APEXCORE-382 Moving docs to asf-site branch
diff --git a/.gitignore b/.gitignore
index 2ee6c7d..f3af33d 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,4 +1,5 @@
.tmp
node_modules
bower_components
-content
\ No newline at end of file
+content
+docs/
\ No newline at end of file
diff --git a/build.sh b/build.sh
index 379e4cf..91e5c89 100755
--- a/build.sh
+++ b/build.sh
@@ -11,6 +11,10 @@
git checkout asf-site
rm -rf content
mv content_tmp content
+# Add documentation to content folder
+echo "Adding documentation from docs/ to content/docs."
+rm -rf content/docs
+cp -rp docs content/docs
git add content
echo "Commiting changes to asf-site branch from master branch."
git commit -m "from $COMMIT_HASH"
diff --git a/docs/apex-3.3/__init__.py b/docs/apex-3.3/__init__.py
deleted file mode 100644
index e69de29..0000000
--- a/docs/apex-3.3/__init__.py
+++ /dev/null
diff --git a/docs/apex-3.3/__init__.pyc b/docs/apex-3.3/__init__.pyc
deleted file mode 100644
index f478a23..0000000
--- a/docs/apex-3.3/__init__.pyc
+++ /dev/null
Binary files differ
diff --git a/docs/apex-3.3/apex_development_setup/index.html b/docs/apex-3.3/apex_development_setup/index.html
deleted file mode 100644
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-
- <li><a class="toctree-l4" href="#development-tools">Development Tools</a></li>
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- <li><a class="toctree-l4" href="#creating-new-apex-project">Creating New Apex Project</a></li>
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-
- <h1 id="apache-apex-development-environment-setup">Apache Apex Development Environment Setup</h1>
-<p>This document discusses the steps needed for setting up a development environment for creating applications that run on the Apache Apex platform.</p>
-<h2 id="development-tools">Development Tools</h2>
-<p>There are a few tools that will be helpful when developing Apache Apex applications, including:</p>
-<ol>
-<li>
-<p><strong>git</strong> - A revision control system (version 1.7.1 or later). There are multiple git clients available for Windows (<a href="http://git-scm.com/download/win">http://git-scm.com/download/win</a> for example), so download and install a client of your choice.</p>
-</li>
-<li>
-<p><strong>java JDK</strong> (not JRE) - Includes the Java Runtime Environment as well as the Java compiler and a variety of tools (version 1.7.0_79 or later). Can be downloaded from the Oracle website.</p>
-</li>
-<li>
-<p><strong>maven</strong> - Apache Maven is a build system for Java projects (version 3.0.5 or later). It can be downloaded from <a href="https://maven.apache.org/download.cgi">https://maven.apache.org/download.cgi</a>.</p>
-</li>
-<li>
-<p><strong>IDE</strong> (Optional) - If you prefer to use an IDE (Integrated Development Environment) such as <em>NetBeans</em>, <em>Eclipse</em> or <em>IntelliJ</em>, install that as well.</p>
-</li>
-</ol>
-<p>After installing these tools, make sure that the directories containing the executable files are in your PATH environment variable.</p>
-<ul>
-<li><strong>Windows</strong> - Open a console window and enter the command <code>echo %PATH%</code> to see the value of the <code>PATH</code> variable and verify that the above directories for Java, git, and maven executables are present. JDK executables like <em>java</em> and <em>javac</em>, the directory might be something like <code>C:\\Program Files\\Java\\jdk1.7.0\_80\\bin</code>; for <em>git</em> it might be <code>C:\\Program Files\\Git\\bin</code>; and for maven it might be <code>C:\\Users\\user\\Software\\apache-maven-3.3.3\\bin</code>. If not, you can change its value clicking on the button at <em>Control Panel</em> ⇨ <em>Advanced System Settings</em> ⇨ <em>Advanced tab</em> ⇨ <em>Environment Variables</em>.</li>
-<li><strong>Linux and Mac</strong> - Open a console/terminal window and enter the command <code>echo $PATH</code> to see the value of the <code>PATH</code> variable and verify that the above directories for Java, git, and maven executables are present. If not, make sure software is downloaded and installed, and optionally PATH reference is added and exported in a <code>~/.profile</code> or <code>~/.bash_profile</code>. For example to add maven located in <code>/sfw/maven/apache-maven-3.3.3</code> to PATH add the line: <code>export PATH=$PATH:/sfw/maven/apache-maven-3.3.3/bin</code></li>
-</ul>
-<p>Confirm by running the following commands and comparing with output that show in the table below:</p>
-<table>
-<colgroup>
-<col width="30%" />
-<col width="70%" />
-</colgroup>
-<tbody>
-<tr class="odd">
-<td align="left"><p>Command</p></td>
-<td align="left"><p>Output</p></td>
-</tr>
-<tr class="even">
-<td align="left"><p><tt>javac -version</tt></p></td>
-<td align="left"><p>javac 1.7.0_80</p></td>
-</tr>
-<tr class="odd">
-<td align="left"><p><tt>java -version</tt></p></td>
-<td align="left"><p>java version "1.7.0_80"</p>
-<p>Java(TM) SE Runtime Environment (build 1.7.0_80-b15)</p>
-<p>Java HotSpot(TM) 64-Bit Server VM (build 24.80-b11, mixed mode)</p></td>
-</tr>
-<tr class="even">
-<td align="left"><p><tt>git --version</tt></p></td>
-<td align="left"><p>git version 2.6.1.windows.1</p></td>
-</tr>
-<tr class="odd">
-<td align="left"><p><tt>mvn --version</tt></p></td>
-<td align="left"><p>Apache Maven 3.3.3 (7994120775791599e205a5524ec3e0dfe41d4a06; 2015-04-22T06:57:37-05:00)</p>
-<p>...</p>
-</td>
-</tr>
-</tbody>
-</table>
-
-<h2 id="creating-new-apex-project">Creating New Apex Project</h2>
-<p>After development tools are configured, you can now use the maven archetype to create a basic Apache Apex project. <strong>Note:</strong> When executing the commands below, replace <code>3.3.0-incubating</code> by <a href="http://apex.apache.org/downloads.html">latest available version</a> of Apache Apex.</p>
-<ul>
-<li>
-<p><strong>Windows</strong> - Create a new Windows command file called <code>newapp.cmd</code> by copying the lines below, and execute it. When you run this file, the properties will be displayed and you will be prompted with <code>Y: :</code>; just press <strong>Enter</strong> to complete the project generation. The caret (^) at the end of some lines indicates that a continuation line follows. </p>
-<pre><code>@echo off
-@rem Script for creating a new application
-setlocal
-mvn archetype:generate ^
- -DarchetypeGroupId=org.apache.apex ^
- -DarchetypeArtifactId=apex-app-archetype -DarchetypeVersion=3.3.0-incubating ^
- -DgroupId=com.example -Dpackage=com.example.myapexapp -DartifactId=myapexapp ^
- -Dversion=1.0-SNAPSHOT
-endlocal
-</code></pre>
-</li>
-<li>
-<p><strong>Linux</strong> - Execute the lines below in a terminal window. New project will be created in the curent working directory. The backslash (\) at the end of the lines indicates continuation.</p>
-<pre><code>mvn archetype:generate \
- -DarchetypeGroupId=org.apache.apex \
- -DarchetypeArtifactId=apex-app-archetype -DarchetypeVersion=3.2.0-incubating \
- -DgroupId=com.example -Dpackage=com.example.myapexapp -DartifactId=myapexapp \
- -Dversion=1.0-SNAPSHOT
-</code></pre>
-</li>
-</ul>
-<p>When the run completes successfully, you should see a new directory named <code>myapexapp</code> containing a maven project for building a basic Apache Apex application. It includes 3 source files:<strong>Application.java</strong>, <strong>RandomNumberGenerator.java</strong> and <strong>ApplicationTest.java</strong>. You can now build the application by stepping into the new directory and running the maven package command:</p>
-<pre><code>cd myapexapp
-mvn clean package -DskipTests
-</code></pre>
-<p>The build should create the application package file <code>myapexapp/target/myapexapp-1.0-SNAPSHOT.apa</code>. This application package can then be used to launch example application via <strong>dtCli</strong>, or other visual management tools. When running, this application will generate a stream of random numbers and print them out, each prefixed by the string <code>hello world:</code>.</p>
-<h2 id="building-apex-demos">Building Apex Demos</h2>
-<p>If you want to see more substantial Apex demo applications and the associated source code, you can follow these simple steps to check out and build them.</p>
-<ol>
-<li>
-<p>Check out the source code repositories:</p>
-<pre><code>git clone https://github.com/apache/incubator-apex-core
-git clone https://github.com/apache/incubator-apex-malhar
-</code></pre>
-</li>
-<li>
-<p>Switch to the appropriate release branch and build each repository:</p>
-<pre><code>cd incubator-apex-core
-mvn clean install -DskipTests
-
-cd incubator-apex-malhar
-mvn clean install -DskipTests
-</code></pre>
-</li>
-</ol>
-<p>The <code>install</code> argument to the <code>mvn</code> command installs resources from each project to your local maven repository (typically <code>.m2/repository</code> under your home directory), and <strong>not</strong> to the system directories, so Administrator privileges are not required. The <code>-DskipTests</code> argument skips running unit tests since they take a long time. If this is a first-time installation, it might take several minutes to complete because maven will download a number of associated plugins.</p>
-<p>After the build completes, you should see the demo application package files in the target directory under each demo subdirectory in <code>incubator-apex-malhar/demos</code>.</p>
-<h2 id="sandbox">Sandbox</h2>
-<p>To jump start development with an Apache Hadoop single node cluster, <a href="https://www.datatorrent.com/download">DataTorrent Sandbox</a> powered by VirtualBox is available on Windows, Linux, or Mac platforms. The sandbox is configured by default to run with 6GB RAM; if your development machine has 16GB or more, you can increase the sandbox RAM to 8GB or more using the VirtualBox console. This will yield better performance and support larger applications. The advantage of developing in the sandbox is that most of the tools (e.g. <em>jdk</em>, <em>git</em>, <em>maven</em>), Hadoop YARN and HDFS, and a distribution of Apache Apex and DataTorrent RTS are pre-installed. The disadvantage is that the sandbox is a memory-limited environment, and requires settings changes and restarts to adjust memory available for development and testing.</p>
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diff --git a/docs/apex-3.3/application_development/index.html b/docs/apex-3.3/application_development/index.html
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- <h1 id="application-developer-guide">Application Developer Guide</h1>
-<p>The Apex platform is designed to process massive amounts of
-real-time events natively in Hadoop. It runs as a YARN (Hadoop 2.x)
-application and leverages Hadoop as a distributed operating
-system. All the basic distributed operating system capabilities of
-Hadoop like resource management (YARN), distributed file system (HDFS),
-multi-tenancy, security, fault-tolerance, and scalability are supported natively
-in all the Apex applications. The platform handles all the details of the application
-execution, including dynamic scaling, state checkpointing and recovery, event
-processing guarantees, etc. allowing you to focus on writing your application logic without
-mixing operational and functional concerns.</p>
-<p>In the platform, building a streaming application can be extremely
-easy and intuitive. The application is represented as a Directed
-Acyclic Graph (DAG) of computation units called <em>Operators</em> interconnected
-by the data-flow edges called <em>Streams</em>. The operators process input
-streams and produce output streams. A library of common operators is
-provided to enable quick application development. In case the desired
-processing is not available in the Operator Library, one can easily
-write a custom operator. We refer those interested in creating their own
-operators to the <a href="../operator_development/">Operator Development Guide</a>.</p>
-<h1 id="running-a-test-application">Running A Test Application</h1>
-<p>If you are starting with the Apex platform for the first time,
-it can be informative to launch an existing application and see it run.
-One of the simplest examples provided in <a href="https://github.com/apache/incubator-apex-malhar">Apex-Malhar repository</a> is a Pi demo application,
-which computes the value of PI using random numbers. After <a href="../apex_development_setup/">setting up development environment</a>
-Pi demo can be launched as follows:</p>
-<ol>
-<li>Open up Apex Malhar files in your IDE (for example Eclipse, IntelliJ, NetBeans, etc)</li>
-<li>Navigate to <code>demos/pi/src/test/java/com/datatorrent/demos/ApplicationTest.java</code></li>
-<li>Run the test for ApplicationTest.java</li>
-<li>View the output in system console</li>
-</ol>
-<p>Congratulations, you just ran your first real-time streaming demo :)
-This demo is very simple and has four operators. The first operator
-emits random integers between 0 to 30, 000. The second operator receives
-these coefficients and emits a hashmap with x and y values each time it
-receives two values. The third operator takes these values and computes
-x**2+y**2. The last operator counts how many computed values from
-the previous operator were less than or equal to 30, 000**2. Assuming
-this count is N, then PI is computed as N/number of values received.
-Here is the code snippet for the PI application. This code populates the
-DAG. Do not worry about what each line does, we will cover these
-concepts later in this document.</p>
-<pre><code class="java">// Generates random numbers
-RandomEventGenerator rand = dag.addOperator("rand", new RandomEventGenerator());
-rand.setMinvalue(0);
-rand.setMaxvalue(30000);
-
-// Generates a round robin HashMap of "x" and "y"
-RoundRobinHashMap<String,Object> rrhm = dag.addOperator("rrhm", new RoundRobinHashMap<String, Object>());
-rrhm.setKeys(new String[] { "x", "y" });
-
-// Calculates pi from x and y
-JavaScriptOperator calc = dag.addOperator("picalc", new Script());
-calc.setPassThru(false);
-calc.put("i",0);
-calc.put("count",0);
-calc.addSetupScript("function pi() { if (x*x+y*y <= "+maxValue*maxValue+") { i++; } count++; return i / count * 4; }");
-calc.setInvoke("pi");
-dag.addStream("rand_rrhm", rand.integer_data, rrhm.data);
-dag.addStream("rrhm_calc", rrhm.map, calc.inBindings);
-
-// puts results on system console
-ConsoleOutputOperator console = dag.addOperator("console", new ConsoleOutputOperator());
-dag.addStream("rand_console",calc.result, console.input);
-</code></pre>
-
-<p>You can review the other demos and see what they do. The examples
-given in the Demos project cover various features of the platform and we
-strongly encourage you to read these to familiarize yourself with the
-platform. In the remaining part of this document we will go through
-details needed for you to develop and run streaming applications in
-Malhar.</p>
-<h2 id="test-application-yahoo-finance-quotes">Test Application: Yahoo! Finance Quotes</h2>
-<p>The PI application was to
-get you started. It is a basic application and does not fully illustrate
-the features of the platform. For the purpose of describing concepts, we
-will consider the test application shown in Figure 1. The application
-downloads tick data from <a href="http://finance.yahoo.com">Yahoo! Finance</a> and computes the
-following for four tickers, namely <a href="http://finance.yahoo.com/q?s=IBM">IBM</a>,
-<a href="http://finance.yahoo.com/q?s=GOOG">GOOG</a>, <a href="http://finance.yahoo.com/q?s=YHOO">YHOO</a>.</p>
-<ol>
-<li>Quote: Consisting of last trade price, last trade time, and
- total volume for the day</li>
-<li>Per-minute chart data: Highest trade price, lowest trade
- price, and volume during that minute</li>
-<li>Simple Moving Average: trade price over 5 minutes</li>
-</ol>
-<p>Total volume must ensure that all trade volume for that day is
-added, i.e. data loss would result in wrong results. Charting data needs
-all the trades in the same minute to go to the same slot, and then on it
-starts afresh, so again data loss would result in wrong results. The
-aggregation for charting data is done over 1 minute. Simple moving
-average computes the average price over a 5 minute sliding window; it
-too would produce wrong results if there is data loss. Figure 1 shows
-the application with no partitioning.</p>
-<p><img alt="" src="../images/application_development/ApplicationDeveloperGuide.html-image00.png" /></p>
-<p>The operator StockTickerInput: StockTickerInput<a href="http://docs.google.com/../apidocs/com/datatorrent/demos/yahoofinance/StockTickInput.html"> </a>is
-the input operator that reads live data from Yahoo! Finance once per
-interval (user configurable in milliseconds), and emits the price, the
-incremental volume, and the last trade time of each stock symbol, thus
-emulating real ticks from the exchange. We utilize the Yahoo! Finance
-CSV web service interface. For example:</p>
-<pre><code>$ GET 'http://download.finance.yahoo.com/d/quotes.csv?s=IBM,GOOG,AAPL,YHOO&f=sl1vt1'
-"IBM",203.966,1513041,"1:43pm"
-"GOOG",762.68,1879741,"1:43pm"
-"AAPL",444.3385,11738366,"1:43pm"
-"YHOO",19.3681,14707163,"1:43pm"
-</code></pre>
-
-<p>Among all the operators in Figure 1, StockTickerInput is the only
-operator that requires extra code because it contains a custom mechanism
-to get the input data. Other operators are used unchanged from the
-Malhar library.</p>
-<p>Here is the class implementation for StockTickInput:</p>
-<pre><code class="java">package com.datatorrent.demos.yahoofinance;
-
-import au.com.bytecode.opencsv.CSVReader;
-import com.datatorrent.annotation.OutputPortFieldAnnotation;
-import com.datatorrent.api.Context.OperatorContext;
-import com.datatorrent.api.DefaultOutputPort;
-import com.datatorrent.api.InputOperator;
-import com.datatorrent.lib.util.KeyValPair;
-import java.io.IOException;
-import java.io.InputStream;
-import java.io.InputStreamReader;
-import java.util.*;
-import org.apache.commons.httpclient.HttpClient;
-import org.apache.commons.httpclient.HttpStatus;
-import org.apache.commons.httpclient.cookie.CookiePolicy;
-import org.apache.commons.httpclient.methods.GetMethod;
-import org.apache.commons.httpclient.params.DefaultHttpParams;
-import org.slf4j.Logger;
-import org.slf4j.LoggerFactory;
-
-/**
- * This operator sends price, volume and time into separate ports and calculates incremental volume.
- */
-public class StockTickInput implements InputOperator
-{
- private static final Logger logger = LoggerFactory.getLogger(StockTickInput.class);
- /**
- * Timeout interval for reading from server. 0 or negative indicates no timeout.
- */
- public int readIntervalMillis = 500;
- /**
- * The URL of the web service resource for the POST request.
- */
- private String url;
- public String[] symbols;
- private transient HttpClient client;
- private transient GetMethod method;
- private HashMap<String, Long> lastVolume = new HashMap<String, Long>();
- private boolean outputEvenIfZeroVolume = false;
- /**
- * The output port to emit price.
- */
- @OutputPortFieldAnnotation(optional = true)
- public final transient DefaultOutputPort<KeyValPair<String, Double>> price = new DefaultOutputPort<KeyValPair<String, Double>>();
- /**
- * The output port to emit incremental volume.
- */
- @OutputPortFieldAnnotation(optional = true)
- public final transient DefaultOutputPort<KeyValPair<String, Long>> volume = new DefaultOutputPort<KeyValPair<String, Long>>();
- /**
- * The output port to emit last traded time.
- */
- @OutputPortFieldAnnotation(optional = true)
- public final transient DefaultOutputPort<KeyValPair<String, String>> time = new DefaultOutputPort<KeyValPair<String, String>>();
-
- /**
- * Prepare URL from symbols and parameters. URL will be something like: http://download.finance.yahoo.com/d/quotes.csv?s=IBM,GOOG,AAPL,YHOO&f=sl1vt1
- *
- * @return the URL
- */
- private String prepareURL()
- {
- String str = "http://download.finance.yahoo.com/d/quotes.csv?s=";
- for (int i = 0; i < symbols.length; i++) {
- if (i != 0) {
- str += ",";
- }
- str += symbols[i];
- }
- str += "&f=sl1vt1&e=.csv";
- return str;
- }
-
- @Override
- public void setup(OperatorContext context)
- {
- url = prepareURL();
- client = new HttpClient();
- method = new GetMethod(url);
- DefaultHttpParams.getDefaultParams().setParameter("http.protocol.cookie-policy", CookiePolicy.BROWSER_COMPATIBILITY);
- }
-
- @Override
- public void teardown()
- {
- }
-
- @Override
- public void emitTuples()
- {
-
- try {
- int statusCode = client.executeMethod(method);
- if (statusCode != HttpStatus.SC_OK) {
- System.err.println("Method failed: " + method.getStatusLine());
- }
- else {
- InputStream istream = method.getResponseBodyAsStream();
- // Process response
- InputStreamReader isr = new InputStreamReader(istream);
- CSVReader reader = new CSVReader(isr);
- List<String[]> myEntries = reader.readAll();
- for (String[] stringArr: myEntries) {
- ArrayList<String> tuple = new ArrayList<String>(Arrays.asList(stringArr));
- if (tuple.size() != 4) {
- return;
- }
- // input csv is <Symbol>,<Price>,<Volume>,<Time>
- String symbol = tuple.get(0);
- double currentPrice = Double.valueOf(tuple.get(1));
- long currentVolume = Long.valueOf(tuple.get(2));
- String timeStamp = tuple.get(3);
- long vol = currentVolume;
- // Sends total volume in first tick, and incremental volume afterwards.
- if (lastVolume.containsKey(symbol)) {
- vol -= lastVolume.get(symbol);
- }
-
- if (vol > 0 || outputEvenIfZeroVolume) {
- price.emit(new KeyValPair<String, Double>(symbol, currentPrice));
- volume.emit(new KeyValPair<String, Long>(symbol, vol));
- time.emit(new KeyValPair<String, String>(symbol, timeStamp));
- lastVolume.put(symbol, currentVolume);
- }
- }
- }
- Thread.sleep(readIntervalMillis);
- }
- catch (InterruptedException ex) {
- logger.debug(ex.toString());
- }
- catch (IOException ex) {
- logger.debug(ex.toString());
- }
- }
-
- @Override
- public void beginWindow(long windowId)
- {
- }
-
- @Override
- public void endWindow()
- {
- }
-
- public void setOutputEvenIfZeroVolume(boolean outputEvenIfZeroVolume)
- {
- this.outputEvenIfZeroVolume = outputEvenIfZeroVolume;
- }
-
-}
-</code></pre>
-
-<p>The operator has three output ports that emit the price of the
-stock, the volume of the stock and the last trade time of the stock,
-declared as public member variables price, volume and time of the class. The tuple of the
-price output port is a key-value
-pair with the stock symbol being the key, and the price being the value.
- The tuple of the volume output
-port is a key value pair with the stock symbol being the key, and the
-incremental volume being the value. The tuple of the time output port is a key value pair with the
-stock symbol being the key, and the last trade time being the
-value.</p>
-<p>Important: Since operators will be
-serialized, all input and output ports need to be declared transient
-because they are stateless and should not be serialized.</p>
-<p>The method setup(OperatorContext)
-contains the code that is necessary for setting up the HTTP
-client for querying Yahoo! Finance.</p>
-<p>Method emitTuples() contains
-the code that reads from Yahoo! Finance, and emits the data to the
-output ports of the operator. emitTuples() will be called one or more times
-within one application window as long as time is allowed within the
-window.</p>
-<p>Note that we want to emulate the tick input stream by having
-incremental volume data with Yahoo! Finance data. We therefore subtract
-the previous volume from the current volume to emulate incremental
-volume for each tick.</p>
-<p>The operator
-DailyVolume: This operator
-reads from the input port, which contains the incremental volume tuples
-from StockTickInput, and
-aggregates the data to provide the cumulative volume. It uses the
-library class SumKeyVal<K,V> provided in math package. In this case,
-SumKeyVal<String,Long>, where K is the stock symbol, V is the
-aggregated volume, with cumulative
-set to true. (Otherwise if cumulativewas set to false, SumKeyVal would
-provide the sum for the application window.) Malhar provides a number
-of built-in operators for simple operations like this so that
-application developers do not have to write them. More examples to
-follow. This operator assumes that the application restarts before the
-market opens every day.</p>
-<p>The operator Quote:
-This operator has three input ports, which are price (from
-StockTickInput), daily_vol (from
-Daily Volume), and time (from
- StockTickInput). This operator
-just consolidates the three data items and and emits the consolidated
-data. It utilizes the class ConsolidatorKeyVal<K> from the
-stream package.</p>
-<p>The operator HighLow: This operator reads from the input port,
-which contains the price tuples from StockTickInput, and provides the high and the
-low price within the application window. It utilizes the library class
- RangeKeyVal<K,V> provided
-in the math package. In this case,
-RangeKeyVal<String,Double>.</p>
-<p>The operator MinuteVolume:
-This operator reads from the input port, which contains the
-volume tuples from StockTickInput,
-and aggregates the data to provide the sum of the volume within one
-minute. Like the operator DailyVolume, this operator also uses
-SumKeyVal<String,Long>, but
-with cumulative set to false. The
-Application Window is set to one minute. We will explain how to set this
-later.</p>
-<p>The operator Chart:
-This operator is very similar to the operator Quote, except that it takes inputs from
-High Low and Minute Vol and outputs the consolidated tuples
-to the output port.</p>
-<p>The operator PriceSMA:
-SMA stands for - Simple Moving Average. It reads from the
-input port, which contains the price tuples from StockTickInput, and
-provides the moving average price of the stock. It utilizes
-SimpleMovingAverage<String,Double>, which is provided in the
- multiwindow package.
-SimpleMovingAverage keeps track of the data of the previous N
-application windows in a sliding manner. For each end window event, it
-provides the average of the data in those application windows.</p>
-<p>The operator Console:
-This operator just outputs the input tuples to the console
-(or stdout). In this example, there are four console operators, which connect to the output
-of Quote, Chart, PriceSMA and VolumeSMA. In
-practice, they should be replaced by operators that use the data to
-produce visualization artifacts like charts.</p>
-<p>Connecting the operators together and constructing the
-DAG: Now that we know the
-operators used, we will create the DAG, set the streaming window size,
-instantiate the operators, and connect the operators together by adding
-streams that connect the output ports with the input ports among those
-operators. This code is in the file YahooFinanceApplication.java. Refer to Figure 1
-again for the graphical representation of the DAG. The last method in
-the code, namely getApplication(),
-does all that. The rest of the methods are just for setting up the
-operators.</p>
-<pre><code class="java">package com.datatorrent.demos.yahoofinance;
-
-import com.datatorrent.api.ApplicationFactory;
-import com.datatorrent.api.Context.OperatorContext;
-import com.datatorrent.api.DAG;
-import com.datatorrent.api.Operator.InputPort;
-import com.datatorrent.lib.io.ConsoleOutputOperator;
-import com.datatorrent.lib.math.RangeKeyVal;
-import com.datatorrent.lib.math.SumKeyVal;
-import com.datatorrent.lib.multiwindow.SimpleMovingAverage;
-import com.datatorrent.lib.stream.ConsolidatorKeyVal;
-import com.datatorrent.lib.util.HighLow;
-import org.apache.hadoop.conf.Configuration;
-
-/**
- * Yahoo! Finance application demo. <p>
- *
- * Get Yahoo finance feed and calculate minute price range, minute volume, simple moving average of 5 minutes.
- */
-public class Application implements StreamingApplication
-{
- private int streamingWindowSizeMilliSeconds = 1000; // 1 second (default is 500ms)
- private int appWindowCountMinute = 60; // 1 minute
- private int appWindowCountSMA = 5 * 60; // 5 minute
-
- /**
- * Get actual Yahoo finance ticks of symbol, last price, total daily volume, and last traded price.
- */
- public StockTickInput getStockTickInputOperator(String name, DAG dag)
- {
- StockTickInput oper = dag.addOperator(name, StockTickInput.class);
- oper.readIntervalMillis = 200;
- return oper;
- }
-
- /**
- * This sends total daily volume by adding volumes from each ticks.
- */
- public SumKeyVal<String, Long> getDailyVolumeOperator(String name, DAG dag)
- {
- SumKeyVal<String, Long> oper = dag.addOperator(name, new SumKeyVal<String, Long>());
- oper.setType(Long.class);
- oper.setCumulative(true);
- return oper;
- }
-
- /**
- * Get aggregated volume of 1 minute and send at the end window of 1 minute.
- */
- public SumKeyVal<String, Long> getMinuteVolumeOperator(String name, DAG dag, int appWindowCount)
- {
- SumKeyVal<String, Long> oper = dag.addOperator(name, new SumKeyVal<String, Long>());
- oper.setType(Long.class);
- oper.setEmitOnlyWhenChanged(true);
-dag.getOperatorMeta(name).getAttributes().put(OperatorContext.APPLICATION_WINDOW_COUNT,appWindowCount);
- return oper;
- }
-
- /**
- * Get High-low range for 1 minute.
- */
- public RangeKeyVal<String, Double> getHighLowOperator(String name, DAG dag, int appWindowCount)
- {
- RangeKeyVal<String, Double> oper = dag.addOperator(name, new RangeKeyVal<String, Double>());
- dag.getOperatorMeta(name).getAttributes().put(OperatorContext.APPLICATION_WINDOW_COUNT,appWindowCount);
- oper.setType(Double.class);
- return oper;
- }
-
- /**
- * Quote (Merge price, daily volume, time)
- */
- public ConsolidatorKeyVal<String,Double,Long,String,?,?> getQuoteOperator(String name, DAG dag)
- {
- ConsolidatorKeyVal<String,Double,Long,String,?,?> oper = dag.addOperator(name, new ConsolidatorKeyVal<String,Double,Long,String,Object,Object>());
- return oper;
- }
-
- /**
- * Chart (Merge minute volume and minute high-low)
- */
- public ConsolidatorKeyVal<String,HighLow,Long,?,?,?> getChartOperator(String name, DAG dag)
- {
- ConsolidatorKeyVal<String,HighLow,Long,?,?,?> oper = dag.addOperator(name, new ConsolidatorKeyVal<String,HighLow,Long,Object,Object,Object>());
- return oper;
- }
-
- /**
- * Get simple moving average of price.
- */
- public SimpleMovingAverage<String, Double> getPriceSimpleMovingAverageOperator(String name, DAG dag, int appWindowCount)
- {
- SimpleMovingAverage<String, Double> oper = dag.addOperator(name, new SimpleMovingAverage<String, Double>());
- oper.setWindowSize(appWindowCount);
- oper.setType(Double.class);
- return oper;
- }
-
- /**
- * Get console for output.
- */
- public InputPort<Object> getConsole(String name, /*String nodeName,*/ DAG dag, String prefix)
- {
- ConsoleOutputOperator oper = dag.addOperator(name, ConsoleOutputOperator.class);
- oper.setStringFormat(prefix + ": %s");
- return oper.input;
- }
-
- /**
- * Create Yahoo Finance Application DAG.
- */
- @Override
- public void populateDAG(DAG dag, Configuration conf)
- {
- dag.getAttributes().put(DAG.STRAM_WINDOW_SIZE_MILLIS,streamingWindowSizeMilliSeconds);
-
- StockTickInput tick = getStockTickInputOperator("StockTickInput", dag);
- SumKeyVal<String, Long> dailyVolume = getDailyVolumeOperator("DailyVolume", dag);
- ConsolidatorKeyVal<String,Double,Long,String,?,?> quoteOperator = getQuoteOperator("Quote", dag);
-
- RangeKeyVal<String, Double> highlow = getHighLowOperator("HighLow", dag, appWindowCountMinute);
- SumKeyVal<String, Long> minuteVolume = getMinuteVolumeOperator("MinuteVolume", dag, appWindowCountMinute);
- ConsolidatorKeyVal<String,HighLow,Long,?,?,?> chartOperator = getChartOperator("Chart", dag);
-
- SimpleMovingAverage<String, Double> priceSMA = getPriceSimpleMovingAverageOperator("PriceSMA", dag, appWindowCountSMA);
- DefaultPartitionCodec<String, Double> codec = new DefaultPartitionCodec<String, Double>();
- dag.setInputPortAttribute(highlow.data, PortContext.STREAM_CODEC, codec);
- dag.setInputPortAttribute(priceSMA.data, PortContext.STREAM_CODEC, codec);
- dag.addStream("price", tick.price, quoteOperator.in1, highlow.data, priceSMA.data);
- dag.addStream("vol", tick.volume, dailyVolume.data, minuteVolume.data);
- dag.addStream("time", tick.time, quoteOperator.in3);
- dag.addStream("daily_vol", dailyVolume.sum, quoteOperator.in2);
-
- dag.addStream("quote_data", quoteOperator.out, getConsole("quoteConsole", dag, "QUOTE"));
-
- dag.addStream("high_low", highlow.range, chartOperator.in1);
- dag.addStream("vol_1min", minuteVolume.sum, chartOperator.in2);
- dag.addStream("chart_data", chartOperator.out, getConsole("chartConsole", dag, "CHART"));
-
- dag.addStream("sma_price", priceSMA.doubleSMA, getConsole("priceSMAConsole", dag, "Price SMA"));
-
- return dag;
- }
-
-}
-</code></pre>
-
-<p>Note that we also set a user-specific sliding window for SMA that
-keeps track of the previous N data points. Do not confuse this with the
-attribute APPLICATION_WINDOW_COUNT.</p>
-<p>In the rest of this chapter we will run through the process of
-running this application. We assume that you are familiar with details
-of your Hadoop infrastructure. For installation
-details please refer to the <a href="http://docs.datatorrent.com/installation/">Installation Guide</a>.</p>
-<h2 id="running-a-test-application_1">Running a Test Application</h2>
-<p>We will now describe how to run the yahoo
-finance application described above in different modes
-(local mode, single node on Hadoop, and multi-nodes on Hadoop).</p>
-<p>The platform runs streaming applications under the control of a
-light-weight Streaming Application Manager (STRAM). Each application has
-its own instance of STRAM. STRAM launches the application and
-continually provides run time monitoring, analysis, and takes action
-such as load scaling or outage recovery as needed. We will discuss
-STRAM in more detail in the next chapter.</p>
-<p>The instructions below assume that the platform was installed in a
-directory <INSTALL_DIR> and the command line interface (CLI) will
-be used to launch the demo application. An application can be run in
-local mode (in IDE or from command line) or on a Hadoop cluster.</p>
-<p>To start the dtCli run</p>
-<pre><code><INSTALL_DIR>/bin/dtcli
-</code></pre>
-<p>The command line prompt appears. To start the application in local mode (the actual version number in the file name may differ)</p>
-<pre><code>dt> launch -local <INSTALL_DIR>/yahoo-finance-demo-3.2.0-SNAPSHOT.apa
-</code></pre>
-<p>To terminate the application in local mode, enter Ctrl-C</p>
-<p>Tu run the application on the Hadoop cluster (the actual version
-number in the file name may differ)</p>
-<pre><code>dt> launch <INSTALL_DIR>/yahoo-finance-demo-3.2.0-SNAPSHOT.apa
-</code></pre>
-<p>To stop the application running in Hadoop, terminate it in the dtCli:</p>
-<pre><code>dt> kill-app
-</code></pre>
-<p>Executing the application in either mode includes the following
-steps. At a top level, STRAM (Streaming Application Manager) validates
-the application (DAG), translates the logical plan to the physical plan
-and then launches the execution engine. The mode determines the
-resources needed and how how they are used.</p>
-<h2 id="local-mode">Local Mode</h2>
-<p>In local mode, the application is run as a single-process with multiple threads. Although a
-few Hadoop classes are needed, there is no dependency on a Hadoop
-cluster or Hadoop services. The local file system is used in place of
-HDFS. This mode allows a quick run of an application in a single process
-sandbox, and hence is the most suitable to debug and analyze the
-application logic. This mode is recommended for developing the
-application and can be used for running applications within the IDE for
-functional testing purposes. Due to limited resources and lack of
-scalability an application running in this single process mode is more
-likely to encounter throughput bottlenecks. A distributed cluster is
-recommended for benchmarking and production testing.</p>
-<h2 id="hadoop-cluster">Hadoop Cluster</h2>
-<p>In this section we discuss various Hadoop cluster setups.</p>
-<h3 id="single-node-cluster">Single Node Cluster</h3>
-<p>In a single node Hadoop cluster all services are deployed on a
-single server (a developer can use his/her development machine as a
-single node cluster). The platform does not distinguish between a single
-or multi-node setup and behaves exactly the same in both cases.</p>
-<p>In this mode, the resource manager, name node, data node, and node
-manager occupy one process each. This is an example of running a
-streaming application as a multi-process application on the same server.
-With prevalence of fast, multi-core systems, this mode is effective for
-debugging, fine tuning, and generic analysis before submitting the job
-to a larger Hadoop cluster. In this mode, execution uses the Hadoop
-services and hence is likely to identify issues that are related to the
-Hadoop environment (such issues will not be uncovered in local mode).
-The throughput will obviously not be as high as on a multi-node Hadoop
-cluster. Additionally, since each container (i.e. Java process) requires
-a significant amount of memory, you will be able to run a much smaller
-number of containers than on a multi-node cluster.</p>
-<h3 id="multi-node-cluster">Multi-Node Cluster</h3>
-<p>In a multi-node Hadoop cluster all the services of Hadoop are
-typically distributed across multiple nodes in a production or
-production-level test environment. Upon launch the application is
-submitted to the Hadoop cluster and executes as a multi-processapplication on multiple nodes.</p>
-<p>Before you start deploying, testing and troubleshooting your
-application on a cluster, you should ensure that Hadoop (version 2.2.0
-or later) is properly installed and
-you have basic skills for working with it.</p>
-<hr />
-<h1 id="apache-apex-platform-overview">Apache Apex Platform Overview</h1>
-<h2 id="streaming-computational-model">Streaming Computational Model</h2>
-<p>In this chapter, we describe the the basics of the real-time streaming platform and its computational model.</p>
-<p>The platform is designed to enable completely asynchronous real time computations done in as unblocked a way as possible with
-minimal overhead .</p>
-<p>Applications running in the platform are represented by a Directed
-Acyclic Graph (DAG) made up of operators and streams. All computations
-are done in memory on arrival of
-the input data, with an option to save the output to disk (HDFS) in a
-non-blocking way. The data that flows between operators consists of
-atomic data elements. Each data element along with its type definition
-(henceforth called schema) is
-called a tuple. An application is a
-design of the flow of these tuples to and from
-the appropriate compute units to enable the computation of the final
-desired results. A message queue (henceforth called
- buffer server) manages tuples streaming
-between compute units in different processes.This server keeps track of
-all consumers, publishers, partitions, and enables replay. More
-information is given in later section.</p>
-<p>The streaming application is monitored by a decision making entity
-called STRAM (streaming application
-manager). STRAM is designed to be a light weight
-controller that has minimal but sufficient interaction with the
-application. This is done via periodic heartbeats. The
-STRAM does the initial launch and periodically analyzes the system
-metrics to decide if any run time action needs to be taken.</p>
-<p>A fundamental building block for the streaming platform
-is the concept of breaking up a stream into equal finite time slices
-called streaming windows. Each window contains the ordered
-set of tuples in that time slice. A typical duration of a window is 500
-ms, but can be configured per application (the Yahoo! Finance
-application configures this value in the properties.xml file to be 1000ms = 1s). Each
-window is preceded by a begin_window event and is terminated by an
-end_window event, and is assigned
-a unique window ID. Even though the platform performs computations at
-the tuple level, bookkeeping is done at the window boundary, making the
-computations within a window an atomic event in the platform. We can
-think of each window as an atomic
-micro-batch of tuples, to be processed together as one
-atomic operation (See Figure 2). </p>
-<p>This atomic batching allows the platform to avoid the very steep
-per tuple bookkeeping cost and instead has a manageable per batch
-bookkeeping cost. This translates to higher throughput, low recovery
-time, and higher scalability. Later in this document we illustrate how
-the atomic micro-batch concept allows more efficient optimization
-algorithms.</p>
-<p>The platform also has in-built support for
-application windows. An application window is part of the
-application specification, and can be a small or large multiple of the
-streaming window. An example from our Yahoo! Finance test application
-is the moving average, calculated over a sliding application window of 5
-minutes which equates to 300 (= 5 * 60) streaming windows.</p>
-<p>Note that these two window concepts are distinct. A streaming
-window is an abstraction of many tuples into a higher atomic event for
-easier management. An application window is a group of consecutive
-streaming windows used for data aggregation (e.g. sum, average, maximum,
-minimum) on a per operator level.</p>
-<p><img alt="" src="../images/application_development/ApplicationDeveloperGuide.html-image02.png" /></p>
-<p>Alongside the platform, a set of
-predefined, benchmarked standard library operator templates is provided
-for ease of use and rapid development of application. These
-operators are open sourced to Apache Software Foundation under the
-project name “Malhar” as part of our efforts to foster community
-innovation. These operators can be used in a DAG as is, while others
-have properties that can be set to specify the
-desired computation. Those interested in details, should refer to
-<a href="https://github.com/apache/incubator-apex-malhar">Apex-Malhar operator library</a>.</p>
-<p>The platform is a Hadoop YARN native
-application. It runs in a Hadoop cluster just like any
-other YARN application (MapReduce etc.) and is designed to seamlessly
-integrate with rest of Hadoop technology stack. It leverages Hadoop as
-much as possible and relies on it as its distributed operating system.
-Hadoop dependencies include resource management, compute/memory/network
-allocation, HDFS, security, fault tolerance, monitoring, metrics,
-multi-tenancy, logging etc. Hadoop classes/concepts are reused as much
-as possible. The aim is to enable enterprises
-to leverage their existing Hadoop infrastructure for real time streaming
-applications. The platform is designed to scale with big
-data applications and scale with Hadoop.</p>
-<p>A streaming application is an asynchronous execution of
-computations across distributed nodes. All computations are done in
-parallel on a distributed cluster. The computation model is designed to
-do as many parallel computations as possible in a non-blocking fashion.
-The task of monitoring of the entire application is done on (streaming)
-window boundaries with a streaming window as an atomic entity. A window
-completion is a quantum of work done. There is no assumption that an
-operator can be interrupted at precisely a particular tuple or window.</p>
-<p>An operator itself also
-cannot assume or predict the exact time a tuple that it emitted would
-get consumed by downstream operators. The operator processes the tuples
-it gets and simply emits new tuples based on its business logic. The
-only guarantee it has is that the upstream operators are processing
-either the current or some later window, and the downstream operator is
-processing either the current or some earlier window. The completion of
-a window (i.e. propagation of the end_window event through an operator) in any
-operator guarantees that all upstream operators have finished processing
-this window. Thus, the end_window event is blocking on an operator
-with multiple outputs, and is a synchronization point in the DAG. The
- begin_window event does not have
-any such restriction, a single begin_window event from any upstream operator
-triggers the operator to start processing tuples.</p>
-<h2 id="streaming-application-manager-stram">Streaming Application Manager (STRAM)</h2>
-<p>Streaming Application Manager (STRAM) is the Hadoop YARN native
-application master. STRAM is the first process that is activated upon
-application launch and orchestrates the streaming application on the
-platform. STRAM is a lightweight controller process. The
-responsibilities of STRAM include</p>
-<ol>
-<li>
-<p>Running the Application</p>
-<ul>
-<li>Read the logical plan of the application (DAG) submitted by the client</li>
-<li>Validate the logical plan</li>
-<li>Translate the logical plan into a physical plan, where certain operators may be partitioned (i.e. replicated) to multiple operators for handling load.</li>
-<li>Request resources (Hadoop containers) from Resource Manager,
- per physical plan</li>
-<li>Based on acquired resources and application attributes, create
- an execution plan by partitioning the DAG into fragments,
- each assigned to different containers.</li>
-<li>Executes the application by deploying each fragment to
- its container. Containers then start stream processing and run
- autonomously, processing one streaming window after another. Each
- container is represented as an instance of the StreamingContainer class, which updates
- STRAM via the heartbeat protocol and processes directions received
- from STRAM.</li>
-</ul>
-</li>
-<li>
-<p>Continually monitoring the application via heartbeats from each StreamingContainer</p>
-</li>
-<li>Collecting Application System Statistics and Logs</li>
-<li>Logging all application-wide decisions taken</li>
-<li>Providing system data on the state of the application via a Web Service.</li>
-<li>
-<p>Supporting Fault Tolerance</p>
-<p>a. Detecting a node outage
-b. Requesting a replacement resource from the Resource Manager
- and scheduling state restoration for the streaming operators
-c. Saving state to Zookeeper</p>
-</li>
-<li>
-<p>Supporting Dynamic Partitioning: Periodically evaluating the SLA and modifying the physical plan if required
- (logical plan does not change).</p>
-</li>
-<li>Enabling Security: Distributing security tokens for distributed components of the execution engine
- and securing web service requests.</li>
-<li>Enabling Dynamic modification of DAG: In the future, we intend to allow for user initiated
- modification of the logical plan to allow for changes to the
- processing logic and functionality.</li>
-</ol>
-<p>An example of the Yahoo! Finance Quote application scheduled on a
-cluster of 5 Hadoop containers (processes) is shown in Figure 3.</p>
-<p><img alt="" src="../images/application_development/ApplicationDeveloperGuide.html-image01.png" /></p>
-<p>An example for the translation from a logical plan to a physical
-plan and an execution plan for a subset of the application is shown in
-Figure 4.</p>
-<p><img alt="" src="../images/application_development/ApplicationDeveloperGuide.html-image04.png" /></p>
-<h2 id="hadoop-components">Hadoop Components</h2>
-<p>In this section we cover some aspects of Hadoop that your
-streaming application interacts with. This section is not meant to
-educate the reader on Hadoop, but just get the reader acquainted with
-the terms. We strongly advise readers to learn Hadoop from other
-sources.</p>
-<p>A streaming application runs as a native Hadoop 2.2 application.
-Hadoop 2.2 does not differentiate between a map-reduce job and other
-applications, and hence as far as Hadoop is concerned, the streaming
-application is just another job. This means that your application
-leverages all the bells and whistles Hadoop provides and is fully
-supported within Hadoop technology stack. The platform is responsible
-for properly integrating itself with the relevant components of Hadoop
-that exist today and those that may emerge in the future</p>
-<p>All investments that leverage multi-tenancy (for example quotas
-and queues), security (for example kerberos), data flow integration (for
-example copying data in-out of HDFS), monitoring, metrics collections,
-etc. will require no changes when streaming applications run on
-Hadoop.</p>
-<h3 id="yarn">YARN</h3>
-<p><a href="http://hadoop.apache.org/docs/current/hadoop-yarn/hadoop-yarn-site">YARN</a>is
-the core library of Hadoop 2.2 that is tasked with resource management
-and works as a distributed application framework. In this section we
-will walk through Yarn's components. In Hadoop 2.2, the old jobTracker
-has been replaced by a combination of ResourceManager (RM) and
-ApplicationMaster (AM).</p>
-<h4 id="resource-manager-rm">Resource Manager (RM)</h4>
-<p><a href="http://hadoop.apache.org/docs/current/hadoop-yarn/hadoop-yarn-site/YARN.html">ResourceManager</a>(RM)
-manages all the distributed resources. It allocates and arbitrates all
-the slots and the resources (cpu, memory, network) of these slots. It
-works with per-node NodeManagers (NMs) and per-application
-ApplicationMasters (AMs). Currently memory usage is monitored by RM; in
-upcoming releases it will have CPU as well as network management. RM is
-shared by map-reduce and streaming applications. Running streaming
-applications requires no changes in the RM.</p>
-<h4 id="application-master-am">Application Master (AM)</h4>
-<p>The AM is the watchdog or monitoring process for your application
-and has the responsibility of negotiating resources with RM and
-interacting with NodeManagers to get the allocated containers started.
-The AM is the starting point of your application and is considered user
-code (not system Hadoop code). The AM itself runs in one container. All
-resource management within the application are managed by the AM. This
-is a critical feature for Hadoop 2.2 where tasks done by jobTracker in
-Hadoop 1.0 have been distributed allowing Hadoop 2.2 to scale much
-beyond Hadoop 1.0. STRAM is a native YARN ApplicationManager.</p>
-<h4 id="node-managers-nm">Node Managers (NM)</h4>
-<p>There is one <a href="http://hadoop.apache.org/docs/current/hadoop-yarn/hadoop-yarn-site/YARN.html">NodeManager</a>(NM)
-per node in the cluster. All the containers (i.e. processes) on that
-node are monitored by the NM. It takes instructions from RM and manages
-resources of that node as per RM instructions. NMs interactions are same
-for map-reduce and for streaming applications. Running streaming
-applications requires no changes in the NM.</p>
-<h4 id="rpc-protocol">RPC Protocol</h4>
-<p>Communication among RM, AM, and NM is done via the Hadoop RPC
-protocol. Streaming applications use the same protocol to send their
-data. No changes are needed in RPC support provided by Hadoop to enable
-communication done by components of your application.</p>
-<h3 id="hdfs">HDFS</h3>
-<p>Hadoop includes a highly fault tolerant, high throughput
-distributed file system (<a href="http://hadoop.apache.org/docs/r1.0.4/hdfs_design.html">HDFS</a>).
-It runs on commodity hardware, and your streaming application will, by
-default, use it. There is no difference between files created by a
-streaming application and those created by map-reduce.</p>
-<h1 id="developing-an-application">Developing An Application</h1>
-<p>In this chapter we describe the methodology to develop an
-application using the Realtime Streaming Platform. The platform was
-designed to make it easy to build and launch sophisticated streaming
-applications with the developer having to deal only with the
-application/business logic. The platform deals with details of where to
-run what operators on which servers and how to correctly route streams
-of data among them.</p>
-<h2 id="development-process">Development Process</h2>
-<p>While the platform does not mandate a specific methodology or set
-of development tools, we have recommendations to maximize productivity
-for the different phases of application development.</p>
-<h4 id="design">Design</h4>
-<ul>
-<li>Identify common, reusable operators. Use a library
- if possible.</li>
-<li>Identify scalability and performance requirements before
- designing the DAG.</li>
-<li>Leverage attributes that the platform supports for scalability
- and performance.</li>
-<li>Use operators that are benchmarked and tested so that later
- surprises are minimized. If you have glue code, create appropriate
- unit tests for it.</li>
-<li>Use THREAD_LOCAL locality for high throughput streams. If all
- the operators on that stream cannot fit in one container,
- try NODE_LOCAL locality. Both THREAD_LOCAL and
- NODE_LOCAL streams avoid the Network Interface Card (NIC)
- completly. The former uses intra-process communication to also avoid
- serialization-deserialization overhead.</li>
-<li>The overall throughput and latencies are are not necessarily
- correlated to the number of operators in a simple way -- the
- relationship is more nuanced. A lot depends on how much work
- individual operators are doing, how many are able to operate in
- parallel, and how much data is flowing through the arcs of the DAG.
- It is, at times, better to break a computation down into its
- constituent simple parts and then stitch them together via streams
- to better utilize the compute resources of the cluster. Decide on a
- per application basis the fine line between complexity of each
- operator vs too many streams. Doing multiple computations in one
- operator does save network I/O, while operators that are too complex
- are hard to maintain.</li>
-<li>Do not use operators that depend on the order of two streams
- as far as possible. In such cases behavior is not idempotent.</li>
-<li>Persist key information to HDFS if possible; it may be useful
- for debugging later.</li>
-<li>Decide on an appropriate fault tolerance mechanism. If some
- data loss is acceptable, use the at-most-once mechanism as it has
- fastest recovery.</li>
-</ul>
-<h4 id="creating-new-project">Creating New Project</h4>
-<p>Please refer to the <a href="../application_packages/">Apex Application Packages</a> for
-the basic steps for creating a new project.</p>
-<h4 id="writing-the-application-code">Writing the application code</h4>
-<p>Preferably use an IDE (Eclipse, Netbeans etc.) that allows you to
-manage dependencies and assists with the Java coding. Specific benefits
-include ease of managing operator library jar files, individual operator
-classes, ports and properties. It will also highlight and assist to
-rectify issues such as type mismatches when adding streams while
-typing.</p>
-<h4 id="testing">Testing</h4>
-<p>Write test cases with JUnit or similar test framework so that code
-is tested as it is written. For such testing, the DAG can run in local
-mode within the IDE. Doing this may involve writing mock input or output
-operators for the integration points with external systems. For example,
-instead of reading from a live data stream, the application in test mode
-can read from and write to files. This can be done with a single
-application DAG by instrumenting a test mode using settings in the
-configuration that is passed to the application factory
-interface.</p>
-<p>Good test coverage will not only eliminate basic validation errors
-such as missing port connections or property constraint violations, but
-also validate the correct processing of the data. The same tests can be
-re-run whenever the application or its dependencies change (operator
-libraries, version of the platform etc.)</p>
-<h4 id="running-an-application">Running an application</h4>
-<p>The platform provides a commandline tool called dtcli for managing applications (launching,
-killing, viewing, etc.). This tool was already discussed above briefly
-in the section entitled Running the Test Application. It will introspect
-the jar file specified with the launch command for applications (classes
-that implement ApplicationFactory) or property files that define
-applications. It will also deploy the dependency jar files from the
-application package to the cluster.</p>
-<p>Dtcli can run the application in local mode (i.e. outside a
-cluster). It is recommended to first run the application in local mode
-in the development environment before launching on the Hadoop cluster.
-This way some of the external system integration and correct
-functionality of the application can be verified in an easier to debug
-environment before testing distributed mode.</p>
-<p>For more details on CLI please refer to the <a href="../dtcli/">dtCli Guide</a>.</p>
-<h2 id="application-api">Application API</h2>
-<p>This section introduces the API to write a streaming application.
-The work involves connecting operators via streams to form the logical
-DAG. The steps are</p>
-<ol>
-<li>
-<p>Instantiate an application (DAG)</p>
-</li>
-<li>
-<p>(Optional) Set Attributes</p>
-<ul>
-<li>Assign application name</li>
-<li>Set any other attributes as per application requirements</li>
-</ul>
-</li>
-<li>
-<p>Create/re-use and instantiate operators</p>
-<ul>
-<li>Assign operator name that is unique within the application</li>
-<li>Declare schema upfront for each operator (and thereby its ports)</li>
-<li>(Optional) Set properties and attributes on the dag as per specification</li>
-<li>Connect ports of operators via streams<ul>
-<li>Each stream connects one output port of an operator to one or more input ports of other operators.</li>
-<li>(Optional) Set attributes on the streams</li>
-</ul>
-</li>
-</ul>
-</li>
-<li>
-<p>Test the application.</p>
-</li>
-</ol>
-<p>There are two methods to create an application, namely Java, and
-Properties file. Java API is for applications being developed by humans,
-and properties file (Hadoop like) is more suited for DAGs generated by
-tools.</p>
-<h3 id="java-api">Java API</h3>
-<p>The Java API is the most common way to create a streaming
-application. It is meant for application developers who prefer to
-leverage the features of Java, and the ease of use and enhanced
-productivity provided by IDEs like NetBeans or Eclipse. Using Java to
-specify the application provides extra validation abilities of Java
-compiler, such as compile time checks for type safety at the time of
-writing the code. Later in this chapter you can read more about
-validation support in the platform.</p>
-<p>The developer specifies the streaming application by implementing
-the ApplicationFactory interface, which is how platform tools (CLI etc.)
-recognize and instantiate applications. Here we show how to create a
-Yahoo! Finance application that streams the last trade price of a ticker
-and computes the high and low price in every 1 min window. Run above
- test application to execute the
-DAG in local mode within the IDE.</p>
-<p>Let us revisit how the Yahoo! Finance test application constructs the DAG:</p>
-<pre><code class="java">public class Application implements StreamingApplication
-{
-
- ...
-
- @Override
- public void populateDAG(DAG dag, Configuration conf)
- {
- dag.getAttributes().attr(DAG.STRAM_WINDOW_SIZE_MILLIS).set(streamingWindowSizeMilliSeconds);
-
- StockTickInput tick = getStockTickInputOperator("StockTickInput", dag);
- SumKeyVal<String, Long> dailyVolume = getDailyVolumeOperator("DailyVolume", dag);
- ConsolidatorKeyVal<String,Double,Long,String,?,?> quoteOperator = getQuoteOperator("Quote", dag);
-
- RangeKeyVal<String, Double> highlow = getHighLowOperator("HighLow", dag, appWindowCountMinute);
- SumKeyVal<String, Long> minuteVolume = getMinuteVolumeOperator("MinuteVolume", dag, appWindowCountMinute);
- ConsolidatorKeyVal<String,HighLow,Long,?,?,?> chartOperator = getChartOperator("Chart", dag);
-
- SimpleMovingAverage<String, Double> priceSMA = getPriceSimpleMovingAverageOperator("PriceSMA", dag, appWindowCountSMA);
-
- dag.addStream("price", tick.price, quoteOperator.in1, highlow.data, priceSMA.data);
- dag.addStream("vol", tick.volume, dailyVolume.data, minuteVolume.data);
- dag.addStream("time", tick.time, quoteOperator.in3);
- dag.addStream("daily_vol", dailyVolume.sum, quoteOperator.in2);
-
- dag.addStream("quote_data", quoteOperator.out, getConsole("quoteConsole", dag, "QUOTE"));
-
- dag.addStream("high_low", highlow.range, chartOperator.in1);
- dag.addStream("vol_1min", minuteVolume.sum, chartOperator.in2);
- dag.addStream("chart_data", chartOperator.out, getConsole("chartConsole", dag, "CHART"));
-
- dag.addStream("sma_price", priceSMA.doubleSMA, getConsole("priceSMAConsole", dag, "Price SMA"));
-
- return dag;
- }
-}
-</code></pre>
-
-<h3 id="property-file-api">Property File API</h3>
-<p>The platform also supports specification of a DAG via a property
-file. The aim here to make it easy for tools to create and run an
-application. This method of specification does not have the Java
-compiler support of compile time check, but since these applications
-would be created by software, they should be correct by construction.
-The syntax is derived from Hadoop properties and should be easy for
-folks who are used to creating software that integrated with
-Hadoop.</p>
-<p>Create an application (DAG): myApplication.properties</p>
-<pre><code># input operator that reads from a file
-dt.operator.inputOp.classname=com.acme.SampleInputOperator
-dt.operator.inputOp.fileName=somefile.txt
-
-# output operator that writes to the console
-dt.operator.outputOp.classname=com.acme.ConsoleOutputOperator
-
-# stream connecting both operators
-dt.stream.inputStream.source=inputOp.outputPort
-dt.stream.inputStream.sinks=outputOp.inputPort
-</code></pre>
-
-<p>Above snippet is intended to convey the basic idea of specifying
-the DAG without using Java. Operators would come from a predefined
-library and referenced in the specification by class name and port names
-(obtained from the library providers documentation or runtime
-introspection by tools). For those interested in details, see later
-sections and refer to the Operation and
-Installation Guide mentioned above.</p>
-<h3 id="attributes">Attributes</h3>
-<p>Attributes impact the runtime behavior of the application. They do
-not impact the functionality. An example of an attribute is application
-name. Setting it changes the application name. Another example is
-streaming window size. Setting it changes the streaming window size from
-the default value to the specified value. Users cannot add new
-attributes, they can only choose from the ones that come packaged and
-pre-supported by the platform. Details of attributes are covered in the
- Operation and Installation
-Guide.</p>
-<h2 id="operators">Operators</h2>
-<p>Operators are basic compute units.
-Operators process each incoming tuple and emit zero or more tuples on
-output ports as per the business logic. The data flow, connectivity,
-fault tolerance (node outage), etc. is taken care of by the platform. As
-an operator developer, all that is needed is to figure out what to do
-with the incoming tuple and when (and which output port) to send out a
-particular output tuple. Correctly designed operators will most likely
-get reused. Operator design needs care and foresight. For details, refer
-to the <a href="../operator_development/">Operator Developer Guide</a>. As an application developer you need to connect operators
-in a way that it implements your business logic. You may also require
-operator customization for functionality and use attributes for
-performance/scalability etc.</p>
-<p>All operators process tuples asynchronously in a distributed
-cluster. An operator cannot assume or predict the exact time a tuple
-that it emitted will get consumed by a downstream operator. An operator
-also cannot predict the exact time when a tuple arrives from an upstream
-operator. The only guarantee is that the upstream operators are
-processing the current or a future window, i.e. the windowId of upstream
-operator is equals or exceeds its own windowId. Conversely the windowId
-of a downstream operator is less than or equals its own windowId. The
-end of a window operation, i.e. the API call to endWindow on an operator
-requires that all upstream operators have finished processing this
-window. This means that completion of processing a window propagates in
-a blocking fashion through an operator. Later sections provides more
-details on streams and data flow of tuples.</p>
-<p>Each operator has a unique name within the DAG as provided by the
-user. This is the name of the operator in the logical plan. The name of
-the operator in the physical plan is an integer assigned to it by STRAM.
-These integers are use the sequence from 1 to N, where N is total number
-of physically unique operators in the DAG. Following the same rule,
-each partitioned instance of a logical operator has its own integer as
-an id. This id along with the Hadoop container name uniquely identifies
-the operator in the execution plan of the DAG. The logical names and the
-physical names are required for web service support. Operators can be
-accessed via both names. These same names are used while interacting
-with dtcli to access an operator.
-Ideally these names should be self-descriptive. For example in Figure 1,
-the node named “Daily volume” has a physical identifier of 2.</p>
-<h3 id="operator-interface">Operator Interface</h3>
-<p>Operator interface in a DAG consists of ports, properties, and attributes.
-Operators interact with other components of the DAG via ports. Functional behavior of the operators
-can be customized via parameters. Run time performance and physical
-instantiation is controlled by attributes. Ports and parameters are
-fields (variables) of the Operator class/object, while attributes are
-meta information that is attached to the operator object via an
-AttributeMap. An operator must have at least one port. Properties are
-optional. Attributes are provided by the platform and always have a
-default value that enables normal functioning of operators.</p>
-<h4 id="ports">Ports</h4>
-<p>Ports are connection points by which an operator receives and
-emits tuples. These should be transient objects instantiated in the
-operator object, that implement particular interfaces. Ports should be
-transient as they contain no state. They have a pre-defined schema and
-can only be connected to other ports with the same schema. An input port
-needs to implement the interface Operator.InputPort and
-interface Sink. A default
-implementation of these is provided by the abstract class DefaultInputPort. An output port needs to
-implement the interface Operator.OutputPort. A default implementation
-of this is provided by the concrete class DefaultOutputPort. These two are a quick way to
-implement the above interfaces, but operator developers have the option
-of providing their own implementations.</p>
-<p>Here are examples of an input and an output port from the operator
-Sum.</p>
-<pre><code class="java">@InputPortFieldAnnotation(name = "data")
-public final transient DefaultInputPort<V> data = new DefaultInputPort<V>() {
- @Override
- public void process(V tuple)
- {
- ...
- }
-}
-@OutputPortFieldAnnotation(optional=true)
-public final transient DefaultOutputPort<V> sum = new DefaultOutputPort<V>(){ … };
-</code></pre>
-
-<p>The process call is in the Sink interface. An emit on an output
-port is done via emit(tuple) call. For the above example it would be
-sum.emit(t), where the type of t is the generic parameter V.</p>
-<p>There is no limit on how many ports an operator can have. However
-any operator must have at least one port. An operator with only one port
-is called an Input Adapter if it has no input port and an Output Adapter
-if it has no output port. These are special operators needed to get/read
-data from outside system/source into the application, or push/write data
-into an outside system/sink. These could be in Hadoop or outside of
-Hadoop. These two operators are in essence gateways for the streaming
-application to communicate with systems outside the application.</p>
-<p>Port connectivity can be validated during compile time by adding
-PortFieldAnnotations shown above. By default all ports have to be
-connected, to allow a port to go unconnected, you need to add
-“optional=true” to the annotation.</p>
-<p>Attributes can be specified for ports that affect the runtime
-behavior. An example of an attribute is parallel partition that specifes
-a parallel computation flow per partition. It is described in detail in
-the Parallel Partitions section. Another example is queue capacity that specifies the buffer size for the
-port. Details of attributes are covered in Operation and Installation Guide.</p>
-<h4 id="properties">Properties</h4>
-<p>Properties are the abstractions by which functional behavior of an
-operator can be customized. They should be non-transient objects
-instantiated in the operator object. They need to be non-transient since
-they are part of the operator state and re-construction of the operator
-object from its checkpointed state must restore the operator to the
-desired state. Properties are optional, i.e. an operator may or may not
-have properties; they are part of user code and their values are not
-interpreted by the platform in any way.</p>
-<p>All non-serializable objects should be declared transient.
-Examples include sockets, session information, etc. These objects should
-be initialized during setup call, which is called every time the
-operator is initialized.</p>
-<h4 id="attributes_1">Attributes</h4>
-<p>Attributes are values assigned to the operators that impact
-run-time. This includes things like the number of partitions, at most
-once or at least once or exactly once recovery modes, etc. Attributes do
-not impact functionality of the operator. Users can change certain
-attributes in runtime. Users cannot add attributes to operators; they
-are pre-defined by the platform. They are interpreted by the platform
-and thus cannot be defined in user created code (like properties).
-Details of attributes are covered in <a href="http://docs.datatorrent.com/configuration/">Configuration Guide</a>.</p>
-<h3 id="operator-state">Operator State</h3>
-<p>The state of an operator is defined as the data that it transfers
-from one window to a future window. Since the computing model of the
-platform is to treat windows like micro-batches, the operator state can
-be checkpointed every Nth window, or every T units of time, where T is significantly greater
-than the streaming window. When an operator is checkpointed, the entire
-object is written to HDFS. The larger the amount of state in an
-operator, the longer it takes to recover from a failure. A stateless
-operator can recover much quicker than a stateful one. The needed
-windows are preserved by the upstream buffer server and are used to
-recompute the lost windows, and also rebuild the buffer server in the
-current container.</p>
-<p>The distinction between Stateless and Stateful is based solely on
-the need to transfer data in the operator from one window to the next.
-The state of an operator is independent of the number of ports.</p>
-<h4 id="stateless">Stateless</h4>
-<p>A Stateless operator is defined as one where no data is needed to
-be kept at the end of every window. This means that all the computations
-of a window can be derived from all the tuples the operator receives
-within that window. This guarantees that the output of any window can be
-reconstructed by simply replaying the tuples that arrived in that
-window. Stateless operators are more efficient in terms of fault
-tolerance, and cost to achieve SLA.</p>
-<h4 id="stateful">Stateful</h4>
-<p>A Stateful operator is defined as one where data is needed to be
-stored at the end of a window for computations occurring in later
-window; a common example is the computation of a sum of values in the
-input tuples.</p>
-<h3 id="operator-api">Operator API</h3>
-<p>The Operator API consists of methods that operator developers may
-need to override. In this section we will discuss the Operator APIs from
-the point of view of an application developer. Knowledge of how an
-operator works internally is critical for writing an application. Those
-interested in the details should refer to Malhar Operator Developer Guide.</p>
-<p>The APIs are available in three modes, namely Single Streaming
-Window, Sliding Application Window, and Aggregate Application Window.
-These are not mutually exclusive, i.e. an operator can use single
-streaming window as well as sliding application window. A physical
-instance of an operator is always processing tuples from a single
-window. The processing of tuples is guaranteed to be sequential, no
-matter which input port the tuples arrive on.</p>
-<p>In the later part of this section we will evaluate three common
-uses of streaming windows by applications. They have different
-characteristics and implications on optimization and recovery mechanisms
-(i.e. algorithm used to recover a node after outage) as discussed later
-in the section.</p>
-<h4 id="streaming-window">Streaming Window</h4>
-<p>Streaming window is atomic micro-batch computation period. The API
-methods relating to a streaming window are as follows</p>
-<pre><code class="java">public void process(<tuple_type> tuple) // Called on the input port on which the tuple arrives
-public void beginWindow(long windowId) // Called at the start of the window as soon as the first begin_window tuple arrives
-public void endWindow() // Called at the end of the window after end_window tuples arrive on all input ports
-public void setup(OperatorContext context) // Called once during initialization of the operator
-public void teardown() // Called once when the operator is being shutdown
-</code></pre>
-
-<p>A tuple can be emitted in any of the three streaming run-time
-calls, namely beginWindow, process, and endWindow but not in setup or
-teardown.</p>
-<h4 id="aggregate-application-window">Aggregate Application Window</h4>
-<p>An operator with an aggregate window is stateful within the
-application window timeframe and possibly stateless at the end of that
-application window. An size of an aggregate application window is an
-operator attribute and is defined as a multiple of the streaming window
-size. The platform recognizes this attribute and optimizes the operator.
-The beginWindow, and endWindow calls are not invoked for those streaming
-windows that do not align with the application window. For example in
-case of streaming window of 0.5 second and application window of 5
-minute, an application window spans 600 streaming windows (5*60*2 =
-600). At the start of the sequence of these 600 atomic streaming
-windows, a beginWindow gets invoked, and at the end of these 600
-streaming windows an endWindow gets invoked. All the intermediate
-streaming windows do not invoke beginWindow or endWindow. Bookkeeping,
-node recovery, stats, UI, etc. continue to work off streaming windows.
-For example if operators are being checkpointed say on an average every
-30th window, then the above application window would have about 20
-checkpoints.</p>
-<h4 id="sliding-application-window">Sliding Application Window</h4>
-<p>A sliding window is computations that requires previous N
-streaming windows. After each streaming window the Nth past window is
-dropped and the new window is added to the computation. An operator with
-sliding window is a stateful operator at end of any window. The sliding
-window period is an attribute and is a multiple of streaming window. The
-platform recognizes this attribute and leverages it during bookkeeping.
-A sliding aggregate window with tolerance to data loss does not have a
-very high bookkeeping cost. The cost of all three recovery mechanisms,
- at most once (data loss tolerant),
-at least once (data loss
-intolerant), and exactly once (data
-loss intolerant and no extra computations) is same as recovery
-mechanisms based on streaming window. STRAM is not able to leverage this
-operator for any extra optimization.</p>
-<h3 id="single-vs-multi-input-operator">Single vs Multi-Input Operator</h3>
-<p>A single-input operator by definition has a single upstream
-operator, since there can only be one writing port for a stream. If an
-operator has a single upstream operator, then the beginWindow on the
-upstream also blocks the beginWindow of the single-input operator. For
-an operator to start processing any window at least one upstream
-operator has to start processing that window. A multi-input operator
-reads from more than one upstream ports. Such an operator would start
-processing as soon as the first begin_window event arrives. However the
-window would not close (i.e. invoke endWindow) till all ports receive
-end_window events for that windowId. Thus the end of a window is a
-blocking event. As we saw earlier, a multi-input operator is also the
-point in the DAG where windows of all upstream operators are
-synchronized. The windows (atomic micro-batches) from a faster (or just
-ahead in processing) upstream operators are queued up till the slower
-upstream operator catches up. STRAM monitors such bottlenecks and takes
-corrective actions. The platform ensures minimal delay, i.e processing
-starts as long as at least one upstream operator has started
-processing.</p>
-<h3 id="recovery-mechanisms">Recovery Mechanisms</h3>
-<p>Application developers can set any of the recovery mechanisms
-below to deal with node outage. In general, the cost of recovery depends
-on the state of the operator, while data integrity is dependant on the
-application. The mechanisms are per window as the platform treats
-windows as atomic compute units. Three recovery mechanisms are
-supported, namely</p>
-<ul>
-<li>At-least-once: All atomic batches are processed at least once.
- No data loss occurs.</li>
-<li>At-most-once: All atomic batches are processed at most once.
- Data loss is possible; this is the most efficient setting.</li>
-<li>Exactly-once: All atomic batches are processed exactly once.
- No data loss occurs; this is the least efficient setting since
- additional work is needed to ensure proper semantics.</li>
-</ul>
-<p>At-least-once is the default. During a recovery event, the
-operator connects to the upstream buffer server and asks for windows to
-be replayed. At-least-once and exactly-once mechanisms start from its
-checkpointed state. At-most-once starts from the next begin-window
-event.</p>
-<p>Recovery mechanisms can be specified per Operator while writing
-the application as shown below.</p>
-<pre><code class="java">Operator o = dag.addOperator(“operator”, …);
-dag.setAttribute(o, OperatorContext.PROCESSING_MODE, ProcessingMode.AT_MOST_ONCE);
-</code></pre>
-
-<p>Also note that once an operator is attributed to AT_MOST_ONCE,
-all the operators downstream to it have to be AT_MOST_ONCE. The client
-will give appropriate warnings or errors if that’s not the case.</p>
-<p>Details are explained in the chapter on Fault Tolerance below.</p>
-<h2 id="streams">Streams</h2>
-<p>A stream is a connector
-(edge) abstraction, and is a fundamental building block of the platform.
-A stream consists of tuples that flow from one port (called the
-output port) to one or more ports
-on other operators (called input ports) another -- so note a potentially
-confusing aspect of this terminology: tuples enter a stream through its
-output port and leave via one or more input ports. A stream has the
-following characteristics</p>
-<ul>
-<li>Tuples are always delivered in the same order in which they
- were emitted.</li>
-<li>Consists of a sequence of windows one after another. Each
- window being a collection of in-order tuples.</li>
-<li>A stream that connects two containers passes through a
- buffer server.</li>
-<li>All streams can be persisted (by default in HDFS).</li>
-<li>Exactly one output port writes to the stream.</li>
-<li>Can be read by one or more input ports.</li>
-<li>Connects operators within an application, not outside
- an application.</li>
-<li>Has an unique name within an application.</li>
-<li>Has attributes which act as hints to STRAM.</li>
-<li>
-<p>Streams have four modes, namely in-line, in-node, in-rack,
- and other. Modes may be overruled (for example due to lack
- of containers). They are defined as follows:</p>
-<ul>
-<li>THREAD_LOCAL: In the same thread, uses thread
- stack (intra-thread). This mode can only be used for a downstream
- operator which has only one input port connected; also called
- in-line.</li>
-<li>CONTAINER_LOCAL: In the same container (intra-process); also
- called in-container.</li>
-<li>NODE_LOCAL: In the same Hadoop node (inter processes, skips
- NIC); also called in-node.</li>
-<li>RACK_LOCAL: On nodes in the same rack; also called
- in-rack.</li>
-<li>unspecified: No guarantee. Could be anywhere within the
- cluster</li>
-</ul>
-</li>
-</ul>
-<p>An example of a stream declaration is given below</p>
-<pre><code class="java">DAG dag = new DAG();
- …
-dag.addStream("views", viewAggregate.sum, cost.data).setLocality(CONTAINER_LOCAL); // A container local stream
-dag.addStream(“clicks”, clickAggregate.sum, rev.data); // An example of unspecified locality
-</code></pre>
-
-<p>The platform guarantees in-order delivery of tuples in a stream.
-STRAM views each stream as collection of ordered windows. Since no tuple
-can exist outside a window, a replay of a stream consists of replay of a
-set of windows. When multiple input ports read the same stream, the
-execution plan of a stream ensures that each input port is logically not
-blocked by the reading of another input port. The schema of a stream is
-same as the schema of the tuple.</p>
-<p>In a stream all tuples emitted by an operator in a window belong
-to that window. A replay of this window would consists of an in-order
-replay of all the tuples. Thus the tuple order within a stream is
-guaranteed. However since an operator may receive multiple streams (for
-example an operator with two input ports), the order of arrival of two
-tuples belonging to different streams is not guaranteed. In general in
-an asynchronous distributed architecture this is expected. Thus the
-operator (specially one with multiple input ports) should not depend on
-the tuple order from two streams. One way to cope with this
-indeterminate order, if necessary, is to wait to get all the tuples of a
-window and emit results in endWindow call. All operator templates
-provided as part of Malhar operator library follow these principles.</p>
-<p>A logical stream gets partitioned into physical streams each
-connecting the partition to the upstream operator. If two different
-attributes are needed on the same stream, it should be split using
-StreamDuplicator operator.</p>
-<p>Modes of the streams are critical for performance. An in-line
-stream is the most optimal as it simply delivers the tuple as-is without
-serialization-deserialization. Streams should be marked
-container_local, specially in case where there is a large tuple volume
-between two operators which then on drops significantly. Since the
-setLocality call merely provides a hint, STRAM may ignore it. An In-node
-stream is not as efficient as an in-line one, but it is clearly better
-than going off-node since it still avoids the potential bottleneck of
-the network card.</p>
-<p>THREAD_LOCAL and CONTAINER_LOCAL streams do not use a buffer
-server as this stream is in a single process. The other two do.</p>
-<h2 id="validating-an-application">Validating an Application</h2>
-<p>The platform provides various ways of validating the application
-specification and data input. An understanding of these checks is very
-important for an application developer since it affects productivity.
-Validation of an application is done in three phases, namely</p>
-<ol>
-<li>Compile Time: Caught during application development, and is
- most cost effective. These checks are mainly done on declarative
- objects and leverages the Java compiler. An example is checking that
- the schemas specified on all ports of a stream are
- mutually compatible.</li>
-<li>Initialization Time: When the application is being
- initialized, before submitting to Hadoop. These checks are related
- to configuration/context of an application, and are done by the
- logical DAG builder implementation. An example is the checking that
- all non-optional ports are connected to other ports.</li>
-<li>Run Time: Validations done when the application is running.
- This is the costliest of all checks. These are checks that can only
- be done at runtime as they involve data. For example divide by 0
- check as part of business logic.</li>
-</ol>
-<h3 id="compile-time">Compile Time</h3>
-<p>Compile time validations apply when an application is specified in
-Java code and include all checks that can be done by Java compiler in
-the development environment (including IDEs like NetBeans or Eclipse).
-Examples include</p>
-<ol>
-<li>Schema Validation: The tuples on ports are POJO (plain old
- java objects) and compiler checks to ensure that all the ports on a
- stream have the same schema.</li>
-<li>Stream Check: Single Output Port and at least one Input port
- per stream. A stream can only have one output port writer. This is
- part of the addStream api. This
- check ensures that developers only connect one output port to
- a stream. The same signature also ensures that there is at least one
- input port for a stream</li>
-<li>Naming: Compile time checks ensures that applications
- components operators, streams are named</li>
-</ol>
-<h3 id="initializationinstantiation-time">Initialization/Instantiation Time</h3>
-<p>Initialization time validations include various checks that are
-done post compile, and before the application starts running in a
-cluster (or local mode). These are mainly configuration/contextual in
-nature. These checks are as critical to proper functionality of the
-application as the compile time validations.</p>
-<p>Examples include</p>
-<ul>
-<li>
-<p><a href="http://docs.oracle.com/javaee/6/tutorial/doc/gircz.html">JavaBeans Validation</a>:
- Examples include</p>
-<ul>
-<li>@Max(): Value must be less than or equal to the number</li>
-<li>@Min(): Value must be greater than or equal to the
- number</li>
-<li>@NotNull: The value of the field or property must not be
- null</li>
-<li>@Pattern(regexp = “....”): Value must match the regular
- expression</li>
-<li>Input port connectivity: By default, every non-optional input
- port must be connected. A port can be declared optional by using an
- annotation: @InputPortFieldAnnotation(name = "...", optional
- = true)</li>
-<li>Output Port Connectivity: Similar. The annotation here is:
- @OutputPortFieldAnnotation(name = "...", optional = true)</li>
-</ul>
-</li>
-<li>
-<p>Unique names in application scope: Operators, streams, must have
- unique names.</p>
-</li>
-<li>Cycles in the dag: DAG cannot have a cycle.</li>
-<li>Unique names in operator scope: Ports, properties, annotations
- must have unique names.</li>
-<li>One stream per port: A port can connect to only one stream.
- This check applies to input as well as output ports even though an
- output port can technically write to two streams. If you must have
- two streams originating from a single output port, use a streamDuplicator operator.</li>
-<li>Application Window Period: Has to be an integral multiple the
- streaming window period.</li>
-</ul>
-<h3 id="run-time">Run Time</h3>
-<p>Run time checks are those that are done when the application is
-running. The real-time streaming platform provides rich run time error
-handling mechanisms. The checks are exclusively done by the application
-business logic, but the platform allows applications to count and audit
-these. Some of these features are in the process of development (backend
-and UI) and this section will be updated as they are developed. Upon
-completion examples will be added to demos to illustrate these.</p>
-<p>Error ports are output ports with error annotations. Since they
-are normal ports, they can be monitored and tuples counted, persisted
-and counts shown in the UI.</p>
-<hr />
-<h1 id="multi-tenancy-and-security">Multi-Tenancy and Security</h1>
-<p>Hadoop is a multi-tenant distributed operating system. Security is
-an intrinsic element of multi-tenancy as without it a cluster cannot be
-reasonably be shared among enterprise applications. Streaming
-applications follow all multi-tenancy security models used in Hadoop as
-they are native Hadoop applications.</p>
-<h2 id="security">Security</h2>
-<p>The platform includes Kerberos support. Both access points, namely
-STRAM and Bufferserver are secure. STRAM passes the token over to
-StreamingContainer, which then gives it to the Bufferserver. The most
-important aspect for an application developer is to note that STRAM is
-the single point of access to ensure security measures are taken by all
-components of the platform.</p>
-<h2 id="resource-limits">Resource Limits</h2>
-<p>Hadoop enforces quotas on resources. This includes hard-disk (name
-space and total disk quota) as well as priority queues for schedulers.
-The platform uses Hadoop resource limits to manage a streaming
-application. In addition network I/O quotas can be enforced. An operator
-can be dynamically partitioned if it reaches its resource limits; these
-limits may be expressed in terms of throughput, latency, or just
-aggregate resource utilization of a container.</p>
-<hr />
-<h1 id="scalability-and-partitioning">Scalability and Partitioning</h1>
-<p>Scalability is a foundational element of this platform and is a
-building block for an eco-system where big-data meets real-time.
-Enterprises need to continually meet SLA as data grows. Without the
-ability to scale as load grows, or new applications with higher loads
-come to fruition, enterprise grade SLA cannot be met. A big issue with
-the streaming application space is that, it is not just about high load,
-but also the fluctuations in it. There is no way to guarantee future
-load requirements and there is a big difference between high and low
-load within a day for the same feed. Traditional streaming platforms
-solve these two cases by simply throwing more hardware at the
-problem.</p>
-<p>Daily spikes are managed by ensuring enough hardware for peak
-load, which then idles during low load, and future needs are handled by
-a very costly re-architecture, or investing heavily in building a
-scalable distributed operating system. Another salient and often
-overlooked cost is the need to manage SLA -- let’s call it buffer capacity. Since this means computing the
-peak load within required time, that translates to allocating enough
-resources over and above peak load as daily peaks fluctuate. For example
-an average peak load of 100 resource units (cpu and/or memory and/or
-network) may mean allocating about 200 resource units to be safe. A
-distributed cluster that cannot dynamically scale up and down, in effect
-pays buffer capacity per application. Another big aspect of streaming
-applications is that the load is not just ingestion rate, more often
-than not, the internal operators produce lot more events than the
-ingestion rate. For example a dimensional data (with, say d dimensions) computation needs 2*d -1 computations per ingested event. A lot
-of applications have over 10 dimensions, i.e over 1000 computations per
-incoming event and these need to be distributed across the cluster,
-thereby causing an explosion in the throughput (events/sec) that needs
-to be managed.</p>
-<p>The platform is designed to handle such cases at a very low cost.
-The platform scales linearly with Hadoop. If applications need more
-resources, the enterprise can simply add more commodity nodes to Hadoop
-without any downtime, and the Hadoop native platform will take care of
-the rest. If some nodes go bad, these can be removed without downtime.
-The daily peaks and valleys in the load are managed by the platform by
-dynamically scaling at the peak and then giving the resources back to
-Hadoop during low load. This means that a properly designed Hadoop
-cluster does several things for enterprises: (a) reduces the cost of
-hardware due to use of commodity hardware (b) shares buffer capacity
-across all applications as peaks of all applications may not align and
-(c) raises the average CPU usage on a 24x7 basis. As a general design
-this is similar to scale that a map-reduce application can deliver. In
-the following sections of this chapter we will see how this is
-done.</p>
-<h2 id="partitioning">Partitioning</h2>
-<p>If all tuples sent through the stream(s) that are connected to the
-input port(s) of an operator in the DAG are received by a single
-physical instance of that operator, that operator can become a
-performance bottleneck. This leads to scalability issues when
-throughput, memory, or CPU needs exceed the processing capacity of that
-single instance.</p>
-<p>To address the problem, the platform offers the capability to
-partition the inflow of data so that it is divided across multiple
-physical instances of a logical operator in the DAG. There are two
-functional ways to partition</p>
-<ul>
-<li>Load balance: Incoming load is simply partitioned
- into stream(s) that go to separate instances of physical operators
- and scalability is achieved via adding more physical operators. Each
- tuple is sent to physical operator (partition) based on a
- round-robin or other similar algorithm. This scheme scales linearly.
- A lot of key based computations can load balance in the platform due
- to the ability to insert Unifiers. For many computations, the
- endWindow and Unifier setup is similar to the combiner and reducer
- mechanism in a Map-Reduce computation.</li>
-<li>Sticky Key: The key assertion is that distribution of tuples
- are sticky, i.e the data with
- same key will always be processed by the same physical operator, no
- matter how many times it is sent through the stream. This stickiness
- will continue even if the number of partitions grows dynamically and
- can eventually be leveraged for advanced features like
- bucket testing. How this is accomplished and what is required to
- develop compliant operators will be explained below.</li>
-</ul>
-<p>We plan to add more partitioning mechanisms proactively to the
-platform over time as needed by emerging usage patterns. The aim is to
-allow enterprises to be able to focus on their business logic, and
-significantly reduce the cost of operability. As an enabling technology
-for managing high loads, this platform provides enterprises with a
-significant innovative edge. Scalability and Partitioning is a
-foundational building block for this platform.</p>
-<h3 id="sticky-partition-vs-round-robin">Sticky Partition vs Round Robin</h3>
-<p>As noted above, partitioning via sticky key is data aware but
-round-robin partitioning is not. An example for non-sticky load
-balancing would be round robin distribution over multiple instances,
-where for example a tuple stream of A, A,
-A with 3 physical operator
-instances would result in processing of a single A by each of the instances, In contrast, sticky
-partitioning means that exactly one instance of the operators will
-process all of the Atuples if they
-fall into the same bucket, while B
-may be processed by another operator. Data aware mapping of
-tuples to partitions (similar to distributed hash table) is accomplished
-via Stream Codecs. In later sections we would show how these two
-approaches can be used in combination.</p>
-<h3 id="stream-codec">Stream Codec</h3>
-<p>The platform does not make assumptions about the tuple
-type, it could be any Java object. The operator developer knows what
-tuple type an input port expects and is capable of processing. Each
-input port has a stream codec associated thatdefines how data is serialized when transmitted over a socket
-stream; it also defines another
-function that computes the partition hash key for the tuple. The engine
-uses that key to determine which physical instance(s) (for a
-partitioned operator) receive that tuple. For this to work, consistent hashing is required.
-The default codec uses the Java Object#hashCode function, which is
-sufficient for basic types such as Integer, String etc. It will also
-work with custom tuple classes as long as they implement hashCode
-appropriately. Reliance on hashCode may not work when generic containers
-are used that do not hash the actual data, such as standard collection
-classes (HashMap etc.), in which case a custom stream codec must be
-assigned to the input port.</p>
-<h3 id="static-partitioning">Static Partitioning</h3>
-<p>DAG designers can specify at design time how they would like
-certain operators to be partitioned. STRAM then instantiates the DAG
-with the physical plan which adheres to the partitioning scheme defined
-by the design. This plan is the initial partition of the application. In
-other words, Static Partitioning is used to tell STRAM to compute the
-physical DAG from a logical DAG once, without taking into consideration
-runtime states or loads of various operators.</p>
-<h3 id="dynamic-partitioning">Dynamic Partitioning</h3>
-<p>In streaming applications the load changes during the day, thus
-creating situations where the number of partitioned operator instances
-needs to adjust dynamically. The load can be measured in terms of
-processing within the DAG based on throughput, or latency, or
-considerations in external system components (time based etc.) that the
-platform may not be aware of. Whatever the trigger, the resource
-requirement for the current processing needs to be adjusted at run-time.
-The platform may detect that operator instances are over or under
-utilized and may need to dynamically adjust the number of instances on
-the fly. More instances of a logical operator may be required (partition
-split) or underutilized operator instances may need decommissioning
-(partition merge). We refer to either of the changes as dynamic
-partitioning. The default partitioning scheme supports split and merge
-of partitions, but without state transfer. The contract of the
-Partitioner interface allows the operator
-developer to implement split/merge and the associated state transfer, if
-necessary.</p>
-<p>Since partitioning is a key scalability measure, our goal is to
-make it as simple as possible without removing the flexibility needed
-for sophisticated applications. Basic partitioning can be enabled at
-compile time through the DAG specification. A slightly involved
-partitioning involves writing custom codecs to calculate data aware
-partitioning scheme. More complex partitioning cases may require users
-to provide a custom implementation of Partitioner, which gives the
-developer full control over state transfer between multiple instances of
-the partitioned operator.</p>
-<h3 id="default-partitioning">Default Partitioning</h3>
-<p>The platform provides a default partitioning implementation that
-can be enabled without implementing Partitioner (or writing any other extra Java
-code), which is designed to support simple sticky partitioning out of
-the box for operators with logic agnostic to the partitioning scheme
-that can be enabled by means of DAG construction alone.</p>
-<p>Typically an operator that can work with the default partitioning
-scheme would have a single input port. If there are multiple input
-ports, only one port will be partitioned (the port first connected in
-the DAG). The number of partitions will be calculated based on the
-initial partition count - set as attribute on the operator in the DAG
-(if the attribute is not present, partitioning is off). Each partition
-will handle tuples based on matching the lower bits of the hash code.
-For example, if the tuple type was Integer and 2 partitions requested,
-all even numbers would go to one operator instance and all odd numbers
-to the other.</p>
-<h4 id="default-dynamic-partitioning">Default Dynamic Partitioning</h4>
-<p>Triggering partition load evaluation and repartitioning action
-itself are separate concerns. Triggers are not specified further here,
-we are planning to support it in a customizable fashion that, for
-example, allows latency or SLA based implementations. Triggers calculate
-a load indicator (signed number) that tells the framework that a given
-partition is either underutilized, operating normally within the
-expected thresholds or overloaded and becoming a bottleneck. The
-indicator is then presented to the partitioning logic (default or custom
-implementation of Partitioner) to provide the opportunity to make any
-needed adjustments.</p>
-<p>The default partitioning logic divides the key space
-according to the lower bits of the hash codes that are generated by the
-stream codec, by assigning each partitioned operator instance via a bit
-mask and the respective value. For example, the operator may have
-initially two partitions, 0and 1, each
-with a bit mask of 1.
-In the case where load evaluation flags partition
-0 as over utilized
-(most data tuples processed yield a hash code with lowest bit cleared),
-apartition split occurs, resulting in 00
-and 10with mask 11. Operator instance 0 will be replaced with 2 new instances and partition
-1 remains unchanged,
-resulting in three active partitions. The same process could repeat if
-most tuples fall into the01 partition, leading to a split into 001 and101
-with mask 111, etc.</p>
-<p>Should load decrease in two sibling partitions, a
-partition merge could
-reverse the split, reducing the mask length and replacing two operators
-with one. Should only one of two sibling partitions be underutilized,
- it cannot be merged.
-Instead, the platform can attempt to deploy the affected operator
-instance along with other operator instances for resource sharing
-amongst underutilized partitions (not implemented yet). Keeping separate
-operator instances allows us to
-pin load increases directly to the affected instance with a single
-specific partition key, which would not be the case had we assigned a
-shared instance to handle multiple keys.</p>
-<h2 id="nxm-partitions">NxM Partitions</h2>
-<p>When two consecutive logical operators are partitioned a special
-optimization is done. Technically the output of the first operator
-should be unified and streamed to the next logical node. But that can
-create a network bottleneck. The platform optimizes this by partitioning
-the output stream of each partition of the first operator as per the
-partitions needed by the next operator. For example if the first
-operator has N partitions and the second operator has M partitions then
-each of the N partitions would send out M streams. The first of each of
-these M streams would be unified and routed to the first of the M
-partitions, and so on. Such an optimization allows for higher
-scalability and eliminates a network bottleneck (one unifier in between
-the two operators) by having M unifiers. This also enables the
-application to perform within the resource limits enforced by YARN.
-STRAM has a much better understanding and estimation of unifier resource
-needs and is thus able to optimize for resource constraints.</p>
-<p>Figure 5 shows a case where we have a 3x2 partition; the single
-intermediate unifier between operator 1 and 2 is
-optimized away. The partition computation for operator 2 is executed on outbound streams of each
-partitions of operator 1. Each
-partition of operator 2 has its own
-CONTAINER_LOCAL unifier. In such a situation, the in-bound network
-tuple flow is split between containers for 2a and 2b each of which take half the traffic. STRAM
-does this by default since it always has better performance.</p>
-<p><img alt="" src="../images/application_development/ApplicationDeveloperGuide.html-image03.png" /></p>
-<h2 id="parallel">Parallel</h2>
-<p>In cases where all the downstream operators use the same
-partitioning scheme and the DAG is network bound an optimization called
-parallel partition is very
-effective. In such a scenario all the downstream operators are also
-partitioned to create computation flow per partition. This optimization
-is extremely efficient for network bound streams, In some cases this
-optimization would also apply for CPU or RAM bounded
-applications.</p>
-<p>In Figure 6a, operator 1 is
-partitioned into 1a and
-1b. Both the downstream operators
-2 and 3 follow the same partition scheme as
-1, however the network I/O between
-1 and 2, and between 2 and 3 is
-high. Then users can decide to optimize using parallel partitions. This
-allows STRAM to completely skip the insertion of intermediate Unifier
-operators between 1 and 2 as well as between 2 and 3; a single unifier
-just before operator 4, is
-adequate by which time tuple flow volume is low.</p>
-<p>Since operator 4 has sufficient resources to manage the combined
-output of multiple instances of operator 3, it need not be partitioned. A further
-optimization can be done by declaring operators 1, 2, and
-3 as THREAD_LOCAL (intra-thread)
-or CONTAINER_LOCAL (intra-process) or NODE_LOCAL (intra-node).
-Parallel partition is not used by default, users have to specify it
-explicitly via an attribute of the input port (reader) of the stream as
-shown below.</p>
-<p><img alt="" src="../images/application_development/ApplicationDeveloperGuide.html-image06.png" /></p>
-<p>The following code shows an example of creating a parallel partition.</p>
-<pre><code class="java">dag.addStream("DenormalizedUserId", idAssigner.userid, uniqUserCount.data);
-dag.setInputPortAttribute(uniqUserCount.data, PortContext.PARTITION_PARALLEL, partitionParallel);
-</code></pre>
-
-<p>Parallel partitions can be used with other partitions, for example
-a parallel partition could be sticky key or load balanced.</p>
-<h2 id="parallel-partitions-with-streams-modes">Parallel Partitions with Streams Modes</h2>
-<p>Parallel partitions can be further optimized if the parallel
-partitions are combined with streams being in-line or in-node or in-rack
-mode. This is very powerful feature and should be used if operators have
-very high throughput within them and the outbound merge does an
-aggregation. For example in Figure 6b, if operator 3 significantly
-reduces the throughput, which usually is a reason to do parallel
-partition, then making the streams in-line or in-node within nodes
-1->2 and 2->3 significantly impacts the performance.</p>
-<p>CONTAINER_LOCAL stream has high bandwidth, and can manage to
-consume massive tuple count without taxing the NIC and networking stack.
-The downside is that all operators (1,2,3) in this case need to be able
-to fit within the resource limits of CPU and memory enforced on a Hadoop
-container. A way around this is to request RM to provide a big
-container. On a highly used Hadoop grid, getting a bigger container may
-be a problem, and operational complexities of managing a Hadoop cluster
-with different container sizes may be higher. If THREAD_LOCAL or
-CONTAINER_LOCAL streams are needed to get the throughput, increasing
-the partition count should be considered. In future STRAM may take this
-decision automatically. Unless there is a very bad skew and sticky key
-partitioning is in use, the approach to partition till each container
-has enough resources works well.</p>
-<p>A NODE_LOCAL stream has lower bandwidth compared to a
-CONTAINER_LOCAL stream, but it works well with the RM in terms of
-respecting container size limits. A NODE_LOCAL parallel partition uses
-local loop back for streams and is much better than using NIC. Though
-NODE_LOCAL stream fits well with similar size containers, it does need
-RM to be able to deliver two containers on the same Hadoop node. On a
-heavily used Hadoop cluster, this may not always be possible. In future
-STRAM would do these trade-offs automatically at run-time.</p>
-<p>A RACK_LOCAL stream has much lower bandwidth than NODE_LOCAL
-stream, as events go through the NIC. But it still is able to better
-manage SLA and latency. Moreover RM has much better ability to give a
-rack local container as opposed to the other two.</p>
-<p>Parallel partitions with CONTAINER_LOCAL streams can be done by
-setting all the intermediate streams to CONTAINER_LOCAL. Parallel
-partitions with THREAD_LOCAL streams can be done by setting all the
-intermediate streams to THREAD_LOCAL. Platform supports the following
-via attributes.</p>
-<ul>
-<li>Parallel-Partition</li>
-<li>Parallel-Partition with THREAD_LOCAL stream</li>
-<li>Parallel-Partition with CONTAINER_LOCAL stream</li>
-<li>Parallel-Partition with NODE_LOCAL stream</li>
-<li>Parallel-Partition with RACK_LOCAL stream</li>
-</ul>
-<p>These attributes would nevertheless be initial starting point and
-STRAM can improve on them at run time.</p>
-<p><img alt="" src="../images/application_development/ApplicationDeveloperGuide.html-image05.png" /></p>
-<h2 id="skew-balancing-partition">Skew Balancing Partition</h2>
-<p>Skew balancing partition is useful to manage skews in the stream
-that is load balanced using a sticky key. Incoming events may have a
-skew, and these may change depending on various factors like time of the
-day or other special circumstances. To manage the uneven load, users can
-set a limit on the ratio of maximum load on a partition to the minimum
-load on a partition. STRAM would use this to dynamically change the
-partitions. For example suppose there are 6 partitions, and the load
-happens to be distributed as follows: one with 40%, and the rest with
-12% each. The ratio of maximum to minimum is 3.33. If the desired ratio
-is set to 2, STRAM would partition the first instance into two
-partitions, each with 20% load to bring the ratio down to the desired
-level. This will be tried repeatedly till partitions are balanced. The
-time period between each attempt is controlled via an attribute to avoid
-rebalancing too frequently. As mentioned earlier, dynamic operations
-include both splitting a partition as well as merging partitions with
-low load.</p>
-<p>Figure 7 shows an example of skew balancing partition. An example
-of 3x1 paritition is shown. Let's say that skew balance is kept at “no
-partition to take up more than 50% load. If in runtime the load type
-changes to create a skew. For example, consider an application in the US
-that is processing a website clickstream. At night in the US, the
-majority of accesses come from the Far East, while in the daytime it
-comes from the Americas. Similarly, in the early morning, the majority
-of the accesses are from east coast of the US, with the skew shifting to
-the west coast as the day progresses. Assume operator 1 is partitioned
-into 1a, 1b, and 1c.</p>
-<p>Let's see what happens if the logical operator 1 gets into a 20%,
-20%, 60% skew as shown in Figure 7. This would trigger the skew
-balancing partition. One example of attaining balance is to merge 1a,
-and 1b to get 1a+1b in a single partition to take the load to 40%; then
-split 1c into two partitions 1ca and 1cb to get 30% on each of them.
-This way STRAM is able to get back to under 50% per partition. As a live
-24x7 application, this kind of skew partitioning can be applied several
-times in a day. Skew-balancing at runtime is a critical feature for SLA
-compliance; it also enables cost savings. This partitioning scheme will
-be available in later release.</p>
-<p><img alt="" src="../images/application_development/ApplicationDeveloperGuide.html-image08.png" /></p>
-<h2 id="skew-unifier-partition">Skew Unifier Partition</h2>
-<p>In this section we would take a look at another way to balance the
-skew. This method is a little less disruptive, but is useful in
-aggregate operators. Let us take the same example as in Figure 7 with
-skew 20%, 20%, and 60%. To manage the load we could have either worked
-on rebalancing the partition, which involves a merge and split of
-partitions to get to a new distribution or by partitioning only the partition with the big skew. Since the
-best way to manage skew is to load balance, if possible, this scheme
-attempts to do so. The method is less useful than the others we discusse
--- the main reason being that if the developer has chosen a sticky key
-partition to start with, it is unlikely that a load balancing scheme can
-help. Assuming that it is worthwhile to load balance, a special
-one-purpose unifier can be inserted for the skew partition. If the cause
-of resource bottleneck is not the I/O, specially the I/O into the
-downstream operator, but is the compute (memory, CPU) power of a
-partition, it makes sense to split the skew partition without having to
-change the in-bound I/O to the upstream operator.</p>
-<p>To trigger this users can set a limit on the ratio of maximum load
-on a partition to the minimum load on a partition, and ask to use this
-scheme. STRAM would use this to load balance.The time period between
-each attempt is controlled via the same attribute to avoid rebalancing
-too frequently.</p>
-<p>Figure 8 shows an example of skew load balancing partition with a
-dedicated unifier. The 20%, 20%, and 60% triggers the skew load
-balancing partition with an unifier. Partition 1c would be split into
-two and it would get its own dedicated unifier. Ideally these two
-additional partitions 1ca and 1cb will get 30% load. This way STRAM is
-able to get back to under 50% per partition. This scheme is very useful
-when the number of partitions is very high and we still have a bad
-skew.</p>
-<p>In the steady state no physical partition is computing more than
-30% of the load. Memory and CPU resources are thus well distributed. The
-unifier that was inserted has to handle 60% of the load, distributed
-more evenly, as opposed to the final unifier that had a 60% skew to
-manage at a much higher total load. This partitioning scheme will be
-available in later release.</p>
-<p><img alt="" src="../images/application_development/ApplicationDeveloperGuide.html-image07.png" /></p>
-<h2 id="cascading-unifier">Cascading Unifier</h2>
-<p>Let's take the case of an upstream operator oprU that connects to a downstream operator
-oprD. Let's assume the application
-is set to scale oprU by load balancing. So this could be either Nx1 or
-NxM partitioning scheme. The upstream operator oprU scales by increasing
-N. An increase in the load triggers more resource needs (CPU, Memory, or
-I/O), which in turn triggers more containers and raises N, the
-downstream node may be impacted in a lot of situations. In this section
-we review a method to shield oprD from dynamic changes in the execution
-plan of oprU. On aggregate operators (Sum, Count, Max, Min, Range …) it
-is better to do load balanced partitioning to avoid impact of skew. This
-works very well as each partition emits tuples at the order of number of
-keys (range) in the incoming stream per application window. But as N
-grows the in-bound I/O to the unifier of oprU that runs in the container
-of oprD goes up proportionately as each upstream partition sends tuples
-of the order of unique keys (range). This means that the partitioning
-would not scale linearly. The platform has mechanisms to manage this and
-get the scale back to being linear.</p>
-<p>Cascading unifiers are implemented by inserting a series of
-intermediate unifiers before the final unifier in the container of oprD.
-Since each unifier guarantees that the outbound I/O would be in order of
-the number of unique keys, the unifier in the oprD container can expect
-to achieve an upper limit on the inbound I/O. The problem is the same
-irrespective of the value of M (1 or more), wherein the amount of
-inbound I/O is proportional to N, not M. Figure 8 illustrates how
-cascading unifier works.</p>
-<p><img alt="" src="../images/application_development/ApplicationDeveloperGuide.html-image09.png" /></p>
-<p>Figure 8 shows an example where a 4x1 partition with single
-unifier is split into three 2x1 partitions to enable the final unifier
-in oprD container to get an upper limit on inbound I/O. This is useful
-to ensure that network I/O to containers is within limits, or within a
-limit specified by users. The platform allows setting an upper limit of
-fan-in of the stream between oprU and oprD. Let's say that this is F (in
-the figure F=2). STRAM would plan N/F (let's call it N1) containers,
-each with one unifier. The inbound fan-in to these unifiers is F. If N1
-> F, another level of unifiers would be inserted. Let's say at some
-point N/(F1*F2*...Fk) < F, where K is the level of unifiers. The
-outbound I/O of each unifier is guaranteed to be under F, specially the
-unifier for oprD. This ensures that the application scales linearly as
-the load grows. The downside is the additional latency imposed by each
-unifier level (a few milliseconds), but the SLA is maintained, and the
-application is able to run within the resource limits imposed by YARN.
-The value of F can be derived from any of the following</p>
-<ul>
-<li>I/O limit on containers to allow proper behavior in an
- multi-tenant environment</li>
-<li>Load on oprD instance</li>
-<li>Buffer server limits on fan-in, fan-out</li>
-<li>Size of reservoir buffer for inbound fan-in</li>
-</ul>
-<p>A more intriguing optimization comes when cascading unifiers are
-combined with node-local execution plan, in which the bounds of two or
-more containers are used and much higher local loopback limits are
-leveraged. In general the first level fan-in limit (F1) and the last
-stage fan-in limit (Fk) need not be same. In fact a much open and better
-leveraged execution plan may indeed have F1 != F2 != … != Fk, as Fk
-determines the fan-in for oprD, while F1, … Fk-1 are fan-ins for
-unifier-only containers. The platform will have these schemes in later
-versions.</p>
-<h2 id="sla">SLA</h2>
-<p>A Service Level Agreement translates to guaranteeing that the
-application would meet the requirements X% of the time. For example six
-sigma X is 99.99966%. For
-real-time streaming applications this translates to requirements for
-latency, throughput, uptime, data loss etc. and that in turn indirectly
-leads to various resource requirements, recovery mechanisms, etc. The
-platform is designed to handle these and features would be released in
-future as they get developed. At a top level, STRAM monitors throughput
-per operator, computes latency per operator, manages uptime and supports
-various recovery mechanisms to handle data loss. A lot of this decision
-making and algorithms will be customizable.</p>
-<hr />
-<h1 id="fault-tolerance">Fault Tolerance</h1>
-<p>Fault tolerance in the platform is defined as the ability to
-recognize the outage of any part of the application, get resources,
-re-initialize the failed operators, and re-compute the lost data. The
-default method is to bring the affected part of the DAG back to a known
-(checkpointed) state and recompute atomic micro batches from there on.
-Thus the default is at least
-once processing mode. An operator can be configured for
-at most once recovery, in which
-case the re-initialized operator starts from next available window; or
-for exactly once recovery, in which
-case the operator only recomputes the window it was processing when the
-outage happened.</p>
-<h2 id="state-of-the-application">State of the Application</h2>
-<p>The state of the application is traditionally defined as the state
-of all operators and streams at any given time. Monitoring state as
-every tuple is processed asynchronously in a distributed environment
-becomes a near impossible task, and cost paid to achieve it is very
-high. Consequently, in the platform, state is not saved per tuple, but
-rather at window boundaries. The platform treats windows as atomic micro
-batches. The state saving task is delegated by STRAM to the individual
-operator or container. This ensures that the bookkeeping cost is very
-low and works in a distributed way. Thus, the state of the application
-is defined as the collection of states of every operator and the set of
-all windows stored in the buffer server. This allows STRAM to rebuild
-any part of the application from the last saved state of the impacted
-operators and the windows retained by the buffer server. The state of an
-operator is intrinsically associated with a window id. Since operators
-can override the default checkpointing period, operators may save state
-at the end of different windows. This works because the buffer server
-saves all windows for as long as they are needed (state in the buffer
-server is purged once STRAM determines that it is not longer needed
-based on checkpointing in downstream operators).</p>
-<p>Operators can be stateless or stateful. A stateless operator
-retains no data between windows. All results of all computations done in
-a window are emitted in that window. Variables in such an operator are
-either transient or are cleared by an end_window event. Such operators
-need no state restoration after an outage. A stateful operator retains
-data between windows and has data in checkpointed state. This data
-(state) is used for computation in future windows. Such an operator
-needs its state restored after an outage. By default the platform
-assumes the operator is stateful. In order to optimize recovery (skip
-processing related to state recovery) for a stateless operator, the
-operator needs to be declared as stateless to STRAM. Operators can
-explicitly mark themselves stateless via an annotation or an
-attribute.</p>
-<p>Recovery mechanisms are explained later in this section. Operator
-developers have to ensure that there is no dependency on the order of
-tuples between two different streams. As mentioned earlier in this
-document, the platform guarantees in-order tuple delivery within a
-single stream, For operators with multiple input ports, a replay may
-result in a different relative order of tuples among the different input
-ports. If the output tuple computation is affected by this relative
-order, the operator may have to wait for the endWindow call (at which
-point it would have seen all the tuples from all input ports in the
-current window), perform order-dependent computations correctly and
-finally, emit results.</p>
-<h2 id="checkpointing">Checkpointing</h2>
-<p>STRAM provides checkpointing parameters to StreamingContainer
-during initialization. A checkpoint period is given to the containers
-that have the window generators. A control tuple is sent at the end of
-checkpoint interval. This tuple traverses through the data path via
-streams and triggers each StreamingContainer in the path to instrument a
-checkpoint of the operator that receives this tuple. This ensures that
-all the operators checkpoint at exactly the same window boundary (except
-in those cases where a different checkpoint interval was configured for
-an operator by the user).</p>
-<p>The only delay is the latency of the control tuple to reach all
-the operators. Checkpoint is thus done between the endWindow call of a
-window and the beginWindow call of the next window. Since most operators
-are computing in parallel (with the exception of those connected by
-THREAD_LOCAL streams) they each checkpoint as and when they are ready
-to process the “checkpoint” control tuple. The asynchronous design of
-the platform means that there is no guarantee that two operators would
-checkpoint at exactly the same time, but there is a guarantee that by
-default they would checkpoint at the same window boundary. This feature
-also ensures that purge of old data can be efficiently done: Once the
-checkpoint window tuple is done traversing the DAG, the checkpoint state
-of the entire DAG increments to this window id at which point prior
-checkpoint data can be discarded.</p>
-<p>In case of an operator that has an application window size that is
-larger than the size of the streaming window, the checkpointing by
-default still happens at same intervals as with other operators. To
-align checkpointing with application window boundary, the application
-developer should set the attribute “CHECKPOINT_WINDOW_COUNT” to
-“APPLICATION_WINDOW_COUNT”. This ensures that the checkpoint happens
-at the end of the application
-window and not within that window.
-Such operators now treat the application window as an atomic computation
-unit. The downside is that it does need the upstream buffer server to
-keep tuples for the entire application window.</p>
-<p>If an operator is completely stateless, i.e. an outbound tuple is
-only emitted in the process call
-and only depends on the tuple of that call, there is no need to align
-checkpointing with application window end. If the operator is stateful
-only within a window, the operator developer should strongly consider
-checkpointing only on the application window boundary.</p>
-<p>Checkpointing involves pausing an operator, serializing the state
-to persistent storage and then resuming the operator. Thus checkpointing
-has a latency cost that can negatively affect computational throughput;
-to minimize that impact, it is important to ensure that checkpointing is
-done with minimal required objects. This means, as mentioned earlier,
-all data that is not part of the operator state should be declared as
-transient so that it is not persisted.</p>
-<p>An operator developer can also create a stateless operator (marked
-with the Stateless annotation). Stateless operators are not
-checkpointed. Obviously, in such an operator, computation should not
-depend on state from a previous window.</p>
-<p>The serialized state of an operator is stored as a file, and is
-the state to which that the operator is restored if an outage happens
-before the next checkpoint. The id of the last completed window (per
-operator) is sent back to STRAM in the next heartbeat. The default
-implementation for serialization uses KRYO. Multiple past checkpoints
-are kept per operator. Depending on the downstream checkpoint, one of
-these are chosen for recovery. Checkpoints and buffer server state are
-purged once STRAM sees windows as fully processed in the DAG.</p>
-<p>A complete recovery of an operator needs the operator to be
-created, its checkpointed state restored and then all the lost atomic
-windows replayed by the upstream buffer server(s). The above design
-keeps the bookkeeping cost low with quick catch up time. In the next
-section we will see how this simple abstraction allows applications to
-recover under different requirements.</p>
-<h2 id="recovery-mechanisms_1">Recovery Mechanisms</h2>
-<p>Recovery mechanism are ways to recover from a container (or an
-operator) outage. In this section we discuss a single container outage.
-Multiple container outages are handled as independent events. Recovery
-requires the upstream buffer server to replay windows and it would
-simply go one more level upstream if the immediate upstream container
-has also failed. If multiple operators are in a container (THREAD_LOCAL
-or CONTAINER_LOCAL stream) the container recovery treats each operator
-as an independent object when figuring out the recovery steps.
-Application developers can set any of the recovery mechanisms discussed
-below for node outage.</p>
-<p>In general, the cost of recovery depends on the state of the
-operator and the recovery mechanism selected, while data loss tolerance
-is specified by the application. For example a data-loss tolerant
-application would prefer at most
-once recovery. All recovery mechanisms treat a streaming
-window as an atomic computation unit. In all three recovery mechanisms
-the new operator connects to the upstream buffer server and asks for
-data from a particular window onwards. Thus all recovery methods
-translate to deciding which atomic units to re-compute and which state
-the new operator resumes from. A partially computed micro-batch is
-always dropped. Such micro-batches are re-computed in at-least-once or
-exactly-once mode and skipped in at-most-once mode. The notiion of an
-atomic micro-batch is a critical guiding principle as it enables very
-low bookkeeping costs, high throughput, low recovery times, and high
-scalability. Within an application each operator can have its own
-recovery mechanism.</p>
-<h3 id="at-least-once">At Least Once</h3>
-<p>At least once recovery is the default recovery mechanism, i.e it
-is used when no mechanism is specified. In this method, the lost
-operator is brought back to its latest viable checkpointed state and the
-upstream buffer server is asked to replay all subsequent windows. There
-is no data loss in recovery. The viable checkpoint state is defined as
-the one whose window id is in the past as compared to all the
-checkpoints of all the downstream operators. All downstream operators
-are restarted at their checkpointed state. They ignore all incoming data
-that belongs to windows prior their checkpointed window. The lost
-windows are thus recomputed and the application catches up with live
-incoming data. This is called " at least
-once" because lost windows are recomputed. For example if
-the streaming window is 0.5 seconds and checkpointing is being done
-every 30 seconds, then upon node outage all windows since the last
-checkpoint (up to 60 windows) need to be re-processed. If the
-application can handle loss of data, then this is not the most optimal
-recovery mechanism.</p>
-<p>In general for this recovery mode, the average time lag on a node
-outage is</p>
-<p><strong>= (CP/2*SW)*T + HC</strong></p>
-<p>where</p>
-<ul>
-<li><strong>CP</strong> - Checkpointing period (default value is 30 seconds)</li>
-<li><strong>SW</strong> - Streaming window period (default value is 0.5 seconds)</li>
-<li><strong>T</strong> - Time taken to re-compute one lost window from data in memory</li>
-<li><strong>HC</strong> - Time it takes to get a new Hadoop Container, or make do with the current ones</li>
-</ul>
-<p>A lower CP is a trade off between cost of checkpointing and the
-need to have to use it in case of outage. Input adapters cannot use
-at-least-once recovery without the support from sources outside Hadoop.
-For an output adapter care may needed if the external system cannot
-handle re-write of the same data.</p>
-<h3 id="at-most-once">At Most Once</h3>
-<p>This recovery mechanism is for applications that can tolerate
-data-loss; they get the quickest recovery in return. The restarted node
-connects to the upstream buffer server, subscribing to data from the
-start of the next window. It then starts processing that window. The
-downstream operators ignore the lost windows and continue to process
-incoming data normally. Thus, this mechanism forces all downstream
-operators to follow.</p>
-<p>For multiple inputs, the operator waits for all ports with the
-at-most-once attribute to get responses from their respective buffer
-servers. Then, the operator starts processing till the end window of the
-latest window id on each input port is reached. In this case the end
-window tuple is non-blocking till the common window id is reached. At
-this point the input ports are now properly synchronized. Upstream nodes
-reconnect under at most
-once paradigm in same way. For example, assume an operator
-has ports in1 and in2 and a checkpointed window of 95. Assume further that the buffer servers of
-operators upstream of in1 and
-in2 respond with window id 100 and
-102 respectively. Then port in1 would continue to process till end window of
-101, while port in2 will wait for in1
-to catch up to 102.
-From then on, both ports process their tuples normally. So windows from
-96 to 99are lost. Window 100
-and 101 has only
-in1 active, and 102 onwards both ports are active. The other
-ports of upstream nodes would also catch up till 102in a similar fashion. This operator may not
-need to be checkpointed. Currently the option to not do checkpoint in
-such cases is not available.</p>
-<p>In general, in this recovery mode, the average time lag on a node
-outage is</p>
-<p><strong>= SW/2 + HC</strong></p>
-<p>where</p>
-<ul>
-<li>
-<p><strong>SW</strong> - Streaming window period (default value is 0.5
-seconds)</p>
-</li>
-<li>
-<p><strong>HC</strong> - Time it takes to get a new Hadoop Container, or make
-do with the current ones</p>
-</li>
-</ul>
-<h3 id="exactly-once">Exactly Once</h3>
-<p>This recovery mechanism is for applications that require no
-data-loss as well are no recomputation. Since a window is an atomic
-compute unit, exactly once applies to the window as a whole. In this
-recovery mode, the operator is brought back to the start of the window
-in which the outage happened and the window is recomputed. The window is
-considered closed when all the data computations are done and end window
-tuple is emitted. Exactly once requires every window to be
-checkpointed. From then on, the operator asks the upstream buffer server
-to send data from the last checkpoint. The upstream node behaves the
-same as in at-most-once recovery. Checkpointing after every streaming
-window is very costly, but users would most often do exactly once per
-application window; if the application window size is substantially
-larger than the streaming window size (which typically is the case) the
-cost of running an operator in this recovery mode may not be as
-high.</p>
-<h3 id="speculative-execution">Speculative Execution</h3>
-<p>In future we looking at possibility of adding speculative execution for the applications. This would be enabled in multiple ways.</p>
-<ol>
-<li>
-<p>At an operator level: The upstream operator would emit to
- two copies. The downstream operator would receive from both copies
- and pick a winner. The winner (primary) would be picked in either of
- the following ways</p>
-<ul>
-<li>Statically as dictated by STRAM</li>
-<li>Dynamically based on whose tuple arrives first. This mode
- needs both copies to guarantee that the computation result would
- have identical functionality</li>
-</ul>
-</li>
-<li>
-<p>At a sub-query level: A part of the application DAG would be
- run in parallel and all upstream operators would feed to two copies
- and all downstream operators would receive from both copies. The
- winners would again be picked in a static or dynamic manner</p>
-</li>
-<li>Entire DAG: Another copy of the application would be run by
- STRAM and the winner would be decided outside the application. In
- this mode the output adapters would both be writing
- the result.</li>
-</ol>
-<p>In all cases the two copies would run on different Hadoop nodes.
-Speculative execution is under development and
-is not yet available.</p>
-<hr />
-<h1 id="dynamic-application-modifications">Dynamic Application Modifications</h1>
-<p>Dynamic application modifications are being worked on and most of
-the features discussed here are now available. The platform supports the
-ability to modify the DAG of the application as per inputs as well as
-set constraints, and will continue to provide abilities to deepen
-features based on this ability. All these changes have one thing in
-common and that is the application does not need to be restarted as
-STRAM will instrument the changes and the streaming will catch-up and
-continue.</p>
-<p>Some examples are</p>
-<ul>
-<li>Dynamic Partitioning: Automatic
- changes in partitioning of computations to match constraints on a
- run time basis. Examples includes STRAM adding resource during spike
- in streams and returning them once spike is gone. Scale up and scale
- down is done automatically without human intervention.</li>
-<li>Modification via constraints: Attributes can be changed via
- Webservices and STRAM would adapt the execution plan to meet these.
- Examples include operations folks asking STRAM to reduce container
- count, or changing network resource restrictions.</li>
-<li>Modification via properties: Properties of operators can be
- changed in run time. This enables application developers to trigger
- a new behavior as need be. Examples include triggering an alert ON.
- The platform supports changes to any property of an operator that
- has a setter function defined.</li>
-<li>Modification of DAG structure: Operators and streams can be
- added to or removed from a running DAG, provided the code of the
- operator being added is already in the classpath of the running
- application master. This enables application developers to add or
- remove processing pipelines on the fly without having to restart
- the application.</li>
-<li>Query Insertion: Addition of sub-queries to currently
- running application. This query would take current streams as inputs
- and start computations as per their specs. Examples insertion of
- SQL-queries on live data streams, dynamic query submission and
- result from STRAM (not yet available).</li>
-</ul>
-<p>Dynamic modifications to applications are foundational part of the
-platform. They enable users to build layers over the applications. Users
-can also save all the changes done since the application launch, and
-therefore predictably get the application to its current state. For
-details refer to <a href="http://docs.datatorrent.com/configuration/">Configuration Guide</a>
-.</p>
-<hr />
-<h1 id="demos">Demos</h1>
-<p>The source code for the demos is available in the open-source
-<a href="https://github.com/apache/incubator-apex-malhar">Apache Apex-Malhar repository</a>.
-All of these do computations in real-time. Developers are encouraged to
-review them as they use various features of the platform and provide an
-opportunity for quick learning.</p>
-
- </div>
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- <h1 id="apache-apex-packages">Apache Apex Packages</h1>
-<h1 id="application-packages">Application Packages</h1>
-<p>An Apache Apex Application Package is a zip file that contains all the
-necessary files to launch an application in Apache Apex. It is the
-standard way for assembling and sharing an Apache Apex application.</p>
-<h2 id="requirements">Requirements</h2>
-<p>You will need have the following installed:</p>
-<ol>
-<li>Apache Maven 3.0 or later (for assembling the App Package)</li>
-<li>Apache Apex 3.2.0 or later (for launching the App Package in your cluster)</li>
-</ol>
-<h2 id="creating-your-first-apex-app-package">Creating Your First Apex App Package</h2>
-<p>You can create an Apex Application Package using your Linux command
-line, or using your favorite IDE.</p>
-<h3 id="using-command-line">Using Command Line</h3>
-<p>First, change to the directory where you put your projects, and create
-an Apex application project using Maven by running the following
-command. Replace "com.example", "mydtapp" and "1.0-SNAPSHOT" with the
-appropriate values (make sure this is all on one line):</p>
-<pre><code>$ mvn archetype:generate \
- -DarchetypeGroupId=org.apache.apex \
- -DarchetypeArtifactId=apex-app-archetype -DarchetypeVersion=3.2.0-incubating \
- -DgroupId=com.example -Dpackage=com.example.mydtapp -DartifactId=mydtapp \
- -Dversion=1.0-SNAPSHOT
-</code></pre>
-<p>This creates a Maven project named "mydtapp". Open it with your favorite
-IDE (e.g. NetBeans, Eclipse, IntelliJ IDEA). In the project, there is a
-sample DAG that generates a number of tuples with a random number and
-prints out "hello world" and the random number in the tuples. The code
-that builds the DAG is in
-src/main/java/com/example/mydtapp/Application.java, and the code that
-runs the unit test for the DAG is in
-src/test/java/com/example/mydtapp/ApplicationTest.java. Try it out by
-running the following command:</p>
-<pre><code>$cd mydtapp; mvn package
-</code></pre>
-<p>This builds the App Package runs the unit test of the DAG. You should
-be getting test output similar to this:</p>
-<pre><code> -------------------------------------------------------
- TESTS
- -------------------------------------------------------
-
- Running com.example.mydtapp.ApplicationTest
- hello world: 0.8015370953286478
- hello world: 0.9785359225545481
- hello world: 0.6322611586644047
- hello world: 0.8460953663451775
- hello world: 0.5719372906929072
- hello world: 0.6361174312337172
- hello world: 0.14873007534816318
- hello world: 0.8866986277418261
- hello world: 0.6346526809866057
- hello world: 0.48587295703904465
- hello world: 0.6436832429676687
-
- ...
-
- Tests run: 1, Failures: 0, Errors: 0, Skipped: 0, Time elapsed: 11.863
- sec
-
- Results :
-
- Tests run: 1, Failures: 0, Errors: 0, Skipped: 0
-</code></pre>
-
-<p>The "mvn package" command creates the App Package file in target
-directory as target/mydtapp-1.0-SNAPSHOT.apa. You will be able to use
-that App Package file to launch this sample application in your actual
-Apex installation.</p>
-<p>Alternatively you can perform the same steps within your IDE (IDEA IntelliJ, Eclipse, NetBeans all support it). Please check the IDE documentation for details.</p>
-<p>Group ID: org.apache.apex
-Artifact ID: apex-app-archetype
-Version: 3.2.0-incubating (or any later version)</p>
-<h2 id="writing-your-own-app-package">Writing Your Own App Package</h2>
-<p>Please refer to the <a href="http://docs.datatorrent.com/create/">Creating Apps</a> on the basics on how to write an Apache Apex application. In your AppPackage project, you can add custom operators (refer to <a href="../operator_development/">Operator Development Guide</a>, project dependencies, default and required configuration properties, pre-set configurations and other metadata.</p>
-<h3 id="adding-and-removing-project-dependencies">Adding (and removing) project dependencies</h3>
-<p>Under the project, you can add project dependencies in pom.xml, or do it
-through your IDE. Here’s the section that describes the dependencies in
-the default pom.xml:</p>
-<pre><code> <dependencies>
- <!-- add your dependencies here -->
- <dependency>
- <groupId>org.apache.apex</groupId>
- <artifactId>malhar-library</artifactId>
- <version>${apex.version}</version>
- <!--
- If you know your application do not need the transitive dependencies that are pulled in by malhar-library,
- Uncomment the following to reduce the size of your app package.
- -->
- <!--
- <exclusions>
- <exclusion>
- <groupId>*</groupId>
- <artifactId>*</artifactId>
- </exclusion>
- </exclusions>
- -->
- </dependency>
- <dependency>
- <groupId>org.apache.apex</groupId>
- <artifactId>apex-engine</artifactId>
- <version>${apex.version}</version>
- <scope>provided</scope>
- </dependency>
- <dependency>
- <groupId>junit</groupId>
- <artifactId>junit</artifactId>
- <version>4.10</version>
- <scope>test</scope>
- </dependency>
- </dependencies>
-</code></pre>
-
-<p>By default, as shown above, the default dependencies include
-malhar-library in compile scope, dt-engine in provided scope, and junit
-in test scope. Do not remove these three dependencies since they are
-necessary for any Apex application. You can, however, exclude
-transitive dependencies from malhar-library to reduce the size of your
-App Package, provided that none of the operators in malhar-library that
-need the transitive dependencies will be used in your application.</p>
-<p>In the sample application, it is safe to remove the transitive
-dependencies from malhar-library, by uncommenting the "exclusions"
-section. It will reduce the size of the sample App Package from 8MB to
-700KB.</p>
-<p>Note that if we exclude *, in some versions of Maven, you may get
-warnings similar to the following:</p>
-<pre><code>
- [WARNING] 'dependencies.dependency.exclusions.exclusion.groupId' for
- org.apache.apex:malhar-library:jar with value '*' does not match a
- valid id pattern.
-
- [WARNING]
- [WARNING] It is highly recommended to fix these problems because they
- threaten the stability of your build.
- [WARNING]
- [WARNING] For this reason, future Maven versions might no longer support
- building such malformed projects.
- [WARNING]
-
-</code></pre>
-
-<p>This is a bug in early versions of Maven 3. The dependency exclusion is
-still valid and it is safe to ignore these warnings.</p>
-<h3 id="application-configuration">Application Configuration</h3>
-<p>A configuration file can be used to configure an application. Different
-kinds of configuration parameters can be specified. They are application
-attributes, operator attributes and properties, port attributes, stream
-properties and application specific properties. They are all specified
-as name value pairs, in XML format, like the following.</p>
-<pre><code><?xml version="1.0"?>
-<configuration>
- <property>
- <name>some_name_1</name>
- <value>some_default_value</value>
- </property>
- <property>
- <name>some_name_2</name>
- <value>some_default_value</value>
- </property>
-</configuration>
-</code></pre>
-
-<h3 id="application-attributes">Application attributes</h3>
-<p>Application attributes are used to specify the platform behavior for the
-application. They can be specified using the parameter
-<code>dt.attr.<attribute></code>. The prefix “dt” is a constant, “attr” is a
-constant denoting an attribute is being specified and <code><attribute></code>
-specifies the name of the attribute. Below is an example snippet setting
-the streaming windows size of the application to be 1000 milliseconds.</p>
-<pre><code> <property>
- <name>dt.attr.STREAMING_WINDOW_SIZE_MILLIS</name>
- <value>1000</value>
- </property>
-</code></pre>
-
-<p>The name tag specifies the attribute and value tag specifies the
-attribute value. The name of the attribute is a JAVA constant name
-identifying the attribute. The constants are defined in
-com.datatorrent.api.Context.DAGContext and the different attributes can
-be specified in the format described above.</p>
-<h3 id="operator-attributes">Operator attributes</h3>
-<p>Operator attributes are used to specify the platform behavior for the
-operator. They can be specified using the parameter
-<code>dt.operator.<operator-name>.attr.<attribute></code>. The prefix “dt” is a
-constant, “operator” is a constant denoting that an operator is being
-specified, <code><operator-name></code> denotes the name of the operator, “attr” is
-the constant denoting that an attribute is being specified and
-<code><attribute></code> is the name of the attribute. The operator name is the
-same name that is specified when the operator is added to the DAG using
-the addOperator method. An example illustrating the specification is
-shown below. It specifies the number of streaming windows for one
-application window of an operator named “input” to be 10</p>
-<pre><code><property>
- <name>dt.operator.input.attr.APPLICATION_WINDOW_COUNT</name>
- <value>10</value>
-</property>
-</code></pre>
-
-<p>The name tag specifies the attribute and value tag specifies the
-attribute value. The name of the attribute is a JAVA constant name
-identifying the attribute. The constants are defined in
-com.datatorrent.api.Context.OperatorContext and the different attributes
-can be specified in the format described above.</p>
-<h3 id="operator-properties">Operator properties</h3>
-<p>Operators can be configured using operator specific properties. The
-properties can be specified using the parameter
-<code>dt.operator.<operator-name>.prop.<property-name></code>. The difference
-between this and the operator attribute specification described above is
-that the keyword “prop” is used to denote that it is a property and
-<code><property-name></code> specifies the property name. An example illustrating
-this is specified below. It specifies the property “hostname” of the
-redis server for a “redis” output operator.</p>
-<pre><code> <property>
- <name>dt.operator.redis.prop.host</name>
- <value>127.0.0.1</value>
- </property>
-</code></pre>
-
-<p>The name tag specifies the property and the value specifies the property
-value. The property name is converted to a setter method which is called
-on the actual operator. The method name is composed by appending the
-word “set” and the property name with the first character of the name
-capitalized. In the above example the setter method would become
-setHost. The method is called using JAVA reflection and the property
-value is passed as an argument. In the above example the method setHost
-will be called on the “redis” operator with “127.0.0.1” as the argument.</p>
-<h3 id="port-attributes">Port attributes</h3>
-<p>Port attributes are used to specify the platform behavior for input and
-output ports. They can be specified using the parameter <code>dt.operator.<operator-name>.inputport.<port-name>.attr.<attribute></code>
-for input port and <code>dt.operator.<operator-name>.outputport.<port-name>.attr.<attribute></code>
-for output port. The keyword “inputport” is used to denote an input port
-and “outputport” to denote an output port. The rest of the specification
-follows the conventions described in other specifications above. An
-example illustrating this is specified below. It specifies the queue
-capacity for an input port named “input” of an operator named “range” to
-be 4k.</p>
-<pre><code><property>
- <name>dt.operator.range.inputport.input.attr.QUEUE_CAPACITY</name>
- <value>4000</value>
-</property>
-</code></pre>
-
-<p>The name tag specifies the attribute and value tag specifies the
-attribute value. The name of the attribute is a JAVA constant name
-identifying the attribute. The constants are defined in
-com.datatorrent.api.Context.PortContext and the different attributes can
-be specified in the format described above.</p>
-<p>The attributes for an output port can also be specified in a similar way
-as described above with a change that keyword “outputport” is used
-instead of “intputport”. A generic keyword “port” can be used to specify
-either an input or an output port. It is useful in the wildcard
-specification described below.</p>
-<h3 id="stream-properties">Stream properties</h3>
-<p>Streams can be configured using stream properties. The properties can be
-specified using the parameter
-<code>dt.stream.<stream-name>.prop.<property-name></code> The constant “stream”
-specifies that it is a stream, <code><stream-name></code> specifies the name of the
-stream and <code><property-name></code> the name of the property. The name of the
-stream is the same name that is passed when the stream is added to the
-DAG using the addStream method. An example illustrating the
-specification is shown below. It sets the locality of the stream named
-“stream1” to container local indicating that the operators the stream is
-connecting be run in the same container.</p>
-<pre><code> <property>
- <name>dt.stream.stream1.prop.locality</name>
- <value>CONTAINER_LOCAL</value>
- </property>
-</code></pre>
-
-<p>The property name is converted into a set method on the stream in the
-same way as described in operator properties section above. In this case
-the method would be setLocality and it will be called in the stream
-“stream1” with the value as the argument.</p>
-<p>Along with the above system defined parameters, the applications can
-define their own specific parameters they can be specified in the
-configuration file. The only condition is that the names of these
-parameters don’t conflict with the system defined parameters or similar
-application parameters defined by other applications. To this end, it is
-recommended that the application parameters have the format
-<code><full-application-class-name>.<param-name>.</code> The
-full-application-class-name is the full JAVA class name of the
-application including the package path and param-name is the name of the
-parameter within the application. The application will still have to
-still read the parameter in using the configuration API of the
-configuration object that is passed in populateDAG.</p>
-<h3 id="wildcards">Wildcards</h3>
-<p>Wildcards and regular expressions can be used in place of names to
-specify a group for applications, operators, ports or streams. For
-example, to specify an attribute for all ports of an operator it can be
-done as follows</p>
-<pre><code><property>
- <name>dt.operator.range.port.*.attr.QUEUE_CAPACITY</name>
- <value>4000</value>
-</property>
-</code></pre>
-
-<p>The wildcard “*” was used instead of the name of the port. Wildcard can
-also be used for operator name, stream name or application name. Regular
-expressions can also be used for names to specify attributes or
-properties for a specific set.</p>
-<h3 id="adding-configuration-properties">Adding configuration properties</h3>
-<p>It is common for applications to require configuration parameters to
-run. For example, the address and port of the database, the location of
-a file for ingestion, etc. You can specify them in
-src/main/resources/META-INF/properties.xml under the App Package
-project. The properties.xml may look like:</p>
-<pre><code><?xml version="1.0"?>
-<configuration>
- <property>
- <name>some_name_1</name>
- </property>
- <property>
- <name>some_name_2</name>
- <value>some_default_value</value>
- </property>
-</configuration>
-</code></pre>
-
-<p>The name of an application-specific property takes the form of:</p>
-<p><code>dt.operator.{opName}.prop.{propName}</code></p>
-<p>The first represents the property with name propName of operator opName.
- Or you can set the application name at run time by setting this
-property:</p>
-<pre><code> dt.attr.APPLICATION_NAME
-</code></pre>
-<p>In this example, property some_name_1 is a required property which
-must be set at launch time, or it must be set by a pre-set configuration
-(see next section). Property some_name_2 is a property that is
-assigned with value some_default_value unless it is overridden at
-launch time.</p>
-<h3 id="adding-pre-set-configurations">Adding pre-set configurations</h3>
-<p>At build time, you can add pre-set configurations to the App Package by
-adding configuration XML files under <code>src/site/conf/<conf>.xml</code>in your
-project. You can then specify which configuration to use at launch
-time. The configuration XML is of the same format of the properties.xml
-file.</p>
-<h3 id="application-specific-properties-file">Application-specific properties file</h3>
-<p>You can also specify properties.xml per application in the application
-package. Just create a file with the name properties-{appName}.xml and
-it will be picked up when you launch the application with the specified
-name within the application package. In short:</p>
-<p>properties.xml: Properties that are global to the Configuration
-Package</p>
-<p>properties-{appName}.xml: Properties that are specific when launching
-an application with the specified appName.</p>
-<h3 id="properties-source-precedence">Properties source precedence</h3>
-<p>If properties with the same key appear in multiple sources (e.g. from
-app package default configuration as META-INF/properties.xml, from app
-package configuration in the conf directory, from launch time defines,
-etc), the precedence of sources, from highest to lowest, is as follows:</p>
-<ol>
-<li>Launch time defines (using -D option in CLI)</li>
-<li>Launch time specified configuration file in file system (using -conf
- option in CLI)</li>
-<li>Launch time specified package configuration (using -apconf option in
- CLI)</li>
-<li>Configuration from \$HOME/.dt/dt-site.xml</li>
-<li>Application defaults within the package as
- META-INF/properties-{appname}.xml</li>
-<li>Package defaults as META-INF/properties.xml</li>
-<li>dt-site.xml in local DT installation</li>
-<li>dt-site.xml stored in HDFS</li>
-</ol>
-<h3 id="other-meta-data">Other meta-data</h3>
-<p>In a Apex App Package project, the pom.xml file contains a
-section that looks like:</p>
-<pre><code><properties>
- <apex.version>3.2.0-incubating</apex.version>
- <apex.apppackage.classpath\>lib*.jar</apex.apppackage.classpath>
-</properties>
-</code></pre>
-
-<p>apex.version is the Apache Apex version that are to be used
-with this Application Package.</p>
-<p>apex.apppackage.classpath is the classpath that is used when
-launching the application in the Application Package. The default is
-lib/*.jar, where lib is where all the dependency jars are kept within
-the Application Package. One reason to change this field is when your
-Application Package needs the classpath in a specific order.</p>
-<h3 id="logging-configuration">Logging configuration</h3>
-<p>Just like other Java projects, you can change the logging configuration
-by having your log4j.properties under src/main/resources. For example,
-if you have the following in src/main/resources/log4j.properties:</p>
-<pre><code> log4j.rootLogger=WARN,CONSOLE
- log4j.appender.CONSOLE=org.apache.log4j.ConsoleAppender
- log4j.appender.CONSOLE.layout=org.apache.log4j.PatternLayout
- log4j.appender.CONSOLE.layout.ConversionPattern=%d{ISO8601} [%t] %-5p
- %c{2} %M - %m%n
-</code></pre>
-
-<p>The root logger’s level is set to WARN and the output is set to the console (stdout).</p>
-<p>Note that by default from project created from the maven archetype,
-there is already a log4j.properties file under src/test/resources and
-that file is only used for the unit test.</p>
-<h2 id="zip-structure-of-application-package">Zip Structure of Application Package</h2>
-<p>Apache Apex Application Package files are zip files. You can examine the content of any Application Package by using unzip -t on your Linux command line.</p>
-<p>There are four top level directories in an Application Package:</p>
-<ol>
-<li>"app" contains the jar files of the DAG code and any custom operators.</li>
-<li>"lib" contains all dependency jars</li>
-<li>"conf" contains all the pre-set configuration XML files.</li>
-<li>"META-INF" contains the MANIFEST.MF file and the properties.xml file.</li>
-<li>“resources” contains any other files</li>
-</ol>
-<h2 id="examining-and-launching-application-packages-through-cli">Examining and Launching Application Packages Through CLI</h2>
-<p>If you are working with Application Packages in the local filesystem, you can use the Apex Command Line Interface (dtcli). </p>
-<h3 id="getting-application-package-meta-information">Getting Application Package Meta Information</h3>
-<p>You can get the meta information about the Application Package using
-this Apex CLI command.</p>
-<pre><code> dt> get-app-package-info <app-package-file>
-</code></pre>
-
-<h3 id="getting-available-operators-in-application-package">Getting Available Operators In Application Package</h3>
-<p>You can get the list of available operators in the Application Package
-using this command.</p>
-<pre><code> dt> get-app-package-operators <app-package-file> <package-prefix>
- [parent-class]
-</code></pre>
-
-<h3 id="getting-properties-of-operators-in-application-package">Getting Properties of Operators in Application Package</h3>
-<p>You can get the list of properties of any operator in the Application
-Package using this command.</p>
-<p>dt> get-app-package-operator-properties <app-package-file> <operator-class></p>
-<h3 id="launching-an-application-package">Launching an Application Package</h3>
-<p>You can launch an application within an Application Package.</p>
-<pre><code>dt> launch [-D property-name=property-value, ...] [-conf config-name]
- [-apconf config-file-within-app-package] <app-package-file>
- [matching-app-name]
-</code></pre>
-
-<p>Note that -conf expects a configuration file in the file system, while -apconf expects a configuration file within the app package.</p>
-<h1 id="configuration-packages">Configuration Packages</h1>
-<p>Sometimes just a configuration file is not enough for launching an application package. If a configuration requires
-additional files to be packaged, you can use an Apex Configuration Package.</p>
-<h2 id="creating-configuration-packages">Creating Configuration Packages</h2>
-<p>Creating Configuration Packages is similar to creating Application Packages. You can create a configuration
-package project using Maven by running the following command. Replace "com.example", "mydtconfig" and "1.0-SNAPSHOT" with the appropriate values:</p>
-<pre><code>$ mvn archetype:generate -DarchetypeGroupId=org.apache.apex \
- -DarchetypeArtifactId=apex-conf-archetype -DarchetypeVersion=3.2.0-incubating \
- -DgroupId=com.example -Dpackage=com.example.mydtconfig -DartifactId=mydtconfig \
- -Dversion=1.0-SNAPSHOT
-</code></pre>
-
-<p>And create the configuration package file by running:</p>
-<pre><code>$ mvn package
-</code></pre>
-
-<p>The "mvn package" command creates the Config Package file in target
-directory as target/mydtconfig.apc. You will be able to use that
-Configuration Package file to launch an Apache Apex application.</p>
-<h2 id="assembling-your-own-configuration-package">Assembling your own configuration package</h2>
-<p>Inside the project created by the archetype, these are the files that
-you should know about when assembling your own configuration package:</p>
-<pre><code>./pom.xml
-./src/main/resources/classpath
-./src/main/resources/files
-./src/main/resources/META-INF/properties.xml
-./src/main/resources/META-INF/properties-{appname}.xml
-</code></pre>
-<h3 id="pomxml">pom.xml</h3>
-<p>Example:</p>
-<pre><code class="xml"> <groupId>com.example</groupId>
- <version>1.0.0</version>
- <artifactId>mydtconf</artifactId>
- <packaging>jar</packaging>
- <!-- change these to the appropriate values -->
- <name>My Apex Application Configuration</name>
- <description>My Custom Application Configuration Description</description>
- <properties>
- <apex.apppackage.name>myapexapp</apex.apppackage.name>
- <apex.apppackage.minversion>1.0.0</apex.apppackage.minversion>
- <apex.apppackage.maxversion>1.9999.9999</apex.apppackage.maxversion>
- <apex.appconf.classpath>classpath/*</apex.appconf.classpath>
- <apex.appconf.files>files/*</apex.appconf.files>
- </properties>
-
-</code></pre>
-
-<p>In pom.xml, you can change the following keys to your desired values</p>
-<ul>
-<li><code><groupId></code></li>
-<li><code><version></code></li>
-<li><code><artifactId></code></li>
-<li><code><name></code></li>
-<li><code><description></code></li>
-</ul>
-<p>You can also change the values of</p>
-<ul>
-<li><code><apex.apppackage.name></code></li>
-<li><code><apex.apppackage.minversion></code></li>
-<li><code><apex.apppackage.maxversion></code></li>
-</ul>
-<p>to reflect what Application Packages can be used with this configuration package. Apex will use this information to check whether a
-configuration package is compatible with the Application Package when you issue a launch command.</p>
-<h3 id="srcmainresourcesclasspath">./src/main/resources/classpath</h3>
-<p>Place any file in this directory that you’d like to be copied to the
-compute machines when launching an application and included in the
-classpath of the application. Example of such files are Java properties
-files and jar files.</p>
-<h3 id="srcmainresourcesfiles">./src/main/resources/files</h3>
-<p>Place any file in this directory that you’d like to be copied to the
-compute machines when launching an application but not included in the
-classpath of the application.</p>
-<h3 id="properties-xml-file">Properties XML file</h3>
-<p>A properties xml file consists of a set of key-value pairs. The set of
-key-value pairs specifies the configuration options the application
-should be launched with.</p>
-<p>Example:</p>
-<pre><code class="xml"><configuration>
- <property>
- <name>some-property-name</name>
- <value>some-property-value</value>
- </property>
- ...
-</configuration>
-</code></pre>
-
-<p>Names of properties XML file:</p>
-<ul>
-<li><strong>properties.xml:</strong> Properties that are global to the Configuration
-Package</li>
-<li><strong>properties-{appName}.xml:</strong> Properties that are specific when launching
-an application with the specified appName within the Application
-Package.</li>
-</ul>
-<p>After you are done with the above, remember to do mvn package to
-generate a new configuration package, which will be located in the
-target directory in your project.</p>
-<h3 id="zip-structure-of-configuration-package">Zip structure of configuration package</h3>
-<p>Apex Application Configuration Package files are zip files. You
-can examine the content of any Application Configuration Package by
-using unzip -t on your Linux command line. The structure of the zip
-file is as follow:</p>
-<pre><code>META-INF
- MANIFEST.MF
- properties.xml
- properties-{appname}.xml
-classpath
- {classpath files}
-files
- {files}
-</code></pre>
-
-<h3 id="launching-with-cli">Launching with CLI</h3>
-<p><code>-conf</code> option of the launch command in CLI supports specifying configuration package in the local filesystem. Example:</p>
-<pre><code>dt\> launch mydtapp-1.0.0.apa -conf mydtconfig.apc
-</code></pre>
-<p>This command expects both the application package and the configuration package to be in the local file system.</p>
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- <h1 id="apache-apex-autometrics">Apache Apex AutoMetrics</h1>
-<h1 id="introduction">Introduction</h1>
-<p>Metrics collect various statistical information about a process which can be very useful for diagnosis. Auto Metrics in Apex can help monitor operators in a running application. The goal of <em>AutoMetric</em> API is to enable operator developer to define relevant metrics for an operator in a simple way which the platform collects and reports automatically.</p>
-<h1 id="specifying-autometrics-in-an-operator">Specifying AutoMetrics in an Operator</h1>
-<p>An <em>AutoMetric</em> can be any object. It can be of a primitive type - int, long, etc. or a complex one. A field or a <code>get</code> method in an operator can be annotated with <code>@AutoMetric</code> to specify that its value is a metric. After every application end window, the platform collects the values of these fields/methods in a map and sends it to application master.</p>
-<pre><code class="java">public class LineReceiver extends BaseOperator
-{
- @AutoMetric
- long length;
-
- @AutoMetric
- long count;
-
- public final transient DefaultInputPort<String> input = new DefaultInputPort<String>()
- {
- @Override
- public void process(String s)
- {
- length += s.length();
- count++;
- }
- };
-
- @Override
- public void beginWindow(long windowId)
- {
- length = 0;
- count = 0;
- }
-}
-</code></pre>
-
-<p>There are 2 auto-metrics declared in the <code>LineReceiver</code>. At the end of each application window, the platform will send a map with 2 entries - <code>[(length, 100), (count, 10)]</code> to the application master.</p>
-<h1 id="aggregating-autometrics-across-partitions">Aggregating AutoMetrics across Partitions</h1>
-<p>When an operator is partitioned, it is useful to aggregate the values of auto-metrics across all its partitions every window to get a logical view of these metrics. The application master performs these aggregations using metrics aggregators.</p>
-<p>The AutoMetric API helps to achieve this by providing an interface for writing aggregators- <code>AutoMetric.Aggregator</code>. Any implementation of <code>AutoMetric.Aggregator</code> can be set as an operator attribute - <code>METRICS_AGGREGATOR</code> for a particular operator which in turn is used for aggregating physical metrics.</p>
-<h2 id="default-aggregators">Default aggregators</h2>
-<p><a href="https://github.com/apache/incubator-apex-core/blob/devel-3/common/src/main/java/com/datatorrent/common/metric/MetricsAggregator.java"><code>MetricsAggregator</code></a> is a simple implementation of <code>AutoMetric.Aggregator</code> that platform uses as a default for summing up primitive types - int, long, float and double.</p>
-<p><code>MetricsAggregator</code> is just a collection of <code>SingleMetricAggregator</code>s. There are multiple implementations of <code>SingleMetricAggregator</code> that perform sum, min, max, avg which are present in Apex core and Apex malhar.</p>
-<p>For the <code>LineReceiver</code> operator, the application developer need not specify any aggregator. The platform will automatically inject an instance of <code>MetricsAggregator</code> that contains two <code>LongSumAggregator</code>s - one for <code>length</code> and one for <code>count</code>. This aggregator will report sum of length and sum of count across all the partitions of <code>LineReceiver</code>.</p>
-<h2 id="building-custom-aggregators">Building custom aggregators</h2>
-<p>Platform cannot perform any meaningful aggregations for non-numeric metrics. In such cases, the operator or application developer can write custom aggregators. Let’s say, if the <code>LineReceiver</code> was modified to have a complex metric as shown below.</p>
-<pre><code class="java">public class AnotherLineReceiver extends BaseOperator
-{
- @AutoMetric
- final LineMetrics lineMetrics = new LineMetrics();
-
- public final transient DefaultInputPort<String> input = new DefaultInputPort<String>()
- {
- @Override
- public void process(String s)
- {
- lineMetrics.length += s.length();
- lineMetrics.count++;
- }
- };
-
- @Override
- public void beginWindow(long windowId)
- {
- lineMetrics.length = 0;
- lineMetrics.count = 0;
- }
-
- public static class LineMetrics implements Serializable
- {
- long length;
- long count;
-
- private static final long serialVersionUID = 201511041908L;
- }
-}
-</code></pre>
-
-<p>Below is a custom aggregator that can calculate average line length across all partitions of <code>AnotherLineReceiver</code>.</p>
-<pre><code class="java">public class AvgLineLengthAggregator implements AutoMetric.Aggregator
-{
-
- Map<String, Object> result = Maps.newHashMap();
-
- @Override
- public Map<String, Object> aggregate(long l, Collection<AutoMetric.PhysicalMetricsContext> collection)
- {
- long totalLength = 0;
- long totalCount = 0;
- for (AutoMetric.PhysicalMetricsContext pmc : collection) {
- AnotherLineReceiver.LineMetrics lm = (AnotherLineReceiver.LineMetrics)pmc.getMetrics().get("lineMetrics");
- totalLength += lm.length;
- totalCount += lm.count;
- }
- result.put("avgLineLength", totalLength/totalCount);
- return result;
- }
-}
-</code></pre>
-
-<p>An instance of above aggregator can be specified as the <code>METRIC_AGGREGATOR</code> for <code>AnotherLineReceiver</code> while creating the DAG as shown below.</p>
-<pre><code class="java"> @Override
- public void populateDAG(DAG dag, Configuration configuration)
- {
- ...
- AnotherLineReceiver lineReceiver = dag.addOperator("LineReceiver", new AnotherLineReceiver());
- dag.setAttribute(lineReceiver, Context.OperatorContext.METRICS_AGGREGATOR, new AvgLineLengthAggregator());
- ...
- }
-</code></pre>
-
-<h1 id="retrieving-autometrics">Retrieving AutoMetrics</h1>
-<p>There are two options for retrieving the AutoMetrics:</p>
-<ul>
-<li>Throught DataTorrent Gateway REST API</li>
-<li>Through REST service on the port of the running STRAM</li>
-</ul>
-<p>The Gateway REST API provides a way to retrieve the latest AutoMetrics for each logical operator. For example:</p>
-<pre><code>GET /ws/v2/applications/{appid}/logicalPlan/operators/{opName}
-{
- ...
- "autoMetrics": {
- "count": "71314",
- "length": "27780706"
- },
- "className": "com.datatorrent.autometric.LineReceiver",
- ...
-}
-</code></pre>
-
-<h1 id="system-metrics">System Metrics</h1>
-<p>System metrics are standard operator metrics provided by the system. Examples include:</p>
-<ul>
-<li>processed tuples per second</li>
-<li>emitted tuples per second</li>
-<li>total tuples processed</li>
-<li>total tuples emitted</li>
-<li>latency</li>
-<li>CPU percentage</li>
-<li>failure count</li>
-<li>checkpoint elapsed time</li>
-</ul>
-<p>The Gateway REST API provides a way to retrieve the latest values for all of the above for each of the logical operators in the application.</p>
-<pre><code>GET /ws/v2/applications/{appid}/logicalPlan/operators/{opName}
-{
- ...
- "cpuPercentageMA": "{cpuPercentageMA}",
- "failureCount": "{failureCount}",
- "latencyMA": "{latencyMA}",
- "totalTuplesEmitted": "{totalTuplesEmitted}",
- "totalTuplesProcessed": "{totalTuplesProcessed}",
- "tuplesEmittedPSMA": "{tuplesEmittedPSMA}",
- "tuplesProcessedPSMA": "{tuplesProcessedPSMA}",
- ...
-}
-</code></pre>
-
-<p>However, just like AutoMetrics, the Gateway only provides the latest metrics. For historical metrics, we will need the help of <a href="http://docs.datatorrent.com/autometrics/#app-data-tracker">App Data Tracker</a>.</p>
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diff --git a/docs/apex-3.3/css/theme_extra.css b/docs/apex-3.3/css/theme_extra.css
deleted file mode 100644
index 9845d00..0000000
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-
- <h1 id="apache-apex-command-line-interface">Apache Apex Command Line Interface</h1>
-<p>dtCli, the Apache Apex command line interface, can be used to launch, monitor, and manage Apache Apex applications. It provides a developer friendly way of interacting with Apache Apex platform. Another advantage of dtCli is to provide scope, by connecting and executing commands in a context of specific application. dtCli enables easy integration with existing enterprise toolset for automated application monitoring and management. Currently the following high level tasks are supported.</p>
-<ul>
-<li>Launch or kill applications</li>
-<li>View system metrics including load, throughput, latency, etc.</li>
-<li>Start or stop tuple recording</li>
-<li>Read operator, stream, port properties and attributes</li>
-<li>Write to operator properties</li>
-<li>Dynamically change the application logical plan</li>
-<li>Create custom macros</li>
-</ul>
-<h2 id="dtcli-commands">dtcli Commands</h2>
-<p>dtCli can be launched by running following command</p>
-<pre><code>dtcli
-</code></pre>
-<p>Help on all commands is available via “help” command in the CLI</p>
-<h3 id="global-commands">Global Commands</h3>
-<pre><code>GLOBAL COMMANDS EXCEPT WHEN CHANGING LOGICAL PLAN:
-
-alias alias-name command
- Create a command alias
-
-begin-macro name
- Begin Macro Definition ($1...$9 to access parameters and type 'end' to end the definition)
-
-connect app-id
- Connect to an app
-
-dump-properties-file out-file jar-file class-name
- Dump the properties file of an app class
-
-echo [arg ...]
- Echo the arguments
-
-exit
- Exit the CLI
-
-get-app-info app-id
- Get the information of an app
-
-get-app-package-info app-package-file
- Get info on the app package file
-
-get-app-package-operator-properties app-package-file operator-class
- Get operator properties within the given app package
-
-get-app-package-operators [options] app-package-file [search-term]
- Get operators within the given app package
- Options:
- -parent Specify the parent class for the operators
-
-get-config-parameter [parameter-name]
- Get the configuration parameter
-
-get-jar-operator-classes [options] jar-files-comma-separated [search-term]
- List operators in a jar list
- Options:
- -parent Specify the parent class for the operators
-
-get-jar-operator-properties jar-files-comma-separated operator-class-name
- List properties in specified operator
-
-help [command]
- Show help
-
-kill-app app-id [app-id ...]
- Kill an app
-
- launch [options] jar-file/json-file/properties-file/app-package-file [matching-app-name]
- Launch an app
- Options:
- -apconf <app package configuration file> Specify an application
- configuration file
- within the app
- package if launching
- an app package.
- -archives <comma separated list of archives> Specify comma
- separated archives
- to be unarchived on
- the compute machines.
- -conf <configuration file> Specify an
- application
- configuration file.
- -D <property=value> Use value for given
- property.
- -exactMatch Only consider
- applications with
- exact app name
- -files <comma separated list of files> Specify comma
- separated files to
- be copied on the
- compute machines.
- -ignorepom Do not run maven to
- find the dependency
- -libjars <comma separated list of libjars> Specify comma
- separated jar files
- or other resource
- files to include in
- the classpath.
- -local Run application in
- local mode.
- -originalAppId <application id> Specify original
- application
- identifier for restart.
- -queue <queue name> Specify the queue to
- launch the application
-
-list-application-attributes
- Lists the application attributes
-list-apps [pattern]
- List applications
-list-operator-attributes
- Lists the operator attributes
-list-port-attributes
- Lists the port attributes
-set-pager on/off
- Set the pager program for output
-show-logical-plan [options] jar-file/app-package-file [class-name]
- List apps in a jar or show logical plan of an app class
- Options:
- -exactMatch Only consider exact match
- for app name
- -ignorepom Do not run maven to find
- the dependency
- -libjars <comma separated list of jars> Specify comma separated
- jar/resource files to
- include in the classpath.
-shutdown-app app-id [app-id ...]
- Shutdown an app
-source file
- Execute the commands in a file
-</code></pre>
-
-<h3 id="commands-after-connecting-to-an-application">Commands after connecting to an application</h3>
-<pre><code>COMMANDS WHEN CONNECTED TO AN APP (via connect <appid>) EXCEPT WHEN CHANGING LOGICAL PLAN:
-
-begin-logical-plan-change
- Begin Logical Plan Change
-dump-properties-file out-file [jar-file] [class-name]
- Dump the properties file of an app class
-get-app-attributes [attribute-name]
- Get attributes of the connected app
-get-app-info [app-id]
- Get the information of an app
-get-operator-attributes operator-name [attribute-name]
- Get attributes of an operator
-get-operator-properties operator-name [property-name]
- Get properties of a logical operator
-get-physical-operator-properties [options] operator-id
- Get properties of a physical operator
- Options:
- -propertyName <property name> The name of the property whose
- value needs to be retrieved
- -waitTime <wait time> How long to wait to get the result
-get-port-attributes operator-name port-name [attribute-name]
- Get attributes of a port
-get-recording-info [operator-id] [start-time]
- Get tuple recording info
-kill-app [app-id ...]
- Kill an app
-kill-container container-id [container-id ...]
- Kill a container
-list-containers
- List containers
-list-operators [pattern]
- List operators
-set-operator-property operator-name property-name property-value
- Set a property of an operator
-set-physical-operator-property operator-id property-name property-value
- Set a property of an operator
-show-logical-plan [options] [jar-file/app-package-file] [class-name]
- Show logical plan of an app class
- Options:
- -exactMatch Only consider exact match
- for app name
- -ignorepom Do not run maven to find
- the dependency
- -libjars <comma separated list of jars> Specify comma separated
- jar/resource files to
- include in the classpath.
-show-physical-plan
- Show physical plan
-shutdown-app [app-id ...]
- Shutdown an app
-start-recording operator-id [port-name] [num-windows]
- Start recording
-stop-recording operator-id [port-name]
- Stop recording
-wait timeout
- Wait for completion of current application
-</code></pre>
-
-<h3 id="commands-when-changing-the-logical-plan">Commands when changing the logical plan</h3>
-<pre><code>COMMANDS WHEN CHANGING LOGICAL PLAN (via begin-logical-plan-change):
-
-abort
- Abort the plan change
-add-stream-sink stream-name to-operator-name to-port-name
- Add a sink to an existing stream
-create-operator operator-name class-name
- Create an operator
-create-stream stream-name from-operator-name from-port-name to-operator-name to-port-name
- Create a stream
-help [command]
- Show help
-remove-operator operator-name
- Remove an operator
-remove-stream stream-name
- Remove a stream
-set-operator-attribute operator-name attr-name attr-value
- Set an attribute of an operator
-set-operator-property operator-name property-name property-value
- Set a property of an operator
-set-port-attribute operator-name port-name attr-name attr-value
- Set an attribute of a port
-set-stream-attribute stream-name attr-name attr-value
- Set an attribute of a stream
-show-queue
- Show the queue of the plan change
-submit
- Submit the plan change
-</code></pre>
-
-<h2 id="examples">Examples</h2>
-<p>An example of defining a custom macro. The macro updates a running application by inserting a new operator. It takes three parameters and executes a logical plan changes.</p>
-<pre><code>dt> begin-macro add-console-output
-macro> begin-logical-plan-change
-macro> create-operator $1 com.datatorrent.lib.io.ConsoleOutputOperator
-macro> create-stream stream_$1 $2 $3 $1 in
-macro> submit
-</code></pre>
-
-<p>Then execute the <code>add-console-output</code> macro like this</p>
-<pre><code>dt> add-console-output xyz opername portname
-</code></pre>
-
-<p>This macro then expands to run the following command</p>
-<pre><code>begin-logical-plan-change
-create-operator xyz com.datatorrent.lib.io.ConsoleOutputOperator
-create-stream stream_xyz opername portname xyz in
-submit
-</code></pre>
-
-<p><em>Note</em>: To perform runtime logical plan changes, like ability to add new operators,
-they must be part of the jar files that were deployed at application launch time.</p>
-
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- <h1 id="apache-apex-incubating">Apache Apex (Incubating)</h1>
-<p>Apex is a Hadoop YARN native big data processing platform, enabling real time stream as well as batch processing for your big data. Apex provides the following benefits:</p>
-<ul>
-<li>High scalability and performance</li>
-<li>Fault tolerance and state management</li>
-<li>Hadoop-native YARN & HDFS implementation</li>
-<li>Event processing guarantees</li>
-<li>Separation of functional and operational concerns</li>
-<li>Simple API supports generic Java code</li>
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-<p>Platform has been demonstated to scale linearly across Hadoop clusters under extreme loads of billions of events per second. Hardware and process failures are quickly recovered with HDFS-backed checkpointing and automatic operator recovery, preserving application state and resuming execution in seconds. Functional and operational specifications are separated. Apex provides a simple API, which enables users to write generic, reusable code. The code is dropped in as-is and platform automatically handles the various operational concerns, such as state management, fault tolerance, scalability, security, metrics, etc. This frees users to focus on functional development, and lets platform provide operability support.</p>
-<p>The core Apex platform is supplemented by Malhar, a library of connector and logic functions, enabling rapid application development. These operators and modules provide access to HDFS, S3, NFS, FTP, and other file systems; Kafka, ActiveMQ, RabbitMQ, JMS, and other message systems; MySql, Cassandra, MongoDB, Redis, HBase, CouchDB, generic JDBC, and other database connectors. In addition to the operators, the library contains a number of demos applications, demonstrating operator features and capabilities. To see the full list of available operators and related documentation, visit <a href="https://github.com/apache/incubator-apex-malhar">Apex Malhar on Github</a></p>
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pmulhuw psadbw pshufw pf2iw pfnacc pfpnacc pi2fw pswapd maskmovdqu clflush movntdq movnti movntpd movdqa movdqu movdq2q movq2dq paddq pmuludq pshufd pshufhw pshuflw pslldq psrldq psubq punpckhqdq punpcklqdq addpd addsd andnpd andpd cmpeqpd cmpeqsd cmplepd cmplesd cmpltpd cmpltsd cmpneqpd cmpneqsd cmpnlepd cmpnlesd cmpnltpd cmpnltsd cmpordpd cmpordsd cmpunordpd cmpunordsd cmppd comisd cvtdq2pd cvtdq2ps cvtpd2dq cvtpd2pi cvtpd2ps cvtpi2pd cvtps2dq cvtps2pd cvtsd2si cvtsd2ss cvtsi2sd cvtss2sd cvttpd2pi cvttpd2dq cvttps2dq cvttsd2si divpd divsd maxpd maxsd minpd minsd movapd movhpd movlpd movmskpd movupd mulpd mulsd orpd shufpd sqrtpd sqrtsd subpd subsd ucomisd unpckhpd unpcklpd xorpd addsubpd addsubps haddpd haddps hsubpd hsubps lddqu movddup movshdup movsldup clgi stgi vmcall vmclear vmfunc vmlaunch vmload vmmcall vmptrld vmptrst vmread vmresume vmrun vmsave vmwrite vmxoff vmxon invept invvpid pabsb pabsw pabsd palignr phaddw phaddd phaddsw phsubw phsubd phsubsw pmaddubsw pmulhrsw pshufb psignb psignw psignd extrq insertq movntsd movntss lzcnt blendpd blendps blendvpd blendvps dppd dpps extractps insertps movntdqa mpsadbw packusdw pblendvb pblendw pcmpeqq pextrb pextrd pextrq phminposuw pinsrb pinsrd pinsrq pmaxsb pmaxsd pmaxud pmaxuw pminsb pminsd pminud pminuw pmovsxbw pmovsxbd pmovsxbq pmovsxwd pmovsxwq pmovsxdq pmovzxbw pmovzxbd pmovzxbq pmovzxwd pmovzxwq pmovzxdq pmuldq pmulld ptest roundpd roundps roundsd roundss crc32 pcmpestri pcmpestrm pcmpistri pcmpistrm pcmpgtq popcnt getsec pfrcpv pfrsqrtv movbe aesenc aesenclast aesdec aesdeclast aesimc aeskeygenassist vaesenc vaesenclast vaesdec vaesdeclast vaesimc vaeskeygenassist vaddpd vaddps vaddsd vaddss vaddsubpd vaddsubps vandpd vandps vandnpd vandnps vblendpd vblendps vblendvpd vblendvps vbroadcastss vbroadcastsd vbroadcastf128 vcmpeq_ospd vcmpeqpd vcmplt_ospd vcmpltpd vcmple_ospd vcmplepd vcmpunord_qpd vcmpunordpd vcmpneq_uqpd vcmpneqpd vcmpnlt_uspd vcmpnltpd vcmpnle_uspd vcmpnlepd vcmpord_qpd vcmpordpd vcmpeq_uqpd vcmpnge_uspd vcmpngepd vcmpngt_uspd vcmpngtpd vcmpfalse_oqpd vcmpfalsepd vcmpneq_oqpd vcmpge_ospd vcmpgepd vcmpgt_ospd vcmpgtpd vcmptrue_uqpd vcmptruepd vcmplt_oqpd vcmple_oqpd vcmpunord_spd vcmpneq_uspd vcmpnlt_uqpd vcmpnle_uqpd vcmpord_spd vcmpeq_uspd vcmpnge_uqpd vcmpngt_uqpd vcmpfalse_ospd vcmpneq_ospd vcmpge_oqpd vcmpgt_oqpd vcmptrue_uspd vcmppd vcmpeq_osps vcmpeqps vcmplt_osps vcmpltps vcmple_osps vcmpleps vcmpunord_qps vcmpunordps vcmpneq_uqps vcmpneqps vcmpnlt_usps vcmpnltps vcmpnle_usps vcmpnleps vcmpord_qps vcmpordps vcmpeq_uqps vcmpnge_usps vcmpngeps vcmpngt_usps vcmpngtps vcmpfalse_oqps vcmpfalseps vcmpneq_oqps vcmpge_osps vcmpgeps vcmpgt_osps vcmpgtps vcmptrue_uqps vcmptrueps vcmplt_oqps vcmple_oqps vcmpunord_sps vcmpneq_usps vcmpnlt_uqps vcmpnle_uqps vcmpord_sps vcmpeq_usps vcmpnge_uqps vcmpngt_uqps vcmpfalse_osps vcmpneq_osps vcmpge_oqps vcmpgt_oqps vcmptrue_usps vcmpps vcmpeq_ossd vcmpeqsd vcmplt_ossd vcmpltsd vcmple_ossd vcmplesd vcmpunord_qsd vcmpunordsd vcmpneq_uqsd vcmpneqsd vcmpnlt_ussd vcmpnltsd vcmpnle_ussd vcmpnlesd vcmpord_qsd vcmpordsd vcmpeq_uqsd vcmpnge_ussd vcmpngesd vcmpngt_ussd vcmpngtsd vcmpfalse_oqsd vcmpfalsesd vcmpneq_oqsd vcmpge_ossd vcmpgesd vcmpgt_ossd vcmpgtsd vcmptrue_uqsd vcmptruesd vcmplt_oqsd vcmple_oqsd vcmpunord_ssd vcmpneq_ussd vcmpnlt_uqsd vcmpnle_uqsd vcmpord_ssd vcmpeq_ussd vcmpnge_uqsd vcmpngt_uqsd vcmpfalse_ossd vcmpneq_ossd vcmpge_oqsd vcmpgt_oqsd vcmptrue_ussd vcmpsd vcmpeq_osss vcmpeqss vcmplt_osss vcmpltss vcmple_osss vcmpless vcmpunord_qss vcmpunordss vcmpneq_uqss vcmpneqss vcmpnlt_usss vcmpnltss vcmpnle_usss vcmpnless vcmpord_qss vcmpordss vcmpeq_uqss vcmpnge_usss vcmpngess vcmpngt_usss vcmpngtss vcmpfalse_oqss vcmpfalsess vcmpneq_oqss vcmpge_osss vcmpgess vcmpgt_osss vcmpgtss vcmptrue_uqss vcmptruess vcmplt_oqss vcmple_oqss vcmpunord_sss vcmpneq_usss vcmpnlt_uqss vcmpnle_uqss vcmpord_sss vcmpeq_usss vcmpnge_uqss vcmpngt_uqss vcmpfalse_osss vcmpneq_osss vcmpge_oqss vcmpgt_oqss vcmptrue_usss vcmpss vcomisd vcomiss vcvtdq2pd vcvtdq2ps vcvtpd2dq vcvtpd2ps vcvtps2dq vcvtps2pd vcvtsd2si vcvtsd2ss vcvtsi2sd vcvtsi2ss vcvtss2sd vcvtss2si vcvttpd2dq vcvttps2dq vcvttsd2si vcvttss2si vdivpd vdivps vdivsd vdivss vdppd vdpps vextractf128 vextractps vhaddpd vhaddps vhsubpd vhsubps vinsertf128 vinsertps vlddqu vldqqu vldmxcsr vmaskmovdqu vmaskmovps vmaskmovpd vmaxpd vmaxps vmaxsd vmaxss vminpd vminps vminsd vminss vmovapd vmovaps vmovd vmovq vmovddup vmovdqa vmovqqa vmovdqu vmovqqu vmovhlps vmovhpd vmovhps vmovlhps vmovlpd vmovlps vmovmskpd vmovmskps vmovntdq vmovntqq vmovntdqa vmovntpd vmovntps vmovsd vmovshdup vmovsldup vmovss vmovupd vmovups vmpsadbw vmulpd vmulps vmulsd vmulss vorpd vorps vpabsb vpabsw vpabsd vpacksswb vpackssdw vpackuswb vpackusdw vpaddb vpaddw vpaddd vpaddq vpaddsb vpaddsw vpaddusb vpaddusw vpalignr vpand vpandn vpavgb vpavgw vpblendvb vpblendw vpcmpestri vpcmpestrm vpcmpistri vpcmpistrm vpcmpeqb vpcmpeqw vpcmpeqd vpcmpeqq vpcmpgtb vpcmpgtw vpcmpgtd vpcmpgtq vpermilpd vpermilps vperm2f128 vpextrb vpextrw vpextrd vpextrq vphaddw vphaddd vphaddsw vphminposuw vphsubw vphsubd vphsubsw vpinsrb vpinsrw vpinsrd vpinsrq vpmaddwd vpmaddubsw vpmaxsb vpmaxsw vpmaxsd vpmaxub vpmaxuw vpmaxud vpminsb vpminsw vpminsd vpminub vpminuw vpminud vpmovmskb vpmovsxbw vpmovsxbd vpmovsxbq vpmovsxwd vpmovsxwq vpmovsxdq vpmovzxbw vpmovzxbd vpmovzxbq vpmovzxwd vpmovzxwq vpmovzxdq vpmulhuw vpmulhrsw vpmulhw vpmullw vpmulld vpmuludq vpmuldq vpor vpsadbw vpshufb vpshufd vpshufhw vpshuflw vpsignb vpsignw vpsignd vpslldq vpsrldq vpsllw vpslld vpsllq vpsraw vpsrad vpsrlw vpsrld vpsrlq vptest vpsubb vpsubw vpsubd vpsubq vpsubsb vpsubsw vpsubusb vpsubusw vpunpckhbw vpunpckhwd vpunpckhdq vpunpckhqdq vpunpcklbw vpunpcklwd vpunpckldq vpunpcklqdq vpxor vrcpps vrcpss vrsqrtps vrsqrtss vroundpd vroundps vroundsd vroundss vshufpd vshufps vsqrtpd vsqrtps vsqrtsd vsqrtss vstmxcsr vsubpd vsubps vsubsd vsubss vtestps vtestpd vucomisd vucomiss vunpckhpd vunpckhps vunpcklpd vunpcklps vxorpd vxorps vzeroall vzeroupper pclmullqlqdq pclmulhqlqdq pclmullqhqdq pclmulhqhqdq pclmulqdq vpclmullqlqdq vpclmulhqlqdq vpclmullqhqdq vpclmulhqhqdq vpclmulqdq vfmadd132ps vfmadd132pd vfmadd312ps vfmadd312pd vfmadd213ps vfmadd213pd vfmadd123ps vfmadd123pd vfmadd231ps vfmadd231pd vfmadd321ps vfmadd321pd vfmaddsub132ps vfmaddsub132pd vfmaddsub312ps vfmaddsub312pd vfmaddsub213ps vfmaddsub213pd vfmaddsub123ps vfmaddsub123pd vfmaddsub231ps vfmaddsub231pd vfmaddsub321ps vfmaddsub321pd vfmsub132ps vfmsub132pd vfmsub312ps vfmsub312pd vfmsub213ps vfmsub213pd vfmsub123ps vfmsub123pd vfmsub231ps vfmsub231pd vfmsub321ps vfmsub321pd vfmsubadd132ps vfmsubadd132pd vfmsubadd312ps vfmsubadd312pd vfmsubadd213ps vfmsubadd213pd vfmsubadd123ps vfmsubadd123pd vfmsubadd231ps vfmsubadd231pd vfmsubadd321ps vfmsubadd321pd vfnmadd132ps vfnmadd132pd vfnmadd312ps vfnmadd312pd vfnmadd213ps vfnmadd213pd vfnmadd123ps vfnmadd123pd vfnmadd231ps vfnmadd231pd vfnmadd321ps vfnmadd321pd vfnmsub132ps vfnmsub132pd vfnmsub312ps vfnmsub312pd vfnmsub213ps vfnmsub213pd vfnmsub123ps vfnmsub123pd vfnmsub231ps vfnmsub231pd vfnmsub321ps vfnmsub321pd vfmadd132ss vfmadd132sd vfmadd312ss vfmadd312sd vfmadd213ss vfmadd213sd vfmadd123ss vfmadd123sd vfmadd231ss vfmadd231sd vfmadd321ss vfmadd321sd vfmsub132ss vfmsub132sd vfmsub312ss vfmsub312sd vfmsub213ss vfmsub213sd vfmsub123ss vfmsub123sd vfmsub231ss vfmsub231sd vfmsub321ss vfmsub321sd vfnmadd132ss vfnmadd132sd vfnmadd312ss vfnmadd312sd vfnmadd213ss vfnmadd213sd vfnmadd123ss vfnmadd123sd vfnmadd231ss vfnmadd231sd vfnmadd321ss vfnmadd321sd vfnmsub132ss vfnmsub132sd vfnmsub312ss vfnmsub312sd vfnmsub213ss vfnmsub213sd vfnmsub123ss vfnmsub123sd vfnmsub231ss vfnmsub231sd vfnmsub321ss vfnmsub321sd rdfsbase rdgsbase rdrand wrfsbase wrgsbase vcvtph2ps vcvtps2ph adcx adox rdseed clac stac xstore xcryptecb xcryptcbc xcryptctr xcryptcfb xcryptofb montmul xsha1 xsha256 llwpcb slwpcb lwpval lwpins vfmaddpd vfmaddps vfmaddsd vfmaddss vfmaddsubpd vfmaddsubps vfmsubaddpd vfmsubaddps vfmsubpd vfmsubps vfmsubsd vfmsubss vfnmaddpd vfnmaddps vfnmaddsd vfnmaddss vfnmsubpd vfnmsubps vfnmsubsd vfnmsubss vfrczpd vfrczps vfrczsd vfrczss vpcmov vpcomb vpcomd vpcomq vpcomub vpcomud vpcomuq vpcomuw vpcomw vphaddbd vphaddbq vphaddbw vphadddq vphaddubd vphaddubq vphaddubw vphaddudq vphadduwd vphadduwq vphaddwd vphaddwq vphsubbw vphsubdq vphsubwd vpmacsdd vpmacsdqh vpmacsdql vpmacssdd vpmacssdqh vpmacssdql vpmacsswd vpmacssww vpmacswd vpmacsww vpmadcsswd vpmadcswd vpperm vprotb vprotd vprotq vprotw vpshab vpshad vpshaq vpshaw vpshlb vpshld vpshlq vpshlw vbroadcasti128 vpblendd vpbroadcastb vpbroadcastw vpbroadcastd vpbroadcastq vpermd vpermpd vpermps vpermq vperm2i128 vextracti128 vinserti128 vpmaskmovd vpmaskmovq vpsllvd vpsllvq vpsravd vpsrlvd vpsrlvq vgatherdpd vgatherqpd vgatherdps vgatherqps vpgatherdd vpgatherqd vpgatherdq vpgatherqq xabort xbegin xend xtest andn bextr blci blcic blsi blsic blcfill blsfill blcmsk blsmsk blsr blcs bzhi mulx pdep pext rorx sarx shlx shrx tzcnt tzmsk t1mskc valignd valignq vblendmpd vblendmps vbroadcastf32x4 vbroadcastf64x4 vbroadcasti32x4 vbroadcasti64x4 vcompresspd vcompressps vcvtpd2udq vcvtps2udq vcvtsd2usi vcvtss2usi vcvttpd2udq vcvttps2udq vcvttsd2usi vcvttss2usi vcvtudq2pd vcvtudq2ps vcvtusi2sd vcvtusi2ss vexpandpd vexpandps vextractf32x4 vextractf64x4 vextracti32x4 vextracti64x4 vfixupimmpd vfixupimmps vfixupimmsd vfixupimmss vgetexppd vgetexpps vgetexpsd vgetexpss vgetmantpd vgetmantps vgetmantsd vgetmantss vinsertf32x4 vinsertf64x4 vinserti32x4 vinserti64x4 vmovdqa32 vmovdqa64 vmovdqu32 vmovdqu64 vpabsq vpandd vpandnd vpandnq vpandq vpblendmd vpblendmq vpcmpltd vpcmpled vpcmpneqd vpcmpnltd vpcmpnled vpcmpd vpcmpltq vpcmpleq vpcmpneqq vpcmpnltq vpcmpnleq vpcmpq vpcmpequd vpcmpltud vpcmpleud vpcmpnequd vpcmpnltud vpcmpnleud vpcmpud vpcmpequq vpcmpltuq vpcmpleuq vpcmpnequq vpcmpnltuq vpcmpnleuq vpcmpuq vpcompressd vpcompressq vpermi2d vpermi2pd vpermi2ps vpermi2q vpermt2d vpermt2pd vpermt2ps vpermt2q vpexpandd vpexpandq vpmaxsq vpmaxuq vpminsq vpminuq vpmovdb vpmovdw vpmovqb vpmovqd vpmovqw vpmovsdb vpmovsdw vpmovsqb vpmovsqd vpmovsqw vpmovusdb vpmovusdw vpmovusqb vpmovusqd vpmovusqw vpord vporq vprold vprolq vprolvd vprolvq vprord vprorq vprorvd vprorvq vpscatterdd vpscatterdq vpscatterqd vpscatterqq vpsraq vpsravq vpternlogd vpternlogq vptestmd vptestmq vptestnmd vptestnmq vpxord vpxorq vrcp14pd vrcp14ps vrcp14sd vrcp14ss vrndscalepd vrndscaleps vrndscalesd vrndscaless vrsqrt14pd vrsqrt14ps vrsqrt14sd vrsqrt14ss vscalefpd vscalefps vscalefsd vscalefss vscatterdpd vscatterdps vscatterqpd vscatterqps vshuff32x4 vshuff64x2 vshufi32x4 vshufi64x2 kandnw kandw kmovw knotw kortestw korw kshiftlw kshiftrw kunpckbw kxnorw kxorw vpbroadcastmb2q vpbroadcastmw2d vpconflictd vpconflictq vplzcntd vplzcntq vexp2pd vexp2ps vrcp28pd vrcp28ps vrcp28sd vrcp28ss vrsqrt28pd vrsqrt28ps vrsqrt28sd vrsqrt28ss vgatherpf0dpd vgatherpf0dps vgatherpf0qpd vgatherpf0qps vgatherpf1dpd vgatherpf1dps vgatherpf1qpd vgatherpf1qps vscatterpf0dpd vscatterpf0dps vscatterpf0qpd vscatterpf0qps vscatterpf1dpd vscatterpf1dps vscatterpf1qpd vscatterpf1qps prefetchwt1 bndmk bndcl bndcu bndcn bndmov bndldx bndstx sha1rnds4 sha1nexte sha1msg1 sha1msg2 sha256rnds2 sha256msg1 sha256msg2 hint_nop0 hint_nop1 hint_nop2 hint_nop3 hint_nop4 hint_nop5 hint_nop6 hint_nop7 hint_nop8 hint_nop9 hint_nop10 hint_nop11 hint_nop12 hint_nop13 hint_nop14 hint_nop15 hint_nop16 hint_nop17 hint_nop18 hint_nop19 hint_nop20 hint_nop21 hint_nop22 hint_nop23 hint_nop24 hint_nop25 hint_nop26 hint_nop27 hint_nop28 hint_nop29 hint_nop30 hint_nop31 hint_nop32 hint_nop33 hint_nop34 hint_nop35 hint_nop36 hint_nop37 hint_nop38 hint_nop39 hint_nop40 hint_nop41 hint_nop42 hint_nop43 hint_nop44 hint_nop45 hint_nop46 hint_nop47 hint_nop48 hint_nop49 hint_nop50 hint_nop51 hint_nop52 hint_nop53 hint_nop54 hint_nop55 hint_nop56 hint_nop57 hint_nop58 hint_nop59 hint_nop60 hint_nop61 hint_nop62 hint_nop63",literal:"ip eip rip al ah bl bh cl ch dl dh sil dil bpl spl r8b r9b r10b r11b r12b r13b r14b r15b ax bx cx dx si di bp sp r8w r9w r10w r11w r12w r13w r14w r15w eax ebx ecx edx esi edi ebp esp eip r8d r9d r10d r11d r12d r13d r14d r15d rax rbx rcx rdx rsi rdi rbp rsp r8 r9 r10 r11 r12 r13 r14 r15 cs ds es fs gs ss st st0 st1 st2 st3 st4 st5 st6 st7 mm0 mm1 mm2 mm3 mm4 mm5 mm6 mm7 xmm0 xmm1 xmm2 xmm3 xmm4 xmm5 xmm6 xmm7 xmm8 xmm9 xmm10 xmm11 xmm12 xmm13 xmm14 xmm15 xmm16 xmm17 xmm18 xmm19 xmm20 xmm21 xmm22 xmm23 xmm24 xmm25 xmm26 xmm27 xmm28 xmm29 xmm30 xmm31 ymm0 ymm1 ymm2 ymm3 ymm4 ymm5 ymm6 ymm7 ymm8 ymm9 ymm10 ymm11 ymm12 ymm13 ymm14 ymm15 ymm16 ymm17 ymm18 ymm19 ymm20 ymm21 ymm22 ymm23 ymm24 ymm25 ymm26 ymm27 ymm28 ymm29 ymm30 ymm31 zmm0 zmm1 zmm2 zmm3 zmm4 zmm5 zmm6 zmm7 zmm8 zmm9 zmm10 zmm11 zmm12 zmm13 zmm14 zmm15 zmm16 zmm17 zmm18 zmm19 zmm20 zmm21 zmm22 zmm23 zmm24 zmm25 zmm26 zmm27 zmm28 zmm29 zmm30 zmm31 k0 k1 k2 k3 k4 k5 k6 k7 bnd0 bnd1 bnd2 bnd3 cr0 cr1 cr2 cr3 cr4 cr8 dr0 dr1 dr2 dr3 dr8 tr3 tr4 tr5 tr6 tr7 r0 r1 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FindMinimumCut FindMinValue FindPermutation FindPostmanTour FindProcessParameters FindRoot FindSequenceFunction FindSettings FindShortestPath FindShortestTour FindThreshold FindVertexCover FindVertexCut Fine FinishDynamic FiniteAbelianGroupCount FiniteGroupCount FiniteGroupData First FirstPassageTimeDistribution FischerGroupFi22 FischerGroupFi23 FischerGroupFi24Prime FisherHypergeometricDistribution FisherRatioTest FisherZDistribution Fit FitAll FittedModel FixedPoint FixedPointList FlashSelection Flat Flatten FlattenAt FlatTopWindow FlipView Floor FlushPrintOutputPacket Fold FoldList Font FontColor FontFamily FontForm FontName FontOpacity FontPostScriptName FontProperties FontReencoding FontSize FontSlant FontSubstitutions FontTracking FontVariations FontWeight For ForAll Format FormatRules FormatType FormatTypeAutoConvert FormatValues FormBox FormBoxOptions FortranForm Forward ForwardBackward Fourier FourierCoefficient FourierCosCoefficient FourierCosSeries FourierCosTransform FourierDCT FourierDCTFilter FourierDCTMatrix FourierDST FourierDSTMatrix FourierMatrix FourierParameters FourierSequenceTransform FourierSeries FourierSinCoefficient FourierSinSeries FourierSinTransform FourierTransform FourierTrigSeries FractionalBrownianMotionProcess FractionalPart FractionBox FractionBoxOptions FractionLine Frame FrameBox FrameBoxOptions Framed FrameInset FrameLabel Frameless FrameMargins FrameStyle FrameTicks FrameTicksStyle FRatioDistribution FrechetDistribution FreeQ FrequencySamplingFilterKernel FresnelC FresnelS Friday FrobeniusNumber FrobeniusSolve FromCharacterCode FromCoefficientRules FromContinuedFraction FromDate FromDigits FromDMS Front FrontEndDynamicExpression FrontEndEventActions FrontEndExecute FrontEndObject FrontEndResource FrontEndResourceString FrontEndStackSize FrontEndToken FrontEndTokenExecute FrontEndValueCache FrontEndVersion FrontFaceColor FrontFaceOpacity Full FullAxes FullDefinition FullForm FullGraphics FullOptions FullSimplify Function FunctionExpand FunctionInterpolation FunctionSpace FussellVeselyImportance GaborFilter GaborMatrix GaborWavelet GainMargins GainPhaseMargins Gamma GammaDistribution GammaRegularized GapPenalty Gather GatherBy GaugeFaceElementFunction GaugeFaceStyle GaugeFrameElementFunction GaugeFrameSize GaugeFrameStyle GaugeLabels GaugeMarkers GaugeStyle GaussianFilter GaussianIntegers GaussianMatrix GaussianWindow GCD GegenbauerC General GeneralizedLinearModelFit GenerateConditions GeneratedCell GeneratedParameters GeneratingFunction Generic GenericCylindricalDecomposition GenomeData GenomeLookup GeodesicClosing GeodesicDilation GeodesicErosion GeodesicOpening GeoDestination GeodesyData GeoDirection GeoDistance GeoGridPosition GeometricBrownianMotionProcess GeometricDistribution GeometricMean GeometricMeanFilter GeometricTransformation GeometricTransformation3DBox GeometricTransformation3DBoxOptions GeometricTransformationBox GeometricTransformationBoxOptions GeoPosition GeoPositionENU GeoPositionXYZ GeoProjectionData GestureHandler GestureHandlerTag Get GetBoundingBoxSizePacket GetContext GetEnvironment GetFileName GetFrontEndOptionsDataPacket GetLinebreakInformationPacket GetMenusPacket GetPageBreakInformationPacket Glaisher GlobalClusteringCoefficient GlobalPreferences GlobalSession Glow GoldenRatio GompertzMakehamDistribution GoodmanKruskalGamma GoodmanKruskalGammaTest Goto Grad Gradient GradientFilter GradientOrientationFilter Graph GraphAssortativity GraphCenter GraphComplement GraphData GraphDensity GraphDiameter GraphDifference GraphDisjointUnion GraphDistance GraphDistanceMatrix GraphElementData GraphEmbedding GraphHighlight GraphHighlightStyle GraphHub Graphics Graphics3D Graphics3DBox Graphics3DBoxOptions GraphicsArray GraphicsBaseline GraphicsBox GraphicsBoxOptions GraphicsColor GraphicsColumn GraphicsComplex GraphicsComplex3DBox GraphicsComplex3DBoxOptions GraphicsComplexBox GraphicsComplexBoxOptions GraphicsContents GraphicsData GraphicsGrid GraphicsGridBox GraphicsGroup GraphicsGroup3DBox GraphicsGroup3DBoxOptions GraphicsGroupBox GraphicsGroupBoxOptions GraphicsGrouping GraphicsHighlightColor GraphicsRow GraphicsSpacing GraphicsStyle GraphIntersection GraphLayout GraphLinkEfficiency GraphPeriphery GraphPlot GraphPlot3D GraphPower GraphPropertyDistribution GraphQ GraphRadius GraphReciprocity GraphRoot GraphStyle GraphUnion Gray GrayLevel GreatCircleDistance Greater GreaterEqual GreaterEqualLess GreaterFullEqual GreaterGreater GreaterLess GreaterSlantEqual GreaterTilde Green Grid GridBaseline GridBox GridBoxAlignment GridBoxBackground GridBoxDividers GridBoxFrame GridBoxItemSize GridBoxItemStyle GridBoxOptions GridBoxSpacings GridCreationSettings GridDefaultElement GridElementStyleOptions GridFrame GridFrameMargins GridGraph GridLines GridLinesStyle GroebnerBasis GroupActionBase GroupCentralizer GroupElementFromWord GroupElementPosition GroupElementQ GroupElements GroupElementToWord GroupGenerators GroupMultiplicationTable GroupOrbits GroupOrder GroupPageBreakWithin GroupSetwiseStabilizer GroupStabilizer GroupStabilizerChain Gudermannian GumbelDistribution HaarWavelet HadamardMatrix HalfNormalDistribution HamiltonianGraphQ HammingDistance HammingWindow HankelH1 HankelH2 HankelMatrix HannPoissonWindow HannWindow HaradaNortonGroupHN HararyGraph HarmonicMean HarmonicMeanFilter HarmonicNumber Hash HashTable Haversine HazardFunction Head HeadCompose Heads HeavisideLambda HeavisidePi HeavisideTheta HeldGroupHe HeldPart HelpBrowserLookup HelpBrowserNotebook HelpBrowserSettings HermiteDecomposition HermiteH HermitianMatrixQ HessenbergDecomposition Hessian HexadecimalCharacter Hexahedron HexahedronBox HexahedronBoxOptions HiddenSurface HighlightGraph HighlightImage HighpassFilter HigmanSimsGroupHS HilbertFilter HilbertMatrix Histogram Histogram3D HistogramDistribution HistogramList HistogramTransform HistogramTransformInterpolation HitMissTransform HITSCentrality HodgeDual HoeffdingD HoeffdingDTest Hold HoldAll HoldAllComplete HoldComplete HoldFirst HoldForm HoldPattern HoldRest HolidayCalendar HomeDirectory HomePage Horizontal HorizontalForm HorizontalGauge HorizontalScrollPosition HornerForm HotellingTSquareDistribution HoytDistribution HTMLSave Hue HumpDownHump HumpEqual HurwitzLerchPhi HurwitzZeta HyperbolicDistribution HypercubeGraph HyperexponentialDistribution Hyperfactorial Hypergeometric0F1 Hypergeometric0F1Regularized Hypergeometric1F1 Hypergeometric1F1Regularized Hypergeometric2F1 Hypergeometric2F1Regularized HypergeometricDistribution HypergeometricPFQ HypergeometricPFQRegularized HypergeometricU Hyperlink HyperlinkCreationSettings Hyphenation HyphenationOptions HypoexponentialDistribution HypothesisTestData I Identity IdentityMatrix If IgnoreCase Im Image Image3D Image3DSlices ImageAccumulate ImageAdd ImageAdjust ImageAlign ImageApply ImageAspectRatio ImageAssemble ImageCache ImageCacheValid ImageCapture ImageChannels ImageClip ImageColorSpace ImageCompose ImageConvolve ImageCooccurrence ImageCorners ImageCorrelate ImageCorrespondingPoints ImageCrop ImageData ImageDataPacket ImageDeconvolve ImageDemosaic ImageDifference ImageDimensions ImageDistance ImageEffect ImageFeatureTrack ImageFileApply ImageFileFilter ImageFileScan ImageFilter ImageForestingComponents ImageForwardTransformation ImageHistogram ImageKeypoints ImageLevels ImageLines ImageMargins ImageMarkers ImageMeasurements ImageMultiply ImageOffset ImagePad ImagePadding ImagePartition ImagePeriodogram ImagePerspectiveTransformation ImageQ ImageRangeCache ImageReflect ImageRegion ImageResize ImageResolution ImageRotate ImageRotated ImageScaled ImageScan ImageSize ImageSizeAction ImageSizeCache ImageSizeMultipliers ImageSizeRaw ImageSubtract ImageTake ImageTransformation ImageTrim ImageType ImageValue ImageValuePositions Implies Import ImportAutoReplacements ImportString ImprovementImportance In IncidenceGraph IncidenceList IncidenceMatrix IncludeConstantBasis IncludeFileExtension IncludePods IncludeSingularTerm Increment Indent IndentingNewlineSpacings IndentMaxFraction IndependenceTest IndependentEdgeSetQ IndependentUnit IndependentVertexSetQ Indeterminate IndexCreationOptions Indexed IndexGraph IndexTag Inequality InexactNumberQ InexactNumbers Infinity Infix Information Inherited InheritScope Initialization InitializationCell InitializationCellEvaluation InitializationCellWarning InlineCounterAssignments InlineCounterIncrements InlineRules Inner Inpaint Input InputAliases InputAssumptions InputAutoReplacements InputField InputFieldBox InputFieldBoxOptions InputForm InputGrouping InputNamePacket InputNotebook InputPacket InputSettings InputStream InputString InputStringPacket InputToBoxFormPacket Insert InsertionPointObject InsertResults Inset Inset3DBox Inset3DBoxOptions InsetBox InsetBoxOptions Install InstallService InString Integer IntegerDigits IntegerExponent IntegerLength IntegerPart IntegerPartitions IntegerQ Integers IntegerString Integral Integrate Interactive InteractiveTradingChart Interlaced Interleaving InternallyBalancedDecomposition InterpolatingFunction InterpolatingPolynomial Interpolation InterpolationOrder InterpolationPoints InterpolationPrecision Interpretation InterpretationBox InterpretationBoxOptions InterpretationFunction InterpretTemplate InterquartileRange Interrupt InterruptSettings Intersection Interval IntervalIntersection IntervalMemberQ IntervalUnion Inverse InverseBetaRegularized InverseCDF InverseChiSquareDistribution InverseContinuousWaveletTransform InverseDistanceTransform InverseEllipticNomeQ InverseErf InverseErfc InverseFourier InverseFourierCosTransform InverseFourierSequenceTransform InverseFourierSinTransform InverseFourierTransform InverseFunction InverseFunctions InverseGammaDistribution InverseGammaRegularized InverseGaussianDistribution InverseGudermannian InverseHaversine InverseJacobiCD InverseJacobiCN InverseJacobiCS InverseJacobiDC InverseJacobiDN InverseJacobiDS InverseJacobiNC InverseJacobiND InverseJacobiNS InverseJacobiSC InverseJacobiSD InverseJacobiSN InverseLaplaceTransform InversePermutation InverseRadon InverseSeries InverseSurvivalFunction InverseWaveletTransform InverseWeierstrassP InverseZTransform Invisible InvisibleApplication InvisibleTimes IrreduciblePolynomialQ IsolatingInterval IsomorphicGraphQ IsotopeData Italic Item ItemBox ItemBoxOptions ItemSize ItemStyle ItoProcess JaccardDissimilarity JacobiAmplitude Jacobian JacobiCD JacobiCN JacobiCS JacobiDC JacobiDN JacobiDS JacobiNC JacobiND JacobiNS JacobiP JacobiSC JacobiSD JacobiSN JacobiSymbol JacobiZeta JankoGroupJ1 JankoGroupJ2 JankoGroupJ3 JankoGroupJ4 JarqueBeraALMTest JohnsonDistribution Join Joined JoinedCurve JoinedCurveBox JoinForm JordanDecomposition JordanModelDecomposition K KagiChart KaiserBesselWindow KaiserWindow KalmanEstimator KalmanFilter KarhunenLoeveDecomposition KaryTree KatzCentrality KCoreComponents KDistribution KelvinBei KelvinBer KelvinKei KelvinKer KendallTau KendallTauTest KernelExecute KernelMixtureDistribution KernelObject Kernels Ket Khinchin KirchhoffGraph KirchhoffMatrix KleinInvariantJ KnightTourGraph KnotData KnownUnitQ KolmogorovSmirnovTest KroneckerDelta KroneckerModelDecomposition KroneckerProduct KroneckerSymbol KuiperTest KumaraswamyDistribution Kurtosis KuwaharaFilter Label Labeled LabeledSlider LabelingFunction LabelStyle LaguerreL LambdaComponents LambertW LanczosWindow LandauDistribution Language LanguageCategory LaplaceDistribution LaplaceTransform Laplacian LaplacianFilter LaplacianGaussianFilter Large Larger Last Latitude LatitudeLongitude LatticeData LatticeReduce Launch LaunchKernels LayeredGraphPlot LayerSizeFunction LayoutInformation LCM LeafCount LeapYearQ LeastSquares LeastSquaresFilterKernel Left LeftArrow LeftArrowBar LeftArrowRightArrow LeftDownTeeVector LeftDownVector LeftDownVectorBar LeftRightArrow LeftRightVector LeftTee LeftTeeArrow LeftTeeVector LeftTriangle LeftTriangleBar LeftTriangleEqual LeftUpDownVector LeftUpTeeVector LeftUpVector LeftUpVectorBar LeftVector LeftVectorBar LegendAppearance Legended LegendFunction LegendLabel LegendLayout LegendMargins LegendMarkers LegendMarkerSize LegendreP LegendreQ LegendreType Length LengthWhile LerchPhi Less LessEqual LessEqualGreater LessFullEqual LessGreater LessLess LessSlantEqual LessTilde LetterCharacter LetterQ Level LeveneTest LeviCivitaTensor LevyDistribution Lexicographic LibraryFunction LibraryFunctionError LibraryFunctionInformation LibraryFunctionLoad LibraryFunctionUnload LibraryLoad LibraryUnload LicenseID LiftingFilterData LiftingWaveletTransform LightBlue LightBrown LightCyan Lighter LightGray LightGreen Lighting LightingAngle LightMagenta LightOrange LightPink LightPurple LightRed LightSources LightYellow Likelihood Limit LimitsPositioning LimitsPositioningTokens LindleyDistribution Line Line3DBox LinearFilter LinearFractionalTransform LinearModelFit LinearOffsetFunction LinearProgramming LinearRecurrence LinearSolve LinearSolveFunction LineBox LineBreak LinebreakAdjustments LineBreakChart LineBreakWithin LineColor LineForm LineGraph LineIndent LineIndentMaxFraction LineIntegralConvolutionPlot LineIntegralConvolutionScale LineLegend LineOpacity LineSpacing LineWrapParts LinkActivate LinkClose LinkConnect LinkConnectedQ LinkCreate LinkError LinkFlush LinkFunction LinkHost LinkInterrupt LinkLaunch LinkMode LinkObject LinkOpen LinkOptions LinkPatterns LinkProtocol LinkRead LinkReadHeld LinkReadyQ Links LinkWrite LinkWriteHeld LiouvilleLambda List Listable ListAnimate ListContourPlot ListContourPlot3D ListConvolve ListCorrelate ListCurvePathPlot ListDeconvolve ListDensityPlot Listen ListFourierSequenceTransform ListInterpolation ListLineIntegralConvolutionPlot ListLinePlot ListLogLinearPlot ListLogLogPlot ListLogPlot ListPicker ListPickerBox ListPickerBoxBackground ListPickerBoxOptions ListPlay ListPlot ListPlot3D ListPointPlot3D ListPolarPlot ListQ ListStreamDensityPlot ListStreamPlot ListSurfacePlot3D ListVectorDensityPlot ListVectorPlot ListVectorPlot3D ListZTransform Literal LiteralSearch LocalClusteringCoefficient LocalizeVariables LocationEquivalenceTest LocationTest Locator LocatorAutoCreate LocatorBox LocatorBoxOptions LocatorCentering LocatorPane LocatorPaneBox LocatorPaneBoxOptions LocatorRegion Locked Log Log10 Log2 LogBarnesG LogGamma LogGammaDistribution LogicalExpand LogIntegral LogisticDistribution LogitModelFit LogLikelihood LogLinearPlot LogLogisticDistribution LogLogPlot LogMultinormalDistribution LogNormalDistribution LogPlot LogRankTest LogSeriesDistribution LongEqual Longest LongestAscendingSequence LongestCommonSequence LongestCommonSequencePositions LongestCommonSubsequence LongestCommonSubsequencePositions LongestMatch LongForm Longitude LongLeftArrow LongLeftRightArrow LongRightArrow Loopback LoopFreeGraphQ LowerCaseQ LowerLeftArrow LowerRightArrow LowerTriangularize LowpassFilter LQEstimatorGains LQGRegulator LQOutputRegulatorGains LQRegulatorGains LUBackSubstitution LucasL LuccioSamiComponents LUDecomposition LyapunovSolve LyonsGroupLy MachineID MachineName MachineNumberQ MachinePrecision MacintoshSystemPageSetup Magenta Magnification Magnify MainSolve MaintainDynamicCaches Majority MakeBoxes MakeExpression MakeRules MangoldtLambda ManhattanDistance Manipulate Manipulator MannWhitneyTest MantissaExponent Manual Map MapAll MapAt MapIndexed MAProcess MapThread MarcumQ MardiaCombinedTest MardiaKurtosisTest MardiaSkewnessTest MarginalDistribution MarkovProcessProperties Masking MatchingDissimilarity MatchLocalNameQ MatchLocalNames MatchQ Material MathematicaNotation MathieuC MathieuCharacteristicA MathieuCharacteristicB MathieuCharacteristicExponent MathieuCPrime MathieuGroupM11 MathieuGroupM12 MathieuGroupM22 MathieuGroupM23 MathieuGroupM24 MathieuS MathieuSPrime MathMLForm MathMLText Matrices MatrixExp MatrixForm MatrixFunction MatrixLog MatrixPlot MatrixPower MatrixQ MatrixRank Max MaxBend MaxDetect MaxExtraBandwidths MaxExtraConditions MaxFeatures MaxFilter Maximize MaxIterations MaxMemoryUsed MaxMixtureKernels MaxPlotPoints MaxPoints MaxRecursion MaxStableDistribution MaxStepFraction MaxSteps MaxStepSize MaxValue MaxwellDistribution McLaughlinGroupMcL Mean MeanClusteringCoefficient MeanDegreeConnectivity MeanDeviation MeanFilter MeanGraphDistance MeanNeighborDegree MeanShift MeanShiftFilter Median MedianDeviation MedianFilter Medium MeijerG MeixnerDistribution MemberQ MemoryConstrained MemoryInUse Menu MenuAppearance MenuCommandKey MenuEvaluator MenuItem MenuPacket MenuSortingValue MenuStyle MenuView MergeDifferences Mesh MeshFunctions MeshRange MeshShading MeshStyle Message MessageDialog MessageList MessageName MessageOptions MessagePacket Messages MessagesNotebook MetaCharacters MetaInformation Method MethodOptions MexicanHatWavelet MeyerWavelet Min MinDetect MinFilter MinimalPolynomial MinimalStateSpaceModel Minimize Minors MinRecursion MinSize MinStableDistribution Minus MinusPlus MinValue Missing MissingDataMethod MittagLefflerE MixedRadix MixedRadixQuantity MixtureDistribution Mod Modal Mode Modular ModularLambda Module Modulus MoebiusMu Moment Momentary MomentConvert MomentEvaluate MomentGeneratingFunction Monday Monitor MonomialList MonomialOrder MonsterGroupM MorletWavelet MorphologicalBinarize MorphologicalBranchPoints MorphologicalComponents MorphologicalEulerNumber MorphologicalGraph MorphologicalPerimeter MorphologicalTransform Most MouseAnnotation MouseAppearance MouseAppearanceTag MouseButtons Mouseover MousePointerNote MousePosition MovingAverage MovingMedian MoyalDistribution MultiedgeStyle MultilaunchWarning MultiLetterItalics MultiLetterStyle MultilineFunction Multinomial MultinomialDistribution MultinormalDistribution MultiplicativeOrder Multiplicity Multiselection MultivariateHypergeometricDistribution MultivariatePoissonDistribution MultivariateTDistribution N NakagamiDistribution NameQ Names NamespaceBox Nand NArgMax NArgMin NBernoulliB NCache NDSolve NDSolveValue Nearest NearestFunction NeedCurrentFrontEndPackagePacket NeedCurrentFrontEndSymbolsPacket NeedlemanWunschSimilarity Needs Negative NegativeBinomialDistribution NegativeMultinomialDistribution NeighborhoodGraph Nest NestedGreaterGreater NestedLessLess NestedScriptRules NestList NestWhile NestWhileList NevilleThetaC NevilleThetaD NevilleThetaN NevilleThetaS NewPrimitiveStyle NExpectation Next NextPrime NHoldAll NHoldFirst NHoldRest NicholsGridLines NicholsPlot NIntegrate NMaximize NMaxValue NMinimize NMinValue NominalVariables NonAssociative NoncentralBetaDistribution NoncentralChiSquareDistribution NoncentralFRatioDistribution NoncentralStudentTDistribution NonCommutativeMultiply NonConstants None NonlinearModelFit NonlocalMeansFilter NonNegative NonPositive Nor NorlundB Norm Normal NormalDistribution NormalGrouping Normalize NormalizedSquaredEuclideanDistance NormalsFunction NormFunction Not NotCongruent NotCupCap NotDoubleVerticalBar Notebook NotebookApply NotebookAutoSave NotebookClose NotebookConvertSettings NotebookCreate NotebookCreateReturnObject NotebookDefault NotebookDelete NotebookDirectory NotebookDynamicExpression NotebookEvaluate NotebookEventActions NotebookFileName NotebookFind NotebookFindReturnObject NotebookGet NotebookGetLayoutInformationPacket NotebookGetMisspellingsPacket NotebookInformation NotebookInterfaceObject NotebookLocate NotebookObject NotebookOpen NotebookOpenReturnObject NotebookPath NotebookPrint NotebookPut NotebookPutReturnObject NotebookRead NotebookResetGeneratedCells Notebooks NotebookSave NotebookSaveAs NotebookSelection NotebookSetupLayoutInformationPacket NotebooksMenu NotebookWrite NotElement NotEqualTilde NotExists NotGreater NotGreaterEqual NotGreaterFullEqual NotGreaterGreater NotGreaterLess NotGreaterSlantEqual NotGreaterTilde NotHumpDownHump NotHumpEqual NotLeftTriangle NotLeftTriangleBar NotLeftTriangleEqual NotLess NotLessEqual NotLessFullEqual NotLessGreater NotLessLess NotLessSlantEqual NotLessTilde NotNestedGreaterGreater NotNestedLessLess NotPrecedes NotPrecedesEqual NotPrecedesSlantEqual NotPrecedesTilde NotReverseElement NotRightTriangle NotRightTriangleBar NotRightTriangleEqual NotSquareSubset NotSquareSubsetEqual NotSquareSuperset NotSquareSupersetEqual NotSubset NotSubsetEqual NotSucceeds NotSucceedsEqual NotSucceedsSlantEqual NotSucceedsTilde NotSuperset NotSupersetEqual NotTilde NotTildeEqual NotTildeFullEqual NotTildeTilde NotVerticalBar NProbability NProduct NProductFactors NRoots NSolve NSum NSumTerms Null NullRecords NullSpace NullWords Number NumberFieldClassNumber NumberFieldDiscriminant NumberFieldFundamentalUnits NumberFieldIntegralBasis NumberFieldNormRepresentatives NumberFieldRegulator NumberFieldRootsOfUnity NumberFieldSignature NumberForm NumberFormat NumberMarks NumberMultiplier NumberPadding NumberPoint NumberQ NumberSeparator NumberSigns NumberString Numerator NumericFunction NumericQ NuttallWindow NValues NyquistGridLines NyquistPlot O ObservabilityGramian ObservabilityMatrix ObservableDecomposition ObservableModelQ OddQ Off Offset OLEData On ONanGroupON OneIdentity Opacity Open OpenAppend Opener OpenerBox OpenerBoxOptions OpenerView OpenFunctionInspectorPacket Opening OpenRead OpenSpecialOptions OpenTemporary OpenWrite Operate OperatingSystem OptimumFlowData Optional OptionInspectorSettings OptionQ Options OptionsPacket OptionsPattern OptionValue OptionValueBox OptionValueBoxOptions Or Orange Order OrderDistribution OrderedQ Ordering Orderless OrnsteinUhlenbeckProcess Orthogonalize Out Outer OutputAutoOverwrite OutputControllabilityMatrix OutputControllableModelQ OutputForm OutputFormData OutputGrouping OutputMathEditExpression OutputNamePacket OutputResponse OutputSizeLimit OutputStream Over OverBar OverDot Overflow OverHat Overlaps Overlay OverlayBox OverlayBoxOptions Overscript OverscriptBox OverscriptBoxOptions OverTilde OverVector OwenT OwnValues PackingMethod PaddedForm Padding PadeApproximant PadLeft PadRight PageBreakAbove PageBreakBelow PageBreakWithin PageFooterLines PageFooters PageHeaderLines PageHeaders PageHeight PageRankCentrality PageWidth PairedBarChart PairedHistogram PairedSmoothHistogram PairedTTest PairedZTest PaletteNotebook PalettePath Pane PaneBox PaneBoxOptions Panel PanelBox PanelBoxOptions Paneled PaneSelector PaneSelectorBox PaneSelectorBoxOptions PaperWidth ParabolicCylinderD ParagraphIndent ParagraphSpacing ParallelArray ParallelCombine ParallelDo ParallelEvaluate Parallelization Parallelize ParallelMap ParallelNeeds ParallelProduct ParallelSubmit ParallelSum ParallelTable ParallelTry Parameter ParameterEstimator ParameterMixtureDistribution ParameterVariables ParametricFunction ParametricNDSolve ParametricNDSolveValue ParametricPlot ParametricPlot3D ParentConnect ParentDirectory ParentForm Parenthesize ParentList ParetoDistribution Part PartialCorrelationFunction PartialD ParticleData Partition PartitionsP PartitionsQ ParzenWindow PascalDistribution PassEventsDown PassEventsUp Paste PasteBoxFormInlineCells PasteButton Path PathGraph PathGraphQ Pattern PatternSequence PatternTest PauliMatrix PaulWavelet Pause PausedTime PDF PearsonChiSquareTest PearsonCorrelationTest PearsonDistribution PerformanceGoal PeriodicInterpolation Periodogram PeriodogramArray PermutationCycles PermutationCyclesQ PermutationGroup PermutationLength PermutationList PermutationListQ PermutationMax PermutationMin PermutationOrder PermutationPower PermutationProduct PermutationReplace Permutations PermutationSupport Permute PeronaMalikFilter Perpendicular PERTDistribution PetersenGraph PhaseMargins Pi Pick PIDData PIDDerivativeFilter PIDFeedforward PIDTune Piecewise PiecewiseExpand PieChart PieChart3D PillaiTrace PillaiTraceTest Pink Pivoting PixelConstrained PixelValue PixelValuePositions Placed Placeholder PlaceholderReplace Plain PlanarGraphQ Play PlayRange Plot Plot3D Plot3Matrix PlotDivision PlotJoined PlotLabel PlotLayout PlotLegends PlotMarkers PlotPoints PlotRange PlotRangeClipping PlotRangePadding PlotRegion PlotStyle Plus PlusMinus Pochhammer PodStates PodWidth Point Point3DBox PointBox PointFigureChart PointForm PointLegend PointSize PoissonConsulDistribution PoissonDistribution PoissonProcess PoissonWindow PolarAxes PolarAxesOrigin PolarGridLines PolarPlot PolarTicks PoleZeroMarkers PolyaAeppliDistribution PolyGamma Polygon Polygon3DBox Polygon3DBoxOptions PolygonBox PolygonBoxOptions PolygonHoleScale PolygonIntersections PolygonScale PolyhedronData PolyLog PolynomialExtendedGCD PolynomialForm PolynomialGCD PolynomialLCM PolynomialMod PolynomialQ PolynomialQuotient PolynomialQuotientRemainder PolynomialReduce PolynomialRemainder Polynomials PopupMenu PopupMenuBox PopupMenuBoxOptions PopupView PopupWindow Position Positive PositiveDefiniteMatrixQ PossibleZeroQ Postfix PostScript Power PowerDistribution PowerExpand PowerMod PowerModList PowerSpectralDensity PowersRepresentations PowerSymmetricPolynomial Precedence PrecedenceForm Precedes PrecedesEqual PrecedesSlantEqual PrecedesTilde Precision PrecisionGoal PreDecrement PredictionRoot PreemptProtect PreferencesPath Prefix PreIncrement Prepend PrependTo PreserveImageOptions Previous PriceGraphDistribution PrimaryPlaceholder Prime PrimeNu PrimeOmega PrimePi PrimePowerQ PrimeQ Primes PrimeZetaP PrimitiveRoot PrincipalComponents PrincipalValue Print PrintAction PrintForm PrintingCopies PrintingOptions PrintingPageRange PrintingStartingPageNumber PrintingStyleEnvironment PrintPrecision PrintTemporary Prism PrismBox PrismBoxOptions PrivateCellOptions PrivateEvaluationOptions PrivateFontOptions PrivateFrontEndOptions PrivateNotebookOptions PrivatePaths Probability ProbabilityDistribution ProbabilityPlot ProbabilityPr ProbabilityScalePlot ProbitModelFit ProcessEstimator ProcessParameterAssumptions ProcessParameterQ ProcessStateDomain ProcessTimeDomain Product ProductDistribution ProductLog ProgressIndicator ProgressIndicatorBox ProgressIndicatorBoxOptions Projection Prolog PromptForm Properties Property PropertyList PropertyValue Proportion Proportional Protect Protected ProteinData Pruning PseudoInverse Purple Put PutAppend Pyramid PyramidBox PyramidBoxOptions QBinomial QFactorial QGamma QHypergeometricPFQ QPochhammer QPolyGamma QRDecomposition QuadraticIrrationalQ Quantile QuantilePlot Quantity QuantityForm QuantityMagnitude QuantityQ QuantityUnit Quartics QuartileDeviation Quartiles QuartileSkewness QueueingNetworkProcess QueueingProcess QueueProperties Quiet Quit Quotient QuotientRemainder RadialityCentrality RadicalBox RadicalBoxOptions RadioButton RadioButtonBar RadioButtonBox RadioButtonBoxOptions Radon RamanujanTau RamanujanTauL RamanujanTauTheta RamanujanTauZ Random RandomChoice RandomComplex RandomFunction RandomGraph RandomImage RandomInteger RandomPermutation RandomPrime RandomReal RandomSample RandomSeed RandomVariate RandomWalkProcess Range RangeFilter RangeSpecification RankedMax RankedMin Raster Raster3D Raster3DBox Raster3DBoxOptions RasterArray RasterBox RasterBoxOptions Rasterize RasterSize Rational RationalFunctions Rationalize Rationals Ratios Raw RawArray RawBoxes RawData RawMedium RayleighDistribution Re Read ReadList ReadProtected Real RealBlockDiagonalForm RealDigits RealExponent Reals Reap Record RecordLists RecordSeparators Rectangle RectangleBox RectangleBoxOptions RectangleChart RectangleChart3D RecurrenceFilter RecurrenceTable RecurringDigitsForm Red Reduce RefBox ReferenceLineStyle ReferenceMarkers ReferenceMarkerStyle Refine ReflectionMatrix ReflectionTransform Refresh RefreshRate RegionBinarize RegionFunction RegionPlot RegionPlot3D RegularExpression Regularization Reinstall Release ReleaseHold ReliabilityDistribution ReliefImage ReliefPlot Remove RemoveAlphaChannel RemoveAsynchronousTask Removed RemoveInputStreamMethod RemoveOutputStreamMethod RemoveProperty RemoveScheduledTask RenameDirectory RenameFile RenderAll RenderingOptions RenewalProcess RenkoChart Repeated RepeatedNull RepeatedString Replace ReplaceAll ReplaceHeldPart ReplaceImageValue ReplaceList ReplacePart ReplacePixelValue ReplaceRepeated Resampling Rescale RescalingTransform ResetDirectory ResetMenusPacket ResetScheduledTask Residue Resolve Rest Resultant ResumePacket Return ReturnExpressionPacket ReturnInputFormPacket ReturnPacket ReturnTextPacket Reverse ReverseBiorthogonalSplineWavelet ReverseElement ReverseEquilibrium ReverseGraph ReverseUpEquilibrium RevolutionAxis RevolutionPlot3D RGBColor RiccatiSolve RiceDistribution RidgeFilter RiemannR RiemannSiegelTheta RiemannSiegelZ Riffle Right RightArrow RightArrowBar RightArrowLeftArrow RightCosetRepresentative RightDownTeeVector RightDownVector RightDownVectorBar RightTee RightTeeArrow RightTeeVector RightTriangle RightTriangleBar RightTriangleEqual RightUpDownVector RightUpTeeVector RightUpVector RightUpVectorBar RightVector RightVectorBar RiskAchievementImportance RiskReductionImportance RogersTanimotoDissimilarity Root RootApproximant RootIntervals RootLocusPlot RootMeanSquare RootOfUnityQ RootReduce Roots RootSum Rotate RotateLabel RotateLeft RotateRight RotationAction RotationBox RotationBoxOptions RotationMatrix RotationTransform Round RoundImplies RoundingRadius Row RowAlignments RowBackgrounds RowBox RowHeights RowLines RowMinHeight RowReduce RowsEqual RowSpacings RSolve RudvalisGroupRu Rule RuleCondition RuleDelayed RuleForm RulerUnits Run RunScheduledTask RunThrough RuntimeAttributes RuntimeOptions RussellRaoDissimilarity SameQ SameTest SampleDepth SampledSoundFunction SampledSoundList SampleRate SamplingPeriod SARIMAProcess SARMAProcess SatisfiabilityCount SatisfiabilityInstances SatisfiableQ Saturday Save Saveable SaveAutoDelete SaveDefinitions SawtoothWave Scale Scaled ScaleDivisions ScaledMousePosition ScaleOrigin ScalePadding ScaleRanges ScaleRangeStyle ScalingFunctions ScalingMatrix ScalingTransform Scan ScheduledTaskActiveQ ScheduledTaskData ScheduledTaskObject ScheduledTasks SchurDecomposition ScientificForm ScreenRectangle ScreenStyleEnvironment ScriptBaselineShifts ScriptLevel ScriptMinSize ScriptRules ScriptSizeMultipliers Scrollbars ScrollingOptions ScrollPosition Sec Sech SechDistribution SectionGrouping SectorChart SectorChart3D SectorOrigin SectorSpacing SeedRandom Select Selectable SelectComponents SelectedCells SelectedNotebook Selection SelectionAnimate SelectionCell SelectionCellCreateCell SelectionCellDefaultStyle SelectionCellParentStyle SelectionCreateCell SelectionDebuggerTag SelectionDuplicateCell SelectionEvaluate SelectionEvaluateCreateCell SelectionMove SelectionPlaceholder SelectionSetStyle SelectWithContents SelfLoops SelfLoopStyle SemialgebraicComponentInstances SendMail Sequence SequenceAlignment SequenceForm SequenceHold SequenceLimit Series SeriesCoefficient SeriesData SessionTime Set SetAccuracy SetAlphaChannel SetAttributes Setbacks SetBoxFormNamesPacket SetDelayed SetDirectory SetEnvironment SetEvaluationNotebook SetFileDate SetFileLoadingContext SetNotebookStatusLine SetOptions SetOptionsPacket SetPrecision SetProperty SetSelectedNotebook SetSharedFunction SetSharedVariable SetSpeechParametersPacket SetStreamPosition SetSystemOptions Setter SetterBar SetterBox SetterBoxOptions Setting SetValue Shading Shallow ShannonWavelet ShapiroWilkTest Share Sharpen ShearingMatrix ShearingTransform ShenCastanMatrix Short ShortDownArrow Shortest ShortestMatch ShortestPathFunction ShortLeftArrow ShortRightArrow ShortUpArrow Show ShowAutoStyles ShowCellBracket ShowCellLabel ShowCellTags ShowClosedCellArea ShowContents ShowControls ShowCursorTracker ShowGroupOpenCloseIcon ShowGroupOpener ShowInvisibleCharacters ShowPageBreaks ShowPredictiveInterface ShowSelection ShowShortBoxForm ShowSpecialCharacters ShowStringCharacters ShowSyntaxStyles ShrinkingDelay ShrinkWrapBoundingBox SiegelTheta SiegelTukeyTest Sign Signature SignedRankTest SignificanceLevel SignPadding SignTest SimilarityRules SimpleGraph SimpleGraphQ Simplify Sin Sinc SinghMaddalaDistribution SingleEvaluation SingleLetterItalics SingleLetterStyle SingularValueDecomposition SingularValueList SingularValuePlot SingularValues Sinh SinhIntegral SinIntegral SixJSymbol Skeleton SkeletonTransform SkellamDistribution Skewness SkewNormalDistribution Skip SliceDistribution Slider Slider2D Slider2DBox Slider2DBoxOptions SliderBox SliderBoxOptions SlideView Slot SlotSequence Small SmallCircle Smaller SmithDelayCompensator SmithWatermanSimilarity SmoothDensityHistogram SmoothHistogram SmoothHistogram3D SmoothKernelDistribution SocialMediaData Socket SokalSneathDissimilarity Solve SolveAlways SolveDelayed Sort SortBy Sound SoundAndGraphics SoundNote SoundVolume Sow Space SpaceForm Spacer Spacings Span SpanAdjustments SpanCharacterRounding SpanFromAbove SpanFromBoth SpanFromLeft SpanLineThickness SpanMaxSize SpanMinSize SpanningCharacters SpanSymmetric SparseArray SpatialGraphDistribution Speak SpeakTextPacket SpearmanRankTest SpearmanRho Spectrogram SpectrogramArray Specularity SpellingCorrection SpellingDictionaries SpellingDictionariesPath SpellingOptions SpellingSuggestionsPacket Sphere SphereBox SphericalBesselJ SphericalBesselY SphericalHankelH1 SphericalHankelH2 SphericalHarmonicY SphericalPlot3D SphericalRegion SpheroidalEigenvalue SpheroidalJoiningFactor SpheroidalPS SpheroidalPSPrime SpheroidalQS SpheroidalQSPrime SpheroidalRadialFactor SpheroidalS1 SpheroidalS1Prime SpheroidalS2 SpheroidalS2Prime Splice SplicedDistribution SplineClosed SplineDegree SplineKnots SplineWeights Split SplitBy SpokenString Sqrt SqrtBox SqrtBoxOptions Square SquaredEuclideanDistance SquareFreeQ SquareIntersection SquaresR SquareSubset SquareSubsetEqual SquareSuperset SquareSupersetEqual SquareUnion SquareWave StabilityMargins StabilityMarginsStyle StableDistribution Stack StackBegin StackComplete StackInhibit StandardDeviation StandardDeviationFilter StandardForm Standardize StandbyDistribution Star StarGraph StartAsynchronousTask StartingStepSize StartOfLine StartOfString StartScheduledTask StartupSound StateDimensions StateFeedbackGains StateOutputEstimator StateResponse StateSpaceModel StateSpaceRealization StateSpaceTransform StationaryDistribution StationaryWaveletPacketTransform StationaryWaveletTransform StatusArea StatusCentrality StepMonitor StieltjesGamma StirlingS1 StirlingS2 StopAsynchronousTask StopScheduledTask StrataVariables StratonovichProcess StreamColorFunction StreamColorFunctionScaling StreamDensityPlot StreamPlot StreamPoints StreamPosition Streams StreamScale StreamStyle String StringBreak StringByteCount StringCases StringCount StringDrop StringExpression StringForm StringFormat StringFreeQ StringInsert StringJoin StringLength StringMatchQ StringPosition StringQ StringReplace StringReplaceList StringReplacePart StringReverse StringRotateLeft StringRotateRight StringSkeleton StringSplit StringTake StringToStream StringTrim StripBoxes StripOnInput StripWrapperBoxes StrokeForm StructuralImportance StructuredArray StructuredSelection StruveH StruveL Stub StudentTDistribution Style StyleBox StyleBoxAutoDelete StyleBoxOptions StyleData StyleDefinitions StyleForm StyleKeyMapping StyleMenuListing StyleNameDialogSettings StyleNames StylePrint StyleSheetPath Subfactorial Subgraph SubMinus SubPlus SubresultantPolynomialRemainders SubresultantPolynomials Subresultants Subscript SubscriptBox SubscriptBoxOptions Subscripted Subset SubsetEqual Subsets SubStar Subsuperscript SubsuperscriptBox SubsuperscriptBoxOptions Subtract SubtractFrom SubValues Succeeds SucceedsEqual SucceedsSlantEqual SucceedsTilde SuchThat Sum SumConvergence Sunday SuperDagger SuperMinus SuperPlus Superscript SuperscriptBox SuperscriptBoxOptions Superset SupersetEqual SuperStar Surd SurdForm SurfaceColor SurfaceGraphics SurvivalDistribution SurvivalFunction SurvivalModel SurvivalModelFit SuspendPacket SuzukiDistribution SuzukiGroupSuz SwatchLegend Switch Symbol SymbolName SymletWavelet Symmetric SymmetricGroup SymmetricMatrixQ SymmetricPolynomial SymmetricReduction Symmetrize SymmetrizedArray SymmetrizedArrayRules SymmetrizedDependentComponents SymmetrizedIndependentComponents SymmetrizedReplacePart SynchronousInitialization SynchronousUpdating Syntax SyntaxForm SyntaxInformation SyntaxLength SyntaxPacket SyntaxQ SystemDialogInput SystemException SystemHelpPath SystemInformation SystemInformationData SystemOpen SystemOptions SystemsModelDelay SystemsModelDelayApproximate SystemsModelDelete SystemsModelDimensions SystemsModelExtract SystemsModelFeedbackConnect SystemsModelLabels SystemsModelOrder SystemsModelParallelConnect SystemsModelSeriesConnect SystemsModelStateFeedbackConnect SystemStub Tab TabFilling Table TableAlignments TableDepth TableDirections TableForm TableHeadings TableSpacing TableView TableViewBox TabSpacings TabView TabViewBox TabViewBoxOptions TagBox TagBoxNote TagBoxOptions TaggingRules TagSet TagSetDelayed TagStyle 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diff --git a/docs/apex-3.3/mkdocs/js/lunr.min.js b/docs/apex-3.3/mkdocs/js/lunr.min.js
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diff --git a/docs/apex-3.3/mkdocs/js/mustache.min.js b/docs/apex-3.3/mkdocs/js/mustache.min.js
deleted file mode 100644
index 7fc6da8..0000000
--- a/docs/apex-3.3/mkdocs/js/mustache.min.js
+++ /dev/null
@@ -1 +0,0 @@
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diff --git a/docs/apex-3.3/mkdocs/js/require.js b/docs/apex-3.3/mkdocs/js/require.js
deleted file mode 100644
index 8638a31..0000000
--- a/docs/apex-3.3/mkdocs/js/require.js
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- RequireJS 2.1.16 Copyright (c) 2010-2015, The Dojo Foundation All Rights Reserved.
- Available via the MIT or new BSD license.
- see: http://github.com/jrburke/requirejs for details
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diff --git a/docs/apex-3.3/mkdocs/js/search-results-template.mustache b/docs/apex-3.3/mkdocs/js/search-results-template.mustache
deleted file mode 100644
index a8b3862..0000000
--- a/docs/apex-3.3/mkdocs/js/search-results-template.mustache
+++ /dev/null
@@ -1,4 +0,0 @@
-<article>
- <h3><a href="{{location}}">{{title}}</a></h3>
- <p>{{summary}}</p>
-</article>
diff --git a/docs/apex-3.3/mkdocs/js/search.js b/docs/apex-3.3/mkdocs/js/search.js
deleted file mode 100644
index 88d563a..0000000
--- a/docs/apex-3.3/mkdocs/js/search.js
+++ /dev/null
@@ -1,88 +0,0 @@
-require([
- base_url + '/mkdocs/js/mustache.min.js',
- base_url + '/mkdocs/js/lunr.min.js',
- 'text!search-results-template.mustache',
- 'text!../search_index.json',
-], function (Mustache, lunr, results_template, data) {
- "use strict";
-
- function getSearchTerm()
- {
- var sPageURL = window.location.search.substring(1);
- var sURLVariables = sPageURL.split('&');
- for (var i = 0; i < sURLVariables.length; i++)
- {
- var sParameterName = sURLVariables[i].split('=');
- if (sParameterName[0] == 'q')
- {
- return decodeURIComponent(sParameterName[1].replace(/\+/g, '%20'));
- }
- }
- }
-
- var index = lunr(function () {
- this.field('title', {boost: 10});
- this.field('text');
- this.ref('location');
- });
-
- data = JSON.parse(data);
- var documents = {};
-
- for (var i=0; i < data.docs.length; i++){
- var doc = data.docs[i];
- doc.location = base_url + doc.location;
- index.add(doc);
- documents[doc.location] = doc;
- }
-
- var search = function(){
-
- var query = document.getElementById('mkdocs-search-query').value;
- var search_results = document.getElementById("mkdocs-search-results");
- while (search_results.firstChild) {
- search_results.removeChild(search_results.firstChild);
- }
-
- if(query === ''){
- return;
- }
-
- var results = index.search(query);
-
- if (results.length > 0){
- for (var i=0; i < results.length; i++){
- var result = results[i];
- doc = documents[result.ref];
- doc.base_url = base_url;
- doc.summary = doc.text.substring(0, 200);
- var html = Mustache.to_html(results_template, doc);
- search_results.insertAdjacentHTML('beforeend', html);
- }
- } else {
- search_results.insertAdjacentHTML('beforeend', "<p>No results found</p>");
- }
-
- if(jQuery){
- /*
- * We currently only automatically hide bootstrap models. This
- * requires jQuery to work.
- */
- jQuery('#mkdocs_search_modal a').click(function(){
- jQuery('#mkdocs_search_modal').modal('hide');
- })
- }
-
- };
-
- var search_input = document.getElementById('mkdocs-search-query');
-
- var term = getSearchTerm();
- if (term){
- search_input.value = term;
- search();
- }
-
- search_input.addEventListener("keyup", search);
-
-});
diff --git a/docs/apex-3.3/mkdocs/js/text.js b/docs/apex-3.3/mkdocs/js/text.js
deleted file mode 100644
index 17921b6..0000000
--- a/docs/apex-3.3/mkdocs/js/text.js
+++ /dev/null
@@ -1,390 +0,0 @@
-/**
- * @license RequireJS text 2.0.12 Copyright (c) 2010-2014, The Dojo Foundation All Rights Reserved.
- * Available via the MIT or new BSD license.
- * see: http://github.com/requirejs/text for details
- */
-/*jslint regexp: true */
-/*global require, XMLHttpRequest, ActiveXObject,
- define, window, process, Packages,
- java, location, Components, FileUtils */
-
-define(['module'], function (module) {
- 'use strict';
-
- var text, fs, Cc, Ci, xpcIsWindows,
- progIds = ['Msxml2.XMLHTTP', 'Microsoft.XMLHTTP', 'Msxml2.XMLHTTP.4.0'],
- xmlRegExp = /^\s*<\?xml(\s)+version=[\'\"](\d)*.(\d)*[\'\"](\s)*\?>/im,
- bodyRegExp = /<body[^>]*>\s*([\s\S]+)\s*<\/body>/im,
- hasLocation = typeof location !== 'undefined' && location.href,
- defaultProtocol = hasLocation && location.protocol && location.protocol.replace(/\:/, ''),
- defaultHostName = hasLocation && location.hostname,
- defaultPort = hasLocation && (location.port || undefined),
- buildMap = {},
- masterConfig = (module.config && module.config()) || {};
-
- text = {
- version: '2.0.12',
-
- strip: function (content) {
- //Strips <?xml ...?> declarations so that external SVG and XML
- //documents can be added to a document without worry. Also, if the string
- //is an HTML document, only the part inside the body tag is returned.
- if (content) {
- content = content.replace(xmlRegExp, "");
- var matches = content.match(bodyRegExp);
- if (matches) {
- content = matches[1];
- }
- } else {
- content = "";
- }
- return content;
- },
-
- jsEscape: function (content) {
- return content.replace(/(['\\])/g, '\\$1')
- .replace(/[\f]/g, "\\f")
- .replace(/[\b]/g, "\\b")
- .replace(/[\n]/g, "\\n")
- .replace(/[\t]/g, "\\t")
- .replace(/[\r]/g, "\\r")
- .replace(/[\u2028]/g, "\\u2028")
- .replace(/[\u2029]/g, "\\u2029");
- },
-
- createXhr: masterConfig.createXhr || function () {
- //Would love to dump the ActiveX crap in here. Need IE 6 to die first.
- var xhr, i, progId;
- if (typeof XMLHttpRequest !== "undefined") {
- return new XMLHttpRequest();
- } else if (typeof ActiveXObject !== "undefined") {
- for (i = 0; i < 3; i += 1) {
- progId = progIds[i];
- try {
- xhr = new ActiveXObject(progId);
- } catch (e) {}
-
- if (xhr) {
- progIds = [progId]; // so faster next time
- break;
- }
- }
- }
-
- return xhr;
- },
-
- /**
- * Parses a resource name into its component parts. Resource names
- * look like: module/name.ext!strip, where the !strip part is
- * optional.
- * @param {String} name the resource name
- * @returns {Object} with properties "moduleName", "ext" and "strip"
- * where strip is a boolean.
- */
- parseName: function (name) {
- var modName, ext, temp,
- strip = false,
- index = name.indexOf("."),
- isRelative = name.indexOf('./') === 0 ||
- name.indexOf('../') === 0;
-
- if (index !== -1 && (!isRelative || index > 1)) {
- modName = name.substring(0, index);
- ext = name.substring(index + 1, name.length);
- } else {
- modName = name;
- }
-
- temp = ext || modName;
- index = temp.indexOf("!");
- if (index !== -1) {
- //Pull off the strip arg.
- strip = temp.substring(index + 1) === "strip";
- temp = temp.substring(0, index);
- if (ext) {
- ext = temp;
- } else {
- modName = temp;
- }
- }
-
- return {
- moduleName: modName,
- ext: ext,
- strip: strip
- };
- },
-
- xdRegExp: /^((\w+)\:)?\/\/([^\/\\]+)/,
-
- /**
- * Is an URL on another domain. Only works for browser use, returns
- * false in non-browser environments. Only used to know if an
- * optimized .js version of a text resource should be loaded
- * instead.
- * @param {String} url
- * @returns Boolean
- */
- useXhr: function (url, protocol, hostname, port) {
- var uProtocol, uHostName, uPort,
- match = text.xdRegExp.exec(url);
- if (!match) {
- return true;
- }
- uProtocol = match[2];
- uHostName = match[3];
-
- uHostName = uHostName.split(':');
- uPort = uHostName[1];
- uHostName = uHostName[0];
-
- return (!uProtocol || uProtocol === protocol) &&
- (!uHostName || uHostName.toLowerCase() === hostname.toLowerCase()) &&
- ((!uPort && !uHostName) || uPort === port);
- },
-
- finishLoad: function (name, strip, content, onLoad) {
- content = strip ? text.strip(content) : content;
- if (masterConfig.isBuild) {
- buildMap[name] = content;
- }
- onLoad(content);
- },
-
- load: function (name, req, onLoad, config) {
- //Name has format: some.module.filext!strip
- //The strip part is optional.
- //if strip is present, then that means only get the string contents
- //inside a body tag in an HTML string. For XML/SVG content it means
- //removing the <?xml ...?> declarations so the content can be inserted
- //into the current doc without problems.
-
- // Do not bother with the work if a build and text will
- // not be inlined.
- if (config && config.isBuild && !config.inlineText) {
- onLoad();
- return;
- }
-
- masterConfig.isBuild = config && config.isBuild;
-
- var parsed = text.parseName(name),
- nonStripName = parsed.moduleName +
- (parsed.ext ? '.' + parsed.ext : ''),
- url = req.toUrl(nonStripName),
- useXhr = (masterConfig.useXhr) ||
- text.useXhr;
-
- // Do not load if it is an empty: url
- if (url.indexOf('empty:') === 0) {
- onLoad();
- return;
- }
-
- //Load the text. Use XHR if possible and in a browser.
- if (!hasLocation || useXhr(url, defaultProtocol, defaultHostName, defaultPort)) {
- text.get(url, function (content) {
- text.finishLoad(name, parsed.strip, content, onLoad);
- }, function (err) {
- if (onLoad.error) {
- onLoad.error(err);
- }
- });
- } else {
- //Need to fetch the resource across domains. Assume
- //the resource has been optimized into a JS module. Fetch
- //by the module name + extension, but do not include the
- //!strip part to avoid file system issues.
- req([nonStripName], function (content) {
- text.finishLoad(parsed.moduleName + '.' + parsed.ext,
- parsed.strip, content, onLoad);
- });
- }
- },
-
- write: function (pluginName, moduleName, write, config) {
- if (buildMap.hasOwnProperty(moduleName)) {
- var content = text.jsEscape(buildMap[moduleName]);
- write.asModule(pluginName + "!" + moduleName,
- "define(function () { return '" +
- content +
- "';});\n");
- }
- },
-
- writeFile: function (pluginName, moduleName, req, write, config) {
- var parsed = text.parseName(moduleName),
- extPart = parsed.ext ? '.' + parsed.ext : '',
- nonStripName = parsed.moduleName + extPart,
- //Use a '.js' file name so that it indicates it is a
- //script that can be loaded across domains.
- fileName = req.toUrl(parsed.moduleName + extPart) + '.js';
-
- //Leverage own load() method to load plugin value, but only
- //write out values that do not have the strip argument,
- //to avoid any potential issues with ! in file names.
- text.load(nonStripName, req, function (value) {
- //Use own write() method to construct full module value.
- //But need to create shell that translates writeFile's
- //write() to the right interface.
- var textWrite = function (contents) {
- return write(fileName, contents);
- };
- textWrite.asModule = function (moduleName, contents) {
- return write.asModule(moduleName, fileName, contents);
- };
-
- text.write(pluginName, nonStripName, textWrite, config);
- }, config);
- }
- };
-
- if (masterConfig.env === 'node' || (!masterConfig.env &&
- typeof process !== "undefined" &&
- process.versions &&
- !!process.versions.node &&
- !process.versions['node-webkit'])) {
- //Using special require.nodeRequire, something added by r.js.
- fs = require.nodeRequire('fs');
-
- text.get = function (url, callback, errback) {
- try {
- var file = fs.readFileSync(url, 'utf8');
- //Remove BOM (Byte Mark Order) from utf8 files if it is there.
- if (file.indexOf('\uFEFF') === 0) {
- file = file.substring(1);
- }
- callback(file);
- } catch (e) {
- if (errback) {
- errback(e);
- }
- }
- };
- } else if (masterConfig.env === 'xhr' || (!masterConfig.env &&
- text.createXhr())) {
- text.get = function (url, callback, errback, headers) {
- var xhr = text.createXhr(), header;
- xhr.open('GET', url, true);
-
- //Allow plugins direct access to xhr headers
- if (headers) {
- for (header in headers) {
- if (headers.hasOwnProperty(header)) {
- xhr.setRequestHeader(header.toLowerCase(), headers[header]);
- }
- }
- }
-
- //Allow overrides specified in config
- if (masterConfig.onXhr) {
- masterConfig.onXhr(xhr, url);
- }
-
- xhr.onreadystatechange = function (evt) {
- var status, err;
- //Do not explicitly handle errors, those should be
- //visible via console output in the browser.
- if (xhr.readyState === 4) {
- status = xhr.status || 0;
- if (status > 399 && status < 600) {
- //An http 4xx or 5xx error. Signal an error.
- err = new Error(url + ' HTTP status: ' + status);
- err.xhr = xhr;
- if (errback) {
- errback(err);
- }
- } else {
- callback(xhr.responseText);
- }
-
- if (masterConfig.onXhrComplete) {
- masterConfig.onXhrComplete(xhr, url);
- }
- }
- };
- xhr.send(null);
- };
- } else if (masterConfig.env === 'rhino' || (!masterConfig.env &&
- typeof Packages !== 'undefined' && typeof java !== 'undefined')) {
- //Why Java, why is this so awkward?
- text.get = function (url, callback) {
- var stringBuffer, line,
- encoding = "utf-8",
- file = new java.io.File(url),
- lineSeparator = java.lang.System.getProperty("line.separator"),
- input = new java.io.BufferedReader(new java.io.InputStreamReader(new java.io.FileInputStream(file), encoding)),
- content = '';
- try {
- stringBuffer = new java.lang.StringBuffer();
- line = input.readLine();
-
- // Byte Order Mark (BOM) - The Unicode Standard, version 3.0, page 324
- // http://www.unicode.org/faq/utf_bom.html
-
- // Note that when we use utf-8, the BOM should appear as "EF BB BF", but it doesn't due to this bug in the JDK:
- // http://bugs.sun.com/bugdatabase/view_bug.do?bug_id=4508058
- if (line && line.length() && line.charAt(0) === 0xfeff) {
- // Eat the BOM, since we've already found the encoding on this file,
- // and we plan to concatenating this buffer with others; the BOM should
- // only appear at the top of a file.
- line = line.substring(1);
- }
-
- if (line !== null) {
- stringBuffer.append(line);
- }
-
- while ((line = input.readLine()) !== null) {
- stringBuffer.append(lineSeparator);
- stringBuffer.append(line);
- }
- //Make sure we return a JavaScript string and not a Java string.
- content = String(stringBuffer.toString()); //String
- } finally {
- input.close();
- }
- callback(content);
- };
- } else if (masterConfig.env === 'xpconnect' || (!masterConfig.env &&
- typeof Components !== 'undefined' && Components.classes &&
- Components.interfaces)) {
- //Avert your gaze!
- Cc = Components.classes;
- Ci = Components.interfaces;
- Components.utils['import']('resource://gre/modules/FileUtils.jsm');
- xpcIsWindows = ('@mozilla.org/windows-registry-key;1' in Cc);
-
- text.get = function (url, callback) {
- var inStream, convertStream, fileObj,
- readData = {};
-
- if (xpcIsWindows) {
- url = url.replace(/\//g, '\\');
- }
-
- fileObj = new FileUtils.File(url);
-
- //XPCOM, you so crazy
- try {
- inStream = Cc['@mozilla.org/network/file-input-stream;1']
- .createInstance(Ci.nsIFileInputStream);
- inStream.init(fileObj, 1, 0, false);
-
- convertStream = Cc['@mozilla.org/intl/converter-input-stream;1']
- .createInstance(Ci.nsIConverterInputStream);
- convertStream.init(inStream, "utf-8", inStream.available(),
- Ci.nsIConverterInputStream.DEFAULT_REPLACEMENT_CHARACTER);
-
- convertStream.readString(inStream.available(), readData);
- convertStream.close();
- inStream.close();
- callback(readData.value);
- } catch (e) {
- throw new Error((fileObj && fileObj.path || '') + ': ' + e);
- }
- };
- }
- return text;
-});
diff --git a/docs/apex-3.3/mkdocs/search_index.json b/docs/apex-3.3/mkdocs/search_index.json
deleted file mode 100644
index 34dd42f..0000000
--- a/docs/apex-3.3/mkdocs/search_index.json
+++ /dev/null
@@ -1,834 +0,0 @@
-{
- "docs": [
- {
- "location": "/",
- "text": "Apache Apex (Incubating)\n\n\nApex is a Hadoop YARN native big data processing platform, enabling real time stream as well as batch processing for your big data. Apex provides the following benefits:\n\n\n\n\nHigh scalability and performance\n\n\nFault tolerance and state management\n\n\nHadoop-native YARN \n HDFS implementation\n\n\nEvent processing guarantees\n\n\nSeparation of functional and operational concerns\n\n\nSimple API supports generic Java code\n\n\n\n\nPlatform has been demonstated to scale linearly across Hadoop clusters under extreme loads of billions of events per second. Hardware and process failures are quickly recovered with HDFS-backed checkpointing and automatic operator recovery, preserving application state and resuming execution in seconds. Functional and operational specifications are separated. Apex provides a simple API, which enables users to write generic, reusable code. The code is dropped in as-is and platform automatically handles the various operational concerns, such as state management, fault tolerance, scalability, security, metrics, etc. This frees users to focus on functional development, and lets platform provide operability support.\n\n\nThe core Apex platform is supplemented by Malhar, a library of connector and logic functions, enabling rapid application development. These operators and modules provide access to HDFS, S3, NFS, FTP, and other file systems; Kafka, ActiveMQ, RabbitMQ, JMS, and other message systems; MySql, Cassandra, MongoDB, Redis, HBase, CouchDB, generic JDBC, and other database connectors. In addition to the operators, the library contains a number of demos applications, demonstrating operator features and capabilities. To see the full list of available operators and related documentation, visit \nApex Malhar on Github\n\n\nFor additional information visit \nApache Apex (incubating)\n.",
- "title": "Apache Apex"
- },
- {
- "location": "/#apache-apex-incubating",
- "text": "Apex is a Hadoop YARN native big data processing platform, enabling real time stream as well as batch processing for your big data. Apex provides the following benefits: High scalability and performance Fault tolerance and state management Hadoop-native YARN HDFS implementation Event processing guarantees Separation of functional and operational concerns Simple API supports generic Java code Platform has been demonstated to scale linearly across Hadoop clusters under extreme loads of billions of events per second. Hardware and process failures are quickly recovered with HDFS-backed checkpointing and automatic operator recovery, preserving application state and resuming execution in seconds. Functional and operational specifications are separated. Apex provides a simple API, which enables users to write generic, reusable code. The code is dropped in as-is and platform automatically handles the various operational concerns, such as state management, fault tolerance, scalability, security, metrics, etc. This frees users to focus on functional development, and lets platform provide operability support. The core Apex platform is supplemented by Malhar, a library of connector and logic functions, enabling rapid application development. These operators and modules provide access to HDFS, S3, NFS, FTP, and other file systems; Kafka, ActiveMQ, RabbitMQ, JMS, and other message systems; MySql, Cassandra, MongoDB, Redis, HBase, CouchDB, generic JDBC, and other database connectors. In addition to the operators, the library contains a number of demos applications, demonstrating operator features and capabilities. To see the full list of available operators and related documentation, visit Apex Malhar on Github For additional information visit Apache Apex (incubating) .",
- "title": "Apache Apex (Incubating)"
- },
- {
- "location": "/apex_development_setup/",
- "text": "Apache Apex Development Environment Setup\n\n\nThis document discusses the steps needed for setting up a development environment for creating applications that run on the Apache Apex platform.\n\n\nDevelopment Tools\n\n\nThere are a few tools that will be helpful when developing Apache Apex applications, including:\n\n\n\n\n\n\ngit\n - A revision control system (version 1.7.1 or later). There are multiple git clients available for Windows (\nhttp://git-scm.com/download/win\n for example), so download and install a client of your choice.\n\n\n\n\n\n\njava JDK\n (not JRE) - Includes the Java Runtime Environment as well as the Java compiler and a variety of tools (version 1.7.0_79 or later). Can be downloaded from the Oracle website.\n\n\n\n\n\n\nmaven\n - Apache Maven is a build system for Java projects (version 3.0.5 or later). It can be downloaded from \nhttps://maven.apache.org/download.cgi\n.\n\n\n\n\n\n\nIDE\n (Optional) - If you prefer to use an IDE (Integrated Development Environment) such as \nNetBeans\n, \nEclipse\n or \nIntelliJ\n, install that as well.\n\n\n\n\n\n\nAfter installing these tools, make sure that the directories containing the executable files are in your PATH environment variable.\n\n\n\n\nWindows\n - Open a console window and enter the command \necho %PATH%\n to see the value of the \nPATH\n variable and verify that the above directories for Java, git, and maven executables are present. JDK executables like \njava\n and \njavac\n, the directory might be something like \nC:\\\\Program Files\\\\Java\\\\jdk1.7.0\\_80\\\\bin\n; for \ngit\n it might be \nC:\\\\Program Files\\\\Git\\\\bin\n; and for maven it might be \nC:\\\\Users\\\\user\\\\Software\\\\apache-maven-3.3.3\\\\bin\n. If not, you can change its value clicking on the button at \nControl Panel\n \n \nAdvanced System Settings\n \n \nAdvanced tab\n \n \nEnvironment Variables\n.\n\n\nLinux and Mac\n - Open a console/terminal window and enter the command \necho $PATH\n to see the value of the \nPATH\n variable and verify that the above directories for Java, git, and maven executables are present. If not, make sure software is downloaded and installed, and optionally PATH reference is added and exported in a \n~/.profile\n or \n~/.bash_profile\n. For example to add maven located in \n/sfw/maven/apache-maven-3.3.3\n to PATH add the line: \nexport PATH=$PATH:/sfw/maven/apache-maven-3.3.3/bin\n\n\n\n\nConfirm by running the following commands and comparing with output that show in the table below:\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nCommand\n\n\nOutput\n\n\n\n\n\n\njavac -version\n\n\njavac 1.7.0_80\n\n\n\n\n\n\njava -version\n\n\njava version \n1.7.0_80\n\n\nJava(TM) SE Runtime Environment (build 1.7.0_80-b15)\n\n\nJava HotSpot(TM) 64-Bit Server VM (build 24.80-b11, mixed mode)\n\n\n\n\n\n\ngit --version\n\n\ngit version 2.6.1.windows.1\n\n\n\n\n\n\nmvn --version\n\n\nApache Maven 3.3.3 (7994120775791599e205a5524ec3e0dfe41d4a06; 2015-04-22T06:57:37-05:00)\n\n\n...\n\n\n\n\n\n\n\n\n\n\n\nCreating New Apex Project\n\n\nAfter development tools are configured, you can now use the maven archetype to create a basic Apache Apex project. \nNote:\n When executing the commands below, replace \n3.3.0-incubating\n by \nlatest available version\n of Apache Apex.\n\n\n\n\n\n\nWindows\n - Create a new Windows command file called \nnewapp.cmd\n by copying the lines below, and execute it. When you run this file, the properties will be displayed and you will be prompted with \nY: :\n; just press \nEnter\n to complete the project generation. The caret (^) at the end of some lines indicates that a continuation line follows. \n\n\n@echo off\n@rem Script for creating a new application\nsetlocal\nmvn archetype:generate ^\n -DarchetypeGroupId=org.apache.apex ^\n -DarchetypeArtifactId=apex-app-archetype -DarchetypeVersion=3.3.0-incubating ^\n -DgroupId=com.example -Dpackage=com.example.myapexapp -DartifactId=myapexapp ^\n -Dversion=1.0-SNAPSHOT\nendlocal\n\n\n\n\n\n\n\nLinux\n - Execute the lines below in a terminal window. New project will be created in the curent working directory. The backslash (\\) at the end of the lines indicates continuation.\n\n\nmvn archetype:generate \\\n -DarchetypeGroupId=org.apache.apex \\\n -DarchetypeArtifactId=apex-app-archetype -DarchetypeVersion=3.2.0-incubating \\\n -DgroupId=com.example -Dpackage=com.example.myapexapp -DartifactId=myapexapp \\\n -Dversion=1.0-SNAPSHOT\n\n\n\n\n\n\n\nWhen the run completes successfully, you should see a new directory named \nmyapexapp\n containing a maven project for building a basic Apache Apex application. It includes 3 source files:\nApplication.java\n, \nRandomNumberGenerator.java\n and \nApplicationTest.java\n. You can now build the application by stepping into the new directory and running the maven package command:\n\n\ncd myapexapp\nmvn clean package -DskipTests\n\n\n\nThe build should create the application package file \nmyapexapp/target/myapexapp-1.0-SNAPSHOT.apa\n. This application package can then be used to launch example application via \ndtCli\n, or other visual management tools. When running, this application will generate a stream of random numbers and print them out, each prefixed by the string \nhello world:\n.\n\n\nBuilding Apex Demos\n\n\nIf you want to see more substantial Apex demo applications and the associated source code, you can follow these simple steps to check out and build them.\n\n\n\n\n\n\nCheck out the source code repositories:\n\n\ngit clone https://github.com/apache/incubator-apex-core\ngit clone https://github.com/apache/incubator-apex-malhar\n\n\n\n\n\n\n\nSwitch to the appropriate release branch and build each repository:\n\n\ncd incubator-apex-core\nmvn clean install -DskipTests\n\ncd incubator-apex-malhar\nmvn clean install -DskipTests\n\n\n\n\n\n\n\nThe \ninstall\n argument to the \nmvn\n command installs resources from each project to your local maven repository (typically \n.m2/repository\n under your home directory), and \nnot\n to the system directories, so Administrator privileges are not required. The \n-DskipTests\n argument skips running unit tests since they take a long time. If this is a first-time installation, it might take several minutes to complete because maven will download a number of associated plugins.\n\n\nAfter the build completes, you should see the demo application package files in the target directory under each demo subdirectory in \nincubator-apex-malhar/demos\n.\n\n\nSandbox\n\n\nTo jump start development with an Apache Hadoop single node cluster, \nDataTorrent Sandbox\n powered by VirtualBox is available on Windows, Linux, or Mac platforms. The sandbox is configured by default to run with 6GB RAM; if your development machine has 16GB or more, you can increase the sandbox RAM to 8GB or more using the VirtualBox console. This will yield better performance and support larger applications. The advantage of developing in the sandbox is that most of the tools (e.g. \njdk\n, \ngit\n, \nmaven\n), Hadoop YARN and HDFS, and a distribution of Apache Apex and DataTorrent RTS are pre-installed. The disadvantage is that the sandbox is a memory-limited environment, and requires settings changes and restarts to adjust memory available for development and testing.",
- "title": "Development Setup"
- },
- {
- "location": "/apex_development_setup/#apache-apex-development-environment-setup",
- "text": "This document discusses the steps needed for setting up a development environment for creating applications that run on the Apache Apex platform.",
- "title": "Apache Apex Development Environment Setup"
- },
- {
- "location": "/apex_development_setup/#development-tools",
- "text": "There are a few tools that will be helpful when developing Apache Apex applications, including: git - A revision control system (version 1.7.1 or later). There are multiple git clients available for Windows ( http://git-scm.com/download/win for example), so download and install a client of your choice. java JDK (not JRE) - Includes the Java Runtime Environment as well as the Java compiler and a variety of tools (version 1.7.0_79 or later). Can be downloaded from the Oracle website. maven - Apache Maven is a build system for Java projects (version 3.0.5 or later). It can be downloaded from https://maven.apache.org/download.cgi . IDE (Optional) - If you prefer to use an IDE (Integrated Development Environment) such as NetBeans , Eclipse or IntelliJ , install that as well. After installing these tools, make sure that the directories containing the executable files are in your PATH environment variable. Windows - Open a console window and enter the command echo %PATH% to see the value of the PATH variable and verify that the above directories for Java, git, and maven executables are present. JDK executables like java and javac , the directory might be something like C:\\\\Program Files\\\\Java\\\\jdk1.7.0\\_80\\\\bin ; for git it might be C:\\\\Program Files\\\\Git\\\\bin ; and for maven it might be C:\\\\Users\\\\user\\\\Software\\\\apache-maven-3.3.3\\\\bin . If not, you can change its value clicking on the button at Control Panel Advanced System Settings Advanced tab Environment Variables . Linux and Mac - Open a console/terminal window and enter the command echo $PATH to see the value of the PATH variable and verify that the above directories for Java, git, and maven executables are present. If not, make sure software is downloaded and installed, and optionally PATH reference is added and exported in a ~/.profile or ~/.bash_profile . For example to add maven located in /sfw/maven/apache-maven-3.3.3 to PATH add the line: export PATH=$PATH:/sfw/maven/apache-maven-3.3.3/bin Confirm by running the following commands and comparing with output that show in the table below: Command Output javac -version javac 1.7.0_80 java -version java version 1.7.0_80 Java(TM) SE Runtime Environment (build 1.7.0_80-b15) Java HotSpot(TM) 64-Bit Server VM (build 24.80-b11, mixed mode) git --version git version 2.6.1.windows.1 mvn --version Apache Maven 3.3.3 (7994120775791599e205a5524ec3e0dfe41d4a06; 2015-04-22T06:57:37-05:00) ...",
- "title": "Development Tools"
- },
- {
- "location": "/apex_development_setup/#creating-new-apex-project",
- "text": "After development tools are configured, you can now use the maven archetype to create a basic Apache Apex project. Note: When executing the commands below, replace 3.3.0-incubating by latest available version of Apache Apex. Windows - Create a new Windows command file called newapp.cmd by copying the lines below, and execute it. When you run this file, the properties will be displayed and you will be prompted with Y: : ; just press Enter to complete the project generation. The caret (^) at the end of some lines indicates that a continuation line follows. @echo off\n@rem Script for creating a new application\nsetlocal\nmvn archetype:generate ^\n -DarchetypeGroupId=org.apache.apex ^\n -DarchetypeArtifactId=apex-app-archetype -DarchetypeVersion=3.3.0-incubating ^\n -DgroupId=com.example -Dpackage=com.example.myapexapp -DartifactId=myapexapp ^\n -Dversion=1.0-SNAPSHOT\nendlocal Linux - Execute the lines below in a terminal window. New project will be created in the curent working directory. The backslash (\\) at the end of the lines indicates continuation. mvn archetype:generate \\\n -DarchetypeGroupId=org.apache.apex \\\n -DarchetypeArtifactId=apex-app-archetype -DarchetypeVersion=3.2.0-incubating \\\n -DgroupId=com.example -Dpackage=com.example.myapexapp -DartifactId=myapexapp \\\n -Dversion=1.0-SNAPSHOT When the run completes successfully, you should see a new directory named myapexapp containing a maven project for building a basic Apache Apex application. It includes 3 source files: Application.java , RandomNumberGenerator.java and ApplicationTest.java . You can now build the application by stepping into the new directory and running the maven package command: cd myapexapp\nmvn clean package -DskipTests The build should create the application package file myapexapp/target/myapexapp-1.0-SNAPSHOT.apa . This application package can then be used to launch example application via dtCli , or other visual management tools. When running, this application will generate a stream of random numbers and print them out, each prefixed by the string hello world: .",
- "title": "Creating New Apex Project"
- },
- {
- "location": "/apex_development_setup/#building-apex-demos",
- "text": "If you want to see more substantial Apex demo applications and the associated source code, you can follow these simple steps to check out and build them. Check out the source code repositories: git clone https://github.com/apache/incubator-apex-core\ngit clone https://github.com/apache/incubator-apex-malhar Switch to the appropriate release branch and build each repository: cd incubator-apex-core\nmvn clean install -DskipTests\n\ncd incubator-apex-malhar\nmvn clean install -DskipTests The install argument to the mvn command installs resources from each project to your local maven repository (typically .m2/repository under your home directory), and not to the system directories, so Administrator privileges are not required. The -DskipTests argument skips running unit tests since they take a long time. If this is a first-time installation, it might take several minutes to complete because maven will download a number of associated plugins. After the build completes, you should see the demo application package files in the target directory under each demo subdirectory in incubator-apex-malhar/demos .",
- "title": "Building Apex Demos"
- },
- {
- "location": "/apex_development_setup/#sandbox",
- "text": "To jump start development with an Apache Hadoop single node cluster, DataTorrent Sandbox powered by VirtualBox is available on Windows, Linux, or Mac platforms. The sandbox is configured by default to run with 6GB RAM; if your development machine has 16GB or more, you can increase the sandbox RAM to 8GB or more using the VirtualBox console. This will yield better performance and support larger applications. The advantage of developing in the sandbox is that most of the tools (e.g. jdk , git , maven ), Hadoop YARN and HDFS, and a distribution of Apache Apex and DataTorrent RTS are pre-installed. The disadvantage is that the sandbox is a memory-limited environment, and requires settings changes and restarts to adjust memory available for development and testing.",
- "title": "Sandbox"
- },
- {
- "location": "/application_development/",
- "text": "Application Developer Guide\n\n\nThe Apex platform is designed to process massive amounts of\nreal-time events natively in Hadoop. It runs as a YARN (Hadoop 2.x) \napplication and leverages Hadoop as a distributed operating\nsystem. All the basic distributed operating system capabilities of\nHadoop like resource management (YARN), distributed file system (HDFS),\nmulti-tenancy, security, fault-tolerance, and scalability are supported natively \nin all the Apex applications. \u00a0The platform handles all the details of the application \nexecution, including dynamic scaling, state checkpointing and recovery, event \nprocessing guarantees, etc. allowing you to focus on writing your application logic without\nmixing operational and functional concerns.\n\n\nIn the platform, building a streaming application can be extremely\neasy and intuitive. \u00a0The application is represented as a Directed\nAcyclic Graph (DAG) of computation units called \nOperators\n interconnected\nby the data-flow edges called \nStreams\n.\u00a0The operators process input\nstreams and produce output streams. A library of common operators is\nprovided to enable quick application development. \u00a0In case the desired\nprocessing is not available in the Operator Library, one can easily\nwrite a custom operator. We refer those interested in creating their own\noperators to the \nOperator Development Guide\n.\n\n\nRunning A Test Application\n\n\nIf you are starting with the Apex platform for the first time,\nit can be informative to launch an existing application and see it run.\nOne of the simplest examples provided in \nApex-Malhar repository\n is a Pi demo application,\nwhich computes the value of PI using random numbers. After \nsetting up development environment\n\nPi demo can be launched as follows:\n\n\n\n\nOpen up Apex Malhar files in your IDE (for example Eclipse, IntelliJ, NetBeans, etc)\n\n\nNavigate to \ndemos/pi/src/test/java/com/datatorrent/demos/ApplicationTest.java\n\n\nRun the test for ApplicationTest.java\n\n\nView the output in system console\n\n\n\n\nCongratulations, you just ran your first real-time streaming demo :) \nThis demo is very simple and has four operators. The first operator\nemits random integers between 0 to 30, 000. The second operator receives\nthese coefficients and emits a hashmap with x and y values each time it\nreceives two values. The third operator takes these values and computes\nx**2+y**2. The last operator counts how many computed values from\nthe previous operator were less than or equal to 30, 000**2. Assuming\nthis count is N, then PI is computed as N/number of values received.\nHere is the code snippet for the PI application. This code populates the\nDAG. Do not worry about what each line does, we will cover these\nconcepts later in this document.\n\n\n// Generates random numbers\nRandomEventGenerator rand = dag.addOperator(\nrand\n, new RandomEventGenerator());\nrand.setMinvalue(0);\nrand.setMaxvalue(30000);\n\n// Generates a round robin HashMap of \nx\n and \ny\n\nRoundRobinHashMap\nString,Object\n rrhm = dag.addOperator(\nrrhm\n, new RoundRobinHashMap\nString, Object\n());\nrrhm.setKeys(new String[] { \nx\n, \ny\n });\n\n// Calculates pi from x and y\nJavaScriptOperator calc = dag.addOperator(\npicalc\n, new Script());\ncalc.setPassThru(false);\ncalc.put(\ni\n,0);\ncalc.put(\ncount\n,0);\ncalc.addSetupScript(\nfunction pi() { if (x*x+y*y \n= \n+maxValue*maxValue+\n) { i++; } count++; return i / count * 4; }\n);\ncalc.setInvoke(\npi\n);\ndag.addStream(\nrand_rrhm\n, rand.integer_data, rrhm.data);\ndag.addStream(\nrrhm_calc\n, rrhm.map, calc.inBindings);\n\n// puts results on system console\nConsoleOutputOperator console = dag.addOperator(\nconsole\n, new ConsoleOutputOperator());\ndag.addStream(\nrand_console\n,calc.result, console.input);\n\n\n\n\nYou can review the other demos and see what they do. The examples\ngiven in the Demos project cover various features of the platform and we\nstrongly encourage you to read these to familiarize yourself with the\nplatform. In the remaining part of this document we will go through\ndetails needed for you to develop and run streaming applications in\nMalhar.\n\n\nTest Application: Yahoo! Finance Quotes\n\n\nThe PI\u00a0application was to\nget you started. It is a basic application and does not fully illustrate\nthe features of the platform. For the purpose of describing concepts, we\nwill consider the test application shown in Figure 1. The application\ndownloads tick data from \nYahoo! Finance\n \u00a0and computes the\nfollowing for four tickers, namely \nIBM\n,\n\nGOOG\n, \nYHOO\n.\n\n\n\n\nQuote: Consisting of last trade price, last trade time, and\n total volume for the day\n\n\nPer-minute chart data: Highest trade price, lowest trade\n price, and volume during that minute\n\n\nSimple Moving Average: trade price over 5 minutes\n\n\n\n\nTotal volume must ensure that all trade volume for that day is\nadded, i.e. data loss would result in wrong results. Charting data needs\nall the trades in the same minute to go to the same slot, and then on it\nstarts afresh, so again data loss would result in wrong results. The\naggregation for charting data is done over 1 minute. Simple moving\naverage computes the average price over a 5 minute sliding window; it\ntoo would produce wrong results if there is data loss. Figure 1 shows\nthe application with no partitioning.\n\n\n\n\nThe operator StockTickerInput:\u00a0StockTickerInput\n\u00a0\nis\nthe input operator that reads live data from Yahoo! Finance once per\ninterval (user configurable in milliseconds), and emits the price, the\nincremental volume, and the last trade time of each stock symbol, thus\nemulating real ticks from the exchange. \u00a0We utilize the Yahoo! Finance\nCSV web service interface. \u00a0For example:\n\n\n$ GET 'http://download.finance.yahoo.com/d/quotes.csv?s=IBM,GOOG,AAPL,YHOO\nf=sl1vt1'\n\nIBM\n,203.966,1513041,\n1:43pm\n\n\nGOOG\n,762.68,1879741,\n1:43pm\n\n\nAAPL\n,444.3385,11738366,\n1:43pm\n\n\nYHOO\n,19.3681,14707163,\n1:43pm\n\n\n\n\n\nAmong all the operators in Figure 1, StockTickerInput is the only\noperator that requires extra code because it contains a custom mechanism\nto get the input data. \u00a0Other operators are used unchanged from the\nMalhar library.\n\n\nHere is the class implementation for StockTickInput:\n\n\npackage com.datatorrent.demos.yahoofinance;\n\nimport au.com.bytecode.opencsv.CSVReader;\nimport com.datatorrent.annotation.OutputPortFieldAnnotation;\nimport com.datatorrent.api.Context.OperatorContext;\nimport com.datatorrent.api.DefaultOutputPort;\nimport com.datatorrent.api.InputOperator;\nimport com.datatorrent.lib.util.KeyValPair;\nimport java.io.IOException;\nimport java.io.InputStream;\nimport java.io.InputStreamReader;\nimport java.util.*;\nimport org.apache.commons.httpclient.HttpClient;\nimport org.apache.commons.httpclient.HttpStatus;\nimport org.apache.commons.httpclient.cookie.CookiePolicy;\nimport org.apache.commons.httpclient.methods.GetMethod;\nimport org.apache.commons.httpclient.params.DefaultHttpParams;\nimport org.slf4j.Logger;\nimport org.slf4j.LoggerFactory;\n\n/**\n * This operator sends price, volume and time into separate ports and calculates incremental volume.\n */\npublic class StockTickInput implements InputOperator\n{\n private static final Logger logger = LoggerFactory.getLogger(StockTickInput.class);\n /**\n * Timeout interval for reading from server. 0 or negative indicates no timeout.\n */\n public int readIntervalMillis = 500;\n /**\n * The URL of the web service resource for the POST request.\n */\n private String url;\n public String[] symbols;\n private transient HttpClient client;\n private transient GetMethod method;\n private HashMap\nString, Long\n lastVolume = new HashMap\nString, Long\n();\n private boolean outputEvenIfZeroVolume = false;\n /**\n * The output port to emit price.\n */\n @OutputPortFieldAnnotation(optional = true)\n public final transient DefaultOutputPort\nKeyValPair\nString, Double\n price = new DefaultOutputPort\nKeyValPair\nString, Double\n();\n /**\n * The output port to emit incremental volume.\n */\n @OutputPortFieldAnnotation(optional = true)\n public final transient DefaultOutputPort\nKeyValPair\nString, Long\n volume = new DefaultOutputPort\nKeyValPair\nString, Long\n();\n /**\n * The output port to emit last traded time.\n */\n @OutputPortFieldAnnotation(optional = true)\n public final transient DefaultOutputPort\nKeyValPair\nString, String\n time = new DefaultOutputPort\nKeyValPair\nString, String\n();\n\n /**\n * Prepare URL from symbols and parameters. URL will be something like: http://download.finance.yahoo.com/d/quotes.csv?s=IBM,GOOG,AAPL,YHOO\nf=sl1vt1\n *\n * @return the URL\n */\n private String prepareURL()\n {\n String str = \nhttp://download.finance.yahoo.com/d/quotes.csv?s=\n;\n for (int i = 0; i \n symbols.length; i++) {\n if (i != 0) {\n str += \n,\n;\n }\n str += symbols[i];\n }\n str += \nf=sl1vt1\ne=.csv\n;\n return str;\n }\n\n @Override\n public void setup(OperatorContext context)\n {\n url = prepareURL();\n client = new HttpClient();\n method = new GetMethod(url);\n DefaultHttpParams.getDefaultParams().setParameter(\nhttp.protocol.cookie-policy\n, CookiePolicy.BROWSER_COMPATIBILITY);\n }\n\n @Override\n public void teardown()\n {\n }\n\n @Override\n public void emitTuples()\n {\n\n try {\n int statusCode = client.executeMethod(method);\n if (statusCode != HttpStatus.SC_OK) {\n System.err.println(\nMethod failed: \n + method.getStatusLine());\n }\n else {\n InputStream istream = method.getResponseBodyAsStream();\n // Process response\n InputStreamReader isr = new InputStreamReader(istream);\n CSVReader reader = new CSVReader(isr);\n List\nString[]\n myEntries = reader.readAll();\n for (String[] stringArr: myEntries) {\n ArrayList\nString\n tuple = new ArrayList\nString\n(Arrays.asList(stringArr));\n if (tuple.size() != 4) {\n return;\n }\n // input csv is \nSymbol\n,\nPrice\n,\nVolume\n,\nTime\n\n String symbol = tuple.get(0);\n double currentPrice = Double.valueOf(tuple.get(1));\n long currentVolume = Long.valueOf(tuple.get(2));\n String timeStamp = tuple.get(3);\n long vol = currentVolume;\n // Sends total volume in first tick, and incremental volume afterwards.\n if (lastVolume.containsKey(symbol)) {\n vol -= lastVolume.get(symbol);\n }\n\n if (vol \n 0 || outputEvenIfZeroVolume) {\n price.emit(new KeyValPair\nString, Double\n(symbol, currentPrice));\n volume.emit(new KeyValPair\nString, Long\n(symbol, vol));\n time.emit(new KeyValPair\nString, String\n(symbol, timeStamp));\n lastVolume.put(symbol, currentVolume);\n }\n }\n }\n Thread.sleep(readIntervalMillis);\n }\n catch (InterruptedException ex) {\n logger.debug(ex.toString());\n }\n catch (IOException ex) {\n logger.debug(ex.toString());\n }\n }\n\n @Override\n public void beginWindow(long windowId)\n {\n }\n\n @Override\n public void endWindow()\n {\n }\n\n public void setOutputEvenIfZeroVolume(boolean outputEvenIfZeroVolume)\n {\n this.outputEvenIfZeroVolume = outputEvenIfZeroVolume;\n }\n\n}\n\n\n\n\nThe operator has three output ports that emit the price of the\nstock, the volume of the stock and the last trade time of the stock,\ndeclared as public member variables price, volume\u00a0and time\u00a0of the class. \u00a0The tuple of the\nprice\u00a0output port is a key-value\npair with the stock symbol being the key, and the price being the value.\n\u00a0The tuple of the volume\u00a0output\nport is a key value pair with the stock symbol being the key, and the\nincremental volume being the value. \u00a0The tuple of the time\u00a0output port is a key value pair with the\nstock symbol being the key, and the last trade time being the\nvalue.\n\n\nImportant: Since operators will be\nserialized, all input and output ports need to be declared transient\nbecause they are stateless and should not be serialized.\n\n\nThe method\u00a0setup(OperatorContext)\ncontains the code that is necessary for setting up the HTTP\nclient for querying Yahoo! Finance.\n\n\nMethod\u00a0emitTuples() contains\nthe code that reads from Yahoo! Finance, and emits the data to the\noutput ports of the operator. \u00a0emitTuples()\u00a0will be called one or more times\nwithin one application window as long as time is allowed within the\nwindow.\n\n\nNote that we want to emulate the tick input stream by having\nincremental volume data with Yahoo! Finance data. \u00a0We therefore subtract\nthe previous volume from the current volume to emulate incremental\nvolume for each tick.\n\n\nThe operator\nDailyVolume:\u00a0This operator\nreads from the input port, which contains the incremental volume tuples\nfrom StockTickInput, and\naggregates the data to provide the cumulative volume. \u00a0It uses the\nlibrary class SumKeyVal\nK,V\n\u00a0provided in math\u00a0package. \u00a0In this case,\nSumKeyVal\nString,Long\n, where K is the stock symbol, V is the\naggregated volume, with cumulative\nset to true. (Otherwise if cumulativewas set to false, SumKeyVal would\nprovide the sum for the application window.) \u00a0Malhar provides a number\nof built-in operators for simple operations like this so that\napplication developers do not have to write them. \u00a0More examples to\nfollow. This operator assumes that the application restarts before the\nmarket opens every day.\n\n\nThe operator Quote:\nThis operator has three input ports, which are price (from\nStockTickInput), daily_vol (from\nDaily Volume), and time (from\n StockTickInput). \u00a0This operator\njust consolidates the three data items and and emits the consolidated\ndata. \u00a0It utilizes the class ConsolidatorKeyVal\nK\n\u00a0from the\nstream\u00a0package.\n\n\nThe operator HighLow:\u00a0This operator reads from the input port,\nwhich contains the price tuples from StockTickInput, and provides the high and the\nlow price within the application window. \u00a0It utilizes the library class\n RangeKeyVal\nK,V\n\u00a0provided\nin the math\u00a0package. In this case,\nRangeKeyVal\nString,Double\n.\n\n\nThe operator MinuteVolume:\nThis operator reads from the input port, which contains the\nvolume tuples from StockTickInput,\nand aggregates the data to provide the sum of the volume within one\nminute. \u00a0Like the operator DailyVolume, this operator also uses\nSumKeyVal\nString,Long\n, but\nwith cumulative set to false. \u00a0The\nApplication Window is set to one minute. We will explain how to set this\nlater.\n\n\nThe operator Chart:\nThis operator is very similar to the operator Quote, except that it takes inputs from\nHigh Low\u00a0and Minute Vol\u00a0and outputs the consolidated tuples\nto the output port.\n\n\nThe operator PriceSMA:\nSMA stands for - Simple Moving Average. It reads from the\ninput port, which contains the price tuples from StockTickInput, and\nprovides the moving average price of the stock. \u00a0It utilizes\nSimpleMovingAverage\nString,Double\n, which is provided in the\n multiwindow\u00a0package.\nSimpleMovingAverage keeps track of the data of the previous N\napplication windows in a sliding manner. \u00a0For each end window event, it\nprovides the average of the data in those application windows.\n\n\nThe operator Console:\nThis operator just outputs the input tuples to the console\n(or stdout). \u00a0In this example, there are four console\u00a0operators, which connect to the output\nof Quote, Chart, PriceSMA and VolumeSMA. \u00a0In\npractice, they should be replaced by operators that use the data to\nproduce visualization artifacts like charts.\n\n\nConnecting the operators together and constructing the\nDAG:\u00a0Now that we know the\noperators used, we will create the DAG, set the streaming window size,\ninstantiate the operators, and connect the operators together by adding\nstreams that connect the output ports with the input ports among those\noperators. \u00a0This code is in the file YahooFinanceApplication.java. Refer to Figure 1\nagain for the graphical representation of the DAG. \u00a0The last method in\nthe code, namely getApplication(),\ndoes all that. \u00a0The rest of the methods are just for setting up the\noperators.\n\n\npackage com.datatorrent.demos.yahoofinance;\n\nimport com.datatorrent.api.ApplicationFactory;\nimport com.datatorrent.api.Context.OperatorContext;\nimport com.datatorrent.api.DAG;\nimport com.datatorrent.api.Operator.InputPort;\nimport com.datatorrent.lib.io.ConsoleOutputOperator;\nimport com.datatorrent.lib.math.RangeKeyVal;\nimport com.datatorrent.lib.math.SumKeyVal;\nimport com.datatorrent.lib.multiwindow.SimpleMovingAverage;\nimport com.datatorrent.lib.stream.ConsolidatorKeyVal;\nimport com.datatorrent.lib.util.HighLow;\nimport org.apache.hadoop.conf.Configuration;\n\n/**\n * Yahoo! Finance application demo. \np\n\n *\n * Get Yahoo finance feed and calculate minute price range, minute volume, simple moving average of 5 minutes.\n */\npublic class Application implements StreamingApplication\n{\n private int streamingWindowSizeMilliSeconds = 1000; // 1 second (default is 500ms)\n private int appWindowCountMinute = 60; // 1 minute\n private int appWindowCountSMA = 5 * 60; // 5 minute\n\n /**\n * Get actual Yahoo finance ticks of symbol, last price, total daily volume, and last traded price.\n */\n public StockTickInput getStockTickInputOperator(String name, DAG dag)\n {\n StockTickInput oper = dag.addOperator(name, StockTickInput.class);\n oper.readIntervalMillis = 200;\n return oper;\n }\n\n /**\n * This sends total daily volume by adding volumes from each ticks.\n */\n public SumKeyVal\nString, Long\n getDailyVolumeOperator(String name, DAG dag)\n {\n SumKeyVal\nString, Long\n oper = dag.addOperator(name, new SumKeyVal\nString, Long\n());\n oper.setType(Long.class);\n oper.setCumulative(true);\n return oper;\n }\n\n /**\n * Get aggregated volume of 1 minute and send at the end window of 1 minute.\n */\n public SumKeyVal\nString, Long\n getMinuteVolumeOperator(String name, DAG dag, int appWindowCount)\n {\n SumKeyVal\nString, Long\n oper = dag.addOperator(name, new SumKeyVal\nString, Long\n());\n oper.setType(Long.class);\n oper.setEmitOnlyWhenChanged(true);\ndag.getOperatorMeta(name).getAttributes().put(OperatorContext.APPLICATION_WINDOW_COUNT,appWindowCount);\n return oper;\n }\n\n /**\n * Get High-low range for 1 minute.\n */\n public RangeKeyVal\nString, Double\n getHighLowOperator(String name, DAG dag, int appWindowCount)\n {\n RangeKeyVal\nString, Double\n oper = dag.addOperator(name, new RangeKeyVal\nString, Double\n());\n dag.getOperatorMeta(name).getAttributes().put(OperatorContext.APPLICATION_WINDOW_COUNT,appWindowCount);\n oper.setType(Double.class);\n return oper;\n }\n\n /**\n * Quote (Merge price, daily volume, time)\n */\n public ConsolidatorKeyVal\nString,Double,Long,String,?,?\n getQuoteOperator(String name, DAG dag)\n {\n ConsolidatorKeyVal\nString,Double,Long,String,?,?\n oper = dag.addOperator(name, new ConsolidatorKeyVal\nString,Double,Long,String,Object,Object\n());\n return oper;\n }\n\n /**\n * Chart (Merge minute volume and minute high-low)\n */\n public ConsolidatorKeyVal\nString,HighLow,Long,?,?,?\n getChartOperator(String name, DAG dag)\n {\n ConsolidatorKeyVal\nString,HighLow,Long,?,?,?\n oper = dag.addOperator(name, new ConsolidatorKeyVal\nString,HighLow,Long,Object,Object,Object\n());\n return oper;\n }\n\n /**\n * Get simple moving average of price.\n */\n public SimpleMovingAverage\nString, Double\n getPriceSimpleMovingAverageOperator(String name, DAG dag, int appWindowCount)\n {\n SimpleMovingAverage\nString, Double\n oper = dag.addOperator(name, new SimpleMovingAverage\nString, Double\n());\n oper.setWindowSize(appWindowCount);\n oper.setType(Double.class);\n return oper;\n }\n\n /**\n * Get console for output.\n */\n public InputPort\nObject\n getConsole(String name, /*String nodeName,*/ DAG dag, String prefix)\n {\n ConsoleOutputOperator oper = dag.addOperator(name, ConsoleOutputOperator.class);\n oper.setStringFormat(prefix + \n: %s\n);\n return oper.input;\n }\n\n /**\n * Create Yahoo Finance Application DAG.\n */\n @Override\n public void populateDAG(DAG dag, Configuration conf)\n {\n dag.getAttributes().put(DAG.STRAM_WINDOW_SIZE_MILLIS,streamingWindowSizeMilliSeconds);\n\n StockTickInput tick = getStockTickInputOperator(\nStockTickInput\n, dag);\n SumKeyVal\nString, Long\n dailyVolume = getDailyVolumeOperator(\nDailyVolume\n, dag);\n ConsolidatorKeyVal\nString,Double,Long,String,?,?\n quoteOperator = getQuoteOperator(\nQuote\n, dag);\n\n RangeKeyVal\nString, Double\n highlow = getHighLowOperator(\nHighLow\n, dag, appWindowCountMinute);\n SumKeyVal\nString, Long\n minuteVolume = getMinuteVolumeOperator(\nMinuteVolume\n, dag, appWindowCountMinute);\n ConsolidatorKeyVal\nString,HighLow,Long,?,?,?\n chartOperator = getChartOperator(\nChart\n, dag);\n\n SimpleMovingAverage\nString, Double\n priceSMA = getPriceSimpleMovingAverageOperator(\nPriceSMA\n, dag, appWindowCountSMA);\n DefaultPartitionCodec\nString, Double\n codec = new DefaultPartitionCodec\nString, Double\n();\n dag.setInputPortAttribute(highlow.data, PortContext.STREAM_CODEC, codec);\n dag.setInputPortAttribute(priceSMA.data, PortContext.STREAM_CODEC, codec);\n dag.addStream(\nprice\n, tick.price, quoteOperator.in1, highlow.data, priceSMA.data);\n dag.addStream(\nvol\n, tick.volume, dailyVolume.data, minuteVolume.data);\n dag.addStream(\ntime\n, tick.time, quoteOperator.in3);\n dag.addStream(\ndaily_vol\n, dailyVolume.sum, quoteOperator.in2);\n\n dag.addStream(\nquote_data\n, quoteOperator.out, getConsole(\nquoteConsole\n, dag, \nQUOTE\n));\n\n dag.addStream(\nhigh_low\n, highlow.range, chartOperator.in1);\n dag.addStream(\nvol_1min\n, minuteVolume.sum, chartOperator.in2);\n dag.addStream(\nchart_data\n, chartOperator.out, getConsole(\nchartConsole\n, dag, \nCHART\n));\n\n dag.addStream(\nsma_price\n, priceSMA.doubleSMA, getConsole(\npriceSMAConsole\n, dag, \nPrice SMA\n));\n\n return dag;\n }\n\n}\n\n\n\n\nNote that we also set a user-specific sliding window for SMA that\nkeeps track of the previous N data points. \u00a0Do not confuse this with the\nattribute APPLICATION_WINDOW_COUNT.\n\n\nIn the rest of this chapter we will run through the process of\nrunning this application. We assume that \u00a0you are familiar with details\nof your Hadoop infrastructure. For installation\ndetails please refer to the \nInstallation Guide\n.\n\n\nRunning a Test Application\n\n\nWe will now describe how to run the yahoo\nfinance application\u00a0described above in different modes\n(local mode, single node on Hadoop, and multi-nodes on Hadoop).\n\n\nThe platform runs streaming applications under the control of a\nlight-weight Streaming Application Manager (STRAM). Each application has\nits own instance of STRAM. STRAM launches the application and\ncontinually provides run time monitoring, analysis, and takes action\nsuch as load scaling or outage recovery as needed. \u00a0We will discuss\nSTRAM in more detail in the next chapter.\n\n\nThe instructions below assume that the platform was installed in a\ndirectory \nINSTALL_DIR\n and the command line interface (CLI) will\nbe used to launch the demo application. An application can be run in\nlocal mode\u00a0(in IDE or from command line) or on a Hadoop cluster.\n\n\nTo start the dtCli run\n\n\nINSTALL_DIR\n/bin/dtcli\n\n\n\nThe command line prompt appears. To start the application in local mode (the actual version number in the file name may differ)\n\n\ndt\n launch -local \nINSTALL_DIR\n/yahoo-finance-demo-3.2.0-SNAPSHOT.apa\n\n\n\nTo terminate the application in local mode, enter Ctrl-C\n\n\nTu run the application on the Hadoop cluster (the actual version\nnumber in the file name may differ)\n\n\ndt\n launch \nINSTALL_DIR\n/yahoo-finance-demo-3.2.0-SNAPSHOT.apa\n\n\n\nTo stop the application running in Hadoop, terminate it in the dtCli:\n\n\ndt\n kill-app\n\n\n\nExecuting the application in either mode includes the following\nsteps. At a top level, STRAM (Streaming Application Manager) validates\nthe application (DAG), translates the logical plan to the physical plan\nand then launches the execution engine. The mode determines the\nresources needed and how how they are used.\n\n\nLocal Mode\n\n\nIn local mode, the application is run as a single-process\u00a0with multiple threads. Although a\nfew Hadoop classes are needed, there is no dependency on a Hadoop\ncluster or Hadoop services. The local file system is used in place of\nHDFS. This mode allows a quick run of an application in a single process\nsandbox, and hence is the most suitable to debug and analyze the\napplication logic. This mode is recommended for developing the\napplication and can be used for running applications within the IDE for\nfunctional testing purposes. Due to limited resources and lack \u00a0of\nscalability an application running in this single process mode is more\nlikely to encounter throughput bottlenecks. A distributed cluster is\nrecommended for benchmarking and production testing.\n\n\nHadoop Cluster\n\n\nIn this section we discuss various Hadoop cluster setups.\n\n\nSingle Node Cluster\n\n\nIn a single node Hadoop cluster all services are deployed on a\nsingle server (a developer can use his/her development machine as a\nsingle node cluster). The platform does not distinguish between a single\nor multi-node setup and behaves exactly the same in both cases.\n\n\nIn this mode, the resource manager, name node, data node, and node\nmanager occupy one process each. This is an example of running a\nstreaming application as a multi-process\u00a0application on the same server.\nWith prevalence of fast, multi-core systems, this mode is effective for\ndebugging, fine tuning, and generic analysis before submitting the job\nto a larger Hadoop cluster. In this mode, execution uses the Hadoop\nservices and hence is likely to identify issues that are related to the\nHadoop environment (such issues will not be uncovered in local mode).\nThe throughput will obviously not be as high as on a multi-node Hadoop\ncluster. Additionally, since each container (i.e. Java process) requires\na significant amount of memory, you will be able to run a much smaller\nnumber of containers than on a multi-node cluster.\n\n\nMulti-Node Cluster\n\n\nIn a multi-node Hadoop cluster all the services of Hadoop are\ntypically distributed across multiple nodes in a production or\nproduction-level test environment. Upon launch the application is\nsubmitted to the Hadoop cluster and executes as a multi-processapplication on\u00a0multiple nodes.\n\n\nBefore you start deploying, testing and troubleshooting your\napplication on a cluster, you should ensure that Hadoop (version 2.2.0\nor later)\u00a0is properly installed and\nyou have basic skills for working with it.\n\n\n\n\nApache Apex Platform Overview\n\n\nStreaming Computational Model\n\n\nIn this chapter, we describe the the basics of the real-time streaming platform and its computational model.\n\n\nThe platform is designed to enable completely asynchronous real time computations\u00a0done in as unblocked a way as possible with\nminimal overhead .\n\n\nApplications running in the platform are represented by a Directed\nAcyclic Graph (DAG) made up of \u00a0operators and streams. All computations\nare done in memory on arrival of\nthe input data, with an option to save the output to disk (HDFS) in a\nnon-blocking way. The data that flows between operators consists of\natomic data elements. Each data element along with its type definition\n(henceforth called schema) is\ncalled a tuple.\u00a0An application is a\ndesign of the flow of these tuples to and from\nthe appropriate compute units to enable the computation of the final\ndesired results.\u00a0A message queue (henceforth called\n\u00a0buffer server) manages tuples streaming\nbetween compute units in different processes.This server keeps track of\nall consumers, publishers, partitions, and enables replay. More\ninformation is given in later section.\n\n\nThe streaming application is monitored by a decision making entity\ncalled STRAM (streaming application\nmanager).\u00a0STRAM is designed to be a light weight\ncontroller that has minimal but sufficient interaction with the\napplication. This is done via periodic heartbeats. The\nSTRAM does the initial launch and periodically analyzes the system\nmetrics to decide if any run time action needs to be taken.\n\n\nA fundamental building block for the streaming platform\nis the concept of breaking up a stream into equal finite time slices\ncalled streaming windows. Each window contains the ordered\nset of tuples in that time slice. A typical duration of a window is 500\nms, but can be configured per application (the Yahoo! Finance\napplication configures this value in the properties.xml\u00a0file to be 1000ms = 1s). Each\nwindow is preceded by a begin_window\u00a0event and is terminated by an\nend_window\u00a0event, and is assigned\na unique window ID. Even though the platform performs computations at\nthe tuple level, bookkeeping is done at the window boundary, making the\ncomputations within a window an atomic event in the platform. \u00a0We can\nthink of each window as an atomic\nmicro-batch\u00a0of tuples, to be processed together as one\natomic operation (See Figure 2). \u00a0\n\n\nThis atomic batching allows the platform to avoid the very steep\nper tuple bookkeeping cost and instead has a manageable per batch\nbookkeeping cost. This translates to higher throughput, low recovery\ntime, and higher scalability. Later in this document we illustrate how\nthe atomic micro-batch concept allows more efficient optimization\nalgorithms.\n\n\nThe platform also has in-built support for\napplication windows.\u00a0 An application window is part of the\napplication specification, and can be a small or large multiple of the\nstreaming window. \u00a0An example from our Yahoo! Finance test application\nis the moving average, calculated over a sliding application window of 5\nminutes which equates to 300 (= 5 * 60) streaming windows.\n\n\nNote that these two window concepts are distinct. \u00a0A streaming\nwindow is an abstraction of many tuples into a higher atomic event for\neasier management. \u00a0An application window is a group of consecutive\nstreaming windows used for data aggregation (e.g. sum, average, maximum,\nminimum) on a per operator level.\n\n\n\n\nAlongside the platform,\u00a0a set of\npredefined, benchmarked standard library operator templates is provided\nfor ease of use and rapid development of application.\u00a0These\noperators are open sourced to Apache Software Foundation under the\nproject name \u201cMalhar\u201d as part of our efforts to foster community\ninnovation. These operators can be used in a DAG as is, while others\nhave properties\u00a0that can be set to specify the\ndesired computation. Those interested in details, should refer to\n\nApex-Malhar operator library\n.\n\n\nThe platform is a Hadoop YARN native\napplication. It runs in a Hadoop cluster just like any\nother YARN application (MapReduce etc.) and is designed to seamlessly\nintegrate with rest of Hadoop technology stack. It leverages Hadoop as\nmuch as possible and relies on it as its distributed operating system.\nHadoop dependencies include resource management, compute/memory/network\nallocation, HDFS, security, fault tolerance, monitoring, metrics,\nmulti-tenancy, logging etc. Hadoop classes/concepts are reused as much\nas possible. The aim is to enable enterprises\nto leverage their existing Hadoop infrastructure for real time streaming\napplications. The platform is designed to scale with big\ndata applications and scale with Hadoop.\n\n\nA streaming application is an asynchronous execution of\ncomputations across distributed nodes. All computations are done in\nparallel on a distributed cluster. The computation model is designed to\ndo as many parallel computations as possible in a non-blocking fashion.\nThe task of monitoring of the entire application is done on (streaming)\nwindow boundaries with a streaming window as an atomic entity. A window\ncompletion is a quantum of work done. There is no assumption that an\noperator can be interrupted at precisely a particular tuple or window.\n\n\nAn operator itself also\ncannot assume or predict the exact time a tuple that it emitted would\nget consumed by downstream operators. The operator processes the tuples\nit gets and simply emits new tuples based on its business logic. The\nonly guarantee it has is that the upstream operators are processing\neither the current or some later window, and the downstream operator is\nprocessing either the current or some earlier window. The completion of\na window (i.e. propagation of the end_window\u00a0event through an operator) in any\noperator guarantees that all upstream operators have finished processing\nthis window. Thus, the end_window\u00a0event is blocking on an operator\nwith multiple outputs, and is a synchronization point in the DAG. The\n begin_window\u00a0event does not have\nany such restriction, a single begin_window\u00a0event from any upstream operator\ntriggers the operator to start processing tuples.\n\n\nStreaming Application Manager (STRAM)\n\n\nStreaming Application Manager (STRAM) is the Hadoop YARN native\napplication master. STRAM is the first process that is activated upon\napplication launch and orchestrates the streaming application on the\nplatform. STRAM is a lightweight controller process. The\nresponsibilities of STRAM include\n\n\n\n\n\n\nRunning the Application\n\n\n\n\nRead the\u00a0logical plan\u00a0of the application (DAG) submitted by the client\n\n\nValidate the logical plan\n\n\nTranslate the logical plan into a physical plan, where certain operators may be partitioned (i.e. replicated) to multiple operators for handling load.\n\n\nRequest resources (Hadoop containers) from Resource Manager,\n per physical plan\n\n\nBased on acquired resources and application attributes, create\n an execution plan\u00a0by partitioning the DAG into fragments,\n each assigned to different containers.\n\n\nExecutes the application by deploying each fragment to\n its container. Containers then start stream processing and run\n autonomously, processing one streaming window after another. Each\n container is represented as an instance of the StreamingContainer\u00a0class, which updates\n STRAM via the heartbeat protocol and processes directions received\n from STRAM.\n\n\n\n\n\n\n\n\nContinually monitoring the application via heartbeats from each StreamingContainer\n\n\n\n\nCollecting Application System Statistics and Logs\n\n\nLogging all application-wide decisions taken\n\n\nProviding system data on the state of the application via a Web Service.\n\n\n\n\nSupporting Fault Tolerance\n\n\na. Detecting a node outage\nb. Requesting a replacement resource from the Resource Manager\n and scheduling state restoration for the streaming operators\nc. Saving state to Zookeeper\n\n\n\n\n\n\nSupporting Dynamic Partitioning:\u00a0Periodically evaluating the SLA and modifying the physical plan if required\n (logical plan does not change).\n\n\n\n\nEnabling Security:\u00a0Distributing security tokens for distributed components of the execution engine\n and securing web service requests.\n\n\nEnabling Dynamic modification of DAG: In the future, we intend to allow for user initiated\n modification of the logical plan to allow for changes to the\n processing logic and functionality.\n\n\n\n\nAn example of the Yahoo! Finance Quote application scheduled on a\ncluster of 5 Hadoop containers (processes) is shown in Figure 3.\n\n\n\n\nAn example for the translation from a logical plan to a physical\nplan and an execution plan for a subset of the application is shown in\nFigure 4.\n\n\n\n\nHadoop Components\n\n\nIn this section we cover some aspects of Hadoop that your\nstreaming application interacts with. This section is not meant to\neducate the reader on Hadoop, but just get the reader acquainted with\nthe terms. We strongly advise readers to learn Hadoop from other\nsources.\n\n\nA streaming application runs as a native Hadoop 2.2 application.\nHadoop 2.2 does not differentiate between a map-reduce job and other\napplications, and hence as far as Hadoop is concerned, the streaming\napplication is just another job. This means that your application\nleverages all the bells and whistles Hadoop provides and is fully\nsupported within Hadoop technology stack. The platform is responsible\nfor properly integrating itself with the relevant components of Hadoop\nthat exist today and those that may emerge in the future\n\n\nAll investments that leverage multi-tenancy (for example quotas\nand queues), security (for example kerberos), data flow integration (for\nexample copying data in-out of HDFS), monitoring, metrics collections,\netc. will require no changes when streaming applications run on\nHadoop.\n\n\nYARN\n\n\nYARN\nis\nthe core library of Hadoop 2.2 that is tasked with resource management\nand works as a distributed application framework. In this section we\nwill walk through Yarn's components. In Hadoop 2.2, the old jobTracker\nhas been replaced by a combination of ResourceManager (RM) and\nApplicationMaster (AM).\n\n\nResource Manager (RM)\n\n\nResourceManager\n(RM)\nmanages all the distributed resources. It allocates and arbitrates all\nthe slots and the resources (cpu, memory, network) of these slots. It\nworks with per-node NodeManagers (NMs) and per-application\nApplicationMasters (AMs). Currently memory usage is monitored by RM; in\nupcoming releases it will have CPU as well as network management. RM is\nshared by map-reduce and streaming applications. Running streaming\napplications requires no changes in the RM.\n\n\nApplication Master (AM)\n\n\nThe AM is the watchdog or monitoring process for your application\nand has the responsibility of negotiating resources with RM and\ninteracting with NodeManagers to get the allocated containers started.\nThe AM is the starting point of your application and is considered user\ncode (not system Hadoop code). The AM itself runs in one container. All\nresource management within the application are managed by the AM. This\nis a critical feature for Hadoop 2.2 where tasks done by jobTracker in\nHadoop 1.0 have been distributed allowing Hadoop 2.2 to scale much\nbeyond Hadoop 1.0. STRAM is a native YARN ApplicationManager.\n\n\nNode Managers (NM)\n\n\nThere is one \nNodeManager\n(NM)\nper node in the cluster. All the containers (i.e. processes) on that\nnode are monitored by the NM. It takes instructions from RM and manages\nresources of that node as per RM instructions. NMs interactions are same\nfor map-reduce and for streaming applications. Running streaming\napplications requires no changes in the NM.\n\n\nRPC Protocol\n\n\nCommunication among RM, AM, and NM is done via the Hadoop RPC\nprotocol. Streaming applications use the same protocol to send their\ndata. No changes are needed in RPC support provided by Hadoop to enable\ncommunication done by components of your application.\n\n\nHDFS\n\n\nHadoop includes a highly fault tolerant, high throughput\ndistributed file system (\nHDFS\n).\nIt runs on commodity hardware, and your streaming application will, by\ndefault, use it. There is no difference between files created by a\nstreaming application and those created by map-reduce.\n\n\nDeveloping An Application\n\n\nIn this chapter we describe the methodology to develop an\napplication using the Realtime Streaming Platform. The platform was\ndesigned to make it easy to build and launch sophisticated streaming\napplications with the developer having to deal only with the\napplication/business logic. The platform deals with details of where to\nrun what operators on which servers and how to correctly route streams\nof data among them.\n\n\nDevelopment Process\n\n\nWhile the platform does not mandate a specific methodology or set\nof development tools, we have recommendations to maximize productivity\nfor the different phases of application development.\n\n\nDesign\n\n\n\n\nIdentify common, reusable operators. Use a library\n if possible.\n\n\nIdentify scalability and performance requirements before\n designing the DAG.\n\n\nLeverage attributes that the platform supports for scalability\n and performance.\n\n\nUse operators that are benchmarked and tested so that later\n surprises are minimized. If you have glue code, create appropriate\n unit tests for it.\n\n\nUse THREAD_LOCAL locality for high throughput streams. If all\n the operators on that stream cannot fit in one container,\n try\u00a0NODE_LOCAL\u00a0locality. Both THREAD_LOCAL and\n NODE_LOCAL streams avoid the Network Interface Card (NIC)\n completly. The former uses intra-process communication to also avoid\n serialization-deserialization overhead.\n\n\nThe overall throughput and latencies are are not necessarily\n correlated to the number of operators in a simple way -- the\n relationship is more nuanced. A lot depends on how much work\n individual operators are doing, how many are able to operate in\n parallel, and how much data is flowing through the arcs of the DAG.\n It is, at times, better to break a computation down into its\n constituent simple parts and then stitch them together via streams\n to better utilize the compute resources of the cluster. Decide on a\n per application basis the fine line between complexity of each\n operator vs too many streams. Doing multiple computations in one\n operator does save network I/O, while operators that are too complex\n are hard to maintain.\n\n\nDo not use operators that depend on the order of two streams\n as far as possible. In such cases behavior is not idempotent.\n\n\nPersist key information to HDFS if possible; it may be useful\n for debugging later.\n\n\nDecide on an appropriate fault tolerance mechanism. If some\n data loss is acceptable, use the at-most-once mechanism as it has\n fastest recovery.\n\n\n\n\nCreating New Project\n\n\nPlease refer to the \nApex Application Packages\n\u00a0for\nthe basic steps for creating a new project.\n\n\nWriting the application code\n\n\nPreferably use an IDE (Eclipse, Netbeans etc.) that allows you to\nmanage dependencies and assists with the Java coding. Specific benefits\ninclude ease of managing operator library jar files, individual operator\nclasses, ports and properties. It will also highlight and assist to\nrectify issues such as type mismatches when adding streams while\ntyping.\n\n\nTesting\n\n\nWrite test cases with JUnit or similar test framework so that code\nis tested as it is written. For such testing, the DAG can run in local\nmode within the IDE. Doing this may involve writing mock input or output\noperators for the integration points with external systems. For example,\ninstead of reading from a live data stream, the application in test mode\ncan read from and write to files. This can be done with a single\napplication DAG by instrumenting a test mode using settings in the\nconfiguration that is passed to the application factory\ninterface.\n\n\nGood test coverage will not only eliminate basic validation errors\nsuch as missing port connections or property constraint violations, but\nalso validate the correct processing of the data. The same tests can be\nre-run whenever the application or its dependencies change (operator\nlibraries, version of the platform etc.)\n\n\nRunning an application\n\n\nThe platform provides a commandline tool called dtcli\u00a0for managing applications (launching,\nkilling, viewing, etc.). This tool was already discussed above briefly\nin the section entitled Running the Test Application. It will introspect\nthe jar file specified with the launch command for applications (classes\nthat implement ApplicationFactory) or property files that define\napplications. It will also deploy the dependency jar files from the\napplication package to the cluster.\n\n\nDtcli can run the application in local mode (i.e. outside a\ncluster). It is recommended to first run the application in local mode\nin the development environment before launching on the Hadoop cluster.\nThis way some of the external system integration and correct\nfunctionality of the application can be verified in an easier to debug\nenvironment before testing distributed mode.\n\n\nFor more details on CLI please refer to the \ndtCli Guide\n.\n\n\nApplication API\n\n\nThis section introduces the API to write a streaming application.\nThe work involves connecting operators via streams to form the logical\nDAG. The steps are\n\n\n\n\n\n\nInstantiate an application (DAG)\n\n\n\n\n\n\n(Optional) Set Attributes\n\n\n\n\nAssign application name\n\n\nSet any other attributes as per application requirements\n\n\n\n\n\n\n\n\nCreate/re-use and instantiate operators\n\n\n\n\nAssign operator name that is unique within the application\n\n\nDeclare schema upfront for each operator (and thereby its ports)\n\n\n(Optional) Set properties\u00a0 and attributes on the dag as per specification\n\n\nConnect ports of operators via streams\n\n\nEach stream connects one output port of an operator to one or more input ports of other operators.\n\n\n(Optional) Set attributes on the streams\n\n\n\n\n\n\n\n\n\n\n\n\nTest the application.\n\n\n\n\n\n\nThere are two methods to create an application, namely Java, and\nProperties file. Java API is for applications being developed by humans,\nand properties file (Hadoop like) is more suited for DAGs generated by\ntools.\n\n\nJava API\n\n\nThe Java API is the most common way to create a streaming\napplication. It is meant for application developers who prefer to\nleverage the features of Java, and the ease of use and enhanced\nproductivity provided by IDEs like NetBeans or Eclipse. Using Java to\nspecify the application provides extra validation abilities of Java\ncompiler, such as compile time checks for type safety at the time of\nwriting the code. Later in this chapter you can read more about\nvalidation support in the platform.\n\n\nThe developer specifies the streaming application by implementing\nthe ApplicationFactory interface, which is how platform tools (CLI etc.)\nrecognize and instantiate applications. Here we show how to create a\nYahoo! Finance application that streams the last trade price of a ticker\nand computes the high and low price in every 1 min window. Run above\n test application\u00a0to execute the\nDAG in local mode within the IDE.\n\n\nLet us revisit how the Yahoo! Finance test application constructs the DAG:\n\n\npublic class Application implements StreamingApplication\n{\n\n ...\n\n @Override\n public void populateDAG(DAG dag, Configuration conf)\n {\n dag.getAttributes().attr(DAG.STRAM_WINDOW_SIZE_MILLIS).set(streamingWindowSizeMilliSeconds);\n\n StockTickInput tick = getStockTickInputOperator(\nStockTickInput\n, dag);\n SumKeyVal\nString, Long\n dailyVolume = getDailyVolumeOperator(\nDailyVolume\n, dag);\n ConsolidatorKeyVal\nString,Double,Long,String,?,?\n quoteOperator = getQuoteOperator(\nQuote\n, dag);\n\n RangeKeyVal\nString, Double\n highlow = getHighLowOperator(\nHighLow\n, dag, appWindowCountMinute);\n SumKeyVal\nString, Long\n minuteVolume = getMinuteVolumeOperator(\nMinuteVolume\n, dag, appWindowCountMinute);\n ConsolidatorKeyVal\nString,HighLow,Long,?,?,?\n chartOperator = getChartOperator(\nChart\n, dag);\n\n SimpleMovingAverage\nString, Double\n priceSMA = getPriceSimpleMovingAverageOperator(\nPriceSMA\n, dag, appWindowCountSMA);\n\n dag.addStream(\nprice\n, tick.price, quoteOperator.in1, highlow.data, priceSMA.data);\n dag.addStream(\nvol\n, tick.volume, dailyVolume.data, minuteVolume.data);\n dag.addStream(\ntime\n, tick.time, quoteOperator.in3);\n dag.addStream(\ndaily_vol\n, dailyVolume.sum, quoteOperator.in2);\n\n dag.addStream(\nquote_data\n, quoteOperator.out, getConsole(\nquoteConsole\n, dag, \nQUOTE\n));\n\n dag.addStream(\nhigh_low\n, highlow.range, chartOperator.in1);\n dag.addStream(\nvol_1min\n, minuteVolume.sum, chartOperator.in2);\n dag.addStream(\nchart_data\n, chartOperator.out, getConsole(\nchartConsole\n, dag, \nCHART\n));\n\n dag.addStream(\nsma_price\n, priceSMA.doubleSMA, getConsole(\npriceSMAConsole\n, dag, \nPrice SMA\n));\n\n return dag;\n }\n}\n\n\n\n\nProperty File API\n\n\nThe platform also supports specification of a DAG via a property\nfile. The aim here to make it easy for tools to create and run an\napplication. This method of specification does not have the Java\ncompiler support of compile time check, but since these applications\nwould be created by software, they should be correct by construction.\nThe syntax is derived from Hadoop properties and should be easy for\nfolks who are used to creating software that integrated with\nHadoop.\n\n\nCreate an application (DAG): myApplication.properties\n\n\n# input operator that reads from a file\ndt.operator.inputOp.classname=com.acme.SampleInputOperator\ndt.operator.inputOp.fileName=somefile.txt\n\n# output operator that writes to the console\ndt.operator.outputOp.classname=com.acme.ConsoleOutputOperator\n\n# stream connecting both operators\ndt.stream.inputStream.source=inputOp.outputPort\ndt.stream.inputStream.sinks=outputOp.inputPort\n\n\n\n\nAbove snippet is intended to convey the basic idea of specifying\nthe DAG without using Java. Operators would come from a predefined\nlibrary and referenced in the specification by class name and port names\n(obtained from the library providers documentation or runtime\nintrospection by tools). For those interested in details, see later\nsections and refer to the Operation and\nInstallation Guide\u00a0mentioned above.\n\n\nAttributes\n\n\nAttributes impact the runtime behavior of the application. They do\nnot impact the functionality. An example of an attribute is application\nname. Setting it changes the application name. Another example is\nstreaming window size. Setting it changes the streaming window size from\nthe default value to the specified value. Users cannot add new\nattributes, they can only choose from the ones that come packaged and\npre-supported by the platform. Details of attributes are covered in the\n Operation and Installation\nGuide.\n\n\nOperators\n\n\nOperators\u00a0are basic compute units.\nOperators process each incoming tuple and emit zero or more tuples on\noutput ports as per the business logic. The data flow, connectivity,\nfault tolerance (node outage), etc. is taken care of by the platform. As\nan operator developer, all that is needed is to figure out what to do\nwith the incoming tuple and when (and which output port) to send out a\nparticular output tuple. Correctly designed operators will most likely\nget reused. Operator design needs care and foresight. For details, refer\nto the \nOperator Developer Guide\n. As an application developer you need to connect operators\nin a way that it implements your business logic. You may also require\noperator customization for functionality and use attributes for\nperformance/scalability etc.\n\n\nAll operators process tuples asynchronously in a distributed\ncluster. An operator cannot assume or predict the exact time a tuple\nthat it emitted will get consumed by a downstream operator. An operator\nalso cannot predict the exact time when a tuple arrives from an upstream\noperator. The only guarantee is that the upstream operators are\nprocessing the current or a future window, i.e. the windowId of upstream\noperator is equals or exceeds its own windowId. Conversely the windowId\nof a downstream operator is less than or equals its own windowId. The\nend of a window operation, i.e. the API call to endWindow on an operator\nrequires that all upstream operators have finished processing this\nwindow. This means that completion of processing a window propagates in\na blocking fashion through an operator. Later sections provides more\ndetails on streams and data flow of tuples.\n\n\nEach operator has a unique name within the DAG as provided by the\nuser. This is the name of the operator in the logical plan. The name of\nthe operator in the physical plan is an integer assigned to it by STRAM.\nThese integers are use the sequence from 1 to N, where N is total number\nof physically unique operators in the DAG. \u00a0Following the same rule,\neach partitioned instance of a logical operator has its own integer as\nan id. This id along with the Hadoop container name uniquely identifies\nthe operator in the execution plan of the DAG. The logical names and the\nphysical names are required for web service support. Operators can be\naccessed via both names. These same names are used while interacting\nwith dtcli\u00a0to access an operator.\nIdeally these names should be self-descriptive. For example in Figure 1,\nthe node named \u201cDaily volume\u201d has a physical identifier of 2.\n\n\nOperator Interface\n\n\nOperator interface in a DAG consists of ports,\u00a0properties,\u00a0and attributes.\nOperators interact with other components of the DAG via ports. Functional behavior of the operators\ncan be customized via parameters. Run time performance and physical\ninstantiation is controlled by attributes. Ports and parameters are\nfields (variables) of the Operator class/object, while attributes are\nmeta information that is attached to the operator object via an\nAttributeMap. An operator must have at least one port. Properties are\noptional. Attributes are provided by the platform and always have a\ndefault value that enables normal functioning of operators.\n\n\nPorts\n\n\nPorts are connection points by which an operator receives and\nemits tuples. These should be transient objects instantiated in the\noperator object, that implement particular interfaces. Ports should be\ntransient as they contain no state. They have a pre-defined schema and\ncan only be connected to other ports with the same schema. An input port\nneeds to implement the interface Operator.InputPort\u00a0and\ninterface Sink. A default\nimplementation of these is provided by the abstract class DefaultInputPort. An output port needs to\nimplement the interface Operator.OutputPort. A default implementation\nof this is provided by the concrete class DefaultOutputPort. These two are a quick way to\nimplement the above interfaces, but operator developers have the option\nof providing their own implementations.\n\n\nHere are examples of an input and an output port from the operator\nSum.\n\n\n@InputPortFieldAnnotation(name = \ndata\n)\npublic final transient DefaultInputPort\nV\n data = new DefaultInputPort\nV\n() {\n @Override\n public void process(V tuple)\n {\n ...\n }\n}\n@OutputPortFieldAnnotation(optional=true)\npublic final transient DefaultOutputPort\nV\n sum = new DefaultOutputPort\nV\n(){ \u2026 };\n\n\n\n\nThe process call is in the Sink interface. An emit on an output\nport is done via emit(tuple) call. For the above example it would be\nsum.emit(t), where the type of t is the generic parameter V.\n\n\nThere is no limit on how many ports an operator can have. However\nany operator must have at least one port. An operator with only one port\nis called an Input Adapter if it has no input port and an Output Adapter\nif it has no output port. These are special operators needed to get/read\ndata from outside system/source into the application, or push/write data\ninto an outside system/sink. These could be in Hadoop or outside of\nHadoop. These two operators are in essence gateways for the streaming\napplication to communicate with systems outside the application.\n\n\nPort connectivity can be validated during compile time by adding\nPortFieldAnnotations shown above. By default all ports have to be\nconnected, to allow a port to go unconnected, you need to add\n\u201coptional=true\u201d to the annotation.\n\n\nAttributes can be specified for ports that affect the runtime\nbehavior. An example of an attribute is parallel partition that specifes\na parallel computation flow per partition. It is described in detail in\nthe Parallel Partitions section. Another example is queue capacity that specifies the buffer size for the\nport. Details of attributes are covered in Operation and Installation Guide.\n\n\nProperties\n\n\nProperties are the abstractions by which functional behavior of an\noperator can be customized. They should be non-transient objects\ninstantiated in the operator object. They need to be non-transient since\nthey are part of the operator state and re-construction of the operator\nobject from its checkpointed state must restore the operator to the\ndesired state. Properties are optional, i.e. an operator may or may not\nhave properties; they are part of user code and their values are not\ninterpreted by the platform in any way.\n\n\nAll non-serializable objects should be declared transient.\nExamples include sockets, session information, etc. These objects should\nbe initialized during setup call, which is called every time the\noperator is initialized.\n\n\nAttributes\n\n\nAttributes are values assigned to the operators that impact\nrun-time. This includes things like the number of partitions, at most\nonce or at least once or exactly once recovery modes, etc. Attributes do\nnot impact functionality of the operator. Users can change certain\nattributes in runtime. Users cannot add attributes to operators; they\nare pre-defined by the platform. They are interpreted by the platform\nand thus cannot be defined in user created code (like properties).\nDetails of attributes are covered in \nConfiguration Guide\n.\n\n\nOperator State\n\n\nThe state of an operator is defined as the data that it transfers\nfrom one window to a future window. Since the computing model of the\nplatform is to treat windows like micro-batches, the operator state can\nbe checkpointed every Nth window, or every T units of time, where T is significantly greater\nthan the streaming window. \u00a0When an operator is checkpointed, the entire\nobject is written to HDFS. \u00a0The larger the amount of state in an\noperator, the longer it takes to recover from a failure. A stateless\noperator can recover much quicker than a stateful one. The needed\nwindows are preserved by the upstream buffer server and are used to\nrecompute the lost windows, and also rebuild the buffer server in the\ncurrent container.\n\n\nThe distinction between Stateless and Stateful is based solely on\nthe need to transfer data in the operator from one window to the next.\nThe state of an operator is independent of the number of ports.\n\n\nStateless\n\n\nA Stateless operator is defined as one where no data is needed to\nbe kept at the end of every window. This means that all the computations\nof a window can be derived from all the tuples the operator receives\nwithin that window. This guarantees that the output of any window can be\nreconstructed by simply replaying the tuples that arrived in that\nwindow. Stateless operators are more efficient in terms of fault\ntolerance, and cost to achieve SLA.\n\n\nStateful\n\n\nA Stateful operator is defined as one where data is needed to be\nstored at the end of a window for computations occurring in later\nwindow; a common example is the computation of a sum of values in the\ninput tuples.\n\n\nOperator API\n\n\nThe Operator API consists of methods that operator developers may\nneed to override. In this section we will discuss the Operator APIs from\nthe point of view of an application developer. Knowledge of how an\noperator works internally is critical for writing an application. Those\ninterested in the details should refer to Malhar Operator Developer Guide.\n\n\nThe APIs are available in three modes, namely Single Streaming\nWindow, Sliding Application Window, and Aggregate Application Window.\nThese are not mutually exclusive, i.e. an operator can use single\nstreaming window as well as sliding application window. A physical\ninstance of an operator is always processing tuples from a single\nwindow. The processing of tuples is guaranteed to be sequential, no\nmatter which input port the tuples arrive on.\n\n\nIn the later part of this section we will evaluate three common\nuses of streaming windows by applications. They have different\ncharacteristics and implications on optimization and recovery mechanisms\n(i.e. algorithm used to recover a node after outage) as discussed later\nin the section.\n\n\nStreaming Window\n\n\nStreaming window is atomic micro-batch computation period. The API\nmethods relating to a streaming window are as follows\n\n\npublic void process(\ntuple_type\n tuple) // Called on the input port on which the tuple arrives\npublic void beginWindow(long windowId) // Called at the start of the window as soon as the first begin_window tuple arrives\npublic void endWindow() // Called at the end of the window after end_window tuples arrive on all input ports\npublic void setup(OperatorContext context) // Called once during initialization of the operator\npublic void teardown() // Called once when the operator is being shutdown\n\n\n\n\nA tuple can be emitted in any of the three streaming run-time\ncalls, namely beginWindow, process, and endWindow but not in setup or\nteardown.\n\n\nAggregate Application Window\n\n\nAn operator with an aggregate window is stateful within the\napplication window timeframe and possibly stateless at the end of that\napplication window. An size of an aggregate application window is an\noperator attribute and is defined as a multiple of the streaming window\nsize. The platform recognizes this attribute and optimizes the operator.\nThe beginWindow, and endWindow calls are not invoked for those streaming\nwindows that do not align with the application window. For example in\ncase of streaming window of 0.5 second and application window of 5\nminute, an application window spans 600 streaming windows (5*60*2 =\n600). At the start of the sequence of these 600 atomic streaming\nwindows, a beginWindow gets invoked, and at the end of these 600\nstreaming windows an endWindow gets invoked. All the intermediate\nstreaming windows do not invoke beginWindow or endWindow. Bookkeeping,\nnode recovery, stats, UI, etc. continue to work off streaming windows.\nFor example if operators are being checkpointed say on an average every\n30th window, then the above application window would have about 20\ncheckpoints.\n\n\nSliding Application Window\n\n\nA sliding window is computations that requires previous N\nstreaming windows. After each streaming window the Nth past window is\ndropped and the new window is added to the computation. An operator with\nsliding window is a stateful operator at end of any window. The sliding\nwindow period is an attribute and is a multiple of streaming window. The\nplatform recognizes this attribute and leverages it during bookkeeping.\nA sliding aggregate window with tolerance to data loss does not have a\nvery high bookkeeping cost. The cost of all three recovery mechanisms,\n at most once\u00a0(data loss tolerant),\nat least once\u00a0(data loss\nintolerant), and exactly once\u00a0(data\nloss intolerant and no extra computations) is same as recovery\nmechanisms based on streaming window. STRAM is not able to leverage this\noperator for any extra optimization.\n\n\nSingle vs Multi-Input Operator\n\n\nA single-input operator by definition has a single upstream\noperator, since there can only be one writing port for a stream. \u00a0If an\noperator has a single upstream operator, then the beginWindow on the\nupstream also blocks the beginWindow of the single-input operator. For\nan operator to start processing any window at least one upstream\noperator has to start processing that window. A multi-input operator\nreads from more than one upstream ports. Such an operator would start\nprocessing as soon as the first begin_window event arrives. However the\nwindow would not close (i.e. invoke endWindow) till all ports receive\nend_window events for that windowId. Thus the end of a window is a\nblocking event. As we saw earlier, a multi-input operator is also the\npoint in the DAG where windows of all upstream operators are\nsynchronized. The windows (atomic micro-batches) from a faster (or just\nahead in processing) upstream operators are queued up till the slower\nupstream operator catches up. STRAM monitors such bottlenecks and takes\ncorrective actions. The platform ensures minimal delay, i.e processing\nstarts as long as at least one upstream operator has started\nprocessing.\n\n\nRecovery Mechanisms\n\n\nApplication developers can set any of the recovery mechanisms\nbelow to deal with node outage. In general, the cost of recovery depends\non the state of the operator, while data integrity is dependant on the\napplication. The mechanisms are per window as the platform treats\nwindows as atomic compute units. Three recovery mechanisms are\nsupported, namely\n\n\n\n\nAt-least-once: All atomic batches are processed at least once.\n No data loss occurs.\n\n\nAt-most-once: All atomic batches are processed at most once.\n Data loss is possible; this is the most efficient setting.\n\n\nExactly-once: All atomic batches are processed exactly once.\n No data loss occurs; this is the least efficient setting since\n additional work is needed to ensure proper semantics.\n\n\n\n\nAt-least-once is the default. During a recovery event, the\noperator connects to the upstream buffer server and asks for windows to\nbe replayed. At-least-once and exactly-once mechanisms start from its\ncheckpointed state. At-most-once starts from the next begin-window\nevent.\n\n\nRecovery mechanisms can be specified per Operator while writing\nthe application as shown below.\n\n\nOperator o = dag.addOperator(\u201coperator\u201d, \u2026);\ndag.setAttribute(o, OperatorContext.PROCESSING_MODE, ProcessingMode.AT_MOST_ONCE);\n\n\n\n\nAlso note that once an operator is attributed to AT_MOST_ONCE,\nall the operators downstream to it have to be AT_MOST_ONCE. The client\nwill give appropriate warnings or errors if that\u2019s not the case.\n\n\nDetails are explained in the chapter on Fault Tolerance below.\n\n\nStreams\n\n\nA stream\u00a0is a connector\n(edge) abstraction, and is a fundamental building block of the platform.\nA stream consists of tuples that flow from one port (called the\noutput\u00a0port) to one or more ports\non other operators (called input\u00a0ports) another -- so note a potentially\nconfusing aspect of this terminology: tuples enter a stream through its\noutput port and leave via one or more input ports. A stream has the\nfollowing characteristics\n\n\n\n\nTuples are always delivered in the same order in which they\n were emitted.\n\n\nConsists of a sequence of windows one after another. Each\n window being a collection of in-order tuples.\n\n\nA stream that connects two containers passes through a\n buffer server.\n\n\nAll streams can be persisted (by default in HDFS).\n\n\nExactly one output port writes to the stream.\n\n\nCan be read by one or more input ports.\n\n\nConnects operators within an application, not outside\n an application.\n\n\nHas an unique name within an application.\n\n\nHas attributes which act as hints to STRAM.\n\n\n\n\nStreams have four modes, namely in-line, in-node, in-rack,\n and other. Modes may be overruled (for example due to lack\n of containers). They are defined as follows:\n\n\n\n\nTHREAD_LOCAL: In the same thread, uses thread\n stack (intra-thread). This mode can only be used for a downstream\n operator which has only one input port connected; also called\n in-line.\n\n\nCONTAINER_LOCAL: In the same container (intra-process); also\n called in-container.\n\n\nNODE_LOCAL: In the same Hadoop node (inter processes, skips\n NIC); also called in-node.\n\n\nRACK_LOCAL: On nodes in the same rack; also called\n in-rack.\n\n\nunspecified: No guarantee. Could be anywhere within the\n cluster\n\n\n\n\n\n\n\n\nAn example of a stream declaration is given below\n\n\nDAG dag = new DAG();\n \u2026\ndag.addStream(\nviews\n, viewAggregate.sum, cost.data).setLocality(CONTAINER_LOCAL); // A container local stream\ndag.addStream(\u201cclicks\u201d, clickAggregate.sum, rev.data); // An example of unspecified locality\n\n\n\n\nThe platform guarantees in-order delivery of tuples in a stream.\nSTRAM views each stream as collection of ordered windows. Since no tuple\ncan exist outside a window, a replay of a stream consists of replay of a\nset of windows. When multiple input ports read the same stream, the\nexecution plan of a stream ensures that each input port is logically not\nblocked by the reading of another input port. The schema of a stream is\nsame as the schema of the tuple.\n\n\nIn a stream all tuples emitted by an operator in a window belong\nto that window. A replay of this window would consists of an in-order\nreplay of all the tuples. Thus the tuple order within a stream is\nguaranteed. However since an operator may receive multiple streams (for\nexample an operator with two input ports), the order of arrival of two\ntuples belonging to different streams is not guaranteed. In general in\nan asynchronous distributed architecture this is expected. Thus the\noperator (specially one with multiple input ports) should not depend on\nthe tuple order from two streams. One way to cope with this\nindeterminate order, if necessary, is to wait to get all the tuples of a\nwindow and emit results in endWindow call. All operator templates\nprovided as part of Malhar operator library follow these principles.\n\n\nA logical stream gets partitioned into physical streams each\nconnecting the partition to the upstream operator. If two different\nattributes are needed on the same stream, it should be split using\nStreamDuplicator\u00a0operator.\n\n\nModes of the streams are critical for performance. An in-line\nstream is the most optimal as it simply delivers the tuple as-is without\nserialization-deserialization. Streams should be marked\ncontainer_local, specially in case where there is a large tuple volume\nbetween two operators which then on drops significantly. Since the\nsetLocality call merely provides a hint, STRAM may ignore it. An In-node\nstream is not as efficient as an in-line one, but it is clearly better\nthan going off-node since it still avoids the potential bottleneck of\nthe network card.\n\n\nTHREAD_LOCAL and CONTAINER_LOCAL streams do not use a buffer\nserver as this stream is in a single process. The other two do.\n\n\nValidating an Application\n\n\nThe platform provides various ways of validating the application\nspecification and data input. An understanding of these checks is very\nimportant for an application developer since it affects productivity.\nValidation of an application is done in three phases, namely\n\n\n\n\nCompile Time: Caught during application development, and is\n most cost effective. These checks are mainly done on declarative\n objects and leverages the Java compiler. An example is checking that\n the schemas specified on all ports of a stream are\n mutually compatible.\n\n\nInitialization Time: When the application is being\n initialized, before submitting to Hadoop. These checks are related\n to configuration/context of an application, and are done by the\n logical DAG builder implementation. An example is the checking that\n all non-optional ports are connected to other ports.\n\n\nRun Time: Validations done when the application is running.\n This is the costliest of all checks. These are checks that can only\n be done at runtime as they involve data. For example divide by 0\n check as part of business logic.\n\n\n\n\nCompile Time\n\n\nCompile time validations apply when an application is specified in\nJava code and include all checks that can be done by Java compiler in\nthe development environment (including IDEs like NetBeans or Eclipse).\nExamples include\n\n\n\n\nSchema Validation: The tuples on ports are POJO (plain old\n java objects) and compiler checks to ensure that all the ports on a\n stream have the same schema.\n\n\nStream Check: Single Output Port and at least one Input port\n per stream. A stream can only have one output port writer. This is\n part of the addStream api. This\n check ensures that developers only connect one output port to\n a stream. The same signature also ensures that there is at least one\n input port for a stream\n\n\nNaming: Compile time checks ensures that applications\n components operators, streams are named\n\n\n\n\nInitialization/Instantiation Time\n\n\nInitialization time validations include various checks that are\ndone post compile, and before the application starts running in a\ncluster (or local mode). These are mainly configuration/contextual in\nnature. These checks are as critical to proper functionality of the\napplication as the compile time validations.\n\n\nExamples include\n\n\n\n\n\n\nJavaBeans Validation\n:\n Examples include\n\n\n\n\n@Max(): Value must be less than or equal to the number\n\n\n@Min(): Value must be greater than or equal to the\n number\n\n\n@NotNull: The value of the field or property must not be\n null\n\n\n@Pattern(regexp = \u201c....\u201d): Value must match the regular\n expression\n\n\nInput port connectivity: By default, every non-optional input\n port must be connected. A port can be declared optional by using an\n annotation: \u00a0 \u00a0 @InputPortFieldAnnotation(name = \"...\", optional\n = true)\n\n\nOutput Port Connectivity: Similar. The annotation here is: \u00a0 \u00a0\n @OutputPortFieldAnnotation(name = \"...\", optional = true)\n\n\n\n\n\n\n\n\nUnique names in application scope: Operators, streams, must have\n unique names.\n\n\n\n\nCycles in the dag: DAG cannot have a cycle.\n\n\nUnique names in operator scope: Ports, properties, annotations\n must have unique names.\n\n\nOne stream per port: A port can connect to only one stream.\n This check applies to input as well as output ports even though an\n output port can technically write to two streams. If you must have\n two streams originating from a single output port, use \u00a0a\u00a0streamDuplicator operator.\n\n\nApplication Window Period: Has to be an integral multiple the\n streaming window period.\n\n\n\n\nRun Time\n\n\nRun time checks are those that are done when the application is\nrunning. The real-time streaming platform provides rich run time error\nhandling mechanisms. The checks are exclusively done by the application\nbusiness logic, but the platform allows applications to count and audit\nthese. Some of these features are in the process of development (backend\nand UI) and this section will be updated as they are developed. Upon\ncompletion examples will be added to demos to illustrate these.\n\n\nError ports are output ports with error annotations. Since they\nare normal ports, they can be monitored and tuples counted, persisted\nand counts shown in the UI.\n\n\n\n\nMulti-Tenancy and Security\n\n\nHadoop is a multi-tenant distributed operating system. Security is\nan intrinsic element of multi-tenancy as without it a cluster cannot be\nreasonably be shared among enterprise applications. Streaming\napplications follow all multi-tenancy security models used in Hadoop as\nthey are native Hadoop applications.\n\n\nSecurity\n\n\nThe platform includes Kerberos support. Both access points, namely\nSTRAM and Bufferserver are secure. STRAM passes the token over to\nStreamingContainer, which then gives it to the Bufferserver. The most\nimportant aspect for an application developer is to note that STRAM is\nthe single point of access to ensure security measures are taken by all\ncomponents of the platform.\n\n\nResource Limits\n\n\nHadoop enforces quotas on resources. This includes hard-disk (name\nspace and total disk quota) as well as priority queues for schedulers.\nThe platform uses Hadoop resource limits to manage a streaming\napplication. In addition network I/O quotas can be enforced. An operator\ncan be dynamically partitioned if it reaches its resource limits; these\nlimits may be expressed in terms of throughput, latency, or just\naggregate resource utilization of a container.\n\n\n\n\nScalability and Partitioning\n\n\nScalability is a foundational element of this platform and is a\nbuilding block for an eco-system where big-data meets real-time.\nEnterprises need to continually meet SLA as data grows. Without the\nability to scale as load grows, or new applications with higher loads\ncome to fruition, enterprise grade SLA cannot be met. A big issue with\nthe streaming application space is that, it is not just about high load,\nbut also the fluctuations in it. There is no way to guarantee future\nload requirements and there is a big difference between high and low\nload within a day for the same feed. Traditional streaming platforms\nsolve these two cases by simply throwing more hardware at the\nproblem.\n\n\nDaily spikes are managed by ensuring enough hardware for peak\nload, which then idles during low load, and future needs are handled by\na very costly re-architecture, or investing heavily in building a\nscalable distributed operating system. Another salient and often\noverlooked cost is the need to manage SLA -- let\u2019s call it buffer capacity. Since this means computing the\npeak load within required time, that translates to allocating enough\nresources over and above peak load as daily peaks fluctuate. For example\nan average peak load of 100 resource units (cpu and/or memory and/or\nnetwork) may mean allocating about 200 resource units to be safe. A\ndistributed cluster that cannot dynamically scale up and down, in effect\npays buffer capacity per application. Another big aspect of streaming\napplications is that the load is not just ingestion rate, more often\nthan not, the internal operators produce lot more events than the\ningestion rate. For example a dimensional data (with, say d\u00a0dimensions) computation needs 2*d -1\u00a0computations per ingested event. A lot\nof applications have over 10 dimensions, i.e over 1000 computations per\nincoming event and these need to be distributed across the cluster,\nthereby causing an explosion in the throughput (events/sec) that needs\nto be managed.\n\n\nThe platform is designed to handle such cases at a very low cost.\nThe platform scales linearly with Hadoop. If applications need more\nresources, the enterprise can simply add more commodity nodes to Hadoop\nwithout any downtime, and the Hadoop native platform will take care of\nthe rest. If some nodes go bad, these can be removed without downtime.\nThe daily peaks and valleys in the load are managed by the platform by\ndynamically scaling at the peak and then giving the resources back to\nHadoop during low load. This means that a properly designed Hadoop\ncluster does several things for enterprises: (a) reduces the cost of\nhardware due to use of commodity hardware (b) shares buffer capacity\nacross all applications as peaks of all applications may not align and\n(c) raises the average CPU usage on a 24x7 basis. As a general design\nthis is similar to scale that a map-reduce application can deliver. In\nthe following sections of this chapter we will see how this is\ndone.\n\n\nPartitioning\n\n\nIf all tuples sent through the stream(s) that are connected to the\ninput port(s) of an operator in the DAG are received by a single\nphysical instance of that operator, that operator can become a\nperformance bottleneck. This leads to scalability issues when\nthroughput, memory, or CPU needs exceed the processing capacity of that\nsingle instance.\n\n\nTo address the problem, the platform offers the capability to\npartition the inflow of data so that it is divided across multiple\nphysical instances of a logical operator in the DAG. There are two\nfunctional ways to partition\n\n\n\n\nLoad balance: Incoming load is simply partitioned\n into stream(s) that go to separate instances of physical operators\n and scalability is achieved via adding more physical operators. Each\n tuple is sent to physical operator (partition) based on a\n round-robin or other similar algorithm. This scheme scales linearly.\n A lot of key based computations can load balance in the platform due\n to the ability to insert Unifiers. For many computations, the\n endWindow and Unifier setup is similar to the combiner and reducer\n mechanism in a Map-Reduce computation.\n\n\nSticky Key: The key assertion is that distribution of tuples\n are sticky, i.e the data with\n same key will always be processed by the same physical operator, no\n matter how many times it is sent through the stream. This stickiness\n will continue even if the number of partitions grows dynamically and\n can eventually be leveraged for advanced features like\n bucket testing. How this is accomplished and what is required to\n develop compliant operators will be explained below.\n\n\n\n\nWe plan to add more partitioning mechanisms proactively to the\nplatform over time as needed by emerging usage patterns. The aim is to\nallow enterprises to be able to focus on their business logic, and\nsignificantly reduce the cost of operability. As an enabling technology\nfor managing high loads, this platform provides enterprises with a\nsignificant innovative edge. Scalability and Partitioning is a\nfoundational building block for this platform.\n\n\nSticky Partition vs Round Robin\n\n\nAs noted above, partitioning via sticky key is data aware but\nround-robin partitioning is not. An example for non-sticky load\nbalancing would be round robin distribution over multiple instances,\nwhere for example a tuple stream of A, A,\nA with 3 physical operator\ninstances would result in processing of a single A by each of the instances, In contrast, sticky\npartitioning means that exactly one instance of the operators will\nprocess all of the Atuples if they\nfall into the same bucket, while B\nmay be processed by another operator. Data aware mapping of\ntuples to partitions (similar to distributed hash table) is accomplished\nvia Stream Codecs. In later sections we would show how these two\napproaches can be used in combination.\n\n\nStream Codec\n\n\nThe platform does not make assumptions about the tuple\ntype, it could be any Java object. The operator developer knows what\ntuple type an input port expects and is capable of processing. Each\ninput port has a stream codec \u00a0associated thatdefines how data is serialized when transmitted over a socket\nstream; it also defines another\nfunction that computes the partition hash key for the tuple. The engine\nuses that key to determine which physical instance(s) \u00a0(for a\npartitioned operator) receive that \u00a0tuple. For this to work, consistent hashing is required.\nThe default codec uses the Java Object#hashCode function, which is\nsufficient for basic types such as Integer, String etc. It will also\nwork with custom tuple classes as long as they implement hashCode\nappropriately. Reliance on hashCode may not work when generic containers\nare used that do not hash the actual data, such as standard collection\nclasses (HashMap etc.), in which case a custom stream codec must be\nassigned to the input port.\n\n\nStatic Partitioning\n\n\nDAG designers can specify at design time how they would like\ncertain operators to be partitioned. STRAM then instantiates the DAG\nwith the physical plan which adheres to the partitioning scheme defined\nby the design. This plan is the initial partition of the application. In\nother words, Static Partitioning is used to tell STRAM to compute the\nphysical DAG from a logical DAG once, without taking into consideration\nruntime states or loads of various operators.\n\n\nDynamic Partitioning\n\n\nIn streaming applications the load changes during the day, thus\ncreating situations where the number of partitioned operator instances\nneeds to adjust dynamically. The load can be measured in terms of\nprocessing within the DAG based on throughput, or latency, or\nconsiderations in external system components (time based etc.) that the\nplatform may not be aware of. Whatever the trigger, the resource\nrequirement for the current processing needs to be adjusted at run-time.\nThe platform may detect that operator instances are over or under\nutilized and may need to dynamically adjust the number of instances on\nthe fly. More instances of a logical operator may be required (partition\nsplit) or underutilized operator instances may need decommissioning\n(partition merge). We refer to either of the changes as dynamic\npartitioning. The default partitioning scheme supports split and merge\nof partitions, but without state transfer. The contract of the\nPartitioner\u00a0interface allows the operator\ndeveloper to implement split/merge and the associated state transfer, if\nnecessary.\n\n\nSince partitioning is a key scalability measure, our goal is to\nmake it as simple as possible without removing the flexibility needed\nfor sophisticated applications. Basic partitioning can be enabled at\ncompile time through the DAG specification. A slightly involved\npartitioning involves writing custom codecs to calculate data aware\npartitioning scheme. More complex partitioning cases may require users\nto provide a custom implementation of Partitioner, which gives the\ndeveloper full control over state transfer between multiple instances of\nthe partitioned operator.\n\n\nDefault Partitioning\n\n\nThe platform provides a default partitioning implementation that\ncan be enabled without implementing Partitioner\u00a0(or writing any other extra Java\ncode), which is designed to support simple sticky partitioning out of\nthe box for operators with logic agnostic to the partitioning scheme\nthat can be enabled by means of DAG construction alone.\n\n\nTypically an operator that can work with the default partitioning\nscheme would have a single input port. If there are multiple input\nports, only one port will be partitioned (the port first connected in\nthe DAG). The number of partitions will be calculated based on the\ninitial partition count - set as attribute on the operator in the DAG\n(if the attribute is not present, partitioning is off). Each partition\nwill handle tuples based on matching the lower bits of the hash code.\nFor example, if the tuple type was Integer and 2 partitions requested,\nall even numbers would go to one operator instance and all odd numbers\nto the other.\n\n\nDefault Dynamic Partitioning\n\n\nTriggering partition load evaluation and repartitioning action\nitself are separate concerns. Triggers are not specified further here,\nwe are planning to support it in a customizable fashion that, for\nexample, allows latency or SLA based implementations. Triggers calculate\na load indicator (signed number) that tells the framework that a given\npartition is either underutilized, operating normally within the\nexpected thresholds or overloaded and becoming a bottleneck. The\nindicator is then presented to the partitioning logic (default or custom\nimplementation of Partitioner) to provide the opportunity to make any\nneeded adjustments.\n\n\nThe default partitioning logic divides the key space\naccording to the lower bits of the hash codes that are generated by the\nstream codec, by assigning each partitioned operator instance via a bit\nmask and the respective value. For example, the operator may have\ninitially two partitions, 0and 1, each\nwith a bit mask of 1.\nIn the case where load evaluation flags partition\n0 as over utilized\n(most data tuples processed yield a hash code with lowest bit cleared),\napartition split\u00a0occurs, resulting in 00\nand 10with mask 11. Operator instance 0 will be replaced with 2 new instances and partition\n1 remains unchanged,\nresulting in three active partitions. The same process could repeat if\nmost tuples fall into the01 partition, leading to a split into 001 and101\nwith mask 111, etc.\n\n\nShould load decrease in two sibling partitions, a\npartition merge\u00a0could\nreverse the split, reducing the mask length and replacing two operators\nwith one. Should only one of two sibling partitions be underutilized,\n it cannot be\u00a0merged.\nInstead, the platform can attempt to deploy the affected operator\ninstance along with other operator instances for resource sharing\namongst underutilized partitions (not implemented yet). Keeping separate\noperator instances allows\u00a0us to\npin load increases directly to the affected instance with a single\nspecific partition key, which would not be the case had we assigned a\nshared instance to handle multiple keys.\n\n\nNxM Partitions\n\n\nWhen two consecutive logical operators are partitioned a special\noptimization is done. Technically the output of the first operator\nshould be unified and streamed to the next logical node. But that can\ncreate a network bottleneck. The platform optimizes this by partitioning\nthe output stream of each partition of the first operator as per the\npartitions needed by the next operator. For example if the first\noperator has N partitions and the second operator has M partitions then\neach of the N partitions would send out M streams. The first of each of\nthese M streams would be unified and routed to the first of the M\npartitions, and so on. Such an optimization allows for higher\nscalability and eliminates a network bottleneck (one unifier in between\nthe two operators) by having M unifiers. This also enables the\napplication to perform within the resource limits enforced by YARN.\nSTRAM has a much better understanding and estimation of unifier resource\nneeds and is thus able to optimize for resource constraints.\n\n\nFigure 5 shows a case where we have a 3x2 partition; the single\nintermediate unifier between operator 1\u00a0and 2\u00a0is\noptimized away. The partition computation for operator 2\u00a0is executed on outbound streams of each\npartitions of operator 1. Each\npartition of operator 2\u00a0has its own\nCONTAINER_LOCAL unifier. In such a situation, the in-bound network\ntuple flow is split between containers for 2a\u00a0and 2b\u00a0each of which take half the traffic. STRAM\ndoes this by default since it always has better performance.\n\n\n\n\nParallel\n\n\nIn cases where all the downstream operators use the same\npartitioning scheme and the DAG is network bound an optimization called\nparallel partition\u00a0is very\neffective. In such a scenario all the downstream operators are also\npartitioned to create computation flow per partition. This optimization\nis extremely efficient for network bound streams, In some cases this\noptimization would also apply for CPU or RAM bounded\napplications.\n\n\nIn Figure 6a, operator 1\u00a0is\npartitioned into 1a\u00a0and\n1b. Both the downstream operators\n2\u00a0and 3\u00a0follow the same partition scheme as\n1, however the network I/O between\n1\u00a0and 2, and between 2\u00a0and 3\u00a0is\nhigh. Then users can decide to optimize using parallel partitions. This\nallows STRAM to completely skip the insertion of intermediate Unifier\noperators between 1 and 2 as well as between 2 and 3; a single unifier\njust before operator 4, is\nadequate by which time tuple flow volume is low.\n\n\nSince operator 4 has sufficient resources to manage the combined\noutput of multiple instances of operator 3, it need not be partitioned. A further\noptimization can be done by declaring operators 1, 2, and\n3\u00a0as THREAD_LOCAL (intra-thread)\nor CONTAINER_LOCAL (intra-process) or NODE_LOCAL (intra-node).\nParallel partition is not used by default, users have to specify it\nexplicitly via an attribute of the input port (reader) of the stream as\nshown below.\n\n\n\n\nThe following code shows an example of creating a parallel partition.\n\n\ndag.addStream(\nDenormalizedUserId\n, idAssigner.userid, uniqUserCount.data);\ndag.setInputPortAttribute(uniqUserCount.data, PortContext.PARTITION_PARALLEL, partitionParallel);\n\n\n\n\nParallel partitions can be used with other partitions, for example\na parallel partition could be sticky key or load balanced.\n\n\nParallel Partitions with Streams Modes\n\n\nParallel partitions can be further optimized if the parallel\npartitions are combined with streams being in-line or in-node or in-rack\nmode. This is very powerful feature and should be used if operators have\nvery high throughput within them and the outbound merge does an\naggregation. For example in Figure 6b, if operator 3 significantly\nreduces the throughput, which usually is a reason to do parallel\npartition, then making the streams in-line or in-node within nodes\n1-\n2 and 2-\n3 significantly impacts the performance.\n\n\nCONTAINER_LOCAL stream has high bandwidth, and can manage to\nconsume massive tuple count without taxing the NIC and networking stack.\nThe downside is that all operators (1,2,3) in this case need to be able\nto fit within the resource limits of CPU and memory enforced on a Hadoop\ncontainer. A way around this is to request RM to provide a big\ncontainer. On a highly used Hadoop grid, getting a bigger container may\nbe a problem, and operational complexities of managing a Hadoop cluster\nwith different container sizes may be higher. If THREAD_LOCAL or\nCONTAINER_LOCAL streams are needed to get the throughput, increasing\nthe partition count should be considered. In future STRAM may take this\ndecision automatically. Unless there is a very bad skew and sticky key\npartitioning is in use, the approach to partition till each container\nhas enough resources works well.\n\n\nA NODE_LOCAL stream has lower bandwidth compared to a\nCONTAINER_LOCAL stream, but it works well with the RM in terms of\nrespecting container size limits. A NODE_LOCAL parallel partition uses\nlocal loop back for streams and is much better than using NIC. Though\nNODE_LOCAL stream fits well with similar size containers, it does need\nRM to be able to deliver two containers on the same Hadoop node. On a\nheavily used Hadoop cluster, this may not always be possible. In future\nSTRAM would do these trade-offs automatically at run-time.\n\n\nA RACK_LOCAL stream has much lower bandwidth than NODE_LOCAL\nstream, as events go through the NIC. But it still is able to better\nmanage SLA and latency. Moreover RM has much better ability to give a\nrack local container as opposed to the other two.\n\n\nParallel partitions with CONTAINER_LOCAL streams can be done by\nsetting all the intermediate streams to CONTAINER_LOCAL. Parallel\npartitions with THREAD_LOCAL streams can be done by setting all the\nintermediate streams to THREAD_LOCAL. Platform supports the following\nvia attributes.\n\n\n\n\nParallel-Partition\n\n\nParallel-Partition with THREAD_LOCAL stream\n\n\nParallel-Partition with CONTAINER_LOCAL stream\n\n\nParallel-Partition with NODE_LOCAL stream\n\n\nParallel-Partition with RACK_LOCAL stream\n\n\n\n\nThese attributes would nevertheless be initial starting point and\nSTRAM can improve on them at run time.\n\n\n\n\nSkew Balancing Partition\n\n\nSkew balancing partition is useful to manage skews in the stream\nthat is load balanced using a sticky key. Incoming events may have a\nskew, and these may change depending on various factors like time of the\nday or other special circumstances. To manage the uneven load, users can\nset a limit on the ratio of maximum load on a partition to the minimum\nload on a partition. STRAM would use this to dynamically change the\npartitions. For example suppose there are 6 partitions, and the load\nhappens to be distributed as follows: one with 40%, and the rest with\n12% each. The ratio of maximum to minimum is 3.33. If the desired ratio\nis set to 2, STRAM would partition the first instance into two\npartitions, each with 20% load to bring the ratio down to the desired\nlevel. This will be tried repeatedly till partitions are balanced. The\ntime period between each attempt is controlled via an attribute to avoid\nrebalancing too frequently. As mentioned earlier, dynamic operations\ninclude both splitting a partition as well as merging partitions with\nlow load.\n\n\nFigure 7 shows an example of skew balancing partition. An example\nof 3x1 paritition is shown. Let's say that skew balance is kept at \u201cno\npartition to take up more than 50% load. If in runtime the load type\nchanges to create a skew. For example, consider an application in the US\nthat is processing a website clickstream. At night in the US, the\nmajority of accesses come from the Far East, while in the daytime it\ncomes from the Americas. Similarly, in the early morning, the majority\nof the accesses are from east coast of the US, with the skew shifting to\nthe west coast as the day progresses. Assume operator 1 is partitioned\ninto 1a, 1b, and 1c.\n\n\nLet's see what happens if the logical operator 1 gets into a 20%,\n20%, 60% skew as shown in Figure 7. This would trigger the skew\nbalancing partition. One example of attaining balance is to merge 1a,\nand 1b to get 1a+1b in a single partition to take the load to 40%; then\nsplit 1c into two partitions 1ca and 1cb to get 30% on each of them.\nThis way STRAM is able to get back to under 50% per partition. As a live\n24x7 application, this kind of skew partitioning can be applied several\ntimes in a day. Skew-balancing at runtime is a critical feature for SLA\ncompliance; it also enables cost savings. This partitioning scheme will\nbe available in later release.\n\n\n\n\nSkew Unifier Partition\n\n\nIn this section we would take a look at another way to balance the\nskew. This method is a little less disruptive, but is useful in\naggregate operators. Let us take the same example as in Figure 7 with\nskew 20%, 20%, and 60%. To manage the load we could have either worked\non rebalancing the partition, which involves a merge and split of\npartitions to get to a new distribution or by partitioning only\u00a0the partition with the big skew. Since the\nbest way to manage skew is to load balance, if possible, this scheme\nattempts to do so. The method is less useful than the others we discusse\n-- the main reason being that if the developer has chosen a sticky key\npartition to start with, it is unlikely that a load balancing scheme can\nhelp. Assuming that it is worthwhile to load balance, a special\none-purpose unifier can be inserted for the skew partition. If the cause\nof resource bottleneck is not the I/O, specially the I/O into the\ndownstream operator, but is the compute (memory, CPU) power of a\npartition, it makes sense to split the skew partition without having to\nchange the in-bound I/O to the upstream operator.\n\n\nTo trigger this users can set a limit on the ratio of maximum load\non a partition to the minimum load on a partition, and ask to use this\nscheme. STRAM would use this to load balance.The time period between\neach attempt is controlled via the same attribute to avoid rebalancing\ntoo frequently.\n\n\nFigure 8 shows an example of skew load balancing partition with a\ndedicated unifier. The 20%, 20%, and 60% triggers the skew load\nbalancing partition with an unifier. Partition 1c would be split into\ntwo and it would get its own dedicated unifier. Ideally these two\nadditional partitions 1ca and 1cb will get 30% load. This way STRAM is\nable to get back to under 50% per partition. This scheme is very useful\nwhen the number of partitions is very high and we still have a bad\nskew.\n\n\nIn the steady state no physical partition is computing more than\n30% of the load. Memory and CPU resources are thus well distributed. The\nunifier that was inserted has to handle 60% of the load, distributed\nmore evenly, as opposed to the final unifier that had a 60% skew to\nmanage at a much higher total load. This partitioning scheme will be\navailable in later release.\n\n\n\n\nCascading Unifier\n\n\nLet's take the case of an upstream operator oprU\u00a0that connects to a downstream operator\noprD. Let's assume the application\nis set to scale oprU by load balancing. So this could be either Nx1 or\nNxM partitioning scheme. The upstream operator oprU scales by increasing\nN. An increase in the load triggers more resource needs (CPU, Memory, or\nI/O), which in turn triggers more containers and raises N, the\ndownstream node may be impacted in a lot of situations. In this section\nwe review a method to shield oprD from dynamic changes in the execution\nplan of oprU. On aggregate operators (Sum, Count, Max, Min, Range \u2026) it\nis better to do load balanced partitioning to avoid impact of skew. This\nworks very well as each partition emits tuples at the order of number of\nkeys (range) in the incoming stream per application window. But as N\ngrows the in-bound I/O to the unifier of oprU that runs in the container\nof oprD goes up proportionately as each upstream partition sends tuples\nof the order of unique keys (range). This means that the partitioning\nwould not scale linearly. The platform has mechanisms to manage this and\nget the scale back to being linear.\n\n\nCascading unifiers are implemented by inserting a series of\nintermediate unifiers before the final unifier in the container of oprD.\nSince each unifier guarantees that the outbound I/O would be in order of\nthe number of unique keys, the unifier in the oprD container can expect\nto achieve an upper limit on the inbound I/O. The problem is the same\nirrespective of the value of M (1 or more), wherein the amount of\ninbound I/O is proportional to N, not M. Figure 8 illustrates how\ncascading unifier works.\n\n\n\n\nFigure 8 shows an example where a 4x1 partition with single\nunifier is split into three 2x1 partitions to enable the final unifier\nin oprD container to get an upper limit on inbound I/O. This is useful\nto ensure that network I/O to containers is within limits, or within a\nlimit specified by users. The platform allows setting an upper limit of\nfan-in of the stream between oprU and oprD. Let's say that this is F (in\nthe figure F=2). STRAM would plan N/F (let's call it N1) containers,\neach with one unifier. The inbound fan-in to these unifiers is F. If N1\n\n F, another level of unifiers would be inserted. Let's say at some\npoint N/(F1*F2*...Fk) \n F, where K is the level of unifiers. The\noutbound I/O of each unifier is guaranteed to be under F, specially the\nunifier for oprD. This ensures that the application scales linearly as\nthe load grows. The downside is the additional latency imposed by each\nunifier level (a few milliseconds), but the SLA is maintained, and the\napplication is able to run within the resource limits imposed by YARN.\nThe value of F can be derived from any of the following\n\n\n\n\nI/O limit on containers to allow proper behavior in an\n multi-tenant environment\n\n\nLoad on oprD instance\n\n\nBuffer server limits on fan-in, fan-out\n\n\nSize of reservoir buffer for inbound fan-in\n\n\n\n\nA more intriguing optimization comes when cascading unifiers are\ncombined with node-local execution plan, in which the bounds of two or\nmore containers are used and much higher local loopback limits are\nleveraged. In general the first level fan-in limit (F1) and the last\nstage fan-in limit (Fk) need not be same. In fact a much open and better\nleveraged execution plan may indeed have F1 != F2 != \u2026 != Fk, as Fk\ndetermines the fan-in for oprD, while F1, \u2026 Fk-1 are fan-ins for\nunifier-only containers. The platform will have these schemes in later\nversions.\n\n\nSLA\n\n\nA Service Level Agreement translates to guaranteeing that the\napplication would meet the requirements X% of the time. For example six\nsigma X is\u00a099.99966%. For\nreal-time streaming applications this translates to requirements for\nlatency, throughput, uptime, data loss etc. and that in turn indirectly\nleads to various resource requirements, recovery mechanisms, etc. The\nplatform is designed to handle these and features would be released in\nfuture as they get developed. At a top level, STRAM monitors throughput\nper operator, computes latency per operator, manages uptime and supports\nvarious recovery mechanisms to handle data loss. A lot of this decision\nmaking and algorithms will be customizable.\n\n\n\n\nFault Tolerance\n\n\nFault tolerance in the platform is defined as the ability to\nrecognize the outage of any part of the application, get resources,\nre-initialize the failed operators, and re-compute the lost data. The\ndefault method is to bring the affected part of the DAG \u00a0back to a known\n(checkpointed) state and recompute atomic micro batches from there on.\nThus the default is at least\nonce\u00a0processing mode. An operator can be configured for\nat most once\u00a0recovery, in which\ncase the re-initialized operator starts from next available window; or\nfor exactly once\u00a0recovery, in which\ncase the operator only recomputes the window it was processing when the\noutage happened.\n\n\nState of the Application\n\n\nThe state of the application is traditionally defined as the state\nof all operators and streams at any given time. Monitoring state as\nevery tuple is processed asynchronously in a distributed environment\nbecomes a near impossible task, and cost paid to achieve it is very\nhigh. Consequently, in the platform, state is not saved per tuple, but\nrather at window boundaries. The platform treats windows as atomic micro\nbatches. The state saving task is delegated by STRAM to the individual\noperator or container. This ensures that the bookkeeping cost is very\nlow and works in a distributed way. Thus, the state of the application\nis defined as the collection of states of every operator and the set of\nall windows stored in the buffer server. This allows STRAM to rebuild\nany part of the application from the last saved state of the impacted\noperators and the windows retained by the buffer server. The state of an\noperator is intrinsically associated with a window id. Since operators\ncan override the default checkpointing period, operators may save state\nat the end of different windows. This works because the buffer server\nsaves all windows for as long as they are needed (state in the buffer\nserver is purged once STRAM determines that it is not longer needed\nbased on checkpointing in downstream operators).\n\n\nOperators can be stateless or stateful. A stateless operator\nretains no data between windows. All results of all computations done in\na window are emitted in that window. Variables in such an operator are\neither transient or are cleared by an end_window event. Such operators\nneed no state restoration after an outage. A stateful operator retains\ndata between windows and has data in checkpointed state. This data\n(state) is used for computation in future windows. Such an operator\nneeds its state restored after an outage. By default the platform\nassumes the operator is stateful. In order to optimize recovery (skip\nprocessing related to state recovery) for a stateless operator, the\noperator needs to be declared as stateless to STRAM. Operators can\nexplicitly mark themselves stateless via an annotation or an\nattribute.\n\n\nRecovery mechanisms are explained later in this section. Operator\ndevelopers have to ensure that there is no dependency on the order of\ntuples between two different streams. As mentioned earlier in this\ndocument, the platform guarantees in-order tuple delivery within a\nsingle stream, For operators with multiple input ports, a replay may\nresult in a different relative order of tuples among the different input\nports. If the output tuple computation is affected by this relative\norder, the operator may have to wait for the endWindow call (at which\npoint it would have seen all the tuples from all input ports in the\ncurrent window), perform order-dependent computations correctly and\nfinally, emit results.\n\n\nCheckpointing\n\n\nSTRAM provides checkpointing parameters to StreamingContainer\nduring initialization. A checkpoint period is given to the containers\nthat have the window generators. A control tuple is sent at the end of\ncheckpoint interval. This tuple traverses through the data path via\nstreams and triggers each StreamingContainer in the path to instrument a\ncheckpoint of the operator that receives this tuple. This ensures that\nall the operators checkpoint at exactly the same window boundary (except\nin those cases where a different checkpoint interval was configured for\nan operator by the user).\n\n\nThe only delay is the latency of the control tuple to reach all\nthe operators. Checkpoint is thus done between the endWindow call of a\nwindow and the beginWindow call of the next window. Since most operators\nare computing in parallel (with the exception of those connected by\nTHREAD_LOCAL streams) they each checkpoint as and when they are ready\nto process the \u201ccheckpoint\u201d control tuple. The asynchronous design of\nthe platform means that there is no guarantee that two operators would\ncheckpoint at exactly the same time, but there is a guarantee that by\ndefault they would checkpoint at the same window boundary. This feature\nalso ensures that purge of old data can be efficiently done: Once the\ncheckpoint window tuple is done traversing the DAG, the checkpoint state\nof the entire DAG increments to this window id at which point prior\ncheckpoint data can be discarded.\n\n\nIn case of an operator that has an application window size that is\nlarger than the size of the streaming window, the checkpointing by\ndefault still happens at same intervals as with other operators. To\nalign checkpointing with application window boundary, the application\ndeveloper should set the attribute \u201cCHECKPOINT_WINDOW_COUNT\u201d to\n\u201cAPPLICATION_WINDOW_COUNT\u201d. This ensures that the checkpoint happens\nat the end\u00a0of the application\nwindow and not within\u00a0that window.\nSuch operators now treat the application window as an atomic computation\nunit. The downside is that it does need the upstream buffer server to\nkeep tuples for the entire application window.\n\n\nIf an operator is completely stateless, i.e. an outbound tuple is\nonly emitted in the process\u00a0call\nand only depends on the tuple of that call, there is no need to align\ncheckpointing with application window end. If the operator is stateful\nonly within a window, the operator developer should strongly consider\ncheckpointing only on the application window boundary.\n\n\nCheckpointing involves pausing an operator, serializing the state\nto persistent storage and then resuming the operator. Thus checkpointing\nhas a latency cost that can negatively affect computational throughput;\nto minimize that impact, it is important to ensure that checkpointing is\ndone with minimal required objects. This means, as mentioned earlier,\nall data that is not part of the operator state should be declared as\ntransient so that it is not persisted.\n\n\nAn operator developer can also create a stateless operator (marked\nwith the Stateless annotation). Stateless operators are not\ncheckpointed. Obviously, in such an operator, computation should not\ndepend on state from a previous window.\n\n\nThe serialized \u00a0state of an operator is stored as a file, and is\nthe state to which that the operator is restored if an outage happens\nbefore the next checkpoint. The id of the last completed window (per\noperator) is sent back to STRAM in the next heartbeat. The default\nimplementation for serialization uses KRYO. Multiple past checkpoints\nare kept per operator. Depending on the downstream checkpoint, one of\nthese are chosen for recovery. Checkpoints and buffer server state are\npurged once STRAM sees windows as fully processed in the DAG.\n\n\nA complete recovery of an operator needs the operator to be\ncreated, its checkpointed state restored and then all the lost atomic\nwindows replayed by the upstream buffer server(s). The above design\nkeeps the bookkeeping cost low with quick catch up time. In the next\nsection we will see how this simple abstraction allows applications to\nrecover under different requirements.\n\n\nRecovery Mechanisms\n\n\nRecovery mechanism are ways to recover from a container (or an\noperator) outage. In this section we discuss a single container outage.\nMultiple container outages are handled as independent events. Recovery\nrequires the upstream buffer server to replay windows and it would\nsimply go one more level upstream if the immediate upstream container\nhas also failed. If multiple operators are in a container (THREAD_LOCAL\nor CONTAINER_LOCAL stream) the container recovery treats each operator\nas an independent object when figuring out the recovery steps.\nApplication developers can set any of the recovery mechanisms discussed\nbelow for node outage.\n\n\nIn general, the cost of recovery depends on the state of the\noperator and the recovery mechanism selected, while data loss tolerance\nis specified by the application. For example a data-loss tolerant\napplication would prefer at most\nonce\u00a0recovery. All recovery mechanisms treat a streaming\nwindow as an atomic computation unit. In all three recovery mechanisms\nthe new operator connects to the upstream buffer server and asks for\ndata from a particular window onwards. Thus all recovery methods\ntranslate to deciding which atomic units to re-compute and which state\nthe new operator resumes from. A partially computed micro-batch is\nalways dropped. Such micro-batches are re-computed in at-least-once or\nexactly-once mode and skipped in at-most-once mode. The notiion of an\natomic micro-batch is a critical guiding principle as it enables very\nlow bookkeeping costs, high throughput, low recovery times, and high\nscalability. Within an application each operator can have its own\nrecovery mechanism.\n\n\nAt Least Once\n\n\nAt least once recovery is the default recovery mechanism, i.e it\nis used when no mechanism is specified. In this method, the lost\noperator is brought back to its latest viable checkpointed state and the\nupstream buffer server is asked to replay all subsequent windows. There\nis no data loss in recovery. The viable checkpoint state is defined as\nthe one whose window id is in the past as compared to all the\ncheckpoints of all the downstream operators. All downstream operators\nare restarted at their checkpointed state. They ignore all incoming data\nthat belongs to windows prior their checkpointed window. The lost\nwindows are thus recomputed and the application catches up with live\nincoming data. This is called \" at least\nonce\"\u00a0because lost windows are recomputed. For example if\nthe streaming window is 0.5 seconds and checkpointing is being done\nevery 30 seconds, then upon node outage all windows since the last\ncheckpoint (up to 60 windows) need to be re-processed. If the\napplication can handle loss of data, then this is not the most optimal\nrecovery mechanism.\n\n\nIn general for this recovery mode, the average time lag on a node\noutage is\n\n\n= (CP/2*SW)*T + HC\n\n\nwhere\n\n\n\n\nCP\n\u00a0\u00a0- Checkpointing period (default value is 30 seconds)\n\n\nSW\n\u00a0\u00a0- Streaming window period (default value is 0.5 seconds)\n\n\nT\n\u00a0\u00a0\u00a0- \u00a0Time taken to re-compute one lost window from data in memory\n\n\nHC\n\u00a0\u00a0- Time it takes to get a new Hadoop Container, or make do with the current ones\n\n\n\n\nA lower CP is a trade off between cost of checkpointing and the\nneed to have to use it in case of outage. Input adapters cannot use\nat-least-once recovery without the support from sources outside Hadoop.\nFor an output adapter care may needed if the external system cannot\nhandle re-write of the same data.\n\n\nAt Most Once\n\n\nThis recovery mechanism is for applications that can tolerate\ndata-loss; they get the quickest recovery in return. The restarted node\nconnects to the upstream buffer server, subscribing to data from the\nstart of the next window. It then starts processing that window. The\ndownstream operators ignore the lost windows and continue to process\nincoming data normally. Thus, this mechanism forces all downstream\noperators to follow.\n\n\nFor multiple inputs, the operator waits for all ports with the\nat-most-once attribute to get responses from their respective buffer\nservers. Then, the operator starts processing till the end window of the\nlatest window id on each input port is reached. In this case the end\nwindow tuple is non-blocking till the common window id is reached. At\nthis point the input ports are now properly synchronized. Upstream nodes\nreconnect under at most\nonce\u00a0paradigm in same way. \u00a0For example, assume an operator\nhas ports in1\u00a0and in2\u00a0and a checkpointed window of 95. Assume further that the buffer servers of\noperators upstream of in1\u00a0and\nin2\u00a0respond with window id 100 and\n102 respectively. Then port in1\u00a0would continue to process till end window of\n101, while port in2\u00a0will wait for in1\nto catch up to 102.\nFrom \u00a0then on, both ports process their tuples normally. So windows from\n96 to 99are lost. Window 100\nand 101 has only\nin1 active, and 102 onwards both ports are active. The other\nports of upstream nodes would also catch up till 102in a similar fashion. This operator may not\nneed to be checkpointed. Currently the option to not do checkpoint in\nsuch cases is not available.\n\n\nIn general, in this recovery mode, the average time lag on a node\noutage is\n\n\n= SW/2 + HC\n\n\nwhere\n\n\n\n\n\n\nSW\n\u00a0- Streaming window period (default value is 0.5\nseconds)\n\n\n\n\n\n\nHC\n\u00a0- Time it takes to get a new Hadoop Container, or make\ndo with the current ones\n\n\n\n\n\n\nExactly Once\n\n\nThis recovery mechanism is for applications that require no\ndata-loss as well are no recomputation. Since a window is an atomic\ncompute unit, exactly once applies to the window as a whole. In this\nrecovery mode, the operator is brought back to the start of the window\nin which the outage happened and the window is recomputed. The window is\nconsidered closed when all the data computations are done and end window\ntuple is emitted. \u00a0Exactly once requires every window to be\ncheckpointed. From then on, the operator asks the upstream buffer server\nto send data from the last checkpoint. The upstream node behaves the\nsame as in at-most-once recovery. Checkpointing after every streaming\nwindow is very costly, but users would most often do exactly once per\napplication window; if the application window size is substantially\nlarger than the streaming window size (which typically is the case) the\ncost of running an operator in this recovery mode may not be as\nhigh.\n\n\nSpeculative Execution\n\n\nIn future we looking at possibility of adding speculative execution for the applications. This would be enabled in multiple ways.\n\n\n\n\n\n\nAt an operator level: The upstream operator would emit to\n two copies. The downstream operator would receive from both copies\n and pick a winner. The winner (primary) would be picked in either of\n the following ways\n\n\n\n\nStatically as dictated by STRAM\n\n\nDynamically based on whose tuple arrives first. This mode\n needs both copies to guarantee that the computation result would\n have identical functionality\n\n\n\n\n\n\n\n\nAt a sub-query level: A part of the application DAG would be\n run in parallel and all upstream operators would feed to two copies\n and all downstream operators would receive from both copies. The\n winners would again be picked in a static or dynamic manner\n\n\n\n\nEntire DAG: Another copy of the application would be run by\n STRAM and the winner would be decided outside the application. In\n this mode the output adapters would both be writing\n the result.\n\n\n\n\nIn all cases the two copies would run on different Hadoop nodes.\nSpeculative execution is under development and\nis not yet available.\n\n\n\n\nDynamic Application Modifications\n\n\nDynamic application modifications are being worked on and most of\nthe features discussed here are now available. The platform supports the\nability to modify the DAG of the application as per inputs as well as\nset constraints, and will continue to provide abilities to deepen\nfeatures based on this ability. All these changes have one thing in\ncommon and that is the application does not need to be restarted as\nSTRAM will instrument the changes and the streaming will catch-up and\ncontinue.\n\n\nSome examples are\n\n\n\n\nDynamic Partitioning:\u00a0Automatic\n changes in partitioning of computations to match constraints on a\n run time basis. Examples includes STRAM adding resource during spike\n in streams and returning them once spike is gone. Scale up and scale\n down is done automatically without human intervention.\n\n\nModification via constraints: Attributes can be changed via\n Webservices and STRAM would adapt the execution plan to meet these.\n Examples include operations folks asking STRAM to reduce container\n count, or changing network resource restrictions.\n\n\nModification via properties: Properties of operators can be\n changed in run time. This enables application developers to trigger\n a new behavior as need be. Examples include triggering an alert ON.\n The platform supports changes to any property of an operator that\n has a setter function defined.\n\n\nModification of DAG structure: Operators and streams can be\n added to or removed from a running DAG, provided the code of the\n operator being added is already in the classpath of the running\n application master. \u00a0This enables application developers to add or\n remove processing pipelines on the fly without having to restart\n the application.\n\n\nQuery Insertion: Addition of sub-queries to currently\n running application. This query would take current streams as inputs\n and start computations as per their specs. Examples insertion of\n SQL-queries on live data streams, dynamic query submission and\n result from STRAM (not yet available).\n\n\n\n\nDynamic modifications to applications are foundational part of the\nplatform. They enable users to build layers over the applications. Users\ncan also save all the changes done since the application launch, and\ntherefore predictably get the application to its current state. For\ndetails refer to \nConfiguration Guide\n\n.\n\n\n\n\nDemos\n\n\nThe source code for the demos is available in the open-source\n\nApache Apex-Malhar repository\n.\nAll of these do computations in real-time. Developers are encouraged to\nreview them as they use various features of the platform and provide an\nopportunity for quick learning.",
- "title": "Applications"
- },
- {
- "location": "/application_development/#application-developer-guide",
- "text": "The Apex platform is designed to process massive amounts of\nreal-time events natively in Hadoop. It runs as a YARN (Hadoop 2.x) \napplication and leverages Hadoop as a distributed operating\nsystem. All the basic distributed operating system capabilities of\nHadoop like resource management (YARN), distributed file system (HDFS),\nmulti-tenancy, security, fault-tolerance, and scalability are supported natively \nin all the Apex applications. \u00a0The platform handles all the details of the application \nexecution, including dynamic scaling, state checkpointing and recovery, event \nprocessing guarantees, etc. allowing you to focus on writing your application logic without\nmixing operational and functional concerns. In the platform, building a streaming application can be extremely\neasy and intuitive. \u00a0The application is represented as a Directed\nAcyclic Graph (DAG) of computation units called Operators interconnected\nby the data-flow edges called Streams .\u00a0The operators process input\nstreams and produce output streams. A library of common operators is\nprovided to enable quick application development. \u00a0In case the desired\nprocessing is not available in the Operator Library, one can easily\nwrite a custom operator. We refer those interested in creating their own\noperators to the Operator Development Guide .",
- "title": "Application Developer Guide"
- },
- {
- "location": "/application_development/#running-a-test-application",
- "text": "If you are starting with the Apex platform for the first time,\nit can be informative to launch an existing application and see it run.\nOne of the simplest examples provided in Apex-Malhar repository is a Pi demo application,\nwhich computes the value of PI using random numbers. After setting up development environment \nPi demo can be launched as follows: Open up Apex Malhar files in your IDE (for example Eclipse, IntelliJ, NetBeans, etc) Navigate to demos/pi/src/test/java/com/datatorrent/demos/ApplicationTest.java Run the test for ApplicationTest.java View the output in system console Congratulations, you just ran your first real-time streaming demo :) \nThis demo is very simple and has four operators. The first operator\nemits random integers between 0 to 30, 000. The second operator receives\nthese coefficients and emits a hashmap with x and y values each time it\nreceives two values. The third operator takes these values and computes\nx**2+y**2. The last operator counts how many computed values from\nthe previous operator were less than or equal to 30, 000**2. Assuming\nthis count is N, then PI is computed as N/number of values received.\nHere is the code snippet for the PI application. This code populates the\nDAG. Do not worry about what each line does, we will cover these\nconcepts later in this document. // Generates random numbers\nRandomEventGenerator rand = dag.addOperator( rand , new RandomEventGenerator());\nrand.setMinvalue(0);\nrand.setMaxvalue(30000);\n\n// Generates a round robin HashMap of x and y \nRoundRobinHashMap String,Object rrhm = dag.addOperator( rrhm , new RoundRobinHashMap String, Object ());\nrrhm.setKeys(new String[] { x , y });\n\n// Calculates pi from x and y\nJavaScriptOperator calc = dag.addOperator( picalc , new Script());\ncalc.setPassThru(false);\ncalc.put( i ,0);\ncalc.put( count ,0);\ncalc.addSetupScript( function pi() { if (x*x+y*y = +maxValue*maxValue+ ) { i++; } count++; return i / count * 4; } );\ncalc.setInvoke( pi );\ndag.addStream( rand_rrhm , rand.integer_data, rrhm.data);\ndag.addStream( rrhm_calc , rrhm.map, calc.inBindings);\n\n// puts results on system console\nConsoleOutputOperator console = dag.addOperator( console , new ConsoleOutputOperator());\ndag.addStream( rand_console ,calc.result, console.input); You can review the other demos and see what they do. The examples\ngiven in the Demos project cover various features of the platform and we\nstrongly encourage you to read these to familiarize yourself with the\nplatform. In the remaining part of this document we will go through\ndetails needed for you to develop and run streaming applications in\nMalhar.",
- "title": "Running A Test Application"
- },
- {
- "location": "/application_development/#test-application-yahoo-finance-quotes",
- "text": "The PI\u00a0application was to\nget you started. It is a basic application and does not fully illustrate\nthe features of the platform. For the purpose of describing concepts, we\nwill consider the test application shown in Figure 1. The application\ndownloads tick data from Yahoo! Finance \u00a0and computes the\nfollowing for four tickers, namely IBM , GOOG , YHOO . Quote: Consisting of last trade price, last trade time, and\n total volume for the day Per-minute chart data: Highest trade price, lowest trade\n price, and volume during that minute Simple Moving Average: trade price over 5 minutes Total volume must ensure that all trade volume for that day is\nadded, i.e. data loss would result in wrong results. Charting data needs\nall the trades in the same minute to go to the same slot, and then on it\nstarts afresh, so again data loss would result in wrong results. The\naggregation for charting data is done over 1 minute. Simple moving\naverage computes the average price over a 5 minute sliding window; it\ntoo would produce wrong results if there is data loss. Figure 1 shows\nthe application with no partitioning. The operator StockTickerInput:\u00a0StockTickerInput \u00a0 is\nthe input operator that reads live data from Yahoo! Finance once per\ninterval (user configurable in milliseconds), and emits the price, the\nincremental volume, and the last trade time of each stock symbol, thus\nemulating real ticks from the exchange. \u00a0We utilize the Yahoo! Finance\nCSV web service interface. \u00a0For example: $ GET 'http://download.finance.yahoo.com/d/quotes.csv?s=IBM,GOOG,AAPL,YHOO f=sl1vt1' IBM ,203.966,1513041, 1:43pm GOOG ,762.68,1879741, 1:43pm AAPL ,444.3385,11738366, 1:43pm YHOO ,19.3681,14707163, 1:43pm Among all the operators in Figure 1, StockTickerInput is the only\noperator that requires extra code because it contains a custom mechanism\nto get the input data. \u00a0Other operators are used unchanged from the\nMalhar library. Here is the class implementation for StockTickInput: package com.datatorrent.demos.yahoofinance;\n\nimport au.com.bytecode.opencsv.CSVReader;\nimport com.datatorrent.annotation.OutputPortFieldAnnotation;\nimport com.datatorrent.api.Context.OperatorContext;\nimport com.datatorrent.api.DefaultOutputPort;\nimport com.datatorrent.api.InputOperator;\nimport com.datatorrent.lib.util.KeyValPair;\nimport java.io.IOException;\nimport java.io.InputStream;\nimport java.io.InputStreamReader;\nimport java.util.*;\nimport org.apache.commons.httpclient.HttpClient;\nimport org.apache.commons.httpclient.HttpStatus;\nimport org.apache.commons.httpclient.cookie.CookiePolicy;\nimport org.apache.commons.httpclient.methods.GetMethod;\nimport org.apache.commons.httpclient.params.DefaultHttpParams;\nimport org.slf4j.Logger;\nimport org.slf4j.LoggerFactory;\n\n/**\n * This operator sends price, volume and time into separate ports and calculates incremental volume.\n */\npublic class StockTickInput implements InputOperator\n{\n private static final Logger logger = LoggerFactory.getLogger(StockTickInput.class);\n /**\n * Timeout interval for reading from server. 0 or negative indicates no timeout.\n */\n public int readIntervalMillis = 500;\n /**\n * The URL of the web service resource for the POST request.\n */\n private String url;\n public String[] symbols;\n private transient HttpClient client;\n private transient GetMethod method;\n private HashMap String, Long lastVolume = new HashMap String, Long ();\n private boolean outputEvenIfZeroVolume = false;\n /**\n * The output port to emit price.\n */\n @OutputPortFieldAnnotation(optional = true)\n public final transient DefaultOutputPort KeyValPair String, Double price = new DefaultOutputPort KeyValPair String, Double ();\n /**\n * The output port to emit incremental volume.\n */\n @OutputPortFieldAnnotation(optional = true)\n public final transient DefaultOutputPort KeyValPair String, Long volume = new DefaultOutputPort KeyValPair String, Long ();\n /**\n * The output port to emit last traded time.\n */\n @OutputPortFieldAnnotation(optional = true)\n public final transient DefaultOutputPort KeyValPair String, String time = new DefaultOutputPort KeyValPair String, String ();\n\n /**\n * Prepare URL from symbols and parameters. URL will be something like: http://download.finance.yahoo.com/d/quotes.csv?s=IBM,GOOG,AAPL,YHOO f=sl1vt1\n *\n * @return the URL\n */\n private String prepareURL()\n {\n String str = http://download.finance.yahoo.com/d/quotes.csv?s= ;\n for (int i = 0; i symbols.length; i++) {\n if (i != 0) {\n str += , ;\n }\n str += symbols[i];\n }\n str += f=sl1vt1 e=.csv ;\n return str;\n }\n\n @Override\n public void setup(OperatorContext context)\n {\n url = prepareURL();\n client = new HttpClient();\n method = new GetMethod(url);\n DefaultHttpParams.getDefaultParams().setParameter( http.protocol.cookie-policy , CookiePolicy.BROWSER_COMPATIBILITY);\n }\n\n @Override\n public void teardown()\n {\n }\n\n @Override\n public void emitTuples()\n {\n\n try {\n int statusCode = client.executeMethod(method);\n if (statusCode != HttpStatus.SC_OK) {\n System.err.println( Method failed: + method.getStatusLine());\n }\n else {\n InputStream istream = method.getResponseBodyAsStream();\n // Process response\n InputStreamReader isr = new InputStreamReader(istream);\n CSVReader reader = new CSVReader(isr);\n List String[] myEntries = reader.readAll();\n for (String[] stringArr: myEntries) {\n ArrayList String tuple = new ArrayList String (Arrays.asList(stringArr));\n if (tuple.size() != 4) {\n return;\n }\n // input csv is Symbol , Price , Volume , Time \n String symbol = tuple.get(0);\n double currentPrice = Double.valueOf(tuple.get(1));\n long currentVolume = Long.valueOf(tuple.get(2));\n String timeStamp = tuple.get(3);\n long vol = currentVolume;\n // Sends total volume in first tick, and incremental volume afterwards.\n if (lastVolume.containsKey(symbol)) {\n vol -= lastVolume.get(symbol);\n }\n\n if (vol 0 || outputEvenIfZeroVolume) {\n price.emit(new KeyValPair String, Double (symbol, currentPrice));\n volume.emit(new KeyValPair String, Long (symbol, vol));\n time.emit(new KeyValPair String, String (symbol, timeStamp));\n lastVolume.put(symbol, currentVolume);\n }\n }\n }\n Thread.sleep(readIntervalMillis);\n }\n catch (InterruptedException ex) {\n logger.debug(ex.toString());\n }\n catch (IOException ex) {\n logger.debug(ex.toString());\n }\n }\n\n @Override\n public void beginWindow(long windowId)\n {\n }\n\n @Override\n public void endWindow()\n {\n }\n\n public void setOutputEvenIfZeroVolume(boolean outputEvenIfZeroVolume)\n {\n this.outputEvenIfZeroVolume = outputEvenIfZeroVolume;\n }\n\n} The operator has three output ports that emit the price of the\nstock, the volume of the stock and the last trade time of the stock,\ndeclared as public member variables price, volume\u00a0and time\u00a0of the class. \u00a0The tuple of the\nprice\u00a0output port is a key-value\npair with the stock symbol being the key, and the price being the value.\n\u00a0The tuple of the volume\u00a0output\nport is a key value pair with the stock symbol being the key, and the\nincremental volume being the value. \u00a0The tuple of the time\u00a0output port is a key value pair with the\nstock symbol being the key, and the last trade time being the\nvalue. Important: Since operators will be\nserialized, all input and output ports need to be declared transient\nbecause they are stateless and should not be serialized. The method\u00a0setup(OperatorContext)\ncontains the code that is necessary for setting up the HTTP\nclient for querying Yahoo! Finance. Method\u00a0emitTuples() contains\nthe code that reads from Yahoo! Finance, and emits the data to the\noutput ports of the operator. \u00a0emitTuples()\u00a0will be called one or more times\nwithin one application window as long as time is allowed within the\nwindow. Note that we want to emulate the tick input stream by having\nincremental volume data with Yahoo! Finance data. \u00a0We therefore subtract\nthe previous volume from the current volume to emulate incremental\nvolume for each tick. The operator\nDailyVolume:\u00a0This operator\nreads from the input port, which contains the incremental volume tuples\nfrom StockTickInput, and\naggregates the data to provide the cumulative volume. \u00a0It uses the\nlibrary class SumKeyVal K,V \u00a0provided in math\u00a0package. \u00a0In this case,\nSumKeyVal String,Long , where K is the stock symbol, V is the\naggregated volume, with cumulative\nset to true. (Otherwise if cumulativewas set to false, SumKeyVal would\nprovide the sum for the application window.) \u00a0Malhar provides a number\nof built-in operators for simple operations like this so that\napplication developers do not have to write them. \u00a0More examples to\nfollow. This operator assumes that the application restarts before the\nmarket opens every day. The operator Quote:\nThis operator has three input ports, which are price (from\nStockTickInput), daily_vol (from\nDaily Volume), and time (from\n StockTickInput). \u00a0This operator\njust consolidates the three data items and and emits the consolidated\ndata. \u00a0It utilizes the class ConsolidatorKeyVal K \u00a0from the\nstream\u00a0package. The operator HighLow:\u00a0This operator reads from the input port,\nwhich contains the price tuples from StockTickInput, and provides the high and the\nlow price within the application window. \u00a0It utilizes the library class\n RangeKeyVal K,V \u00a0provided\nin the math\u00a0package. In this case,\nRangeKeyVal String,Double . The operator MinuteVolume:\nThis operator reads from the input port, which contains the\nvolume tuples from StockTickInput,\nand aggregates the data to provide the sum of the volume within one\nminute. \u00a0Like the operator DailyVolume, this operator also uses\nSumKeyVal String,Long , but\nwith cumulative set to false. \u00a0The\nApplication Window is set to one minute. We will explain how to set this\nlater. The operator Chart:\nThis operator is very similar to the operator Quote, except that it takes inputs from\nHigh Low\u00a0and Minute Vol\u00a0and outputs the consolidated tuples\nto the output port. The operator PriceSMA:\nSMA stands for - Simple Moving Average. It reads from the\ninput port, which contains the price tuples from StockTickInput, and\nprovides the moving average price of the stock. \u00a0It utilizes\nSimpleMovingAverage String,Double , which is provided in the\n multiwindow\u00a0package.\nSimpleMovingAverage keeps track of the data of the previous N\napplication windows in a sliding manner. \u00a0For each end window event, it\nprovides the average of the data in those application windows. The operator Console:\nThis operator just outputs the input tuples to the console\n(or stdout). \u00a0In this example, there are four console\u00a0operators, which connect to the output\nof Quote, Chart, PriceSMA and VolumeSMA. \u00a0In\npractice, they should be replaced by operators that use the data to\nproduce visualization artifacts like charts. Connecting the operators together and constructing the\nDAG:\u00a0Now that we know the\noperators used, we will create the DAG, set the streaming window size,\ninstantiate the operators, and connect the operators together by adding\nstreams that connect the output ports with the input ports among those\noperators. \u00a0This code is in the file YahooFinanceApplication.java. Refer to Figure 1\nagain for the graphical representation of the DAG. \u00a0The last method in\nthe code, namely getApplication(),\ndoes all that. \u00a0The rest of the methods are just for setting up the\noperators. package com.datatorrent.demos.yahoofinance;\n\nimport com.datatorrent.api.ApplicationFactory;\nimport com.datatorrent.api.Context.OperatorContext;\nimport com.datatorrent.api.DAG;\nimport com.datatorrent.api.Operator.InputPort;\nimport com.datatorrent.lib.io.ConsoleOutputOperator;\nimport com.datatorrent.lib.math.RangeKeyVal;\nimport com.datatorrent.lib.math.SumKeyVal;\nimport com.datatorrent.lib.multiwindow.SimpleMovingAverage;\nimport com.datatorrent.lib.stream.ConsolidatorKeyVal;\nimport com.datatorrent.lib.util.HighLow;\nimport org.apache.hadoop.conf.Configuration;\n\n/**\n * Yahoo! Finance application demo. p \n *\n * Get Yahoo finance feed and calculate minute price range, minute volume, simple moving average of 5 minutes.\n */\npublic class Application implements StreamingApplication\n{\n private int streamingWindowSizeMilliSeconds = 1000; // 1 second (default is 500ms)\n private int appWindowCountMinute = 60; // 1 minute\n private int appWindowCountSMA = 5 * 60; // 5 minute\n\n /**\n * Get actual Yahoo finance ticks of symbol, last price, total daily volume, and last traded price.\n */\n public StockTickInput getStockTickInputOperator(String name, DAG dag)\n {\n StockTickInput oper = dag.addOperator(name, StockTickInput.class);\n oper.readIntervalMillis = 200;\n return oper;\n }\n\n /**\n * This sends total daily volume by adding volumes from each ticks.\n */\n public SumKeyVal String, Long getDailyVolumeOperator(String name, DAG dag)\n {\n SumKeyVal String, Long oper = dag.addOperator(name, new SumKeyVal String, Long ());\n oper.setType(Long.class);\n oper.setCumulative(true);\n return oper;\n }\n\n /**\n * Get aggregated volume of 1 minute and send at the end window of 1 minute.\n */\n public SumKeyVal String, Long getMinuteVolumeOperator(String name, DAG dag, int appWindowCount)\n {\n SumKeyVal String, Long oper = dag.addOperator(name, new SumKeyVal String, Long ());\n oper.setType(Long.class);\n oper.setEmitOnlyWhenChanged(true);\ndag.getOperatorMeta(name).getAttributes().put(OperatorContext.APPLICATION_WINDOW_COUNT,appWindowCount);\n return oper;\n }\n\n /**\n * Get High-low range for 1 minute.\n */\n public RangeKeyVal String, Double getHighLowOperator(String name, DAG dag, int appWindowCount)\n {\n RangeKeyVal String, Double oper = dag.addOperator(name, new RangeKeyVal String, Double ());\n dag.getOperatorMeta(name).getAttributes().put(OperatorContext.APPLICATION_WINDOW_COUNT,appWindowCount);\n oper.setType(Double.class);\n return oper;\n }\n\n /**\n * Quote (Merge price, daily volume, time)\n */\n public ConsolidatorKeyVal String,Double,Long,String,?,? getQuoteOperator(String name, DAG dag)\n {\n ConsolidatorKeyVal String,Double,Long,String,?,? oper = dag.addOperator(name, new ConsolidatorKeyVal String,Double,Long,String,Object,Object ());\n return oper;\n }\n\n /**\n * Chart (Merge minute volume and minute high-low)\n */\n public ConsolidatorKeyVal String,HighLow,Long,?,?,? getChartOperator(String name, DAG dag)\n {\n ConsolidatorKeyVal String,HighLow,Long,?,?,? oper = dag.addOperator(name, new ConsolidatorKeyVal String,HighLow,Long,Object,Object,Object ());\n return oper;\n }\n\n /**\n * Get simple moving average of price.\n */\n public SimpleMovingAverage String, Double getPriceSimpleMovingAverageOperator(String name, DAG dag, int appWindowCount)\n {\n SimpleMovingAverage String, Double oper = dag.addOperator(name, new SimpleMovingAverage String, Double ());\n oper.setWindowSize(appWindowCount);\n oper.setType(Double.class);\n return oper;\n }\n\n /**\n * Get console for output.\n */\n public InputPort Object getConsole(String name, /*String nodeName,*/ DAG dag, String prefix)\n {\n ConsoleOutputOperator oper = dag.addOperator(name, ConsoleOutputOperator.class);\n oper.setStringFormat(prefix + : %s );\n return oper.input;\n }\n\n /**\n * Create Yahoo Finance Application DAG.\n */\n @Override\n public void populateDAG(DAG dag, Configuration conf)\n {\n dag.getAttributes().put(DAG.STRAM_WINDOW_SIZE_MILLIS,streamingWindowSizeMilliSeconds);\n\n StockTickInput tick = getStockTickInputOperator( StockTickInput , dag);\n SumKeyVal String, Long dailyVolume = getDailyVolumeOperator( DailyVolume , dag);\n ConsolidatorKeyVal String,Double,Long,String,?,? quoteOperator = getQuoteOperator( Quote , dag);\n\n RangeKeyVal String, Double highlow = getHighLowOperator( HighLow , dag, appWindowCountMinute);\n SumKeyVal String, Long minuteVolume = getMinuteVolumeOperator( MinuteVolume , dag, appWindowCountMinute);\n ConsolidatorKeyVal String,HighLow,Long,?,?,? chartOperator = getChartOperator( Chart , dag);\n\n SimpleMovingAverage String, Double priceSMA = getPriceSimpleMovingAverageOperator( PriceSMA , dag, appWindowCountSMA);\n DefaultPartitionCodec String, Double codec = new DefaultPartitionCodec String, Double ();\n dag.setInputPortAttribute(highlow.data, PortContext.STREAM_CODEC, codec);\n dag.setInputPortAttribute(priceSMA.data, PortContext.STREAM_CODEC, codec);\n dag.addStream( price , tick.price, quoteOperator.in1, highlow.data, priceSMA.data);\n dag.addStream( vol , tick.volume, dailyVolume.data, minuteVolume.data);\n dag.addStream( time , tick.time, quoteOperator.in3);\n dag.addStream( daily_vol , dailyVolume.sum, quoteOperator.in2);\n\n dag.addStream( quote_data , quoteOperator.out, getConsole( quoteConsole , dag, QUOTE ));\n\n dag.addStream( high_low , highlow.range, chartOperator.in1);\n dag.addStream( vol_1min , minuteVolume.sum, chartOperator.in2);\n dag.addStream( chart_data , chartOperator.out, getConsole( chartConsole , dag, CHART ));\n\n dag.addStream( sma_price , priceSMA.doubleSMA, getConsole( priceSMAConsole , dag, Price SMA ));\n\n return dag;\n }\n\n} Note that we also set a user-specific sliding window for SMA that\nkeeps track of the previous N data points. \u00a0Do not confuse this with the\nattribute APPLICATION_WINDOW_COUNT. In the rest of this chapter we will run through the process of\nrunning this application. We assume that \u00a0you are familiar with details\nof your Hadoop infrastructure. For installation\ndetails please refer to the Installation Guide .",
- "title": "Test Application: Yahoo! Finance Quotes"
- },
- {
- "location": "/application_development/#running-a-test-application_1",
- "text": "We will now describe how to run the yahoo\nfinance application\u00a0described above in different modes\n(local mode, single node on Hadoop, and multi-nodes on Hadoop). The platform runs streaming applications under the control of a\nlight-weight Streaming Application Manager (STRAM). Each application has\nits own instance of STRAM. STRAM launches the application and\ncontinually provides run time monitoring, analysis, and takes action\nsuch as load scaling or outage recovery as needed. \u00a0We will discuss\nSTRAM in more detail in the next chapter. The instructions below assume that the platform was installed in a\ndirectory INSTALL_DIR and the command line interface (CLI) will\nbe used to launch the demo application. An application can be run in\nlocal mode\u00a0(in IDE or from command line) or on a Hadoop cluster. To start the dtCli run INSTALL_DIR /bin/dtcli The command line prompt appears. To start the application in local mode (the actual version number in the file name may differ) dt launch -local INSTALL_DIR /yahoo-finance-demo-3.2.0-SNAPSHOT.apa To terminate the application in local mode, enter Ctrl-C Tu run the application on the Hadoop cluster (the actual version\nnumber in the file name may differ) dt launch INSTALL_DIR /yahoo-finance-demo-3.2.0-SNAPSHOT.apa To stop the application running in Hadoop, terminate it in the dtCli: dt kill-app Executing the application in either mode includes the following\nsteps. At a top level, STRAM (Streaming Application Manager) validates\nthe application (DAG), translates the logical plan to the physical plan\nand then launches the execution engine. The mode determines the\nresources needed and how how they are used.",
- "title": "Running a Test Application"
- },
- {
- "location": "/application_development/#local-mode",
- "text": "In local mode, the application is run as a single-process\u00a0with multiple threads. Although a\nfew Hadoop classes are needed, there is no dependency on a Hadoop\ncluster or Hadoop services. The local file system is used in place of\nHDFS. This mode allows a quick run of an application in a single process\nsandbox, and hence is the most suitable to debug and analyze the\napplication logic. This mode is recommended for developing the\napplication and can be used for running applications within the IDE for\nfunctional testing purposes. Due to limited resources and lack \u00a0of\nscalability an application running in this single process mode is more\nlikely to encounter throughput bottlenecks. A distributed cluster is\nrecommended for benchmarking and production testing.",
- "title": "Local Mode"
- },
- {
- "location": "/application_development/#hadoop-cluster",
- "text": "In this section we discuss various Hadoop cluster setups.",
- "title": "Hadoop Cluster"
- },
- {
- "location": "/application_development/#single-node-cluster",
- "text": "In a single node Hadoop cluster all services are deployed on a\nsingle server (a developer can use his/her development machine as a\nsingle node cluster). The platform does not distinguish between a single\nor multi-node setup and behaves exactly the same in both cases. In this mode, the resource manager, name node, data node, and node\nmanager occupy one process each. This is an example of running a\nstreaming application as a multi-process\u00a0application on the same server.\nWith prevalence of fast, multi-core systems, this mode is effective for\ndebugging, fine tuning, and generic analysis before submitting the job\nto a larger Hadoop cluster. In this mode, execution uses the Hadoop\nservices and hence is likely to identify issues that are related to the\nHadoop environment (such issues will not be uncovered in local mode).\nThe throughput will obviously not be as high as on a multi-node Hadoop\ncluster. Additionally, since each container (i.e. Java process) requires\na significant amount of memory, you will be able to run a much smaller\nnumber of containers than on a multi-node cluster.",
- "title": "Single Node Cluster"
- },
- {
- "location": "/application_development/#multi-node-cluster",
- "text": "In a multi-node Hadoop cluster all the services of Hadoop are\ntypically distributed across multiple nodes in a production or\nproduction-level test environment. Upon launch the application is\nsubmitted to the Hadoop cluster and executes as a multi-processapplication on\u00a0multiple nodes. Before you start deploying, testing and troubleshooting your\napplication on a cluster, you should ensure that Hadoop (version 2.2.0\nor later)\u00a0is properly installed and\nyou have basic skills for working with it.",
- "title": "Multi-Node Cluster"
- },
- {
- "location": "/application_development/#apache-apex-platform-overview",
- "text": "",
- "title": "Apache Apex Platform Overview"
- },
- {
- "location": "/application_development/#streaming-computational-model",
- "text": "In this chapter, we describe the the basics of the real-time streaming platform and its computational model. The platform is designed to enable completely asynchronous real time computations\u00a0done in as unblocked a way as possible with\nminimal overhead . Applications running in the platform are represented by a Directed\nAcyclic Graph (DAG) made up of \u00a0operators and streams. All computations\nare done in memory on arrival of\nthe input data, with an option to save the output to disk (HDFS) in a\nnon-blocking way. The data that flows between operators consists of\natomic data elements. Each data element along with its type definition\n(henceforth called schema) is\ncalled a tuple.\u00a0An application is a\ndesign of the flow of these tuples to and from\nthe appropriate compute units to enable the computation of the final\ndesired results.\u00a0A message queue (henceforth called\n\u00a0buffer server) manages tuples streaming\nbetween compute units in different processes.This server keeps track of\nall consumers, publishers, partitions, and enables replay. More\ninformation is given in later section. The streaming application is monitored by a decision making entity\ncalled STRAM (streaming application\nmanager).\u00a0STRAM is designed to be a light weight\ncontroller that has minimal but sufficient interaction with the\napplication. This is done via periodic heartbeats. The\nSTRAM does the initial launch and periodically analyzes the system\nmetrics to decide if any run time action needs to be taken. A fundamental building block for the streaming platform\nis the concept of breaking up a stream into equal finite time slices\ncalled streaming windows. Each window contains the ordered\nset of tuples in that time slice. A typical duration of a window is 500\nms, but can be configured per application (the Yahoo! Finance\napplication configures this value in the properties.xml\u00a0file to be 1000ms = 1s). Each\nwindow is preceded by a begin_window\u00a0event and is terminated by an\nend_window\u00a0event, and is assigned\na unique window ID. Even though the platform performs computations at\nthe tuple level, bookkeeping is done at the window boundary, making the\ncomputations within a window an atomic event in the platform. \u00a0We can\nthink of each window as an atomic\nmicro-batch\u00a0of tuples, to be processed together as one\natomic operation (See Figure 2). \u00a0 This atomic batching allows the platform to avoid the very steep\nper tuple bookkeeping cost and instead has a manageable per batch\nbookkeeping cost. This translates to higher throughput, low recovery\ntime, and higher scalability. Later in this document we illustrate how\nthe atomic micro-batch concept allows more efficient optimization\nalgorithms. The platform also has in-built support for\napplication windows.\u00a0 An application window is part of the\napplication specification, and can be a small or large multiple of the\nstreaming window. \u00a0An example from our Yahoo! Finance test application\nis the moving average, calculated over a sliding application window of 5\nminutes which equates to 300 (= 5 * 60) streaming windows. Note that these two window concepts are distinct. \u00a0A streaming\nwindow is an abstraction of many tuples into a higher atomic event for\neasier management. \u00a0An application window is a group of consecutive\nstreaming windows used for data aggregation (e.g. sum, average, maximum,\nminimum) on a per operator level. Alongside the platform,\u00a0a set of\npredefined, benchmarked standard library operator templates is provided\nfor ease of use and rapid development of application.\u00a0These\noperators are open sourced to Apache Software Foundation under the\nproject name \u201cMalhar\u201d as part of our efforts to foster community\ninnovation. These operators can be used in a DAG as is, while others\nhave properties\u00a0that can be set to specify the\ndesired computation. Those interested in details, should refer to Apex-Malhar operator library . The platform is a Hadoop YARN native\napplication. It runs in a Hadoop cluster just like any\nother YARN application (MapReduce etc.) and is designed to seamlessly\nintegrate with rest of Hadoop technology stack. It leverages Hadoop as\nmuch as possible and relies on it as its distributed operating system.\nHadoop dependencies include resource management, compute/memory/network\nallocation, HDFS, security, fault tolerance, monitoring, metrics,\nmulti-tenancy, logging etc. Hadoop classes/concepts are reused as much\nas possible. The aim is to enable enterprises\nto leverage their existing Hadoop infrastructure for real time streaming\napplications. The platform is designed to scale with big\ndata applications and scale with Hadoop. A streaming application is an asynchronous execution of\ncomputations across distributed nodes. All computations are done in\nparallel on a distributed cluster. The computation model is designed to\ndo as many parallel computations as possible in a non-blocking fashion.\nThe task of monitoring of the entire application is done on (streaming)\nwindow boundaries with a streaming window as an atomic entity. A window\ncompletion is a quantum of work done. There is no assumption that an\noperator can be interrupted at precisely a particular tuple or window. An operator itself also\ncannot assume or predict the exact time a tuple that it emitted would\nget consumed by downstream operators. The operator processes the tuples\nit gets and simply emits new tuples based on its business logic. The\nonly guarantee it has is that the upstream operators are processing\neither the current or some later window, and the downstream operator is\nprocessing either the current or some earlier window. The completion of\na window (i.e. propagation of the end_window\u00a0event through an operator) in any\noperator guarantees that all upstream operators have finished processing\nthis window. Thus, the end_window\u00a0event is blocking on an operator\nwith multiple outputs, and is a synchronization point in the DAG. The\n begin_window\u00a0event does not have\nany such restriction, a single begin_window\u00a0event from any upstream operator\ntriggers the operator to start processing tuples.",
- "title": "Streaming Computational Model"
- },
- {
- "location": "/application_development/#streaming-application-manager-stram",
- "text": "Streaming Application Manager (STRAM) is the Hadoop YARN native\napplication master. STRAM is the first process that is activated upon\napplication launch and orchestrates the streaming application on the\nplatform. STRAM is a lightweight controller process. The\nresponsibilities of STRAM include Running the Application Read the\u00a0logical plan\u00a0of the application (DAG) submitted by the client Validate the logical plan Translate the logical plan into a physical plan, where certain operators may be partitioned (i.e. replicated) to multiple operators for handling load. Request resources (Hadoop containers) from Resource Manager,\n per physical plan Based on acquired resources and application attributes, create\n an execution plan\u00a0by partitioning the DAG into fragments,\n each assigned to different containers. Executes the application by deploying each fragment to\n its container. Containers then start stream processing and run\n autonomously, processing one streaming window after another. Each\n container is represented as an instance of the StreamingContainer\u00a0class, which updates\n STRAM via the heartbeat protocol and processes directions received\n from STRAM. Continually monitoring the application via heartbeats from each StreamingContainer Collecting Application System Statistics and Logs Logging all application-wide decisions taken Providing system data on the state of the application via a Web Service. Supporting Fault Tolerance a. Detecting a node outage\nb. Requesting a replacement resource from the Resource Manager\n and scheduling state restoration for the streaming operators\nc. Saving state to Zookeeper Supporting Dynamic Partitioning:\u00a0Periodically evaluating the SLA and modifying the physical plan if required\n (logical plan does not change). Enabling Security:\u00a0Distributing security tokens for distributed components of the execution engine\n and securing web service requests. Enabling Dynamic modification of DAG: In the future, we intend to allow for user initiated\n modification of the logical plan to allow for changes to the\n processing logic and functionality. An example of the Yahoo! Finance Quote application scheduled on a\ncluster of 5 Hadoop containers (processes) is shown in Figure 3. An example for the translation from a logical plan to a physical\nplan and an execution plan for a subset of the application is shown in\nFigure 4.",
- "title": "Streaming Application Manager (STRAM)"
- },
- {
- "location": "/application_development/#hadoop-components",
- "text": "In this section we cover some aspects of Hadoop that your\nstreaming application interacts with. This section is not meant to\neducate the reader on Hadoop, but just get the reader acquainted with\nthe terms. We strongly advise readers to learn Hadoop from other\nsources. A streaming application runs as a native Hadoop 2.2 application.\nHadoop 2.2 does not differentiate between a map-reduce job and other\napplications, and hence as far as Hadoop is concerned, the streaming\napplication is just another job. This means that your application\nleverages all the bells and whistles Hadoop provides and is fully\nsupported within Hadoop technology stack. The platform is responsible\nfor properly integrating itself with the relevant components of Hadoop\nthat exist today and those that may emerge in the future All investments that leverage multi-tenancy (for example quotas\nand queues), security (for example kerberos), data flow integration (for\nexample copying data in-out of HDFS), monitoring, metrics collections,\netc. will require no changes when streaming applications run on\nHadoop.",
- "title": "Hadoop Components"
- },
- {
- "location": "/application_development/#yarn",
- "text": "YARN is\nthe core library of Hadoop 2.2 that is tasked with resource management\nand works as a distributed application framework. In this section we\nwill walk through Yarn's components. In Hadoop 2.2, the old jobTracker\nhas been replaced by a combination of ResourceManager (RM) and\nApplicationMaster (AM).",
- "title": "YARN"
- },
- {
- "location": "/application_development/#resource-manager-rm",
- "text": "ResourceManager (RM)\nmanages all the distributed resources. It allocates and arbitrates all\nthe slots and the resources (cpu, memory, network) of these slots. It\nworks with per-node NodeManagers (NMs) and per-application\nApplicationMasters (AMs). Currently memory usage is monitored by RM; in\nupcoming releases it will have CPU as well as network management. RM is\nshared by map-reduce and streaming applications. Running streaming\napplications requires no changes in the RM.",
- "title": "Resource Manager (RM)"
- },
- {
- "location": "/application_development/#application-master-am",
- "text": "The AM is the watchdog or monitoring process for your application\nand has the responsibility of negotiating resources with RM and\ninteracting with NodeManagers to get the allocated containers started.\nThe AM is the starting point of your application and is considered user\ncode (not system Hadoop code). The AM itself runs in one container. All\nresource management within the application are managed by the AM. This\nis a critical feature for Hadoop 2.2 where tasks done by jobTracker in\nHadoop 1.0 have been distributed allowing Hadoop 2.2 to scale much\nbeyond Hadoop 1.0. STRAM is a native YARN ApplicationManager.",
- "title": "Application Master (AM)"
- },
- {
- "location": "/application_development/#node-managers-nm",
- "text": "There is one NodeManager (NM)\nper node in the cluster. All the containers (i.e. processes) on that\nnode are monitored by the NM. It takes instructions from RM and manages\nresources of that node as per RM instructions. NMs interactions are same\nfor map-reduce and for streaming applications. Running streaming\napplications requires no changes in the NM.",
- "title": "Node Managers (NM)"
- },
- {
- "location": "/application_development/#rpc-protocol",
- "text": "Communication among RM, AM, and NM is done via the Hadoop RPC\nprotocol. Streaming applications use the same protocol to send their\ndata. No changes are needed in RPC support provided by Hadoop to enable\ncommunication done by components of your application.",
- "title": "RPC Protocol"
- },
- {
- "location": "/application_development/#hdfs",
- "text": "Hadoop includes a highly fault tolerant, high throughput\ndistributed file system ( HDFS ).\nIt runs on commodity hardware, and your streaming application will, by\ndefault, use it. There is no difference between files created by a\nstreaming application and those created by map-reduce.",
- "title": "HDFS"
- },
- {
- "location": "/application_development/#developing-an-application",
- "text": "In this chapter we describe the methodology to develop an\napplication using the Realtime Streaming Platform. The platform was\ndesigned to make it easy to build and launch sophisticated streaming\napplications with the developer having to deal only with the\napplication/business logic. The platform deals with details of where to\nrun what operators on which servers and how to correctly route streams\nof data among them.",
- "title": "Developing An Application"
- },
- {
- "location": "/application_development/#development-process",
- "text": "While the platform does not mandate a specific methodology or set\nof development tools, we have recommendations to maximize productivity\nfor the different phases of application development.",
- "title": "Development Process"
- },
- {
- "location": "/application_development/#design",
- "text": "Identify common, reusable operators. Use a library\n if possible. Identify scalability and performance requirements before\n designing the DAG. Leverage attributes that the platform supports for scalability\n and performance. Use operators that are benchmarked and tested so that later\n surprises are minimized. If you have glue code, create appropriate\n unit tests for it. Use THREAD_LOCAL locality for high throughput streams. If all\n the operators on that stream cannot fit in one container,\n try\u00a0NODE_LOCAL\u00a0locality. Both THREAD_LOCAL and\n NODE_LOCAL streams avoid the Network Interface Card (NIC)\n completly. The former uses intra-process communication to also avoid\n serialization-deserialization overhead. The overall throughput and latencies are are not necessarily\n correlated to the number of operators in a simple way -- the\n relationship is more nuanced. A lot depends on how much work\n individual operators are doing, how many are able to operate in\n parallel, and how much data is flowing through the arcs of the DAG.\n It is, at times, better to break a computation down into its\n constituent simple parts and then stitch them together via streams\n to better utilize the compute resources of the cluster. Decide on a\n per application basis the fine line between complexity of each\n operator vs too many streams. Doing multiple computations in one\n operator does save network I/O, while operators that are too complex\n are hard to maintain. Do not use operators that depend on the order of two streams\n as far as possible. In such cases behavior is not idempotent. Persist key information to HDFS if possible; it may be useful\n for debugging later. Decide on an appropriate fault tolerance mechanism. If some\n data loss is acceptable, use the at-most-once mechanism as it has\n fastest recovery.",
- "title": "Design"
- },
- {
- "location": "/application_development/#creating-new-project",
- "text": "Please refer to the Apex Application Packages \u00a0for\nthe basic steps for creating a new project.",
- "title": "Creating New Project"
- },
- {
- "location": "/application_development/#writing-the-application-code",
- "text": "Preferably use an IDE (Eclipse, Netbeans etc.) that allows you to\nmanage dependencies and assists with the Java coding. Specific benefits\ninclude ease of managing operator library jar files, individual operator\nclasses, ports and properties. It will also highlight and assist to\nrectify issues such as type mismatches when adding streams while\ntyping.",
- "title": "Writing the application code"
- },
- {
- "location": "/application_development/#testing",
- "text": "Write test cases with JUnit or similar test framework so that code\nis tested as it is written. For such testing, the DAG can run in local\nmode within the IDE. Doing this may involve writing mock input or output\noperators for the integration points with external systems. For example,\ninstead of reading from a live data stream, the application in test mode\ncan read from and write to files. This can be done with a single\napplication DAG by instrumenting a test mode using settings in the\nconfiguration that is passed to the application factory\ninterface. Good test coverage will not only eliminate basic validation errors\nsuch as missing port connections or property constraint violations, but\nalso validate the correct processing of the data. The same tests can be\nre-run whenever the application or its dependencies change (operator\nlibraries, version of the platform etc.)",
- "title": "Testing"
- },
- {
- "location": "/application_development/#running-an-application",
- "text": "The platform provides a commandline tool called dtcli\u00a0for managing applications (launching,\nkilling, viewing, etc.). This tool was already discussed above briefly\nin the section entitled Running the Test Application. It will introspect\nthe jar file specified with the launch command for applications (classes\nthat implement ApplicationFactory) or property files that define\napplications. It will also deploy the dependency jar files from the\napplication package to the cluster. Dtcli can run the application in local mode (i.e. outside a\ncluster). It is recommended to first run the application in local mode\nin the development environment before launching on the Hadoop cluster.\nThis way some of the external system integration and correct\nfunctionality of the application can be verified in an easier to debug\nenvironment before testing distributed mode. For more details on CLI please refer to the dtCli Guide .",
- "title": "Running an application"
- },
- {
- "location": "/application_development/#application-api",
- "text": "This section introduces the API to write a streaming application.\nThe work involves connecting operators via streams to form the logical\nDAG. The steps are Instantiate an application (DAG) (Optional) Set Attributes Assign application name Set any other attributes as per application requirements Create/re-use and instantiate operators Assign operator name that is unique within the application Declare schema upfront for each operator (and thereby its ports) (Optional) Set properties\u00a0 and attributes on the dag as per specification Connect ports of operators via streams Each stream connects one output port of an operator to one or more input ports of other operators. (Optional) Set attributes on the streams Test the application. There are two methods to create an application, namely Java, and\nProperties file. Java API is for applications being developed by humans,\nand properties file (Hadoop like) is more suited for DAGs generated by\ntools.",
- "title": "Application API"
- },
- {
- "location": "/application_development/#java-api",
- "text": "The Java API is the most common way to create a streaming\napplication. It is meant for application developers who prefer to\nleverage the features of Java, and the ease of use and enhanced\nproductivity provided by IDEs like NetBeans or Eclipse. Using Java to\nspecify the application provides extra validation abilities of Java\ncompiler, such as compile time checks for type safety at the time of\nwriting the code. Later in this chapter you can read more about\nvalidation support in the platform. The developer specifies the streaming application by implementing\nthe ApplicationFactory interface, which is how platform tools (CLI etc.)\nrecognize and instantiate applications. Here we show how to create a\nYahoo! Finance application that streams the last trade price of a ticker\nand computes the high and low price in every 1 min window. Run above\n test application\u00a0to execute the\nDAG in local mode within the IDE. Let us revisit how the Yahoo! Finance test application constructs the DAG: public class Application implements StreamingApplication\n{\n\n ...\n\n @Override\n public void populateDAG(DAG dag, Configuration conf)\n {\n dag.getAttributes().attr(DAG.STRAM_WINDOW_SIZE_MILLIS).set(streamingWindowSizeMilliSeconds);\n\n StockTickInput tick = getStockTickInputOperator( StockTickInput , dag);\n SumKeyVal String, Long dailyVolume = getDailyVolumeOperator( DailyVolume , dag);\n ConsolidatorKeyVal String,Double,Long,String,?,? quoteOperator = getQuoteOperator( Quote , dag);\n\n RangeKeyVal String, Double highlow = getHighLowOperator( HighLow , dag, appWindowCountMinute);\n SumKeyVal String, Long minuteVolume = getMinuteVolumeOperator( MinuteVolume , dag, appWindowCountMinute);\n ConsolidatorKeyVal String,HighLow,Long,?,?,? chartOperator = getChartOperator( Chart , dag);\n\n SimpleMovingAverage String, Double priceSMA = getPriceSimpleMovingAverageOperator( PriceSMA , dag, appWindowCountSMA);\n\n dag.addStream( price , tick.price, quoteOperator.in1, highlow.data, priceSMA.data);\n dag.addStream( vol , tick.volume, dailyVolume.data, minuteVolume.data);\n dag.addStream( time , tick.time, quoteOperator.in3);\n dag.addStream( daily_vol , dailyVolume.sum, quoteOperator.in2);\n\n dag.addStream( quote_data , quoteOperator.out, getConsole( quoteConsole , dag, QUOTE ));\n\n dag.addStream( high_low , highlow.range, chartOperator.in1);\n dag.addStream( vol_1min , minuteVolume.sum, chartOperator.in2);\n dag.addStream( chart_data , chartOperator.out, getConsole( chartConsole , dag, CHART ));\n\n dag.addStream( sma_price , priceSMA.doubleSMA, getConsole( priceSMAConsole , dag, Price SMA ));\n\n return dag;\n }\n}",
- "title": "Java API"
- },
- {
- "location": "/application_development/#property-file-api",
- "text": "The platform also supports specification of a DAG via a property\nfile. The aim here to make it easy for tools to create and run an\napplication. This method of specification does not have the Java\ncompiler support of compile time check, but since these applications\nwould be created by software, they should be correct by construction.\nThe syntax is derived from Hadoop properties and should be easy for\nfolks who are used to creating software that integrated with\nHadoop. Create an application (DAG): myApplication.properties # input operator that reads from a file\ndt.operator.inputOp.classname=com.acme.SampleInputOperator\ndt.operator.inputOp.fileName=somefile.txt\n\n# output operator that writes to the console\ndt.operator.outputOp.classname=com.acme.ConsoleOutputOperator\n\n# stream connecting both operators\ndt.stream.inputStream.source=inputOp.outputPort\ndt.stream.inputStream.sinks=outputOp.inputPort Above snippet is intended to convey the basic idea of specifying\nthe DAG without using Java. Operators would come from a predefined\nlibrary and referenced in the specification by class name and port names\n(obtained from the library providers documentation or runtime\nintrospection by tools). For those interested in details, see later\nsections and refer to the Operation and\nInstallation Guide\u00a0mentioned above.",
- "title": "Property File API"
- },
- {
- "location": "/application_development/#attributes",
- "text": "Attributes impact the runtime behavior of the application. They do\nnot impact the functionality. An example of an attribute is application\nname. Setting it changes the application name. Another example is\nstreaming window size. Setting it changes the streaming window size from\nthe default value to the specified value. Users cannot add new\nattributes, they can only choose from the ones that come packaged and\npre-supported by the platform. Details of attributes are covered in the\n Operation and Installation\nGuide.",
- "title": "Attributes"
- },
- {
- "location": "/application_development/#operators",
- "text": "Operators\u00a0are basic compute units.\nOperators process each incoming tuple and emit zero or more tuples on\noutput ports as per the business logic. The data flow, connectivity,\nfault tolerance (node outage), etc. is taken care of by the platform. As\nan operator developer, all that is needed is to figure out what to do\nwith the incoming tuple and when (and which output port) to send out a\nparticular output tuple. Correctly designed operators will most likely\nget reused. Operator design needs care and foresight. For details, refer\nto the Operator Developer Guide . As an application developer you need to connect operators\nin a way that it implements your business logic. You may also require\noperator customization for functionality and use attributes for\nperformance/scalability etc. All operators process tuples asynchronously in a distributed\ncluster. An operator cannot assume or predict the exact time a tuple\nthat it emitted will get consumed by a downstream operator. An operator\nalso cannot predict the exact time when a tuple arrives from an upstream\noperator. The only guarantee is that the upstream operators are\nprocessing the current or a future window, i.e. the windowId of upstream\noperator is equals or exceeds its own windowId. Conversely the windowId\nof a downstream operator is less than or equals its own windowId. The\nend of a window operation, i.e. the API call to endWindow on an operator\nrequires that all upstream operators have finished processing this\nwindow. This means that completion of processing a window propagates in\na blocking fashion through an operator. Later sections provides more\ndetails on streams and data flow of tuples. Each operator has a unique name within the DAG as provided by the\nuser. This is the name of the operator in the logical plan. The name of\nthe operator in the physical plan is an integer assigned to it by STRAM.\nThese integers are use the sequence from 1 to N, where N is total number\nof physically unique operators in the DAG. \u00a0Following the same rule,\neach partitioned instance of a logical operator has its own integer as\nan id. This id along with the Hadoop container name uniquely identifies\nthe operator in the execution plan of the DAG. The logical names and the\nphysical names are required for web service support. Operators can be\naccessed via both names. These same names are used while interacting\nwith dtcli\u00a0to access an operator.\nIdeally these names should be self-descriptive. For example in Figure 1,\nthe node named \u201cDaily volume\u201d has a physical identifier of 2.",
- "title": "Operators"
- },
- {
- "location": "/application_development/#operator-interface",
- "text": "Operator interface in a DAG consists of ports,\u00a0properties,\u00a0and attributes.\nOperators interact with other components of the DAG via ports. Functional behavior of the operators\ncan be customized via parameters. Run time performance and physical\ninstantiation is controlled by attributes. Ports and parameters are\nfields (variables) of the Operator class/object, while attributes are\nmeta information that is attached to the operator object via an\nAttributeMap. An operator must have at least one port. Properties are\noptional. Attributes are provided by the platform and always have a\ndefault value that enables normal functioning of operators.",
- "title": "Operator Interface"
- },
- {
- "location": "/application_development/#ports",
- "text": "Ports are connection points by which an operator receives and\nemits tuples. These should be transient objects instantiated in the\noperator object, that implement particular interfaces. Ports should be\ntransient as they contain no state. They have a pre-defined schema and\ncan only be connected to other ports with the same schema. An input port\nneeds to implement the interface Operator.InputPort\u00a0and\ninterface Sink. A default\nimplementation of these is provided by the abstract class DefaultInputPort. An output port needs to\nimplement the interface Operator.OutputPort. A default implementation\nof this is provided by the concrete class DefaultOutputPort. These two are a quick way to\nimplement the above interfaces, but operator developers have the option\nof providing their own implementations. Here are examples of an input and an output port from the operator\nSum. @InputPortFieldAnnotation(name = data )\npublic final transient DefaultInputPort V data = new DefaultInputPort V () {\n @Override\n public void process(V tuple)\n {\n ...\n }\n}\n@OutputPortFieldAnnotation(optional=true)\npublic final transient DefaultOutputPort V sum = new DefaultOutputPort V (){ \u2026 }; The process call is in the Sink interface. An emit on an output\nport is done via emit(tuple) call. For the above example it would be\nsum.emit(t), where the type of t is the generic parameter V. There is no limit on how many ports an operator can have. However\nany operator must have at least one port. An operator with only one port\nis called an Input Adapter if it has no input port and an Output Adapter\nif it has no output port. These are special operators needed to get/read\ndata from outside system/source into the application, or push/write data\ninto an outside system/sink. These could be in Hadoop or outside of\nHadoop. These two operators are in essence gateways for the streaming\napplication to communicate with systems outside the application. Port connectivity can be validated during compile time by adding\nPortFieldAnnotations shown above. By default all ports have to be\nconnected, to allow a port to go unconnected, you need to add\n\u201coptional=true\u201d to the annotation. Attributes can be specified for ports that affect the runtime\nbehavior. An example of an attribute is parallel partition that specifes\na parallel computation flow per partition. It is described in detail in\nthe Parallel Partitions section. Another example is queue capacity that specifies the buffer size for the\nport. Details of attributes are covered in Operation and Installation Guide.",
- "title": "Ports"
- },
- {
- "location": "/application_development/#properties",
- "text": "Properties are the abstractions by which functional behavior of an\noperator can be customized. They should be non-transient objects\ninstantiated in the operator object. They need to be non-transient since\nthey are part of the operator state and re-construction of the operator\nobject from its checkpointed state must restore the operator to the\ndesired state. Properties are optional, i.e. an operator may or may not\nhave properties; they are part of user code and their values are not\ninterpreted by the platform in any way. All non-serializable objects should be declared transient.\nExamples include sockets, session information, etc. These objects should\nbe initialized during setup call, which is called every time the\noperator is initialized.",
- "title": "Properties"
- },
- {
- "location": "/application_development/#attributes_1",
- "text": "Attributes are values assigned to the operators that impact\nrun-time. This includes things like the number of partitions, at most\nonce or at least once or exactly once recovery modes, etc. Attributes do\nnot impact functionality of the operator. Users can change certain\nattributes in runtime. Users cannot add attributes to operators; they\nare pre-defined by the platform. They are interpreted by the platform\nand thus cannot be defined in user created code (like properties).\nDetails of attributes are covered in Configuration Guide .",
- "title": "Attributes"
- },
- {
- "location": "/application_development/#operator-state",
- "text": "The state of an operator is defined as the data that it transfers\nfrom one window to a future window. Since the computing model of the\nplatform is to treat windows like micro-batches, the operator state can\nbe checkpointed every Nth window, or every T units of time, where T is significantly greater\nthan the streaming window. \u00a0When an operator is checkpointed, the entire\nobject is written to HDFS. \u00a0The larger the amount of state in an\noperator, the longer it takes to recover from a failure. A stateless\noperator can recover much quicker than a stateful one. The needed\nwindows are preserved by the upstream buffer server and are used to\nrecompute the lost windows, and also rebuild the buffer server in the\ncurrent container. The distinction between Stateless and Stateful is based solely on\nthe need to transfer data in the operator from one window to the next.\nThe state of an operator is independent of the number of ports.",
- "title": "Operator State"
- },
- {
- "location": "/application_development/#stateless",
- "text": "A Stateless operator is defined as one where no data is needed to\nbe kept at the end of every window. This means that all the computations\nof a window can be derived from all the tuples the operator receives\nwithin that window. This guarantees that the output of any window can be\nreconstructed by simply replaying the tuples that arrived in that\nwindow. Stateless operators are more efficient in terms of fault\ntolerance, and cost to achieve SLA.",
- "title": "Stateless"
- },
- {
- "location": "/application_development/#stateful",
- "text": "A Stateful operator is defined as one where data is needed to be\nstored at the end of a window for computations occurring in later\nwindow; a common example is the computation of a sum of values in the\ninput tuples.",
- "title": "Stateful"
- },
- {
- "location": "/application_development/#operator-api",
- "text": "The Operator API consists of methods that operator developers may\nneed to override. In this section we will discuss the Operator APIs from\nthe point of view of an application developer. Knowledge of how an\noperator works internally is critical for writing an application. Those\ninterested in the details should refer to Malhar Operator Developer Guide. The APIs are available in three modes, namely Single Streaming\nWindow, Sliding Application Window, and Aggregate Application Window.\nThese are not mutually exclusive, i.e. an operator can use single\nstreaming window as well as sliding application window. A physical\ninstance of an operator is always processing tuples from a single\nwindow. The processing of tuples is guaranteed to be sequential, no\nmatter which input port the tuples arrive on. In the later part of this section we will evaluate three common\nuses of streaming windows by applications. They have different\ncharacteristics and implications on optimization and recovery mechanisms\n(i.e. algorithm used to recover a node after outage) as discussed later\nin the section.",
- "title": "Operator API"
- },
- {
- "location": "/application_development/#streaming-window",
- "text": "Streaming window is atomic micro-batch computation period. The API\nmethods relating to a streaming window are as follows public void process( tuple_type tuple) // Called on the input port on which the tuple arrives\npublic void beginWindow(long windowId) // Called at the start of the window as soon as the first begin_window tuple arrives\npublic void endWindow() // Called at the end of the window after end_window tuples arrive on all input ports\npublic void setup(OperatorContext context) // Called once during initialization of the operator\npublic void teardown() // Called once when the operator is being shutdown A tuple can be emitted in any of the three streaming run-time\ncalls, namely beginWindow, process, and endWindow but not in setup or\nteardown.",
- "title": "Streaming Window"
- },
- {
- "location": "/application_development/#aggregate-application-window",
- "text": "An operator with an aggregate window is stateful within the\napplication window timeframe and possibly stateless at the end of that\napplication window. An size of an aggregate application window is an\noperator attribute and is defined as a multiple of the streaming window\nsize. The platform recognizes this attribute and optimizes the operator.\nThe beginWindow, and endWindow calls are not invoked for those streaming\nwindows that do not align with the application window. For example in\ncase of streaming window of 0.5 second and application window of 5\nminute, an application window spans 600 streaming windows (5*60*2 =\n600). At the start of the sequence of these 600 atomic streaming\nwindows, a beginWindow gets invoked, and at the end of these 600\nstreaming windows an endWindow gets invoked. All the intermediate\nstreaming windows do not invoke beginWindow or endWindow. Bookkeeping,\nnode recovery, stats, UI, etc. continue to work off streaming windows.\nFor example if operators are being checkpointed say on an average every\n30th window, then the above application window would have about 20\ncheckpoints.",
- "title": "Aggregate Application Window"
- },
- {
- "location": "/application_development/#sliding-application-window",
- "text": "A sliding window is computations that requires previous N\nstreaming windows. After each streaming window the Nth past window is\ndropped and the new window is added to the computation. An operator with\nsliding window is a stateful operator at end of any window. The sliding\nwindow period is an attribute and is a multiple of streaming window. The\nplatform recognizes this attribute and leverages it during bookkeeping.\nA sliding aggregate window with tolerance to data loss does not have a\nvery high bookkeeping cost. The cost of all three recovery mechanisms,\n at most once\u00a0(data loss tolerant),\nat least once\u00a0(data loss\nintolerant), and exactly once\u00a0(data\nloss intolerant and no extra computations) is same as recovery\nmechanisms based on streaming window. STRAM is not able to leverage this\noperator for any extra optimization.",
- "title": "Sliding Application Window"
- },
- {
- "location": "/application_development/#single-vs-multi-input-operator",
- "text": "A single-input operator by definition has a single upstream\noperator, since there can only be one writing port for a stream. \u00a0If an\noperator has a single upstream operator, then the beginWindow on the\nupstream also blocks the beginWindow of the single-input operator. For\nan operator to start processing any window at least one upstream\noperator has to start processing that window. A multi-input operator\nreads from more than one upstream ports. Such an operator would start\nprocessing as soon as the first begin_window event arrives. However the\nwindow would not close (i.e. invoke endWindow) till all ports receive\nend_window events for that windowId. Thus the end of a window is a\nblocking event. As we saw earlier, a multi-input operator is also the\npoint in the DAG where windows of all upstream operators are\nsynchronized. The windows (atomic micro-batches) from a faster (or just\nahead in processing) upstream operators are queued up till the slower\nupstream operator catches up. STRAM monitors such bottlenecks and takes\ncorrective actions. The platform ensures minimal delay, i.e processing\nstarts as long as at least one upstream operator has started\nprocessing.",
- "title": "Single vs Multi-Input Operator"
- },
- {
- "location": "/application_development/#recovery-mechanisms",
- "text": "Application developers can set any of the recovery mechanisms\nbelow to deal with node outage. In general, the cost of recovery depends\non the state of the operator, while data integrity is dependant on the\napplication. The mechanisms are per window as the platform treats\nwindows as atomic compute units. Three recovery mechanisms are\nsupported, namely At-least-once: All atomic batches are processed at least once.\n No data loss occurs. At-most-once: All atomic batches are processed at most once.\n Data loss is possible; this is the most efficient setting. Exactly-once: All atomic batches are processed exactly once.\n No data loss occurs; this is the least efficient setting since\n additional work is needed to ensure proper semantics. At-least-once is the default. During a recovery event, the\noperator connects to the upstream buffer server and asks for windows to\nbe replayed. At-least-once and exactly-once mechanisms start from its\ncheckpointed state. At-most-once starts from the next begin-window\nevent. Recovery mechanisms can be specified per Operator while writing\nthe application as shown below. Operator o = dag.addOperator(\u201coperator\u201d, \u2026);\ndag.setAttribute(o, OperatorContext.PROCESSING_MODE, ProcessingMode.AT_MOST_ONCE); Also note that once an operator is attributed to AT_MOST_ONCE,\nall the operators downstream to it have to be AT_MOST_ONCE. The client\nwill give appropriate warnings or errors if that\u2019s not the case. Details are explained in the chapter on Fault Tolerance below.",
- "title": "Recovery Mechanisms"
- },
- {
- "location": "/application_development/#streams",
- "text": "A stream\u00a0is a connector\n(edge) abstraction, and is a fundamental building block of the platform.\nA stream consists of tuples that flow from one port (called the\noutput\u00a0port) to one or more ports\non other operators (called input\u00a0ports) another -- so note a potentially\nconfusing aspect of this terminology: tuples enter a stream through its\noutput port and leave via one or more input ports. A stream has the\nfollowing characteristics Tuples are always delivered in the same order in which they\n were emitted. Consists of a sequence of windows one after another. Each\n window being a collection of in-order tuples. A stream that connects two containers passes through a\n buffer server. All streams can be persisted (by default in HDFS). Exactly one output port writes to the stream. Can be read by one or more input ports. Connects operators within an application, not outside\n an application. Has an unique name within an application. Has attributes which act as hints to STRAM. Streams have four modes, namely in-line, in-node, in-rack,\n and other. Modes may be overruled (for example due to lack\n of containers). They are defined as follows: THREAD_LOCAL: In the same thread, uses thread\n stack (intra-thread). This mode can only be used for a downstream\n operator which has only one input port connected; also called\n in-line. CONTAINER_LOCAL: In the same container (intra-process); also\n called in-container. NODE_LOCAL: In the same Hadoop node (inter processes, skips\n NIC); also called in-node. RACK_LOCAL: On nodes in the same rack; also called\n in-rack. unspecified: No guarantee. Could be anywhere within the\n cluster An example of a stream declaration is given below DAG dag = new DAG();\n \u2026\ndag.addStream( views , viewAggregate.sum, cost.data).setLocality(CONTAINER_LOCAL); // A container local stream\ndag.addStream(\u201cclicks\u201d, clickAggregate.sum, rev.data); // An example of unspecified locality The platform guarantees in-order delivery of tuples in a stream.\nSTRAM views each stream as collection of ordered windows. Since no tuple\ncan exist outside a window, a replay of a stream consists of replay of a\nset of windows. When multiple input ports read the same stream, the\nexecution plan of a stream ensures that each input port is logically not\nblocked by the reading of another input port. The schema of a stream is\nsame as the schema of the tuple. In a stream all tuples emitted by an operator in a window belong\nto that window. A replay of this window would consists of an in-order\nreplay of all the tuples. Thus the tuple order within a stream is\nguaranteed. However since an operator may receive multiple streams (for\nexample an operator with two input ports), the order of arrival of two\ntuples belonging to different streams is not guaranteed. In general in\nan asynchronous distributed architecture this is expected. Thus the\noperator (specially one with multiple input ports) should not depend on\nthe tuple order from two streams. One way to cope with this\nindeterminate order, if necessary, is to wait to get all the tuples of a\nwindow and emit results in endWindow call. All operator templates\nprovided as part of Malhar operator library follow these principles. A logical stream gets partitioned into physical streams each\nconnecting the partition to the upstream operator. If two different\nattributes are needed on the same stream, it should be split using\nStreamDuplicator\u00a0operator. Modes of the streams are critical for performance. An in-line\nstream is the most optimal as it simply delivers the tuple as-is without\nserialization-deserialization. Streams should be marked\ncontainer_local, specially in case where there is a large tuple volume\nbetween two operators which then on drops significantly. Since the\nsetLocality call merely provides a hint, STRAM may ignore it. An In-node\nstream is not as efficient as an in-line one, but it is clearly better\nthan going off-node since it still avoids the potential bottleneck of\nthe network card. THREAD_LOCAL and CONTAINER_LOCAL streams do not use a buffer\nserver as this stream is in a single process. The other two do.",
- "title": "Streams"
- },
- {
- "location": "/application_development/#validating-an-application",
- "text": "The platform provides various ways of validating the application\nspecification and data input. An understanding of these checks is very\nimportant for an application developer since it affects productivity.\nValidation of an application is done in three phases, namely Compile Time: Caught during application development, and is\n most cost effective. These checks are mainly done on declarative\n objects and leverages the Java compiler. An example is checking that\n the schemas specified on all ports of a stream are\n mutually compatible. Initialization Time: When the application is being\n initialized, before submitting to Hadoop. These checks are related\n to configuration/context of an application, and are done by the\n logical DAG builder implementation. An example is the checking that\n all non-optional ports are connected to other ports. Run Time: Validations done when the application is running.\n This is the costliest of all checks. These are checks that can only\n be done at runtime as they involve data. For example divide by 0\n check as part of business logic.",
- "title": "Validating an Application"
- },
- {
- "location": "/application_development/#compile-time",
- "text": "Compile time validations apply when an application is specified in\nJava code and include all checks that can be done by Java compiler in\nthe development environment (including IDEs like NetBeans or Eclipse).\nExamples include Schema Validation: The tuples on ports are POJO (plain old\n java objects) and compiler checks to ensure that all the ports on a\n stream have the same schema. Stream Check: Single Output Port and at least one Input port\n per stream. A stream can only have one output port writer. This is\n part of the addStream api. This\n check ensures that developers only connect one output port to\n a stream. The same signature also ensures that there is at least one\n input port for a stream Naming: Compile time checks ensures that applications\n components operators, streams are named",
- "title": "Compile Time"
- },
- {
- "location": "/application_development/#initializationinstantiation-time",
- "text": "Initialization time validations include various checks that are\ndone post compile, and before the application starts running in a\ncluster (or local mode). These are mainly configuration/contextual in\nnature. These checks are as critical to proper functionality of the\napplication as the compile time validations. Examples include JavaBeans Validation :\n Examples include @Max(): Value must be less than or equal to the number @Min(): Value must be greater than or equal to the\n number @NotNull: The value of the field or property must not be\n null @Pattern(regexp = \u201c....\u201d): Value must match the regular\n expression Input port connectivity: By default, every non-optional input\n port must be connected. A port can be declared optional by using an\n annotation: \u00a0 \u00a0 @InputPortFieldAnnotation(name = \"...\", optional\n = true) Output Port Connectivity: Similar. The annotation here is: \u00a0 \u00a0\n @OutputPortFieldAnnotation(name = \"...\", optional = true) Unique names in application scope: Operators, streams, must have\n unique names. Cycles in the dag: DAG cannot have a cycle. Unique names in operator scope: Ports, properties, annotations\n must have unique names. One stream per port: A port can connect to only one stream.\n This check applies to input as well as output ports even though an\n output port can technically write to two streams. If you must have\n two streams originating from a single output port, use \u00a0a\u00a0streamDuplicator operator. Application Window Period: Has to be an integral multiple the\n streaming window period.",
- "title": "Initialization/Instantiation Time"
- },
- {
- "location": "/application_development/#run-time",
- "text": "Run time checks are those that are done when the application is\nrunning. The real-time streaming platform provides rich run time error\nhandling mechanisms. The checks are exclusively done by the application\nbusiness logic, but the platform allows applications to count and audit\nthese. Some of these features are in the process of development (backend\nand UI) and this section will be updated as they are developed. Upon\ncompletion examples will be added to demos to illustrate these. Error ports are output ports with error annotations. Since they\nare normal ports, they can be monitored and tuples counted, persisted\nand counts shown in the UI.",
- "title": "Run Time"
- },
- {
- "location": "/application_development/#multi-tenancy-and-security",
- "text": "Hadoop is a multi-tenant distributed operating system. Security is\nan intrinsic element of multi-tenancy as without it a cluster cannot be\nreasonably be shared among enterprise applications. Streaming\napplications follow all multi-tenancy security models used in Hadoop as\nthey are native Hadoop applications.",
- "title": "Multi-Tenancy and Security"
- },
- {
- "location": "/application_development/#security",
- "text": "The platform includes Kerberos support. Both access points, namely\nSTRAM and Bufferserver are secure. STRAM passes the token over to\nStreamingContainer, which then gives it to the Bufferserver. The most\nimportant aspect for an application developer is to note that STRAM is\nthe single point of access to ensure security measures are taken by all\ncomponents of the platform.",
- "title": "Security"
- },
- {
- "location": "/application_development/#resource-limits",
- "text": "Hadoop enforces quotas on resources. This includes hard-disk (name\nspace and total disk quota) as well as priority queues for schedulers.\nThe platform uses Hadoop resource limits to manage a streaming\napplication. In addition network I/O quotas can be enforced. An operator\ncan be dynamically partitioned if it reaches its resource limits; these\nlimits may be expressed in terms of throughput, latency, or just\naggregate resource utilization of a container.",
- "title": "Resource Limits"
- },
- {
- "location": "/application_development/#scalability-and-partitioning",
- "text": "Scalability is a foundational element of this platform and is a\nbuilding block for an eco-system where big-data meets real-time.\nEnterprises need to continually meet SLA as data grows. Without the\nability to scale as load grows, or new applications with higher loads\ncome to fruition, enterprise grade SLA cannot be met. A big issue with\nthe streaming application space is that, it is not just about high load,\nbut also the fluctuations in it. There is no way to guarantee future\nload requirements and there is a big difference between high and low\nload within a day for the same feed. Traditional streaming platforms\nsolve these two cases by simply throwing more hardware at the\nproblem. Daily spikes are managed by ensuring enough hardware for peak\nload, which then idles during low load, and future needs are handled by\na very costly re-architecture, or investing heavily in building a\nscalable distributed operating system. Another salient and often\noverlooked cost is the need to manage SLA -- let\u2019s call it buffer capacity. Since this means computing the\npeak load within required time, that translates to allocating enough\nresources over and above peak load as daily peaks fluctuate. For example\nan average peak load of 100 resource units (cpu and/or memory and/or\nnetwork) may mean allocating about 200 resource units to be safe. A\ndistributed cluster that cannot dynamically scale up and down, in effect\npays buffer capacity per application. Another big aspect of streaming\napplications is that the load is not just ingestion rate, more often\nthan not, the internal operators produce lot more events than the\ningestion rate. For example a dimensional data (with, say d\u00a0dimensions) computation needs 2*d -1\u00a0computations per ingested event. A lot\nof applications have over 10 dimensions, i.e over 1000 computations per\nincoming event and these need to be distributed across the cluster,\nthereby causing an explosion in the throughput (events/sec) that needs\nto be managed. The platform is designed to handle such cases at a very low cost.\nThe platform scales linearly with Hadoop. If applications need more\nresources, the enterprise can simply add more commodity nodes to Hadoop\nwithout any downtime, and the Hadoop native platform will take care of\nthe rest. If some nodes go bad, these can be removed without downtime.\nThe daily peaks and valleys in the load are managed by the platform by\ndynamically scaling at the peak and then giving the resources back to\nHadoop during low load. This means that a properly designed Hadoop\ncluster does several things for enterprises: (a) reduces the cost of\nhardware due to use of commodity hardware (b) shares buffer capacity\nacross all applications as peaks of all applications may not align and\n(c) raises the average CPU usage on a 24x7 basis. As a general design\nthis is similar to scale that a map-reduce application can deliver. In\nthe following sections of this chapter we will see how this is\ndone.",
- "title": "Scalability and Partitioning"
- },
- {
- "location": "/application_development/#partitioning",
- "text": "If all tuples sent through the stream(s) that are connected to the\ninput port(s) of an operator in the DAG are received by a single\nphysical instance of that operator, that operator can become a\nperformance bottleneck. This leads to scalability issues when\nthroughput, memory, or CPU needs exceed the processing capacity of that\nsingle instance. To address the problem, the platform offers the capability to\npartition the inflow of data so that it is divided across multiple\nphysical instances of a logical operator in the DAG. There are two\nfunctional ways to partition Load balance: Incoming load is simply partitioned\n into stream(s) that go to separate instances of physical operators\n and scalability is achieved via adding more physical operators. Each\n tuple is sent to physical operator (partition) based on a\n round-robin or other similar algorithm. This scheme scales linearly.\n A lot of key based computations can load balance in the platform due\n to the ability to insert Unifiers. For many computations, the\n endWindow and Unifier setup is similar to the combiner and reducer\n mechanism in a Map-Reduce computation. Sticky Key: The key assertion is that distribution of tuples\n are sticky, i.e the data with\n same key will always be processed by the same physical operator, no\n matter how many times it is sent through the stream. This stickiness\n will continue even if the number of partitions grows dynamically and\n can eventually be leveraged for advanced features like\n bucket testing. How this is accomplished and what is required to\n develop compliant operators will be explained below. We plan to add more partitioning mechanisms proactively to the\nplatform over time as needed by emerging usage patterns. The aim is to\nallow enterprises to be able to focus on their business logic, and\nsignificantly reduce the cost of operability. As an enabling technology\nfor managing high loads, this platform provides enterprises with a\nsignificant innovative edge. Scalability and Partitioning is a\nfoundational building block for this platform.",
- "title": "Partitioning"
- },
- {
- "location": "/application_development/#sticky-partition-vs-round-robin",
- "text": "As noted above, partitioning via sticky key is data aware but\nround-robin partitioning is not. An example for non-sticky load\nbalancing would be round robin distribution over multiple instances,\nwhere for example a tuple stream of A, A,\nA with 3 physical operator\ninstances would result in processing of a single A by each of the instances, In contrast, sticky\npartitioning means that exactly one instance of the operators will\nprocess all of the Atuples if they\nfall into the same bucket, while B\nmay be processed by another operator. Data aware mapping of\ntuples to partitions (similar to distributed hash table) is accomplished\nvia Stream Codecs. In later sections we would show how these two\napproaches can be used in combination.",
- "title": "Sticky Partition vs Round Robin"
- },
- {
- "location": "/application_development/#stream-codec",
- "text": "The platform does not make assumptions about the tuple\ntype, it could be any Java object. The operator developer knows what\ntuple type an input port expects and is capable of processing. Each\ninput port has a stream codec \u00a0associated thatdefines how data is serialized when transmitted over a socket\nstream; it also defines another\nfunction that computes the partition hash key for the tuple. The engine\nuses that key to determine which physical instance(s) \u00a0(for a\npartitioned operator) receive that \u00a0tuple. For this to work, consistent hashing is required.\nThe default codec uses the Java Object#hashCode function, which is\nsufficient for basic types such as Integer, String etc. It will also\nwork with custom tuple classes as long as they implement hashCode\nappropriately. Reliance on hashCode may not work when generic containers\nare used that do not hash the actual data, such as standard collection\nclasses (HashMap etc.), in which case a custom stream codec must be\nassigned to the input port.",
- "title": "Stream Codec"
- },
- {
- "location": "/application_development/#static-partitioning",
- "text": "DAG designers can specify at design time how they would like\ncertain operators to be partitioned. STRAM then instantiates the DAG\nwith the physical plan which adheres to the partitioning scheme defined\nby the design. This plan is the initial partition of the application. In\nother words, Static Partitioning is used to tell STRAM to compute the\nphysical DAG from a logical DAG once, without taking into consideration\nruntime states or loads of various operators.",
- "title": "Static Partitioning"
- },
- {
- "location": "/application_development/#dynamic-partitioning",
- "text": "In streaming applications the load changes during the day, thus\ncreating situations where the number of partitioned operator instances\nneeds to adjust dynamically. The load can be measured in terms of\nprocessing within the DAG based on throughput, or latency, or\nconsiderations in external system components (time based etc.) that the\nplatform may not be aware of. Whatever the trigger, the resource\nrequirement for the current processing needs to be adjusted at run-time.\nThe platform may detect that operator instances are over or under\nutilized and may need to dynamically adjust the number of instances on\nthe fly. More instances of a logical operator may be required (partition\nsplit) or underutilized operator instances may need decommissioning\n(partition merge). We refer to either of the changes as dynamic\npartitioning. The default partitioning scheme supports split and merge\nof partitions, but without state transfer. The contract of the\nPartitioner\u00a0interface allows the operator\ndeveloper to implement split/merge and the associated state transfer, if\nnecessary. Since partitioning is a key scalability measure, our goal is to\nmake it as simple as possible without removing the flexibility needed\nfor sophisticated applications. Basic partitioning can be enabled at\ncompile time through the DAG specification. A slightly involved\npartitioning involves writing custom codecs to calculate data aware\npartitioning scheme. More complex partitioning cases may require users\nto provide a custom implementation of Partitioner, which gives the\ndeveloper full control over state transfer between multiple instances of\nthe partitioned operator.",
- "title": "Dynamic Partitioning"
- },
- {
- "location": "/application_development/#default-partitioning",
- "text": "The platform provides a default partitioning implementation that\ncan be enabled without implementing Partitioner\u00a0(or writing any other extra Java\ncode), which is designed to support simple sticky partitioning out of\nthe box for operators with logic agnostic to the partitioning scheme\nthat can be enabled by means of DAG construction alone. Typically an operator that can work with the default partitioning\nscheme would have a single input port. If there are multiple input\nports, only one port will be partitioned (the port first connected in\nthe DAG). The number of partitions will be calculated based on the\ninitial partition count - set as attribute on the operator in the DAG\n(if the attribute is not present, partitioning is off). Each partition\nwill handle tuples based on matching the lower bits of the hash code.\nFor example, if the tuple type was Integer and 2 partitions requested,\nall even numbers would go to one operator instance and all odd numbers\nto the other.",
- "title": "Default Partitioning"
- },
- {
- "location": "/application_development/#default-dynamic-partitioning",
- "text": "Triggering partition load evaluation and repartitioning action\nitself are separate concerns. Triggers are not specified further here,\nwe are planning to support it in a customizable fashion that, for\nexample, allows latency or SLA based implementations. Triggers calculate\na load indicator (signed number) that tells the framework that a given\npartition is either underutilized, operating normally within the\nexpected thresholds or overloaded and becoming a bottleneck. The\nindicator is then presented to the partitioning logic (default or custom\nimplementation of Partitioner) to provide the opportunity to make any\nneeded adjustments. The default partitioning logic divides the key space\naccording to the lower bits of the hash codes that are generated by the\nstream codec, by assigning each partitioned operator instance via a bit\nmask and the respective value. For example, the operator may have\ninitially two partitions, 0and 1, each\nwith a bit mask of 1.\nIn the case where load evaluation flags partition\n0 as over utilized\n(most data tuples processed yield a hash code with lowest bit cleared),\napartition split\u00a0occurs, resulting in 00\nand 10with mask 11. Operator instance 0 will be replaced with 2 new instances and partition\n1 remains unchanged,\nresulting in three active partitions. The same process could repeat if\nmost tuples fall into the01 partition, leading to a split into 001 and101\nwith mask 111, etc. Should load decrease in two sibling partitions, a\npartition merge\u00a0could\nreverse the split, reducing the mask length and replacing two operators\nwith one. Should only one of two sibling partitions be underutilized,\n it cannot be\u00a0merged.\nInstead, the platform can attempt to deploy the affected operator\ninstance along with other operator instances for resource sharing\namongst underutilized partitions (not implemented yet). Keeping separate\noperator instances allows\u00a0us to\npin load increases directly to the affected instance with a single\nspecific partition key, which would not be the case had we assigned a\nshared instance to handle multiple keys.",
- "title": "Default Dynamic Partitioning"
- },
- {
- "location": "/application_development/#nxm-partitions",
- "text": "When two consecutive logical operators are partitioned a special\noptimization is done. Technically the output of the first operator\nshould be unified and streamed to the next logical node. But that can\ncreate a network bottleneck. The platform optimizes this by partitioning\nthe output stream of each partition of the first operator as per the\npartitions needed by the next operator. For example if the first\noperator has N partitions and the second operator has M partitions then\neach of the N partitions would send out M streams. The first of each of\nthese M streams would be unified and routed to the first of the M\npartitions, and so on. Such an optimization allows for higher\nscalability and eliminates a network bottleneck (one unifier in between\nthe two operators) by having M unifiers. This also enables the\napplication to perform within the resource limits enforced by YARN.\nSTRAM has a much better understanding and estimation of unifier resource\nneeds and is thus able to optimize for resource constraints. Figure 5 shows a case where we have a 3x2 partition; the single\nintermediate unifier between operator 1\u00a0and 2\u00a0is\noptimized away. The partition computation for operator 2\u00a0is executed on outbound streams of each\npartitions of operator 1. Each\npartition of operator 2\u00a0has its own\nCONTAINER_LOCAL unifier. In such a situation, the in-bound network\ntuple flow is split between containers for 2a\u00a0and 2b\u00a0each of which take half the traffic. STRAM\ndoes this by default since it always has better performance.",
- "title": "NxM Partitions"
- },
- {
- "location": "/application_development/#parallel",
- "text": "In cases where all the downstream operators use the same\npartitioning scheme and the DAG is network bound an optimization called\nparallel partition\u00a0is very\neffective. In such a scenario all the downstream operators are also\npartitioned to create computation flow per partition. This optimization\nis extremely efficient for network bound streams, In some cases this\noptimization would also apply for CPU or RAM bounded\napplications. In Figure 6a, operator 1\u00a0is\npartitioned into 1a\u00a0and\n1b. Both the downstream operators\n2\u00a0and 3\u00a0follow the same partition scheme as\n1, however the network I/O between\n1\u00a0and 2, and between 2\u00a0and 3\u00a0is\nhigh. Then users can decide to optimize using parallel partitions. This\nallows STRAM to completely skip the insertion of intermediate Unifier\noperators between 1 and 2 as well as between 2 and 3; a single unifier\njust before operator 4, is\nadequate by which time tuple flow volume is low. Since operator 4 has sufficient resources to manage the combined\noutput of multiple instances of operator 3, it need not be partitioned. A further\noptimization can be done by declaring operators 1, 2, and\n3\u00a0as THREAD_LOCAL (intra-thread)\nor CONTAINER_LOCAL (intra-process) or NODE_LOCAL (intra-node).\nParallel partition is not used by default, users have to specify it\nexplicitly via an attribute of the input port (reader) of the stream as\nshown below. The following code shows an example of creating a parallel partition. dag.addStream( DenormalizedUserId , idAssigner.userid, uniqUserCount.data);\ndag.setInputPortAttribute(uniqUserCount.data, PortContext.PARTITION_PARALLEL, partitionParallel); Parallel partitions can be used with other partitions, for example\na parallel partition could be sticky key or load balanced.",
- "title": "Parallel"
- },
- {
- "location": "/application_development/#parallel-partitions-with-streams-modes",
- "text": "Parallel partitions can be further optimized if the parallel\npartitions are combined with streams being in-line or in-node or in-rack\nmode. This is very powerful feature and should be used if operators have\nvery high throughput within them and the outbound merge does an\naggregation. For example in Figure 6b, if operator 3 significantly\nreduces the throughput, which usually is a reason to do parallel\npartition, then making the streams in-line or in-node within nodes\n1- 2 and 2- 3 significantly impacts the performance. CONTAINER_LOCAL stream has high bandwidth, and can manage to\nconsume massive tuple count without taxing the NIC and networking stack.\nThe downside is that all operators (1,2,3) in this case need to be able\nto fit within the resource limits of CPU and memory enforced on a Hadoop\ncontainer. A way around this is to request RM to provide a big\ncontainer. On a highly used Hadoop grid, getting a bigger container may\nbe a problem, and operational complexities of managing a Hadoop cluster\nwith different container sizes may be higher. If THREAD_LOCAL or\nCONTAINER_LOCAL streams are needed to get the throughput, increasing\nthe partition count should be considered. In future STRAM may take this\ndecision automatically. Unless there is a very bad skew and sticky key\npartitioning is in use, the approach to partition till each container\nhas enough resources works well. A NODE_LOCAL stream has lower bandwidth compared to a\nCONTAINER_LOCAL stream, but it works well with the RM in terms of\nrespecting container size limits. A NODE_LOCAL parallel partition uses\nlocal loop back for streams and is much better than using NIC. Though\nNODE_LOCAL stream fits well with similar size containers, it does need\nRM to be able to deliver two containers on the same Hadoop node. On a\nheavily used Hadoop cluster, this may not always be possible. In future\nSTRAM would do these trade-offs automatically at run-time. A RACK_LOCAL stream has much lower bandwidth than NODE_LOCAL\nstream, as events go through the NIC. But it still is able to better\nmanage SLA and latency. Moreover RM has much better ability to give a\nrack local container as opposed to the other two. Parallel partitions with CONTAINER_LOCAL streams can be done by\nsetting all the intermediate streams to CONTAINER_LOCAL. Parallel\npartitions with THREAD_LOCAL streams can be done by setting all the\nintermediate streams to THREAD_LOCAL. Platform supports the following\nvia attributes. Parallel-Partition Parallel-Partition with THREAD_LOCAL stream Parallel-Partition with CONTAINER_LOCAL stream Parallel-Partition with NODE_LOCAL stream Parallel-Partition with RACK_LOCAL stream These attributes would nevertheless be initial starting point and\nSTRAM can improve on them at run time.",
- "title": "Parallel Partitions with Streams Modes"
- },
- {
- "location": "/application_development/#skew-balancing-partition",
- "text": "Skew balancing partition is useful to manage skews in the stream\nthat is load balanced using a sticky key. Incoming events may have a\nskew, and these may change depending on various factors like time of the\nday or other special circumstances. To manage the uneven load, users can\nset a limit on the ratio of maximum load on a partition to the minimum\nload on a partition. STRAM would use this to dynamically change the\npartitions. For example suppose there are 6 partitions, and the load\nhappens to be distributed as follows: one with 40%, and the rest with\n12% each. The ratio of maximum to minimum is 3.33. If the desired ratio\nis set to 2, STRAM would partition the first instance into two\npartitions, each with 20% load to bring the ratio down to the desired\nlevel. This will be tried repeatedly till partitions are balanced. The\ntime period between each attempt is controlled via an attribute to avoid\nrebalancing too frequently. As mentioned earlier, dynamic operations\ninclude both splitting a partition as well as merging partitions with\nlow load. Figure 7 shows an example of skew balancing partition. An example\nof 3x1 paritition is shown. Let's say that skew balance is kept at \u201cno\npartition to take up more than 50% load. If in runtime the load type\nchanges to create a skew. For example, consider an application in the US\nthat is processing a website clickstream. At night in the US, the\nmajority of accesses come from the Far East, while in the daytime it\ncomes from the Americas. Similarly, in the early morning, the majority\nof the accesses are from east coast of the US, with the skew shifting to\nthe west coast as the day progresses. Assume operator 1 is partitioned\ninto 1a, 1b, and 1c. Let's see what happens if the logical operator 1 gets into a 20%,\n20%, 60% skew as shown in Figure 7. This would trigger the skew\nbalancing partition. One example of attaining balance is to merge 1a,\nand 1b to get 1a+1b in a single partition to take the load to 40%; then\nsplit 1c into two partitions 1ca and 1cb to get 30% on each of them.\nThis way STRAM is able to get back to under 50% per partition. As a live\n24x7 application, this kind of skew partitioning can be applied several\ntimes in a day. Skew-balancing at runtime is a critical feature for SLA\ncompliance; it also enables cost savings. This partitioning scheme will\nbe available in later release.",
- "title": "Skew Balancing Partition"
- },
- {
- "location": "/application_development/#skew-unifier-partition",
- "text": "In this section we would take a look at another way to balance the\nskew. This method is a little less disruptive, but is useful in\naggregate operators. Let us take the same example as in Figure 7 with\nskew 20%, 20%, and 60%. To manage the load we could have either worked\non rebalancing the partition, which involves a merge and split of\npartitions to get to a new distribution or by partitioning only\u00a0the partition with the big skew. Since the\nbest way to manage skew is to load balance, if possible, this scheme\nattempts to do so. The method is less useful than the others we discusse\n-- the main reason being that if the developer has chosen a sticky key\npartition to start with, it is unlikely that a load balancing scheme can\nhelp. Assuming that it is worthwhile to load balance, a special\none-purpose unifier can be inserted for the skew partition. If the cause\nof resource bottleneck is not the I/O, specially the I/O into the\ndownstream operator, but is the compute (memory, CPU) power of a\npartition, it makes sense to split the skew partition without having to\nchange the in-bound I/O to the upstream operator. To trigger this users can set a limit on the ratio of maximum load\non a partition to the minimum load on a partition, and ask to use this\nscheme. STRAM would use this to load balance.The time period between\neach attempt is controlled via the same attribute to avoid rebalancing\ntoo frequently. Figure 8 shows an example of skew load balancing partition with a\ndedicated unifier. The 20%, 20%, and 60% triggers the skew load\nbalancing partition with an unifier. Partition 1c would be split into\ntwo and it would get its own dedicated unifier. Ideally these two\nadditional partitions 1ca and 1cb will get 30% load. This way STRAM is\nable to get back to under 50% per partition. This scheme is very useful\nwhen the number of partitions is very high and we still have a bad\nskew. In the steady state no physical partition is computing more than\n30% of the load. Memory and CPU resources are thus well distributed. The\nunifier that was inserted has to handle 60% of the load, distributed\nmore evenly, as opposed to the final unifier that had a 60% skew to\nmanage at a much higher total load. This partitioning scheme will be\navailable in later release.",
- "title": "Skew Unifier Partition"
- },
- {
- "location": "/application_development/#cascading-unifier",
- "text": "Let's take the case of an upstream operator oprU\u00a0that connects to a downstream operator\noprD. Let's assume the application\nis set to scale oprU by load balancing. So this could be either Nx1 or\nNxM partitioning scheme. The upstream operator oprU scales by increasing\nN. An increase in the load triggers more resource needs (CPU, Memory, or\nI/O), which in turn triggers more containers and raises N, the\ndownstream node may be impacted in a lot of situations. In this section\nwe review a method to shield oprD from dynamic changes in the execution\nplan of oprU. On aggregate operators (Sum, Count, Max, Min, Range \u2026) it\nis better to do load balanced partitioning to avoid impact of skew. This\nworks very well as each partition emits tuples at the order of number of\nkeys (range) in the incoming stream per application window. But as N\ngrows the in-bound I/O to the unifier of oprU that runs in the container\nof oprD goes up proportionately as each upstream partition sends tuples\nof the order of unique keys (range). This means that the partitioning\nwould not scale linearly. The platform has mechanisms to manage this and\nget the scale back to being linear. Cascading unifiers are implemented by inserting a series of\nintermediate unifiers before the final unifier in the container of oprD.\nSince each unifier guarantees that the outbound I/O would be in order of\nthe number of unique keys, the unifier in the oprD container can expect\nto achieve an upper limit on the inbound I/O. The problem is the same\nirrespective of the value of M (1 or more), wherein the amount of\ninbound I/O is proportional to N, not M. Figure 8 illustrates how\ncascading unifier works. Figure 8 shows an example where a 4x1 partition with single\nunifier is split into three 2x1 partitions to enable the final unifier\nin oprD container to get an upper limit on inbound I/O. This is useful\nto ensure that network I/O to containers is within limits, or within a\nlimit specified by users. The platform allows setting an upper limit of\nfan-in of the stream between oprU and oprD. Let's say that this is F (in\nthe figure F=2). STRAM would plan N/F (let's call it N1) containers,\neach with one unifier. The inbound fan-in to these unifiers is F. If N1 F, another level of unifiers would be inserted. Let's say at some\npoint N/(F1*F2*...Fk) F, where K is the level of unifiers. The\noutbound I/O of each unifier is guaranteed to be under F, specially the\nunifier for oprD. This ensures that the application scales linearly as\nthe load grows. The downside is the additional latency imposed by each\nunifier level (a few milliseconds), but the SLA is maintained, and the\napplication is able to run within the resource limits imposed by YARN.\nThe value of F can be derived from any of the following I/O limit on containers to allow proper behavior in an\n multi-tenant environment Load on oprD instance Buffer server limits on fan-in, fan-out Size of reservoir buffer for inbound fan-in A more intriguing optimization comes when cascading unifiers are\ncombined with node-local execution plan, in which the bounds of two or\nmore containers are used and much higher local loopback limits are\nleveraged. In general the first level fan-in limit (F1) and the last\nstage fan-in limit (Fk) need not be same. In fact a much open and better\nleveraged execution plan may indeed have F1 != F2 != \u2026 != Fk, as Fk\ndetermines the fan-in for oprD, while F1, \u2026 Fk-1 are fan-ins for\nunifier-only containers. The platform will have these schemes in later\nversions.",
- "title": "Cascading Unifier"
- },
- {
- "location": "/application_development/#sla",
- "text": "A Service Level Agreement translates to guaranteeing that the\napplication would meet the requirements X% of the time. For example six\nsigma X is\u00a099.99966%. For\nreal-time streaming applications this translates to requirements for\nlatency, throughput, uptime, data loss etc. and that in turn indirectly\nleads to various resource requirements, recovery mechanisms, etc. The\nplatform is designed to handle these and features would be released in\nfuture as they get developed. At a top level, STRAM monitors throughput\nper operator, computes latency per operator, manages uptime and supports\nvarious recovery mechanisms to handle data loss. A lot of this decision\nmaking and algorithms will be customizable.",
- "title": "SLA"
- },
- {
- "location": "/application_development/#fault-tolerance",
- "text": "Fault tolerance in the platform is defined as the ability to\nrecognize the outage of any part of the application, get resources,\nre-initialize the failed operators, and re-compute the lost data. The\ndefault method is to bring the affected part of the DAG \u00a0back to a known\n(checkpointed) state and recompute atomic micro batches from there on.\nThus the default is at least\nonce\u00a0processing mode. An operator can be configured for\nat most once\u00a0recovery, in which\ncase the re-initialized operator starts from next available window; or\nfor exactly once\u00a0recovery, in which\ncase the operator only recomputes the window it was processing when the\noutage happened.",
- "title": "Fault Tolerance"
- },
- {
- "location": "/application_development/#state-of-the-application",
- "text": "The state of the application is traditionally defined as the state\nof all operators and streams at any given time. Monitoring state as\nevery tuple is processed asynchronously in a distributed environment\nbecomes a near impossible task, and cost paid to achieve it is very\nhigh. Consequently, in the platform, state is not saved per tuple, but\nrather at window boundaries. The platform treats windows as atomic micro\nbatches. The state saving task is delegated by STRAM to the individual\noperator or container. This ensures that the bookkeeping cost is very\nlow and works in a distributed way. Thus, the state of the application\nis defined as the collection of states of every operator and the set of\nall windows stored in the buffer server. This allows STRAM to rebuild\nany part of the application from the last saved state of the impacted\noperators and the windows retained by the buffer server. The state of an\noperator is intrinsically associated with a window id. Since operators\ncan override the default checkpointing period, operators may save state\nat the end of different windows. This works because the buffer server\nsaves all windows for as long as they are needed (state in the buffer\nserver is purged once STRAM determines that it is not longer needed\nbased on checkpointing in downstream operators). Operators can be stateless or stateful. A stateless operator\nretains no data between windows. All results of all computations done in\na window are emitted in that window. Variables in such an operator are\neither transient or are cleared by an end_window event. Such operators\nneed no state restoration after an outage. A stateful operator retains\ndata between windows and has data in checkpointed state. This data\n(state) is used for computation in future windows. Such an operator\nneeds its state restored after an outage. By default the platform\nassumes the operator is stateful. In order to optimize recovery (skip\nprocessing related to state recovery) for a stateless operator, the\noperator needs to be declared as stateless to STRAM. Operators can\nexplicitly mark themselves stateless via an annotation or an\nattribute. Recovery mechanisms are explained later in this section. Operator\ndevelopers have to ensure that there is no dependency on the order of\ntuples between two different streams. As mentioned earlier in this\ndocument, the platform guarantees in-order tuple delivery within a\nsingle stream, For operators with multiple input ports, a replay may\nresult in a different relative order of tuples among the different input\nports. If the output tuple computation is affected by this relative\norder, the operator may have to wait for the endWindow call (at which\npoint it would have seen all the tuples from all input ports in the\ncurrent window), perform order-dependent computations correctly and\nfinally, emit results.",
- "title": "State of the Application"
- },
- {
- "location": "/application_development/#checkpointing",
- "text": "STRAM provides checkpointing parameters to StreamingContainer\nduring initialization. A checkpoint period is given to the containers\nthat have the window generators. A control tuple is sent at the end of\ncheckpoint interval. This tuple traverses through the data path via\nstreams and triggers each StreamingContainer in the path to instrument a\ncheckpoint of the operator that receives this tuple. This ensures that\nall the operators checkpoint at exactly the same window boundary (except\nin those cases where a different checkpoint interval was configured for\nan operator by the user). The only delay is the latency of the control tuple to reach all\nthe operators. Checkpoint is thus done between the endWindow call of a\nwindow and the beginWindow call of the next window. Since most operators\nare computing in parallel (with the exception of those connected by\nTHREAD_LOCAL streams) they each checkpoint as and when they are ready\nto process the \u201ccheckpoint\u201d control tuple. The asynchronous design of\nthe platform means that there is no guarantee that two operators would\ncheckpoint at exactly the same time, but there is a guarantee that by\ndefault they would checkpoint at the same window boundary. This feature\nalso ensures that purge of old data can be efficiently done: Once the\ncheckpoint window tuple is done traversing the DAG, the checkpoint state\nof the entire DAG increments to this window id at which point prior\ncheckpoint data can be discarded. In case of an operator that has an application window size that is\nlarger than the size of the streaming window, the checkpointing by\ndefault still happens at same intervals as with other operators. To\nalign checkpointing with application window boundary, the application\ndeveloper should set the attribute \u201cCHECKPOINT_WINDOW_COUNT\u201d to\n\u201cAPPLICATION_WINDOW_COUNT\u201d. This ensures that the checkpoint happens\nat the end\u00a0of the application\nwindow and not within\u00a0that window.\nSuch operators now treat the application window as an atomic computation\nunit. The downside is that it does need the upstream buffer server to\nkeep tuples for the entire application window. If an operator is completely stateless, i.e. an outbound tuple is\nonly emitted in the process\u00a0call\nand only depends on the tuple of that call, there is no need to align\ncheckpointing with application window end. If the operator is stateful\nonly within a window, the operator developer should strongly consider\ncheckpointing only on the application window boundary. Checkpointing involves pausing an operator, serializing the state\nto persistent storage and then resuming the operator. Thus checkpointing\nhas a latency cost that can negatively affect computational throughput;\nto minimize that impact, it is important to ensure that checkpointing is\ndone with minimal required objects. This means, as mentioned earlier,\nall data that is not part of the operator state should be declared as\ntransient so that it is not persisted. An operator developer can also create a stateless operator (marked\nwith the Stateless annotation). Stateless operators are not\ncheckpointed. Obviously, in such an operator, computation should not\ndepend on state from a previous window. The serialized \u00a0state of an operator is stored as a file, and is\nthe state to which that the operator is restored if an outage happens\nbefore the next checkpoint. The id of the last completed window (per\noperator) is sent back to STRAM in the next heartbeat. The default\nimplementation for serialization uses KRYO. Multiple past checkpoints\nare kept per operator. Depending on the downstream checkpoint, one of\nthese are chosen for recovery. Checkpoints and buffer server state are\npurged once STRAM sees windows as fully processed in the DAG. A complete recovery of an operator needs the operator to be\ncreated, its checkpointed state restored and then all the lost atomic\nwindows replayed by the upstream buffer server(s). The above design\nkeeps the bookkeeping cost low with quick catch up time. In the next\nsection we will see how this simple abstraction allows applications to\nrecover under different requirements.",
- "title": "Checkpointing"
- },
- {
- "location": "/application_development/#recovery-mechanisms_1",
- "text": "Recovery mechanism are ways to recover from a container (or an\noperator) outage. In this section we discuss a single container outage.\nMultiple container outages are handled as independent events. Recovery\nrequires the upstream buffer server to replay windows and it would\nsimply go one more level upstream if the immediate upstream container\nhas also failed. If multiple operators are in a container (THREAD_LOCAL\nor CONTAINER_LOCAL stream) the container recovery treats each operator\nas an independent object when figuring out the recovery steps.\nApplication developers can set any of the recovery mechanisms discussed\nbelow for node outage. In general, the cost of recovery depends on the state of the\noperator and the recovery mechanism selected, while data loss tolerance\nis specified by the application. For example a data-loss tolerant\napplication would prefer at most\nonce\u00a0recovery. All recovery mechanisms treat a streaming\nwindow as an atomic computation unit. In all three recovery mechanisms\nthe new operator connects to the upstream buffer server and asks for\ndata from a particular window onwards. Thus all recovery methods\ntranslate to deciding which atomic units to re-compute and which state\nthe new operator resumes from. A partially computed micro-batch is\nalways dropped. Such micro-batches are re-computed in at-least-once or\nexactly-once mode and skipped in at-most-once mode. The notiion of an\natomic micro-batch is a critical guiding principle as it enables very\nlow bookkeeping costs, high throughput, low recovery times, and high\nscalability. Within an application each operator can have its own\nrecovery mechanism.",
- "title": "Recovery Mechanisms"
- },
- {
- "location": "/application_development/#at-least-once",
- "text": "At least once recovery is the default recovery mechanism, i.e it\nis used when no mechanism is specified. In this method, the lost\noperator is brought back to its latest viable checkpointed state and the\nupstream buffer server is asked to replay all subsequent windows. There\nis no data loss in recovery. The viable checkpoint state is defined as\nthe one whose window id is in the past as compared to all the\ncheckpoints of all the downstream operators. All downstream operators\nare restarted at their checkpointed state. They ignore all incoming data\nthat belongs to windows prior their checkpointed window. The lost\nwindows are thus recomputed and the application catches up with live\nincoming data. This is called \" at least\nonce\"\u00a0because lost windows are recomputed. For example if\nthe streaming window is 0.5 seconds and checkpointing is being done\nevery 30 seconds, then upon node outage all windows since the last\ncheckpoint (up to 60 windows) need to be re-processed. If the\napplication can handle loss of data, then this is not the most optimal\nrecovery mechanism. In general for this recovery mode, the average time lag on a node\noutage is = (CP/2*SW)*T + HC where CP \u00a0\u00a0- Checkpointing period (default value is 30 seconds) SW \u00a0\u00a0- Streaming window period (default value is 0.5 seconds) T \u00a0\u00a0\u00a0- \u00a0Time taken to re-compute one lost window from data in memory HC \u00a0\u00a0- Time it takes to get a new Hadoop Container, or make do with the current ones A lower CP is a trade off between cost of checkpointing and the\nneed to have to use it in case of outage. Input adapters cannot use\nat-least-once recovery without the support from sources outside Hadoop.\nFor an output adapter care may needed if the external system cannot\nhandle re-write of the same data.",
- "title": "At Least Once"
- },
- {
- "location": "/application_development/#at-most-once",
- "text": "This recovery mechanism is for applications that can tolerate\ndata-loss; they get the quickest recovery in return. The restarted node\nconnects to the upstream buffer server, subscribing to data from the\nstart of the next window. It then starts processing that window. The\ndownstream operators ignore the lost windows and continue to process\nincoming data normally. Thus, this mechanism forces all downstream\noperators to follow. For multiple inputs, the operator waits for all ports with the\nat-most-once attribute to get responses from their respective buffer\nservers. Then, the operator starts processing till the end window of the\nlatest window id on each input port is reached. In this case the end\nwindow tuple is non-blocking till the common window id is reached. At\nthis point the input ports are now properly synchronized. Upstream nodes\nreconnect under at most\nonce\u00a0paradigm in same way. \u00a0For example, assume an operator\nhas ports in1\u00a0and in2\u00a0and a checkpointed window of 95. Assume further that the buffer servers of\noperators upstream of in1\u00a0and\nin2\u00a0respond with window id 100 and\n102 respectively. Then port in1\u00a0would continue to process till end window of\n101, while port in2\u00a0will wait for in1\nto catch up to 102.\nFrom \u00a0then on, both ports process their tuples normally. So windows from\n96 to 99are lost. Window 100\nand 101 has only\nin1 active, and 102 onwards both ports are active. The other\nports of upstream nodes would also catch up till 102in a similar fashion. This operator may not\nneed to be checkpointed. Currently the option to not do checkpoint in\nsuch cases is not available. In general, in this recovery mode, the average time lag on a node\noutage is = SW/2 + HC where SW \u00a0- Streaming window period (default value is 0.5\nseconds) HC \u00a0- Time it takes to get a new Hadoop Container, or make\ndo with the current ones",
- "title": "At Most Once"
- },
- {
- "location": "/application_development/#exactly-once",
- "text": "This recovery mechanism is for applications that require no\ndata-loss as well are no recomputation. Since a window is an atomic\ncompute unit, exactly once applies to the window as a whole. In this\nrecovery mode, the operator is brought back to the start of the window\nin which the outage happened and the window is recomputed. The window is\nconsidered closed when all the data computations are done and end window\ntuple is emitted. \u00a0Exactly once requires every window to be\ncheckpointed. From then on, the operator asks the upstream buffer server\nto send data from the last checkpoint. The upstream node behaves the\nsame as in at-most-once recovery. Checkpointing after every streaming\nwindow is very costly, but users would most often do exactly once per\napplication window; if the application window size is substantially\nlarger than the streaming window size (which typically is the case) the\ncost of running an operator in this recovery mode may not be as\nhigh.",
- "title": "Exactly Once"
- },
- {
- "location": "/application_development/#speculative-execution",
- "text": "In future we looking at possibility of adding speculative execution for the applications. This would be enabled in multiple ways. At an operator level: The upstream operator would emit to\n two copies. The downstream operator would receive from both copies\n and pick a winner. The winner (primary) would be picked in either of\n the following ways Statically as dictated by STRAM Dynamically based on whose tuple arrives first. This mode\n needs both copies to guarantee that the computation result would\n have identical functionality At a sub-query level: A part of the application DAG would be\n run in parallel and all upstream operators would feed to two copies\n and all downstream operators would receive from both copies. The\n winners would again be picked in a static or dynamic manner Entire DAG: Another copy of the application would be run by\n STRAM and the winner would be decided outside the application. In\n this mode the output adapters would both be writing\n the result. In all cases the two copies would run on different Hadoop nodes.\nSpeculative execution is under development and\nis not yet available.",
- "title": "Speculative Execution"
- },
- {
- "location": "/application_development/#dynamic-application-modifications",
- "text": "Dynamic application modifications are being worked on and most of\nthe features discussed here are now available. The platform supports the\nability to modify the DAG of the application as per inputs as well as\nset constraints, and will continue to provide abilities to deepen\nfeatures based on this ability. All these changes have one thing in\ncommon and that is the application does not need to be restarted as\nSTRAM will instrument the changes and the streaming will catch-up and\ncontinue. Some examples are Dynamic Partitioning:\u00a0Automatic\n changes in partitioning of computations to match constraints on a\n run time basis. Examples includes STRAM adding resource during spike\n in streams and returning them once spike is gone. Scale up and scale\n down is done automatically without human intervention. Modification via constraints: Attributes can be changed via\n Webservices and STRAM would adapt the execution plan to meet these.\n Examples include operations folks asking STRAM to reduce container\n count, or changing network resource restrictions. Modification via properties: Properties of operators can be\n changed in run time. This enables application developers to trigger\n a new behavior as need be. Examples include triggering an alert ON.\n The platform supports changes to any property of an operator that\n has a setter function defined. Modification of DAG structure: Operators and streams can be\n added to or removed from a running DAG, provided the code of the\n operator being added is already in the classpath of the running\n application master. \u00a0This enables application developers to add or\n remove processing pipelines on the fly without having to restart\n the application. Query Insertion: Addition of sub-queries to currently\n running application. This query would take current streams as inputs\n and start computations as per their specs. Examples insertion of\n SQL-queries on live data streams, dynamic query submission and\n result from STRAM (not yet available). Dynamic modifications to applications are foundational part of the\nplatform. They enable users to build layers over the applications. Users\ncan also save all the changes done since the application launch, and\ntherefore predictably get the application to its current state. For\ndetails refer to Configuration Guide \n.",
- "title": "Dynamic Application Modifications"
- },
- {
- "location": "/application_development/#demos",
- "text": "The source code for the demos is available in the open-source Apache Apex-Malhar repository .\nAll of these do computations in real-time. Developers are encouraged to\nreview them as they use various features of the platform and provide an\nopportunity for quick learning.",
- "title": "Demos"
- },
- {
- "location": "/application_packages/",
- "text": "Apache Apex Packages\n\n\nApplication Packages\n\n\nAn Apache Apex Application Package is a zip file that contains all the\nnecessary files to launch an application in Apache Apex. It is the\nstandard way for assembling and sharing an Apache Apex application.\n\n\nRequirements\n\n\nYou will need have the following installed:\n\n\n\n\nApache Maven 3.0 or later (for assembling the App Package)\n\n\nApache Apex 3.2.0 or later (for launching the App Package in your cluster)\n\n\n\n\nCreating Your First Apex App Package\n\n\nYou can create an Apex Application Package using your Linux command\nline, or using your favorite IDE.\n\n\nUsing Command Line\n\n\nFirst, change to the directory where you put your projects, and create\nan Apex application project using Maven by running the following\ncommand. Replace \"com.example\", \"mydtapp\" and \"1.0-SNAPSHOT\" with the\nappropriate values (make sure this is all on one line):\n\n\n$ mvn archetype:generate \\\n -DarchetypeGroupId=org.apache.apex \\\n -DarchetypeArtifactId=apex-app-archetype -DarchetypeVersion=3.2.0-incubating \\\n -DgroupId=com.example -Dpackage=com.example.mydtapp -DartifactId=mydtapp \\\n -Dversion=1.0-SNAPSHOT\n\n\n\nThis creates a Maven project named \"mydtapp\". Open it with your favorite\nIDE (e.g. NetBeans, Eclipse, IntelliJ IDEA). In the project, there is a\nsample DAG that generates a number of tuples with a random number and\nprints out \"hello world\" and the random number in the tuples. The code\nthat builds the DAG is in\nsrc/main/java/com/example/mydtapp/Application.java, and the code that\nruns the unit test for the DAG is in\nsrc/test/java/com/example/mydtapp/ApplicationTest.java. Try it out by\nrunning the following command:\n\n\n$cd mydtapp; mvn package\n\n\n\nThis builds the App Package runs the unit test of the DAG. You should\nbe getting test output similar to this:\n\n\n -------------------------------------------------------\n TESTS\n -------------------------------------------------------\n\n Running com.example.mydtapp.ApplicationTest\n hello world: 0.8015370953286478\n hello world: 0.9785359225545481\n hello world: 0.6322611586644047\n hello world: 0.8460953663451775\n hello world: 0.5719372906929072\n hello world: 0.6361174312337172\n hello world: 0.14873007534816318\n hello world: 0.8866986277418261\n hello world: 0.6346526809866057\n hello world: 0.48587295703904465\n hello world: 0.6436832429676687\n\n ...\n\n Tests run: 1, Failures: 0, Errors: 0, Skipped: 0, Time elapsed: 11.863\n sec\n\n Results :\n\n Tests run: 1, Failures: 0, Errors: 0, Skipped: 0\n\n\n\n\nThe \"mvn package\" command creates the App Package file in target\ndirectory as target/mydtapp-1.0-SNAPSHOT.apa. You will be able to use\nthat App Package file to launch this sample application in your actual\nApex installation.\n\n\nAlternatively you can perform the same steps within your IDE (IDEA IntelliJ, Eclipse, NetBeans all support it). Please check the IDE documentation for details.\n\n\nGroup ID: org.apache.apex\nArtifact ID: apex-app-archetype\nVersion: 3.2.0-incubating (or any later version)\n\n\nWriting Your Own App Package\n\n\nPlease refer to the \nCreating Apps\n on the basics on how to write an Apache Apex application. In your AppPackage project, you can add custom operators (refer to \nOperator Development Guide\n, project dependencies, default and required configuration properties, pre-set configurations and other metadata.\n\n\nAdding (and removing) project dependencies\n\n\nUnder the project, you can add project dependencies in pom.xml, or do it\nthrough your IDE. Here\u2019s the section that describes the dependencies in\nthe default pom.xml:\n\n\n \ndependencies\n\n \n!-- add your dependencies here --\n\n \ndependency\n\n \ngroupId\norg.apache.apex\n/groupId\n\n \nartifactId\nmalhar-library\n/artifactId\n\n \nversion\n${apex.version}\n/version\n\n \n!--\n If you know your application do not need the transitive dependencies that are pulled in by malhar-library,\n Uncomment the following to reduce the size of your app package.\n --\n\n \n!--\n \nexclusions\n\n \nexclusion\n\n \ngroupId\n*\n/groupId\n\n \nartifactId\n*\n/artifactId\n\n \n/exclusion\n\n \n/exclusions\n\n --\n\n \n/dependency\n\n \ndependency\n\n \ngroupId\norg.apache.apex\n/groupId\n\n \nartifactId\napex-engine\n/artifactId\n\n \nversion\n${apex.version}\n/version\n\n \nscope\nprovided\n/scope\n\n \n/dependency\n\n \ndependency\n\n \ngroupId\njunit\n/groupId\n\n \nartifactId\njunit\n/artifactId\n\n \nversion\n4.10\n/version\n\n \nscope\ntest\n/scope\n\n \n/dependency\n\n \n/dependencies\n\n\n\n\n\nBy default, as shown above, the default dependencies include\nmalhar-library in compile scope, dt-engine in provided scope, and junit\nin test scope. Do not remove these three dependencies since they are\nnecessary for any Apex application. You can, however, exclude\ntransitive dependencies from malhar-library to reduce the size of your\nApp Package, provided that none of the operators in malhar-library that\nneed the transitive dependencies will be used in your application.\n\n\nIn the sample application, it is safe to remove the transitive\ndependencies from malhar-library, by uncommenting the \"exclusions\"\nsection. It will reduce the size of the sample App Package from 8MB to\n700KB.\n\n\nNote that if we exclude *, in some versions of Maven, you may get\nwarnings similar to the following:\n\n\n\n [WARNING] 'dependencies.dependency.exclusions.exclusion.groupId' for\n org.apache.apex:malhar-library:jar with value '*' does not match a\n valid id pattern.\n\n [WARNING]\n [WARNING] It is highly recommended to fix these problems because they\n threaten the stability of your build.\n [WARNING]\n [WARNING] For this reason, future Maven versions might no longer support\n building such malformed projects.\n [WARNING]\n\n\n\n\n\nThis is a bug in early versions of Maven 3. The dependency exclusion is\nstill valid and it is safe to ignore these warnings.\n\n\nApplication Configuration\n\n\nA configuration file can be used to configure an application. Different\nkinds of configuration parameters can be specified. They are application\nattributes, operator attributes and properties, port attributes, stream\nproperties and application specific properties. They are all specified\nas name value pairs, in XML format, like the following.\n\n\n?xml version=\n1.0\n?\n\n\nconfiguration\n\n \nproperty\n\n \nname\nsome_name_1\n/name\n\n \nvalue\nsome_default_value\n/value\n\n \n/property\n\n \nproperty\n\n \nname\nsome_name_2\n/name\n\n \nvalue\nsome_default_value\n/value\n\n \n/property\n\n\n/configuration\n\n\n\n\n\nApplication attributes\n\n\nApplication attributes are used to specify the platform behavior for the\napplication. They can be specified using the parameter\n\ndt.attr.\nattribute\n. The prefix \u201cdt\u201d is a constant, \u201cattr\u201d is a\nconstant denoting an attribute is being specified and \nattribute\n\nspecifies the name of the attribute. Below is an example snippet setting\nthe streaming windows size of the application to be 1000 milliseconds.\n\n\n \nproperty\n\n \nname\ndt.attr.STREAMING_WINDOW_SIZE_MILLIS\n/name\n\n \nvalue\n1000\n/value\n\n \n/property\n\n\n\n\n\nThe name tag specifies the attribute and value tag specifies the\nattribute value. The name of the attribute is a JAVA constant name\nidentifying the attribute. The constants are defined in\ncom.datatorrent.api.Context.DAGContext and the different attributes can\nbe specified in the format described above.\n\n\nOperator attributes\n\n\nOperator attributes are used to specify the platform behavior for the\noperator. They can be specified using the parameter\n\ndt.operator.\noperator-name\n.attr.\nattribute\n. The prefix \u201cdt\u201d is a\nconstant, \u201coperator\u201d is a constant denoting that an operator is being\nspecified, \noperator-name\n denotes the name of the operator, \u201cattr\u201d is\nthe constant denoting that an attribute is being specified and\n\nattribute\n is the name of the attribute. The operator name is the\nsame name that is specified when the operator is added to the DAG using\nthe addOperator method. An example illustrating the specification is\nshown below. It specifies the number of streaming windows for one\napplication window of an operator named \u201cinput\u201d to be 10\n\n\nproperty\n\n \nname\ndt.operator.input.attr.APPLICATION_WINDOW_COUNT\n/name\n\n \nvalue\n10\n/value\n\n\n/property\n\n\n\n\n\nThe name tag specifies the attribute and value tag specifies the\nattribute value. The name of the attribute is a JAVA constant name\nidentifying the attribute. The constants are defined in\ncom.datatorrent.api.Context.OperatorContext and the different attributes\ncan be specified in the format described above.\n\n\nOperator properties\n\n\nOperators can be configured using operator specific properties. The\nproperties can be specified using the parameter\n\ndt.operator.\noperator-name\n.prop.\nproperty-name\n. The difference\nbetween this and the operator attribute specification described above is\nthat the keyword \u201cprop\u201d is used to denote that it is a property and\n\nproperty-name\n specifies the property name. An example illustrating\nthis is specified below. It specifies the property \u201chostname\u201d of the\nredis server for a \u201credis\u201d output operator.\n\n\n \nproperty\n\n \nname\ndt.operator.redis.prop.host\n/name\n\n \nvalue\n127.0.0.1\n/value\n\n \n/property\n\n\n\n\n\nThe name tag specifies the property and the value specifies the property\nvalue. The property name is converted to a setter method which is called\non the actual operator. The method name is composed by appending the\nword \u201cset\u201d and the property name with the first character of the name\ncapitalized. In the above example the setter method would become\nsetHost. The method is called using JAVA reflection and the property\nvalue is passed as an argument. In the above example the method setHost\nwill be called on the \u201credis\u201d operator with \u201c127.0.0.1\u201d as the argument.\n\n\nPort attributes\n\n\nPort attributes are used to specify the platform behavior for input and\noutput ports. They can be specified using the parameter \ndt.operator.\noperator-name\n.inputport.\nport-name\n.attr.\nattribute\n\nfor input port and \ndt.operator.\noperator-name\n.outputport.\nport-name\n.attr.\nattribute\n\nfor output port. The keyword \u201cinputport\u201d is used to denote an input port\nand \u201coutputport\u201d to denote an output port. The rest of the specification\nfollows the conventions described in other specifications above. An\nexample illustrating this is specified below. It specifies the queue\ncapacity for an input port named \u201cinput\u201d of an operator named \u201crange\u201d to\nbe 4k.\n\n\nproperty\n\n \nname\ndt.operator.range.inputport.input.attr.QUEUE_CAPACITY\n/name\n\n \nvalue\n4000\n/value\n\n\n/property\n\n\n\n\n\nThe name tag specifies the attribute and value tag specifies the\nattribute value. The name of the attribute is a JAVA constant name\nidentifying the attribute. The constants are defined in\ncom.datatorrent.api.Context.PortContext and the different attributes can\nbe specified in the format described above.\n\n\nThe attributes for an output port can also be specified in a similar way\nas described above with a change that keyword \u201coutputport\u201d is used\ninstead of \u201cintputport\u201d. A generic keyword \u201cport\u201d can be used to specify\neither an input or an output port. It is useful in the wildcard\nspecification described below.\n\n\nStream properties\n\n\nStreams can be configured using stream properties. The properties can be\nspecified using the parameter\n\ndt.stream.\nstream-name\n.prop.\nproperty-name\n The constant \u201cstream\u201d\nspecifies that it is a stream, \nstream-name\n specifies the name of the\nstream and \nproperty-name\n the name of the property. The name of the\nstream is the same name that is passed when the stream is added to the\nDAG using the addStream method. An example illustrating the\nspecification is shown below. It sets the locality of the stream named\n\u201cstream1\u201d to container local indicating that the operators the stream is\nconnecting be run in the same container.\n\n\n \nproperty\n\n \nname\ndt.stream.stream1.prop.locality\n/name\n\n \nvalue\nCONTAINER_LOCAL\n/value\n\n \n/property\n\n\n\n\n\nThe property name is converted into a set method on the stream in the\nsame way as described in operator properties section above. In this case\nthe method would be setLocality and it will be called in the stream\n\u201cstream1\u201d with the value as the argument.\n\n\nAlong with the above system defined parameters, the applications can\ndefine their own specific parameters they can be specified in the\nconfiguration file. The only condition is that the names of these\nparameters don\u2019t conflict with the system defined parameters or similar\napplication parameters defined by other applications. To this end, it is\nrecommended that the application parameters have the format\n\nfull-application-class-name\n.\nparam-name\n.\n The\nfull-application-class-name is the full JAVA class name of the\napplication including the package path and param-name is the name of the\nparameter within the application. The application will still have to\nstill read the parameter in using the configuration API of the\nconfiguration object that is passed in populateDAG.\n\n\nWildcards\n\n\nWildcards and regular expressions can be used in place of names to\nspecify a group for applications, operators, ports or streams. For\nexample, to specify an attribute for all ports of an operator it can be\ndone as follows\n\n\nproperty\n\n \nname\ndt.operator.range.port.*.attr.QUEUE_CAPACITY\n/name\n\n \nvalue\n4000\n/value\n\n\n/property\n\n\n\n\n\nThe wildcard \u201c*\u201d was used instead of the name of the port. Wildcard can\nalso be used for operator name, stream name or application name. Regular\nexpressions can also be used for names to specify attributes or\nproperties for a specific set.\n\n\nAdding configuration properties\n\n\nIt is common for applications to require configuration parameters to\nrun. For example, the address and port of the database, the location of\na file for ingestion, etc. You can specify them in\nsrc/main/resources/META-INF/properties.xml under the App Package\nproject. The properties.xml may look like:\n\n\n?xml version=\n1.0\n?\n\n\nconfiguration\n\n \nproperty\n\n \nname\nsome_name_1\n/name\n\n \n/property\n\n \nproperty\n\n \nname\nsome_name_2\n/name\n\n \nvalue\nsome_default_value\n/value\n\n \n/property\n\n\n/configuration\n\n\n\n\n\nThe name of an application-specific property takes the form of:\n\n\ndt.operator.{opName}.prop.{propName}\n\n\nThe first represents the property with name propName of operator opName.\n Or you can set the application name at run time by setting this\nproperty:\n\n\n dt.attr.APPLICATION_NAME\n\n\n\nIn this example, property some_name_1 is a required property which\nmust be set at launch time, or it must be set by a pre-set configuration\n(see next section). Property some_name_2 is a property that is\nassigned with value some_default_value unless it is overridden at\nlaunch time.\n\n\nAdding pre-set configurations\n\n\nAt build time, you can add pre-set configurations to the App Package by\nadding configuration XML files under \nsrc/site/conf/\nconf\n.xml\nin your\nproject. You can then specify which configuration to use at launch\ntime. The configuration XML is of the same format of the properties.xml\nfile.\n\n\nApplication-specific properties file\n\n\nYou can also specify properties.xml per application in the application\npackage. Just create a file with the name properties-{appName}.xml and\nit will be picked up when you launch the application with the specified\nname within the application package. In short:\n\n\nproperties.xml: Properties that are global to the Configuration\nPackage\n\n\nproperties-{appName}.xml: Properties that are specific when launching\nan application with the specified appName.\n\n\nProperties source precedence\n\n\nIf properties with the same key appear in multiple sources (e.g. from\napp package default configuration as META-INF/properties.xml, from app\npackage configuration in the conf directory, from launch time defines,\netc), the precedence of sources, from highest to lowest, is as follows:\n\n\n\n\nLaunch time defines (using -D option in CLI)\n\n\nLaunch time specified configuration file in file system (using -conf\n option in CLI)\n\n\nLaunch time specified package configuration (using -apconf option in\n CLI)\n\n\nConfiguration from \\$HOME/.dt/dt-site.xml\n\n\nApplication defaults within the package as\n META-INF/properties-{appname}.xml\n\n\nPackage defaults as META-INF/properties.xml\n\n\ndt-site.xml in local DT installation\n\n\ndt-site.xml stored in HDFS\n\n\n\n\nOther meta-data\n\n\nIn a Apex App Package project, the pom.xml file contains a\nsection that looks like:\n\n\nproperties\n\n \napex.version\n3.2.0-incubating\n/apex.version\n\n \napex.apppackage.classpath\\\nlib*.jar\n/apex.apppackage.classpath\n\n\n/properties\n\n\n\n\n\napex.version is the Apache Apex version that are to be used\nwith this Application Package.\n\n\napex.apppackage.classpath is the classpath that is used when\nlaunching the application in the Application Package. The default is\nlib/*.jar, where lib is where all the dependency jars are kept within\nthe Application Package. One reason to change this field is when your\nApplication Package needs the classpath in a specific order.\n\n\nLogging configuration\n\n\nJust like other Java projects, you can change the logging configuration\nby having your log4j.properties under src/main/resources. For example,\nif you have the following in src/main/resources/log4j.properties:\n\n\n log4j.rootLogger=WARN,CONSOLE\n log4j.appender.CONSOLE=org.apache.log4j.ConsoleAppender\n log4j.appender.CONSOLE.layout=org.apache.log4j.PatternLayout\n log4j.appender.CONSOLE.layout.ConversionPattern=%d{ISO8601} [%t] %-5p\n %c{2} %M - %m%n\n\n\n\n\nThe root logger\u2019s level is set to WARN and the output is set to the console (stdout).\n\n\nNote that by default from project created from the maven archetype,\nthere is already a log4j.properties file under src/test/resources and\nthat file is only used for the unit test.\n\n\nZip Structure of Application Package\n\n\nApache Apex Application Package files are zip files. You can examine the content of any Application Package by using unzip -t on your Linux command line.\n\n\nThere are four top level directories in an Application Package:\n\n\n\n\n\"app\" contains the jar files of the DAG code and any custom operators.\n\n\n\"lib\" contains all dependency jars\n\n\n\"conf\" contains all the pre-set configuration XML files.\n\n\n\"META-INF\" contains the MANIFEST.MF file and the properties.xml file.\n\n\n\u201cresources\u201d contains any other files\n\n\n\n\nExamining and Launching Application Packages Through CLI\n\n\nIf you are working with Application Packages in the local filesystem, you can use the Apex Command Line Interface (dtcli). \n\n\nGetting Application Package Meta Information\n\n\nYou can get the meta information about the Application Package using\nthis Apex CLI command.\n\n\n dt\n get-app-package-info \napp-package-file\n\n\n\n\n\nGetting Available Operators In Application Package\n\n\nYou can get the list of available operators in the Application Package\nusing this command.\n\n\n dt\n get-app-package-operators \napp-package-file\n \npackage-prefix\n\n [parent-class]\n\n\n\n\nGetting Properties of Operators in Application Package\n\n\nYou can get the list of properties of any operator in the Application\nPackage using this command.\n\n\ndt\n get-app-package-operator-properties \n \n\n\nLaunching an Application Package\n\n\nYou can launch an application within an Application Package.\n\n\ndt\n launch [-D property-name=property-value, ...] [-conf config-name]\n [-apconf config-file-within-app-package] \napp-package-file\n\n [matching-app-name]\n\n\n\n\nNote that -conf expects a configuration file in the file system, while -apconf expects a configuration file within the app package.\n\n\nConfiguration Packages\n\n\nSometimes just a configuration file is not enough for launching an application package. If a configuration requires\nadditional files to be packaged, you can use an Apex Configuration Package.\n\n\nCreating Configuration Packages\n\n\nCreating Configuration Packages is similar to creating Application Packages. You can create a configuration \npackage project using Maven by running the following command. Replace \"com.example\", \"mydtconfig\" and \"1.0-SNAPSHOT\" with the appropriate values:\n\n\n$ mvn archetype:generate -DarchetypeGroupId=org.apache.apex \\\n -DarchetypeArtifactId=apex-conf-archetype -DarchetypeVersion=3.2.0-incubating \\\n -DgroupId=com.example -Dpackage=com.example.mydtconfig -DartifactId=mydtconfig \\\n -Dversion=1.0-SNAPSHOT\n\n\n\n\nAnd create the configuration package file by running:\n\n\n$ mvn package\n\n\n\n\nThe \"mvn package\" command creates the Config Package file in target\ndirectory as target/mydtconfig.apc. You will be able to use that\nConfiguration Package file to launch an Apache Apex application.\n\n\nAssembling your own configuration package\n\n\nInside the project created by the archetype, these are the files that\nyou should know about when assembling your own configuration package:\n\n\n./pom.xml\n./src/main/resources/classpath\n./src/main/resources/files\n./src/main/resources/META-INF/properties.xml\n./src/main/resources/META-INF/properties-{appname}.xml\n\n\n\npom.xml\n\n\nExample:\n\n\n \ngroupId\ncom.example\n/groupId\n\n \nversion\n1.0.0\n/version\n\n \nartifactId\nmydtconf\n/artifactId\n\n \npackaging\njar\n/packaging\n\n \n!-- change these to the appropriate values --\n\n \nname\nMy Apex Application Configuration\n/name\n\n \ndescription\nMy Custom Application Configuration Description\n/description\n\n \nproperties\n\n \napex.apppackage.name\nmyapexapp\n/apex.apppackage.name\n\n \napex.apppackage.minversion\n1.0.0\n/apex.apppackage.minversion\n\n \napex.apppackage.maxversion\n1.9999.9999\n/apex.apppackage.maxversion\n\n \napex.appconf.classpath\nclasspath/*\n/apex.appconf.classpath\n\n \napex.appconf.files\nfiles/*\n/apex.appconf.files\n\n \n/properties\n\n\n\n\n\n\nIn pom.xml, you can change the following keys to your desired values\n\n\n\n\ngroupId\n\n\nversion\n\n\nartifactId\n\n\nname\n\n\ndescription\n\n\n\n\nYou can also change the values of\n\n\n\n\napex.apppackage.name\n\n\napex.apppackage.minversion\n\n\napex.apppackage.maxversion\n\n\n\n\nto reflect what Application Packages can be used with this configuration package. Apex will use this information to check whether a\nconfiguration package is compatible with the Application Package when you issue a launch command.\n\n\n./src/main/resources/classpath\n\n\nPlace any file in this directory that you\u2019d like to be copied to the\ncompute machines when launching an application and included in the\nclasspath of the application. Example of such files are Java properties\nfiles and jar files.\n\n\n./src/main/resources/files\n\n\nPlace any file in this directory that you\u2019d like to be copied to the\ncompute machines when launching an application but not included in the\nclasspath of the application.\n\n\nProperties XML file\n\n\nA properties xml file consists of a set of key-value pairs. The set of\nkey-value pairs specifies the configuration options the application\nshould be launched with.\n\n\nExample:\n\n\nconfiguration\n\n \nproperty\n\n \nname\nsome-property-name\n/name\n\n \nvalue\nsome-property-value\n/value\n\n \n/property\n\n ...\n\n/configuration\n\n\n\n\n\nNames of properties XML file:\n\n\n\n\nproperties.xml:\n Properties that are global to the Configuration\nPackage\n\n\nproperties-{appName}.xml:\n Properties that are specific when launching\nan application with the specified appName within the Application\nPackage.\n\n\n\n\nAfter you are done with the above, remember to do mvn package to\ngenerate a new configuration package, which will be located in the\ntarget directory in your project.\n\n\nZip structure of configuration package\n\n\nApex Application Configuration Package files are zip files. You\ncan examine the content of any Application Configuration Package by\nusing unzip -t on your Linux command line. The structure of the zip\nfile is as follow:\n\n\nMETA-INF\n MANIFEST.MF\n properties.xml\n properties-{appname}.xml\nclasspath\n {classpath files}\nfiles\n {files}\n\n\n\n\nLaunching with CLI\n\n\n-conf\n option of the launch command in CLI supports specifying configuration package in the local filesystem. Example:\n\n\ndt\\\n launch mydtapp-1.0.0.apa -conf mydtconfig.apc\n\n\n\nThis command expects both the application package and the configuration package to be in the local file system.",
- "title": "Packages"
- },
- {
- "location": "/application_packages/#apache-apex-packages",
- "text": "",
- "title": "Apache Apex Packages"
- },
- {
- "location": "/application_packages/#application-packages",
- "text": "An Apache Apex Application Package is a zip file that contains all the\nnecessary files to launch an application in Apache Apex. It is the\nstandard way for assembling and sharing an Apache Apex application.",
- "title": "Application Packages"
- },
- {
- "location": "/application_packages/#requirements",
- "text": "You will need have the following installed: Apache Maven 3.0 or later (for assembling the App Package) Apache Apex 3.2.0 or later (for launching the App Package in your cluster)",
- "title": "Requirements"
- },
- {
- "location": "/application_packages/#creating-your-first-apex-app-package",
- "text": "You can create an Apex Application Package using your Linux command\nline, or using your favorite IDE.",
- "title": "Creating Your First Apex App Package"
- },
- {
- "location": "/application_packages/#using-command-line",
- "text": "First, change to the directory where you put your projects, and create\nan Apex application project using Maven by running the following\ncommand. Replace \"com.example\", \"mydtapp\" and \"1.0-SNAPSHOT\" with the\nappropriate values (make sure this is all on one line): $ mvn archetype:generate \\\n -DarchetypeGroupId=org.apache.apex \\\n -DarchetypeArtifactId=apex-app-archetype -DarchetypeVersion=3.2.0-incubating \\\n -DgroupId=com.example -Dpackage=com.example.mydtapp -DartifactId=mydtapp \\\n -Dversion=1.0-SNAPSHOT This creates a Maven project named \"mydtapp\". Open it with your favorite\nIDE (e.g. NetBeans, Eclipse, IntelliJ IDEA). In the project, there is a\nsample DAG that generates a number of tuples with a random number and\nprints out \"hello world\" and the random number in the tuples. The code\nthat builds the DAG is in\nsrc/main/java/com/example/mydtapp/Application.java, and the code that\nruns the unit test for the DAG is in\nsrc/test/java/com/example/mydtapp/ApplicationTest.java. Try it out by\nrunning the following command: $cd mydtapp; mvn package This builds the App Package runs the unit test of the DAG. You should\nbe getting test output similar to this: -------------------------------------------------------\n TESTS\n -------------------------------------------------------\n\n Running com.example.mydtapp.ApplicationTest\n hello world: 0.8015370953286478\n hello world: 0.9785359225545481\n hello world: 0.6322611586644047\n hello world: 0.8460953663451775\n hello world: 0.5719372906929072\n hello world: 0.6361174312337172\n hello world: 0.14873007534816318\n hello world: 0.8866986277418261\n hello world: 0.6346526809866057\n hello world: 0.48587295703904465\n hello world: 0.6436832429676687\n\n ...\n\n Tests run: 1, Failures: 0, Errors: 0, Skipped: 0, Time elapsed: 11.863\n sec\n\n Results :\n\n Tests run: 1, Failures: 0, Errors: 0, Skipped: 0 The \"mvn package\" command creates the App Package file in target\ndirectory as target/mydtapp-1.0-SNAPSHOT.apa. You will be able to use\nthat App Package file to launch this sample application in your actual\nApex installation. Alternatively you can perform the same steps within your IDE (IDEA IntelliJ, Eclipse, NetBeans all support it). Please check the IDE documentation for details. Group ID: org.apache.apex\nArtifact ID: apex-app-archetype\nVersion: 3.2.0-incubating (or any later version)",
- "title": "Using Command Line"
- },
- {
- "location": "/application_packages/#writing-your-own-app-package",
- "text": "Please refer to the Creating Apps on the basics on how to write an Apache Apex application. In your AppPackage project, you can add custom operators (refer to Operator Development Guide , project dependencies, default and required configuration properties, pre-set configurations and other metadata.",
- "title": "Writing Your Own App Package"
- },
- {
- "location": "/application_packages/#adding-and-removing-project-dependencies",
- "text": "Under the project, you can add project dependencies in pom.xml, or do it\nthrough your IDE. Here\u2019s the section that describes the dependencies in\nthe default pom.xml: dependencies \n !-- add your dependencies here -- \n dependency \n groupId org.apache.apex /groupId \n artifactId malhar-library /artifactId \n version ${apex.version} /version \n !--\n If you know your application do not need the transitive dependencies that are pulled in by malhar-library,\n Uncomment the following to reduce the size of your app package.\n -- \n !--\n exclusions \n exclusion \n groupId * /groupId \n artifactId * /artifactId \n /exclusion \n /exclusions \n -- \n /dependency \n dependency \n groupId org.apache.apex /groupId \n artifactId apex-engine /artifactId \n version ${apex.version} /version \n scope provided /scope \n /dependency \n dependency \n groupId junit /groupId \n artifactId junit /artifactId \n version 4.10 /version \n scope test /scope \n /dependency \n /dependencies By default, as shown above, the default dependencies include\nmalhar-library in compile scope, dt-engine in provided scope, and junit\nin test scope. Do not remove these three dependencies since they are\nnecessary for any Apex application. You can, however, exclude\ntransitive dependencies from malhar-library to reduce the size of your\nApp Package, provided that none of the operators in malhar-library that\nneed the transitive dependencies will be used in your application. In the sample application, it is safe to remove the transitive\ndependencies from malhar-library, by uncommenting the \"exclusions\"\nsection. It will reduce the size of the sample App Package from 8MB to\n700KB. Note that if we exclude *, in some versions of Maven, you may get\nwarnings similar to the following: \n [WARNING] 'dependencies.dependency.exclusions.exclusion.groupId' for\n org.apache.apex:malhar-library:jar with value '*' does not match a\n valid id pattern.\n\n [WARNING]\n [WARNING] It is highly recommended to fix these problems because they\n threaten the stability of your build.\n [WARNING]\n [WARNING] For this reason, future Maven versions might no longer support\n building such malformed projects.\n [WARNING] This is a bug in early versions of Maven 3. The dependency exclusion is\nstill valid and it is safe to ignore these warnings.",
- "title": "Adding (and removing) project dependencies"
- },
- {
- "location": "/application_packages/#application-configuration",
- "text": "A configuration file can be used to configure an application. Different\nkinds of configuration parameters can be specified. They are application\nattributes, operator attributes and properties, port attributes, stream\nproperties and application specific properties. They are all specified\nas name value pairs, in XML format, like the following. ?xml version= 1.0 ? configuration \n property \n name some_name_1 /name \n value some_default_value /value \n /property \n property \n name some_name_2 /name \n value some_default_value /value \n /property /configuration",
- "title": "Application Configuration"
- },
- {
- "location": "/application_packages/#application-attributes",
- "text": "Application attributes are used to specify the platform behavior for the\napplication. They can be specified using the parameter dt.attr. attribute . The prefix \u201cdt\u201d is a constant, \u201cattr\u201d is a\nconstant denoting an attribute is being specified and attribute \nspecifies the name of the attribute. Below is an example snippet setting\nthe streaming windows size of the application to be 1000 milliseconds. property \n name dt.attr.STREAMING_WINDOW_SIZE_MILLIS /name \n value 1000 /value \n /property The name tag specifies the attribute and value tag specifies the\nattribute value. The name of the attribute is a JAVA constant name\nidentifying the attribute. The constants are defined in\ncom.datatorrent.api.Context.DAGContext and the different attributes can\nbe specified in the format described above.",
- "title": "Application attributes"
- },
- {
- "location": "/application_packages/#operator-attributes",
- "text": "Operator attributes are used to specify the platform behavior for the\noperator. They can be specified using the parameter dt.operator. operator-name .attr. attribute . The prefix \u201cdt\u201d is a\nconstant, \u201coperator\u201d is a constant denoting that an operator is being\nspecified, operator-name denotes the name of the operator, \u201cattr\u201d is\nthe constant denoting that an attribute is being specified and attribute is the name of the attribute. The operator name is the\nsame name that is specified when the operator is added to the DAG using\nthe addOperator method. An example illustrating the specification is\nshown below. It specifies the number of streaming windows for one\napplication window of an operator named \u201cinput\u201d to be 10 property \n name dt.operator.input.attr.APPLICATION_WINDOW_COUNT /name \n value 10 /value /property The name tag specifies the attribute and value tag specifies the\nattribute value. The name of the attribute is a JAVA constant name\nidentifying the attribute. The constants are defined in\ncom.datatorrent.api.Context.OperatorContext and the different attributes\ncan be specified in the format described above.",
- "title": "Operator attributes"
- },
- {
- "location": "/application_packages/#operator-properties",
- "text": "Operators can be configured using operator specific properties. The\nproperties can be specified using the parameter dt.operator. operator-name .prop. property-name . The difference\nbetween this and the operator attribute specification described above is\nthat the keyword \u201cprop\u201d is used to denote that it is a property and property-name specifies the property name. An example illustrating\nthis is specified below. It specifies the property \u201chostname\u201d of the\nredis server for a \u201credis\u201d output operator. property \n name dt.operator.redis.prop.host /name \n value 127.0.0.1 /value \n /property The name tag specifies the property and the value specifies the property\nvalue. The property name is converted to a setter method which is called\non the actual operator. The method name is composed by appending the\nword \u201cset\u201d and the property name with the first character of the name\ncapitalized. In the above example the setter method would become\nsetHost. The method is called using JAVA reflection and the property\nvalue is passed as an argument. In the above example the method setHost\nwill be called on the \u201credis\u201d operator with \u201c127.0.0.1\u201d as the argument.",
- "title": "Operator properties"
- },
- {
- "location": "/application_packages/#port-attributes",
- "text": "Port attributes are used to specify the platform behavior for input and\noutput ports. They can be specified using the parameter dt.operator. operator-name .inputport. port-name .attr. attribute \nfor input port and dt.operator. operator-name .outputport. port-name .attr. attribute \nfor output port. The keyword \u201cinputport\u201d is used to denote an input port\nand \u201coutputport\u201d to denote an output port. The rest of the specification\nfollows the conventions described in other specifications above. An\nexample illustrating this is specified below. It specifies the queue\ncapacity for an input port named \u201cinput\u201d of an operator named \u201crange\u201d to\nbe 4k. property \n name dt.operator.range.inputport.input.attr.QUEUE_CAPACITY /name \n value 4000 /value /property The name tag specifies the attribute and value tag specifies the\nattribute value. The name of the attribute is a JAVA constant name\nidentifying the attribute. The constants are defined in\ncom.datatorrent.api.Context.PortContext and the different attributes can\nbe specified in the format described above. The attributes for an output port can also be specified in a similar way\nas described above with a change that keyword \u201coutputport\u201d is used\ninstead of \u201cintputport\u201d. A generic keyword \u201cport\u201d can be used to specify\neither an input or an output port. It is useful in the wildcard\nspecification described below.",
- "title": "Port attributes"
- },
- {
- "location": "/application_packages/#stream-properties",
- "text": "Streams can be configured using stream properties. The properties can be\nspecified using the parameter dt.stream. stream-name .prop. property-name The constant \u201cstream\u201d\nspecifies that it is a stream, stream-name specifies the name of the\nstream and property-name the name of the property. The name of the\nstream is the same name that is passed when the stream is added to the\nDAG using the addStream method. An example illustrating the\nspecification is shown below. It sets the locality of the stream named\n\u201cstream1\u201d to container local indicating that the operators the stream is\nconnecting be run in the same container. property \n name dt.stream.stream1.prop.locality /name \n value CONTAINER_LOCAL /value \n /property The property name is converted into a set method on the stream in the\nsame way as described in operator properties section above. In this case\nthe method would be setLocality and it will be called in the stream\n\u201cstream1\u201d with the value as the argument. Along with the above system defined parameters, the applications can\ndefine their own specific parameters they can be specified in the\nconfiguration file. The only condition is that the names of these\nparameters don\u2019t conflict with the system defined parameters or similar\napplication parameters defined by other applications. To this end, it is\nrecommended that the application parameters have the format full-application-class-name . param-name . The\nfull-application-class-name is the full JAVA class name of the\napplication including the package path and param-name is the name of the\nparameter within the application. The application will still have to\nstill read the parameter in using the configuration API of the\nconfiguration object that is passed in populateDAG.",
- "title": "Stream properties"
- },
- {
- "location": "/application_packages/#wildcards",
- "text": "Wildcards and regular expressions can be used in place of names to\nspecify a group for applications, operators, ports or streams. For\nexample, to specify an attribute for all ports of an operator it can be\ndone as follows property \n name dt.operator.range.port.*.attr.QUEUE_CAPACITY /name \n value 4000 /value /property The wildcard \u201c*\u201d was used instead of the name of the port. Wildcard can\nalso be used for operator name, stream name or application name. Regular\nexpressions can also be used for names to specify attributes or\nproperties for a specific set.",
- "title": "Wildcards"
- },
- {
- "location": "/application_packages/#adding-configuration-properties",
- "text": "It is common for applications to require configuration parameters to\nrun. For example, the address and port of the database, the location of\na file for ingestion, etc. You can specify them in\nsrc/main/resources/META-INF/properties.xml under the App Package\nproject. The properties.xml may look like: ?xml version= 1.0 ? configuration \n property \n name some_name_1 /name \n /property \n property \n name some_name_2 /name \n value some_default_value /value \n /property /configuration The name of an application-specific property takes the form of: dt.operator.{opName}.prop.{propName} The first represents the property with name propName of operator opName.\n Or you can set the application name at run time by setting this\nproperty: dt.attr.APPLICATION_NAME In this example, property some_name_1 is a required property which\nmust be set at launch time, or it must be set by a pre-set configuration\n(see next section). Property some_name_2 is a property that is\nassigned with value some_default_value unless it is overridden at\nlaunch time.",
- "title": "Adding configuration properties"
- },
- {
- "location": "/application_packages/#adding-pre-set-configurations",
- "text": "At build time, you can add pre-set configurations to the App Package by\nadding configuration XML files under src/site/conf/ conf .xml in your\nproject. You can then specify which configuration to use at launch\ntime. The configuration XML is of the same format of the properties.xml\nfile.",
- "title": "Adding pre-set configurations"
- },
- {
- "location": "/application_packages/#application-specific-properties-file",
- "text": "You can also specify properties.xml per application in the application\npackage. Just create a file with the name properties-{appName}.xml and\nit will be picked up when you launch the application with the specified\nname within the application package. In short: properties.xml: Properties that are global to the Configuration\nPackage properties-{appName}.xml: Properties that are specific when launching\nan application with the specified appName.",
- "title": "Application-specific properties file"
- },
- {
- "location": "/application_packages/#properties-source-precedence",
- "text": "If properties with the same key appear in multiple sources (e.g. from\napp package default configuration as META-INF/properties.xml, from app\npackage configuration in the conf directory, from launch time defines,\netc), the precedence of sources, from highest to lowest, is as follows: Launch time defines (using -D option in CLI) Launch time specified configuration file in file system (using -conf\n option in CLI) Launch time specified package configuration (using -apconf option in\n CLI) Configuration from \\$HOME/.dt/dt-site.xml Application defaults within the package as\n META-INF/properties-{appname}.xml Package defaults as META-INF/properties.xml dt-site.xml in local DT installation dt-site.xml stored in HDFS",
- "title": "Properties source precedence"
- },
- {
- "location": "/application_packages/#other-meta-data",
- "text": "In a Apex App Package project, the pom.xml file contains a\nsection that looks like: properties \n apex.version 3.2.0-incubating /apex.version \n apex.apppackage.classpath\\ lib*.jar /apex.apppackage.classpath /properties apex.version is the Apache Apex version that are to be used\nwith this Application Package. apex.apppackage.classpath is the classpath that is used when\nlaunching the application in the Application Package. The default is\nlib/*.jar, where lib is where all the dependency jars are kept within\nthe Application Package. One reason to change this field is when your\nApplication Package needs the classpath in a specific order.",
- "title": "Other meta-data"
- },
- {
- "location": "/application_packages/#logging-configuration",
- "text": "Just like other Java projects, you can change the logging configuration\nby having your log4j.properties under src/main/resources. For example,\nif you have the following in src/main/resources/log4j.properties: log4j.rootLogger=WARN,CONSOLE\n log4j.appender.CONSOLE=org.apache.log4j.ConsoleAppender\n log4j.appender.CONSOLE.layout=org.apache.log4j.PatternLayout\n log4j.appender.CONSOLE.layout.ConversionPattern=%d{ISO8601} [%t] %-5p\n %c{2} %M - %m%n The root logger\u2019s level is set to WARN and the output is set to the console (stdout). Note that by default from project created from the maven archetype,\nthere is already a log4j.properties file under src/test/resources and\nthat file is only used for the unit test.",
- "title": "Logging configuration"
- },
- {
- "location": "/application_packages/#zip-structure-of-application-package",
- "text": "Apache Apex Application Package files are zip files. You can examine the content of any Application Package by using unzip -t on your Linux command line. There are four top level directories in an Application Package: \"app\" contains the jar files of the DAG code and any custom operators. \"lib\" contains all dependency jars \"conf\" contains all the pre-set configuration XML files. \"META-INF\" contains the MANIFEST.MF file and the properties.xml file. \u201cresources\u201d contains any other files",
- "title": "Zip Structure of Application Package"
- },
- {
- "location": "/application_packages/#examining-and-launching-application-packages-through-cli",
- "text": "If you are working with Application Packages in the local filesystem, you can use the Apex Command Line Interface (dtcli).",
- "title": "Examining and Launching Application Packages Through CLI"
- },
- {
- "location": "/application_packages/#getting-application-package-meta-information",
- "text": "You can get the meta information about the Application Package using\nthis Apex CLI command. dt get-app-package-info app-package-file",
- "title": "Getting Application Package Meta Information"
- },
- {
- "location": "/application_packages/#getting-available-operators-in-application-package",
- "text": "You can get the list of available operators in the Application Package\nusing this command. dt get-app-package-operators app-package-file package-prefix \n [parent-class]",
- "title": "Getting Available Operators In Application Package"
- },
- {
- "location": "/application_packages/#getting-properties-of-operators-in-application-package",
- "text": "You can get the list of properties of any operator in the Application\nPackage using this command. dt get-app-package-operator-properties",
- "title": "Getting Properties of Operators in Application Package"
- },
- {
- "location": "/application_packages/#launching-an-application-package",
- "text": "You can launch an application within an Application Package. dt launch [-D property-name=property-value, ...] [-conf config-name]\n [-apconf config-file-within-app-package] app-package-file \n [matching-app-name] Note that -conf expects a configuration file in the file system, while -apconf expects a configuration file within the app package.",
- "title": "Launching an Application Package"
- },
- {
- "location": "/application_packages/#configuration-packages",
- "text": "Sometimes just a configuration file is not enough for launching an application package. If a configuration requires\nadditional files to be packaged, you can use an Apex Configuration Package.",
- "title": "Configuration Packages"
- },
- {
- "location": "/application_packages/#creating-configuration-packages",
- "text": "Creating Configuration Packages is similar to creating Application Packages. You can create a configuration \npackage project using Maven by running the following command. Replace \"com.example\", \"mydtconfig\" and \"1.0-SNAPSHOT\" with the appropriate values: $ mvn archetype:generate -DarchetypeGroupId=org.apache.apex \\\n -DarchetypeArtifactId=apex-conf-archetype -DarchetypeVersion=3.2.0-incubating \\\n -DgroupId=com.example -Dpackage=com.example.mydtconfig -DartifactId=mydtconfig \\\n -Dversion=1.0-SNAPSHOT And create the configuration package file by running: $ mvn package The \"mvn package\" command creates the Config Package file in target\ndirectory as target/mydtconfig.apc. You will be able to use that\nConfiguration Package file to launch an Apache Apex application.",
- "title": "Creating Configuration Packages"
- },
- {
- "location": "/application_packages/#assembling-your-own-configuration-package",
- "text": "Inside the project created by the archetype, these are the files that\nyou should know about when assembling your own configuration package: ./pom.xml\n./src/main/resources/classpath\n./src/main/resources/files\n./src/main/resources/META-INF/properties.xml\n./src/main/resources/META-INF/properties-{appname}.xml",
- "title": "Assembling your own configuration package"
- },
- {
- "location": "/application_packages/#pomxml",
- "text": "Example: groupId com.example /groupId \n version 1.0.0 /version \n artifactId mydtconf /artifactId \n packaging jar /packaging \n !-- change these to the appropriate values -- \n name My Apex Application Configuration /name \n description My Custom Application Configuration Description /description \n properties \n apex.apppackage.name myapexapp /apex.apppackage.name \n apex.apppackage.minversion 1.0.0 /apex.apppackage.minversion \n apex.apppackage.maxversion 1.9999.9999 /apex.apppackage.maxversion \n apex.appconf.classpath classpath/* /apex.appconf.classpath \n apex.appconf.files files/* /apex.appconf.files \n /properties In pom.xml, you can change the following keys to your desired values groupId version artifactId name description You can also change the values of apex.apppackage.name apex.apppackage.minversion apex.apppackage.maxversion to reflect what Application Packages can be used with this configuration package. Apex will use this information to check whether a\nconfiguration package is compatible with the Application Package when you issue a launch command.",
- "title": "pom.xml"
- },
- {
- "location": "/application_packages/#srcmainresourcesclasspath",
- "text": "Place any file in this directory that you\u2019d like to be copied to the\ncompute machines when launching an application and included in the\nclasspath of the application. Example of such files are Java properties\nfiles and jar files.",
- "title": "./src/main/resources/classpath"
- },
- {
- "location": "/application_packages/#srcmainresourcesfiles",
- "text": "Place any file in this directory that you\u2019d like to be copied to the\ncompute machines when launching an application but not included in the\nclasspath of the application.",
- "title": "./src/main/resources/files"
- },
- {
- "location": "/application_packages/#properties-xml-file",
- "text": "A properties xml file consists of a set of key-value pairs. The set of\nkey-value pairs specifies the configuration options the application\nshould be launched with. Example: configuration \n property \n name some-property-name /name \n value some-property-value /value \n /property \n ... /configuration Names of properties XML file: properties.xml: Properties that are global to the Configuration\nPackage properties-{appName}.xml: Properties that are specific when launching\nan application with the specified appName within the Application\nPackage. After you are done with the above, remember to do mvn package to\ngenerate a new configuration package, which will be located in the\ntarget directory in your project.",
- "title": "Properties XML file"
- },
- {
- "location": "/application_packages/#zip-structure-of-configuration-package",
- "text": "Apex Application Configuration Package files are zip files. You\ncan examine the content of any Application Configuration Package by\nusing unzip -t on your Linux command line. The structure of the zip\nfile is as follow: META-INF\n MANIFEST.MF\n properties.xml\n properties-{appname}.xml\nclasspath\n {classpath files}\nfiles\n {files}",
- "title": "Zip structure of configuration package"
- },
- {
- "location": "/application_packages/#launching-with-cli",
- "text": "-conf option of the launch command in CLI supports specifying configuration package in the local filesystem. Example: dt\\ launch mydtapp-1.0.0.apa -conf mydtconfig.apc This command expects both the application package and the configuration package to be in the local file system.",
- "title": "Launching with CLI"
- },
- {
- "location": "/operator_development/",
- "text": "Operator Development Guide\n\n\nOperators are basic building blocks of an application built to run on\nApache Apex\u00a0platform. An application may consist of one or more\noperators each of which define some logical operation to be done on the\ntuples arriving at the operator. These operators are connected together\nusing streams forming a Directed Acyclic Graph (DAG). In other words, a streaming\napplication is represented by a DAG that consists of operations (called operators) and\ndata flow (called streams).\n\n\nIn this document we will discuss details on how an operator works and\nits internals. This document is intended to serve the following purposes\n\n\n\n\nApache Apex Operators\n\u00a0- Introduction to operator terminology and concepts.\n\n\nWriting Custom Operators\n\u00a0- Designing, coding and testing new operators from scratch. Includes code examples.\n\n\nOperator Reference\n - Details of operator internals, lifecycle, and best practices and optimizations.\n\n\n\n\n\n\nApache Apex Operators \n\n\nOperators - \u201cWhat\u201d in a nutshell\n\n\nOperators are independent units of logical operations which can\ncontribute in executing the business logic of a use case. For example,\nin an ETL workflow, a filtering operation can be represented by a single\noperator. This filtering operator will be responsible for doing just one\ntask in the ETL pipeline, i.e. filter incoming tuples. Operators do not\nimpose any restrictions on what can or cannot be done as part of a\noperator. An operator may as well contain the entire business logic.\nHowever, it is recommended, that the operators are light weight\nindependent tasks, in\norder to take advantage of the distributed framework that Apache Apex\nprovides.\u00a0The structure of a streaming application shares resemblance\nwith the way CPU pipelining works. CPU pipelining breaks down the\ncomputation engine into different stages viz. instruction fetch,\ninstruction decode, etc. so that each of them can perform their task on\ndifferent instructions\nparallely. Similarly,\nApache Apex APIs allow the user to break down their tasks into different\nstages so that all of the tasks can be executed on different tuples\nparallely.\n\n\n\n\nOperators - \u201cHow\u201d in a nutshell\n\n\nAn Apache Apex application runs as a YARN application. Hence, each of\nthe operators that the application DAG contains, runs in one of the\ncontainers provisioned by YARN.\u00a0Further, Apache Apex exposes APIs to\nallow the user to request bundling multiple operators in a single node,\na single container or even a single thread. We shall look at these calls\nin the reference sections [cite reference sections]. For now, consider\nan operator as some piece of code that runs on some machine of a YARN\ncluster.\n\n\nTypes of Operators\n\n\nAn operator works on one tuple at a time. These tuples may be supplied\nby other operators in the application or by external sources,\nsuch as a database or a message bus. Similarly, after the tuples are\nprocessed, these may be passed on to other operators, or stored into an external system. \nTherea are 3 type of operators based on function: \n\n\n\n\nInput Adapter\n - This is one of the starting points in\n the\u00a0application DAG and is responsible for getting tuples from an\n external system. At the same time, such data may also be generated\n by the operator itself, without interacting with the outside\n world.\u00a0These input tuples will form the initial universe of\n data\u00a0that the application works on.\n\n\nGeneric Operator\n - This type of operator accepts input tuples from\n the previous operators and passes\u00a0them on to the following operators\n in the DAG.\n\n\nOutput Adapter\n - This is one of the ending points in the application\n DAG and is responsible for writing the data out to some external\n system.\n\n\n\n\nNote: There can be multiple operators of all types in an application\nDAG.\n\n\nOperators Position in a DAG\n\n\nWe may refer to operators depending on their position with respect to\none another. For any operator opr (see image below), there are two types of operators.\n\n\n\n\nUpstream operators\n - These are the operators from which there is a\n directed path to opr\u00a0in the application DAG.\n\n\nDownstream operators\n - These are the operators to which there is a\n directed path from opr\u00a0in the application DAG.\n\n\n\n\nNote that there are no cycles formed in the application\u00a0DAG.\n\n\n\n\nPorts\n\n\nOperators in a DAG are connected together via directed flows\ncalled streams. Each\u00a0stream\u00a0has end-points located on the operators\ncalled ports. Therea are 2 types of ports.\n\n\n\n\nInput Port\n - This is a\u00a0port through which an operator accepts input\n tuples\u00a0from an upstream operator.\n\n\nOutput port\n - This is a\u00a0port through which an operator passes on the\n processed data to downstream operators.\n\n\n\n\nLooking at the number of input ports, an Input Adapter is an operator\nwith no input ports, a Generic operator has both input and output ports,\nwhile an Output Adapter has no output ports. At the same time, note that\nan operator may act as an Input Adapter while at the same time have an\ninput port. In such cases, the operator is getting data from two\ndifferent sources, viz.\u00a0the input stream from the input port and an\nexternal source.\n\n\n\n\n\n\nHow Operator Works\n\n\nAn operator passes through various stages during its lifetime. Each\nstage is an API call that the Streaming Application Master makes for an\noperator. \u00a0The following figure illustrates the stages through which an\noperator passes.\n\n\n\n\n\n\nThe \nsetup()\n call initializes the operator and prepares itself to\n start processing tuples.\n\n\nThe \nbeginWindow()\n call marks the beginning\u00a0of an\u00a0application window\n and allows for any processing to be done before a window starts.\n\n\nThe \nprocess()\n call belongs to the \nInputPort\n and gets triggered when\n any tuple arrives at the Input port of the operator. This call is\n specific only to Generic and Output adapters, since Input Adapters\n do not have an input port. This is made for all the tuples at the\n input port until the end window marker tuple is received on the\n input port.\n\n\nThe \nemitTuples()\n is the counterpart of \nprocess()\n call for Input\n Adapters.\n This call is used by Input adapters to emit any tuples that are\n fetched from the external systems, or generated by the operator.\n This method is called continuously until the pre-configured window\n time is elapsed, at which the end window marker tuple is sent out on\n the output port.\n\n\nThe \nendWindow()\n call marks the end of the window and allows for any\n processing to be done after the window ends.\n\n\nThe \nteardown()\n call is used for gracefully shutting down the\n operator and releasing any resources held by the operator.\n\n\n\n\nDeveloping Custom Operators \n\n\nAbout this tutorial\n\n\nThis tutorial will guide the user towards developing a operator from\nscratch. It includes all aspects of writing an operator including\ndesign, code and unit testing.\n\n\nIntroduction\n\n\nIn this tutorial, we will design and write, from scratch, an operator\ncalled Word Count. This operator will accept tuples of type String,\ncount the number of occurrences for each word appearing in the tuple and\nsend out the updated counts for all the words encountered in the tuple.\nFurther, the operator will also accept a file path on HDFS which will\ncontain the stop-words which need to be ignored when counting\noccurrences.\n\n\nDesign\n\n\nDesign of the operator must be finalized before starting to write an\noperator. Many aspects including the functionality, the data sources,\nthe types involved etc. need to be first finalized before writing the\noperator. Let us dive into each of these while considering the Word\nCount\u00a0operator.\n\n\nFunctionality\n\n\nWe can define the scope of operator functionality using the following\ntasks:\n\n\n\n\nParse the input tuple to identify the words in the tuple\n\n\nIdentify the stop-words in the tuple by looking up the stop-word\n file as configured\n\n\nFor each non-stop-word in the tuple, count the occurrences in that\n tuple and add it to a global counts\n\n\n\n\nLet\u2019s consider an example. Suppose we have the following tuples flow\ninto the Word Count operator.\n\n\n\n\nHumpty dumpty sat on a wall\n\n\nHumpty dumpty had a great fall\n\n\n\n\nInitially counts for all words\u00a0is 0. Once the first tuple is processed,\nthe counts that must be emitted are:\n\n\nhumpty - 1\ndumpty - 1\nsat - 1\nwall - 1\n\n\n\n\nNote that we are ignoring the stop-words, \u201con\u201d and \u201ca\u201d in this case.\nAlso note that as a rule, we\u2019ll ignore the case of the words when\ncounting occurrences.\n\n\nSimilarly, after the second tuple is processed, the counts that must be\nemitted are:\n\n\nhumpty - 2\ndumpty - 2\ngreat - 1\nfall - 1\n\n\n\n\nAgain, we ignore the words \n\u201chad\u201d\n and \n\u201ca\u201d\n since these are stop-words.\n\n\nNote that the most recent count for any word is correct count for that\nword. In other words, any new output for a word, invalidated all the\nprevious counts for that word.\n\n\nInputs\n\n\nAs seen from the example\u00a0above, the following\u00a0inputs are expected for\nthe operator:\n\n\n\n\nInput stream whose tuple type is String\n\n\nInput HDFS file path, pointing to a file containing stop-words\n\n\n\n\nOnly one input port is needed. The stop-word file will be small enough\nto be read completely in a single read. In addition this will be a one\ntime activity for the lifetime of the operator. This does not need a\nseparate input port.\n\n\n\n\nOutputs\n\n\nWe can define the output for this operator in multiple ways.\n\n\n\n\nThe operator may send out the set of counts for which the counts\n have changed after processing each tuple.\n\n\nSome applications might not need an update after every tuple, but\n only after\u00a0a certain time duration.\n\n\n\n\nLet us try and implement both these options depending on the\nconfiguration. Let us define a\u00a0boolean configuration parameter\n\n\u201csendPerTuple\u201d\n. The value of this parameter will indicate whether the\nupdated counts for words need to be emitted after processing each\ntuple\u00a0(true)\u00a0or after a certain time duration (false).\n\n\nThe type of information the operator will be sending out on the output\nport is the same for all the cases. This will be a \n key, value \n\u00a0pair,\nwhere the key\u00a0is the word while, the value is the latest count for that\nword. This means we just need one output port on which this information\nwill go out.\n\n\n\n\nConfiguration\n\n\nWe have the following configuration parameters:\n\n\n\n\nstopWordFilePath\n\u00a0- This parameter will store the path to the stop\n word file on HDFS as configured by the user.\n\n\nsendPerTuple\n\u00a0- This parameter decides whether we send out the\n updated counts after processing each tuple or at the end of a\n window. When set to true, the operator will send out the updated\n counts after each tuple, else it will send at the end of\n each\u00a0window.\n\n\n\n\nCode\n\n\nThe source code for the tutorial can be found here:\n\n\nhttps://github.com/DataTorrent/examples/tree/master/tutorials/operatorTutorial\n\n\nOperator Reference \n\n\nThe Operator Class\n\n\nThe operator will exist physically as a class which implements the\nOperator\u00a0interface. This interface will require implementations for the\nfollowing method calls:\n\n\n\n\nsetup(OperatorContext context)\n\n\nbeginWindow(long windowId)\n\n\nendWindow()\n\n\ntearDown()\n\n\n\n\nIn order to simplify the creation of an operator, Apache\u00a0Apex\nlibrary also provides a base class \u201cBaseOperator\u201d which has empty\nimplementations for these methods. Please refer to the \nApex Operators\n\u00a0section and the\n\nReference\n\u00a0section for details on these.\n\n\nWe extend the class \u201cBaseOperator\u201d to create our own operator\n\u201cWordCountOperator\u201d.\n\n\npublic class WordCountOperator extends BaseOperator\n{\n}\n\n\n\n\nClass (Operator) properties\n\n\nWe define the following class variables:\n\n\n\n\nsendPerTuple\n\u00a0- Configures the output frequency from the operator\n\n\n\n\nprivate boolean sendPerTuple = true; // default\n\n\n\n\n\n\nstopWordFilePath\n\u00a0- Stores the path to the stop words file on HDFS\n\n\n\n\nprivate String stopWordFilePath;\u00a0// no default\n\n\n\n\n\n\nstopWords\n\u00a0- Stores the stop words read from the configured file\n\n\n\n\nprivate transient String[] stopWords;\n\n\n\n\n\n\nglobalCounts\n\u00a0- A Map which stores the counts of all the words\n encountered so far. Note that this variable is non transient, which\n means that this variable is saved as part of the checkpoint and can be recovered in event of a crash.\n\n\n\n\nprivate Map\nString, Long\n globalCounts;\n\n\n\n\n\n\nupdatedCounts\n\u00a0- A Map which stores the counts for only the most\n recent tuple(s). sendPerTuple configuration determines whether to store the most recent or the recent\n window worth of tuples.\n\n\n\n\nprivate transient Map\nString, Long\n updatedCounts;\n\n\n\n\n\n\ninput\n - The input port for the operator. The type of this input port\n is String\u00a0which means it will only accept tuples of type String. The\n definition of an input port requires implementation of a method\n called process(String tuple), which should\u00a0have the processing logic\n for the input tuple which \u00a0arrives at this input port. We delegate\n this task to another method called processTuple(String tuple). This\n helps in keeping the operator classes extensible by overriding the\n processing logic for the input tuples.\n\n\n\n\npublic transient DefaultInputPort\nString\n input = new \u00a0 \u00a0\nDefaultInputPort\nString\n()\n{\n\u00a0\u00a0\u00a0\u00a0@Override\n\u00a0\u00a0\u00a0\u00a0public void process(String tuple)\n\u00a0\u00a0\u00a0\u00a0{\n \u00a0\u00a0\u00a0\u00a0processTuple(tuple);\n\u00a0\u00a0\u00a0\u00a0}\n};\n\n\n\n\n\n\noutput - The output port for the operator. The type of this port is\n Entry \n String, Long \n, which means the operator will emit \n word,\n count \n pairs for the updated counts.\n\n\n\n\npublic transient DefaultOutputPort \nEntry\nString, Long\n output = new\nDefaultOutputPort\nEntry\nString,Long\n();\n\n\n\n\nThe Constructor\n\n\nThe constructor is the place where we initialize the non-transient data\nstructures,\u00a0since\nconstructor is called just once per activation of an operator. With regards to Word Count\u00a0operator, we initialize the globalCounts variable in the constructor.\n\n\nglobalCounts = Maps.newHashMap();\n\n\n\n\nSetup call\n\n\nThe setup method is called only once during an operator lifetime and its purpose is to allow \nthe operator to set itself up for processing incoming streams. Transient objects in the operator are\nnot serialized and checkpointed. Hence, it is essential that such objects initialized in the setup call. \nIn case of operator failure, the operator will be redeployed (most likely on a different container). The setup method called by the Apache Apex engine allows the operator to prepare for execution in the new container.\n\n\nThe following tasks are executed as part of the setup call:\n\n\n\n\nRead the stop-word list from HDFS and store it in the\n stopWords\u00a0array\n\n\nInitialize updatedCounts\u00a0variable. This will store the updated\n counts for words in most recent tuples processed by the operator.\n As a transient variable, the value will be lost when operator fails.\n\n\n\n\nBegin Window call\n\n\nThe begin window call signals the start of an application window. With \nregards to Word Count Operator, we are expecting updated counts for the most recent window of\ndata if the sendPerTuple\u00a0is set to false. Hence, we clear the updatedCounts\u00a0variable in the begin window\ncall and start accumulating the counts till the end window call.\n\n\nProcess Tuple call\n\n\nThe processTuple\u00a0method is called by the process\u00a0method of the input\nport, input. This method defines the processing logic for the current\ntuple that is received at the input port. As part of this method, we\nidentify the words in the current tuple and update the globalCounts\u00a0and\nthe updatedCounts\u00a0variables. In addition, if the sendPerTuple\u00a0variable\nis set to true, we also emit the words\u00a0and corresponding counts in\nupdatedCounts\u00a0to the output port. Note\u00a0that in this case (sendPerTuple =\ntrue), we clear the updatedCounts\u00a0variable in every call to\nprocessTuple.\n\n\nEnd Window call\n\n\nThis call signals the end of an application window. With regards to Word\nCount Operator, we emit the updatedCounts\u00a0to the output port if the\nsendPerTuple\u00a0flag is set to false.\n\n\nTeardown call\n\n\nThis method allows the operator to gracefully shut down itself after\nreleasing the resources that it has acquired. With regards to our operator,\nwe call the shutDown\u00a0method which shuts down the operator along with any\ndownstream operators.\n\n\nTesting your Operator\n\n\nAs part of testing our operator, we test the following two facets:\n\n\n\n\nTest output of the operator after processing a single tuple\n\n\nTest output of the operator after processing of a window of tuples\n\n\n\n\nThe unit tests for the WordCount operator are available in the class\nWordCountOperatorTest.java. We simulate the behavior of the engine by\nusing the test utilities provided by Apache Apex libraries. We simulate\nthe setup, beginWindow, process\u00a0method of the input port and\nendWindow\u00a0calls and compare the output received at the simulated output\nports.\n\n\n\n\nInvoke constructor; non-transients initialized.\n\n\nCopy state from checkpoint -- initialized values from step 1 are\nreplaced.\n\n\n\n\nMalhar Operator Library\n\n\nTo see the full list of Apex Malhar operators along with related documentation, visit \nApex Malhar on Github",
- "title": "Operators"
- },
- {
- "location": "/operator_development/#operator-development-guide",
- "text": "Operators are basic building blocks of an application built to run on\nApache Apex\u00a0platform. An application may consist of one or more\noperators each of which define some logical operation to be done on the\ntuples arriving at the operator. These operators are connected together\nusing streams forming a Directed Acyclic Graph (DAG). In other words, a streaming\napplication is represented by a DAG that consists of operations (called operators) and\ndata flow (called streams). In this document we will discuss details on how an operator works and\nits internals. This document is intended to serve the following purposes Apache Apex Operators \u00a0- Introduction to operator terminology and concepts. Writing Custom Operators \u00a0- Designing, coding and testing new operators from scratch. Includes code examples. Operator Reference - Details of operator internals, lifecycle, and best practices and optimizations.",
- "title": "Operator Development Guide"
- },
- {
- "location": "/operator_development/#apache-apex-operators",
- "text": "",
- "title": "Apache Apex Operators "
- },
- {
- "location": "/operator_development/#operators-what-in-a-nutshell",
- "text": "Operators are independent units of logical operations which can\ncontribute in executing the business logic of a use case. For example,\nin an ETL workflow, a filtering operation can be represented by a single\noperator. This filtering operator will be responsible for doing just one\ntask in the ETL pipeline, i.e. filter incoming tuples. Operators do not\nimpose any restrictions on what can or cannot be done as part of a\noperator. An operator may as well contain the entire business logic.\nHowever, it is recommended, that the operators are light weight\nindependent tasks, in\norder to take advantage of the distributed framework that Apache Apex\nprovides.\u00a0The structure of a streaming application shares resemblance\nwith the way CPU pipelining works. CPU pipelining breaks down the\ncomputation engine into different stages viz. instruction fetch,\ninstruction decode, etc. so that each of them can perform their task on\ndifferent instructions\nparallely. Similarly,\nApache Apex APIs allow the user to break down their tasks into different\nstages so that all of the tasks can be executed on different tuples\nparallely.",
- "title": "Operators - \u201cWhat\u201d in a nutshell"
- },
- {
- "location": "/operator_development/#operators-how-in-a-nutshell",
- "text": "An Apache Apex application runs as a YARN application. Hence, each of\nthe operators that the application DAG contains, runs in one of the\ncontainers provisioned by YARN.\u00a0Further, Apache Apex exposes APIs to\nallow the user to request bundling multiple operators in a single node,\na single container or even a single thread. We shall look at these calls\nin the reference sections [cite reference sections]. For now, consider\nan operator as some piece of code that runs on some machine of a YARN\ncluster.",
- "title": "Operators - \u201cHow\u201d in a nutshell"
- },
- {
- "location": "/operator_development/#types-of-operators",
- "text": "An operator works on one tuple at a time. These tuples may be supplied\nby other operators in the application or by external sources,\nsuch as a database or a message bus. Similarly, after the tuples are\nprocessed, these may be passed on to other operators, or stored into an external system. \nTherea are 3 type of operators based on function: Input Adapter - This is one of the starting points in\n the\u00a0application DAG and is responsible for getting tuples from an\n external system. At the same time, such data may also be generated\n by the operator itself, without interacting with the outside\n world.\u00a0These input tuples will form the initial universe of\n data\u00a0that the application works on. Generic Operator - This type of operator accepts input tuples from\n the previous operators and passes\u00a0them on to the following operators\n in the DAG. Output Adapter - This is one of the ending points in the application\n DAG and is responsible for writing the data out to some external\n system. Note: There can be multiple operators of all types in an application\nDAG.",
- "title": "Types of Operators"
- },
- {
- "location": "/operator_development/#operators-position-in-a-dag",
- "text": "We may refer to operators depending on their position with respect to\none another. For any operator opr (see image below), there are two types of operators. Upstream operators - These are the operators from which there is a\n directed path to opr\u00a0in the application DAG. Downstream operators - These are the operators to which there is a\n directed path from opr\u00a0in the application DAG. Note that there are no cycles formed in the application\u00a0DAG.",
- "title": "Operators Position in a DAG"
- },
- {
- "location": "/operator_development/#ports",
- "text": "Operators in a DAG are connected together via directed flows\ncalled streams. Each\u00a0stream\u00a0has end-points located on the operators\ncalled ports. Therea are 2 types of ports. Input Port - This is a\u00a0port through which an operator accepts input\n tuples\u00a0from an upstream operator. Output port - This is a\u00a0port through which an operator passes on the\n processed data to downstream operators. Looking at the number of input ports, an Input Adapter is an operator\nwith no input ports, a Generic operator has both input and output ports,\nwhile an Output Adapter has no output ports. At the same time, note that\nan operator may act as an Input Adapter while at the same time have an\ninput port. In such cases, the operator is getting data from two\ndifferent sources, viz.\u00a0the input stream from the input port and an\nexternal source.",
- "title": "Ports"
- },
- {
- "location": "/operator_development/#how-operator-works",
- "text": "An operator passes through various stages during its lifetime. Each\nstage is an API call that the Streaming Application Master makes for an\noperator. \u00a0The following figure illustrates the stages through which an\noperator passes. The setup() call initializes the operator and prepares itself to\n start processing tuples. The beginWindow() call marks the beginning\u00a0of an\u00a0application window\n and allows for any processing to be done before a window starts. The process() call belongs to the InputPort and gets triggered when\n any tuple arrives at the Input port of the operator. This call is\n specific only to Generic and Output adapters, since Input Adapters\n do not have an input port. This is made for all the tuples at the\n input port until the end window marker tuple is received on the\n input port. The emitTuples() is the counterpart of process() call for Input\n Adapters.\n This call is used by Input adapters to emit any tuples that are\n fetched from the external systems, or generated by the operator.\n This method is called continuously until the pre-configured window\n time is elapsed, at which the end window marker tuple is sent out on\n the output port. The endWindow() call marks the end of the window and allows for any\n processing to be done after the window ends. The teardown() call is used for gracefully shutting down the\n operator and releasing any resources held by the operator.",
- "title": "How Operator Works"
- },
- {
- "location": "/operator_development/#developing-custom-operators",
- "text": "",
- "title": "Developing Custom Operators "
- },
- {
- "location": "/operator_development/#about-this-tutorial",
- "text": "This tutorial will guide the user towards developing a operator from\nscratch. It includes all aspects of writing an operator including\ndesign, code and unit testing.",
- "title": "About this tutorial"
- },
- {
- "location": "/operator_development/#introduction",
- "text": "In this tutorial, we will design and write, from scratch, an operator\ncalled Word Count. This operator will accept tuples of type String,\ncount the number of occurrences for each word appearing in the tuple and\nsend out the updated counts for all the words encountered in the tuple.\nFurther, the operator will also accept a file path on HDFS which will\ncontain the stop-words which need to be ignored when counting\noccurrences.",
- "title": "Introduction"
- },
- {
- "location": "/operator_development/#design",
- "text": "Design of the operator must be finalized before starting to write an\noperator. Many aspects including the functionality, the data sources,\nthe types involved etc. need to be first finalized before writing the\noperator. Let us dive into each of these while considering the Word\nCount\u00a0operator.",
- "title": "Design"
- },
- {
- "location": "/operator_development/#functionality",
- "text": "We can define the scope of operator functionality using the following\ntasks: Parse the input tuple to identify the words in the tuple Identify the stop-words in the tuple by looking up the stop-word\n file as configured For each non-stop-word in the tuple, count the occurrences in that\n tuple and add it to a global counts Let\u2019s consider an example. Suppose we have the following tuples flow\ninto the Word Count operator. Humpty dumpty sat on a wall Humpty dumpty had a great fall Initially counts for all words\u00a0is 0. Once the first tuple is processed,\nthe counts that must be emitted are: humpty - 1\ndumpty - 1\nsat - 1\nwall - 1 Note that we are ignoring the stop-words, \u201con\u201d and \u201ca\u201d in this case.\nAlso note that as a rule, we\u2019ll ignore the case of the words when\ncounting occurrences. Similarly, after the second tuple is processed, the counts that must be\nemitted are: humpty - 2\ndumpty - 2\ngreat - 1\nfall - 1 Again, we ignore the words \u201chad\u201d and \u201ca\u201d since these are stop-words. Note that the most recent count for any word is correct count for that\nword. In other words, any new output for a word, invalidated all the\nprevious counts for that word.",
- "title": "Functionality"
- },
- {
- "location": "/operator_development/#inputs",
- "text": "As seen from the example\u00a0above, the following\u00a0inputs are expected for\nthe operator: Input stream whose tuple type is String Input HDFS file path, pointing to a file containing stop-words Only one input port is needed. The stop-word file will be small enough\nto be read completely in a single read. In addition this will be a one\ntime activity for the lifetime of the operator. This does not need a\nseparate input port.",
- "title": "Inputs"
- },
- {
- "location": "/operator_development/#outputs",
- "text": "We can define the output for this operator in multiple ways. The operator may send out the set of counts for which the counts\n have changed after processing each tuple. Some applications might not need an update after every tuple, but\n only after\u00a0a certain time duration. Let us try and implement both these options depending on the\nconfiguration. Let us define a\u00a0boolean configuration parameter \u201csendPerTuple\u201d . The value of this parameter will indicate whether the\nupdated counts for words need to be emitted after processing each\ntuple\u00a0(true)\u00a0or after a certain time duration (false). The type of information the operator will be sending out on the output\nport is the same for all the cases. This will be a key, value \u00a0pair,\nwhere the key\u00a0is the word while, the value is the latest count for that\nword. This means we just need one output port on which this information\nwill go out.",
- "title": "Outputs"
- },
- {
- "location": "/operator_development/#configuration",
- "text": "We have the following configuration parameters: stopWordFilePath \u00a0- This parameter will store the path to the stop\n word file on HDFS as configured by the user. sendPerTuple \u00a0- This parameter decides whether we send out the\n updated counts after processing each tuple or at the end of a\n window. When set to true, the operator will send out the updated\n counts after each tuple, else it will send at the end of\n each\u00a0window.",
- "title": "Configuration"
- },
- {
- "location": "/operator_development/#code",
- "text": "The source code for the tutorial can be found here: https://github.com/DataTorrent/examples/tree/master/tutorials/operatorTutorial",
- "title": "Code"
- },
- {
- "location": "/operator_development/#operator-reference",
- "text": "",
- "title": "Operator Reference "
- },
- {
- "location": "/operator_development/#the-operator-class",
- "text": "The operator will exist physically as a class which implements the\nOperator\u00a0interface. This interface will require implementations for the\nfollowing method calls: setup(OperatorContext context) beginWindow(long windowId) endWindow() tearDown() In order to simplify the creation of an operator, Apache\u00a0Apex\nlibrary also provides a base class \u201cBaseOperator\u201d which has empty\nimplementations for these methods. Please refer to the Apex Operators \u00a0section and the Reference \u00a0section for details on these. We extend the class \u201cBaseOperator\u201d to create our own operator\n\u201cWordCountOperator\u201d. public class WordCountOperator extends BaseOperator\n{\n}",
- "title": "The Operator Class"
- },
- {
- "location": "/operator_development/#class-operator-properties",
- "text": "We define the following class variables: sendPerTuple \u00a0- Configures the output frequency from the operator private boolean sendPerTuple = true; // default stopWordFilePath \u00a0- Stores the path to the stop words file on HDFS private String stopWordFilePath;\u00a0// no default stopWords \u00a0- Stores the stop words read from the configured file private transient String[] stopWords; globalCounts \u00a0- A Map which stores the counts of all the words\n encountered so far. Note that this variable is non transient, which\n means that this variable is saved as part of the checkpoint and can be recovered in event of a crash. private Map String, Long globalCounts; updatedCounts \u00a0- A Map which stores the counts for only the most\n recent tuple(s). sendPerTuple configuration determines whether to store the most recent or the recent\n window worth of tuples. private transient Map String, Long updatedCounts; input - The input port for the operator. The type of this input port\n is String\u00a0which means it will only accept tuples of type String. The\n definition of an input port requires implementation of a method\n called process(String tuple), which should\u00a0have the processing logic\n for the input tuple which \u00a0arrives at this input port. We delegate\n this task to another method called processTuple(String tuple). This\n helps in keeping the operator classes extensible by overriding the\n processing logic for the input tuples. public transient DefaultInputPort String input = new \u00a0 \u00a0\nDefaultInputPort String ()\n{\n\u00a0\u00a0\u00a0\u00a0@Override\n\u00a0\u00a0\u00a0\u00a0public void process(String tuple)\n\u00a0\u00a0\u00a0\u00a0{\n \u00a0\u00a0\u00a0\u00a0processTuple(tuple);\n\u00a0\u00a0\u00a0\u00a0}\n}; output - The output port for the operator. The type of this port is\n Entry String, Long , which means the operator will emit word,\n count pairs for the updated counts. public transient DefaultOutputPort Entry String, Long output = new\nDefaultOutputPort Entry String,Long ();",
- "title": "Class (Operator) properties"
- },
- {
- "location": "/operator_development/#the-constructor",
- "text": "The constructor is the place where we initialize the non-transient data\nstructures,\u00a0since\nconstructor is called just once per activation of an operator. With regards to Word Count\u00a0operator, we initialize the globalCounts variable in the constructor. globalCounts = Maps.newHashMap();",
- "title": "The Constructor"
- },
- {
- "location": "/operator_development/#setup-call",
- "text": "The setup method is called only once during an operator lifetime and its purpose is to allow \nthe operator to set itself up for processing incoming streams. Transient objects in the operator are\nnot serialized and checkpointed. Hence, it is essential that such objects initialized in the setup call. \nIn case of operator failure, the operator will be redeployed (most likely on a different container). The setup method called by the Apache Apex engine allows the operator to prepare for execution in the new container. The following tasks are executed as part of the setup call: Read the stop-word list from HDFS and store it in the\n stopWords\u00a0array Initialize updatedCounts\u00a0variable. This will store the updated\n counts for words in most recent tuples processed by the operator.\n As a transient variable, the value will be lost when operator fails.",
- "title": "Setup call"
- },
- {
- "location": "/operator_development/#begin-window-call",
- "text": "The begin window call signals the start of an application window. With \nregards to Word Count Operator, we are expecting updated counts for the most recent window of\ndata if the sendPerTuple\u00a0is set to false. Hence, we clear the updatedCounts\u00a0variable in the begin window\ncall and start accumulating the counts till the end window call.",
- "title": "Begin Window call"
- },
- {
- "location": "/operator_development/#process-tuple-call",
- "text": "The processTuple\u00a0method is called by the process\u00a0method of the input\nport, input. This method defines the processing logic for the current\ntuple that is received at the input port. As part of this method, we\nidentify the words in the current tuple and update the globalCounts\u00a0and\nthe updatedCounts\u00a0variables. In addition, if the sendPerTuple\u00a0variable\nis set to true, we also emit the words\u00a0and corresponding counts in\nupdatedCounts\u00a0to the output port. Note\u00a0that in this case (sendPerTuple =\ntrue), we clear the updatedCounts\u00a0variable in every call to\nprocessTuple.",
- "title": "Process Tuple call"
- },
- {
- "location": "/operator_development/#end-window-call",
- "text": "This call signals the end of an application window. With regards to Word\nCount Operator, we emit the updatedCounts\u00a0to the output port if the\nsendPerTuple\u00a0flag is set to false.",
- "title": "End Window call"
- },
- {
- "location": "/operator_development/#teardown-call",
- "text": "This method allows the operator to gracefully shut down itself after\nreleasing the resources that it has acquired. With regards to our operator,\nwe call the shutDown\u00a0method which shuts down the operator along with any\ndownstream operators.",
- "title": "Teardown call"
- },
- {
- "location": "/operator_development/#testing-your-operator",
- "text": "As part of testing our operator, we test the following two facets: Test output of the operator after processing a single tuple Test output of the operator after processing of a window of tuples The unit tests for the WordCount operator are available in the class\nWordCountOperatorTest.java. We simulate the behavior of the engine by\nusing the test utilities provided by Apache Apex libraries. We simulate\nthe setup, beginWindow, process\u00a0method of the input port and\nendWindow\u00a0calls and compare the output received at the simulated output\nports. Invoke constructor; non-transients initialized. Copy state from checkpoint -- initialized values from step 1 are\nreplaced.",
- "title": "Testing your Operator"
- },
- {
- "location": "/operator_development/#malhar-operator-library",
- "text": "To see the full list of Apex Malhar operators along with related documentation, visit Apex Malhar on Github",
- "title": "Malhar Operator Library"
- },
- {
- "location": "/autometrics/",
- "text": "Apache Apex AutoMetrics\n\n\nIntroduction\n\n\nMetrics collect various statistical information about a process which can be very useful for diagnosis. Auto Metrics in Apex can help monitor operators in a running application. The goal of \nAutoMetric\n API is to enable operator developer to define relevant metrics for an operator in a simple way which the platform collects and reports automatically.\n\n\nSpecifying AutoMetrics in an Operator\n\n\nAn \nAutoMetric\n can be any object. It can be of a primitive type - int, long, etc. or a complex one. A field or a \nget\n method in an operator can be annotated with \n@AutoMetric\n to specify that its value is a metric. After every application end window, the platform collects the values of these fields/methods in a map and sends it to application master.\n\n\npublic class LineReceiver extends BaseOperator\n{\n @AutoMetric\n long length;\n\n @AutoMetric\n long count;\n\n public final transient DefaultInputPort\nString\n input = new DefaultInputPort\nString\n()\n {\n @Override\n public void process(String s)\n {\n length += s.length();\n count++;\n }\n };\n\n @Override\n public void beginWindow(long windowId)\n {\n length = 0;\n count = 0;\n }\n}\n\n\n\n\nThere are 2 auto-metrics declared in the \nLineReceiver\n. At the end of each application window, the platform will send a map with 2 entries - \n[(length, 100), (count, 10)]\n to the application master.\n\n\nAggregating AutoMetrics across Partitions\n\n\nWhen an operator is partitioned, it is useful to aggregate the values of auto-metrics across all its partitions every window to get a logical view of these metrics. The application master performs these aggregations using metrics aggregators.\n\n\nThe AutoMetric API helps to achieve this by providing an interface for writing aggregators- \nAutoMetric.Aggregator\n. Any implementation of \nAutoMetric.Aggregator\n can be set as an operator attribute - \nMETRICS_AGGREGATOR\n for a particular operator which in turn is used for aggregating physical metrics.\n\n\nDefault aggregators\n\n\nMetricsAggregator\n is a simple implementation of \nAutoMetric.Aggregator\n that platform uses as a default for summing up primitive types - int, long, float and double.\n\n\nMetricsAggregator\n is just a collection of \nSingleMetricAggregator\ns. There are multiple implementations of \nSingleMetricAggregator\n that perform sum, min, max, avg which are present in Apex core and Apex malhar.\n\n\nFor the \nLineReceiver\n operator, the application developer need not specify any aggregator. The platform will automatically inject an instance of \nMetricsAggregator\n that contains two \nLongSumAggregator\ns - one for \nlength\n and one for \ncount\n. This aggregator will report sum of length and sum of count across all the partitions of \nLineReceiver\n.\n\n\nBuilding custom aggregators\n\n\nPlatform cannot perform any meaningful aggregations for non-numeric metrics. In such cases, the operator or application developer can write custom aggregators. Let\u2019s say, if the \nLineReceiver\n was modified to have a complex metric as shown below.\n\n\npublic class AnotherLineReceiver extends BaseOperator\n{\n @AutoMetric\n final LineMetrics lineMetrics = new LineMetrics();\n\n public final transient DefaultInputPort\nString\n input = new DefaultInputPort\nString\n()\n {\n @Override\n public void process(String s)\n {\n lineMetrics.length += s.length();\n lineMetrics.count++;\n }\n };\n\n @Override\n public void beginWindow(long windowId)\n {\n lineMetrics.length = 0;\n lineMetrics.count = 0;\n }\n\n public static class LineMetrics implements Serializable\n {\n long length;\n long count;\n\n private static final long serialVersionUID = 201511041908L;\n }\n}\n\n\n\n\nBelow is a custom aggregator that can calculate average line length across all partitions of \nAnotherLineReceiver\n.\n\n\npublic class AvgLineLengthAggregator implements AutoMetric.Aggregator\n{\n\n Map\nString, Object\n result = Maps.newHashMap();\n\n @Override\n public Map\nString, Object\n aggregate(long l, Collection\nAutoMetric.PhysicalMetricsContext\n collection)\n {\n long totalLength = 0;\n long totalCount = 0;\n for (AutoMetric.PhysicalMetricsContext pmc : collection) {\n AnotherLineReceiver.LineMetrics lm = (AnotherLineReceiver.LineMetrics)pmc.getMetrics().get(\nlineMetrics\n);\n totalLength += lm.length;\n totalCount += lm.count;\n }\n result.put(\navgLineLength\n, totalLength/totalCount);\n return result;\n }\n}\n\n\n\n\nAn instance of above aggregator can be specified as the \nMETRIC_AGGREGATOR\n for \nAnotherLineReceiver\n while creating the DAG as shown below.\n\n\n @Override\n public void populateDAG(DAG dag, Configuration configuration)\n {\n ...\n AnotherLineReceiver lineReceiver = dag.addOperator(\nLineReceiver\n, new AnotherLineReceiver());\n dag.setAttribute(lineReceiver, Context.OperatorContext.METRICS_AGGREGATOR, new AvgLineLengthAggregator());\n ...\n }\n\n\n\n\nRetrieving AutoMetrics\n\n\nThere are two options for retrieving the AutoMetrics:\n\n\n\n\nThrought DataTorrent Gateway REST API\n\n\nThrough REST service on the port of the running STRAM\n\n\n\n\nThe Gateway REST API provides a way to retrieve the latest AutoMetrics for each logical operator. For example:\n\n\nGET /ws/v2/applications/{appid}/logicalPlan/operators/{opName}\n{\n ...\n \nautoMetrics\n: {\n \ncount\n: \n71314\n,\n \nlength\n: \n27780706\n\n },\n \nclassName\n: \ncom.datatorrent.autometric.LineReceiver\n,\n ...\n}\n\n\n\n\nSystem Metrics\n\n\nSystem metrics are standard operator metrics provided by the system. Examples include:\n\n\n\n\nprocessed tuples per second\n\n\nemitted tuples per second\n\n\ntotal tuples processed\n\n\ntotal tuples emitted\n\n\nlatency\n\n\nCPU percentage\n\n\nfailure count\n\n\ncheckpoint elapsed time\n\n\n\n\nThe Gateway REST API provides a way to retrieve the latest values for all of the above for each of the logical operators in the application.\n\n\nGET /ws/v2/applications/{appid}/logicalPlan/operators/{opName}\n{\n ...\n \ncpuPercentageMA\n: \n{cpuPercentageMA}\n,\n \nfailureCount\n: \n{failureCount}\n,\n \nlatencyMA\n: \n{latencyMA}\n, \n \ntotalTuplesEmitted\n: \n{totalTuplesEmitted}\n,\n \ntotalTuplesProcessed\n: \n{totalTuplesProcessed}\n,\n \ntuplesEmittedPSMA\n: \n{tuplesEmittedPSMA}\n,\n \ntuplesProcessedPSMA\n: \n{tuplesProcessedPSMA}\n,\n ...\n}\n\n\n\n\nHowever, just like AutoMetrics, the Gateway only provides the latest metrics. For historical metrics, we will need the help of \nApp Data Tracker\n.",
- "title": "AutoMetric API"
- },
- {
- "location": "/autometrics/#apache-apex-autometrics",
- "text": "",
- "title": "Apache Apex AutoMetrics"
- },
- {
- "location": "/autometrics/#introduction",
- "text": "Metrics collect various statistical information about a process which can be very useful for diagnosis. Auto Metrics in Apex can help monitor operators in a running application. The goal of AutoMetric API is to enable operator developer to define relevant metrics for an operator in a simple way which the platform collects and reports automatically.",
- "title": "Introduction"
- },
- {
- "location": "/autometrics/#specifying-autometrics-in-an-operator",
- "text": "An AutoMetric can be any object. It can be of a primitive type - int, long, etc. or a complex one. A field or a get method in an operator can be annotated with @AutoMetric to specify that its value is a metric. After every application end window, the platform collects the values of these fields/methods in a map and sends it to application master. public class LineReceiver extends BaseOperator\n{\n @AutoMetric\n long length;\n\n @AutoMetric\n long count;\n\n public final transient DefaultInputPort String input = new DefaultInputPort String ()\n {\n @Override\n public void process(String s)\n {\n length += s.length();\n count++;\n }\n };\n\n @Override\n public void beginWindow(long windowId)\n {\n length = 0;\n count = 0;\n }\n} There are 2 auto-metrics declared in the LineReceiver . At the end of each application window, the platform will send a map with 2 entries - [(length, 100), (count, 10)] to the application master.",
- "title": "Specifying AutoMetrics in an Operator"
- },
- {
- "location": "/autometrics/#aggregating-autometrics-across-partitions",
- "text": "When an operator is partitioned, it is useful to aggregate the values of auto-metrics across all its partitions every window to get a logical view of these metrics. The application master performs these aggregations using metrics aggregators. The AutoMetric API helps to achieve this by providing an interface for writing aggregators- AutoMetric.Aggregator . Any implementation of AutoMetric.Aggregator can be set as an operator attribute - METRICS_AGGREGATOR for a particular operator which in turn is used for aggregating physical metrics.",
- "title": "Aggregating AutoMetrics across Partitions"
- },
- {
- "location": "/autometrics/#default-aggregators",
- "text": "MetricsAggregator is a simple implementation of AutoMetric.Aggregator that platform uses as a default for summing up primitive types - int, long, float and double. MetricsAggregator is just a collection of SingleMetricAggregator s. There are multiple implementations of SingleMetricAggregator that perform sum, min, max, avg which are present in Apex core and Apex malhar. For the LineReceiver operator, the application developer need not specify any aggregator. The platform will automatically inject an instance of MetricsAggregator that contains two LongSumAggregator s - one for length and one for count . This aggregator will report sum of length and sum of count across all the partitions of LineReceiver .",
- "title": "Default aggregators"
- },
- {
- "location": "/autometrics/#building-custom-aggregators",
- "text": "Platform cannot perform any meaningful aggregations for non-numeric metrics. In such cases, the operator or application developer can write custom aggregators. Let\u2019s say, if the LineReceiver was modified to have a complex metric as shown below. public class AnotherLineReceiver extends BaseOperator\n{\n @AutoMetric\n final LineMetrics lineMetrics = new LineMetrics();\n\n public final transient DefaultInputPort String input = new DefaultInputPort String ()\n {\n @Override\n public void process(String s)\n {\n lineMetrics.length += s.length();\n lineMetrics.count++;\n }\n };\n\n @Override\n public void beginWindow(long windowId)\n {\n lineMetrics.length = 0;\n lineMetrics.count = 0;\n }\n\n public static class LineMetrics implements Serializable\n {\n long length;\n long count;\n\n private static final long serialVersionUID = 201511041908L;\n }\n} Below is a custom aggregator that can calculate average line length across all partitions of AnotherLineReceiver . public class AvgLineLengthAggregator implements AutoMetric.Aggregator\n{\n\n Map String, Object result = Maps.newHashMap();\n\n @Override\n public Map String, Object aggregate(long l, Collection AutoMetric.PhysicalMetricsContext collection)\n {\n long totalLength = 0;\n long totalCount = 0;\n for (AutoMetric.PhysicalMetricsContext pmc : collection) {\n AnotherLineReceiver.LineMetrics lm = (AnotherLineReceiver.LineMetrics)pmc.getMetrics().get( lineMetrics );\n totalLength += lm.length;\n totalCount += lm.count;\n }\n result.put( avgLineLength , totalLength/totalCount);\n return result;\n }\n} An instance of above aggregator can be specified as the METRIC_AGGREGATOR for AnotherLineReceiver while creating the DAG as shown below. @Override\n public void populateDAG(DAG dag, Configuration configuration)\n {\n ...\n AnotherLineReceiver lineReceiver = dag.addOperator( LineReceiver , new AnotherLineReceiver());\n dag.setAttribute(lineReceiver, Context.OperatorContext.METRICS_AGGREGATOR, new AvgLineLengthAggregator());\n ...\n }",
- "title": "Building custom aggregators"
- },
- {
- "location": "/autometrics/#retrieving-autometrics",
- "text": "There are two options for retrieving the AutoMetrics: Throught DataTorrent Gateway REST API Through REST service on the port of the running STRAM The Gateway REST API provides a way to retrieve the latest AutoMetrics for each logical operator. For example: GET /ws/v2/applications/{appid}/logicalPlan/operators/{opName}\n{\n ...\n autoMetrics : {\n count : 71314 ,\n length : 27780706 \n },\n className : com.datatorrent.autometric.LineReceiver ,\n ...\n}",
- "title": "Retrieving AutoMetrics"
- },
- {
- "location": "/autometrics/#system-metrics",
- "text": "System metrics are standard operator metrics provided by the system. Examples include: processed tuples per second emitted tuples per second total tuples processed total tuples emitted latency CPU percentage failure count checkpoint elapsed time The Gateway REST API provides a way to retrieve the latest values for all of the above for each of the logical operators in the application. GET /ws/v2/applications/{appid}/logicalPlan/operators/{opName}\n{\n ...\n cpuPercentageMA : {cpuPercentageMA} ,\n failureCount : {failureCount} ,\n latencyMA : {latencyMA} , \n totalTuplesEmitted : {totalTuplesEmitted} ,\n totalTuplesProcessed : {totalTuplesProcessed} ,\n tuplesEmittedPSMA : {tuplesEmittedPSMA} ,\n tuplesProcessedPSMA : {tuplesProcessedPSMA} ,\n ...\n} However, just like AutoMetrics, the Gateway only provides the latest metrics. For historical metrics, we will need the help of App Data Tracker .",
- "title": "System Metrics"
- },
- {
- "location": "/dtcli/",
- "text": "Apache Apex Command Line Interface\n\n\ndtCli, the Apache Apex command line interface, can be used to launch, monitor, and manage Apache Apex applications. It provides a developer friendly way of interacting with Apache Apex platform. Another advantage of dtCli is to provide scope, by connecting and executing commands in a context of specific application. dtCli enables easy integration with existing enterprise toolset for automated application monitoring and management. Currently the following high level tasks are supported.\n\n\n\n\nLaunch or kill applications\n\n\nView system metrics including load, throughput, latency, etc.\n\n\nStart or stop tuple recording\n\n\nRead operator, stream, port properties and attributes\n\n\nWrite to operator properties\n\n\nDynamically change the application logical plan\n\n\nCreate custom macros\n\n\n\n\ndtcli Commands\n\n\ndtCli can be launched by running following command\n\n\ndtcli\n\n\n\nHelp on all commands is available via \u201chelp\u201d command in the CLI\n\n\nGlobal Commands\n\n\nGLOBAL COMMANDS EXCEPT WHEN CHANGING LOGICAL PLAN:\n\nalias alias-name command\n Create a command alias\n\nbegin-macro name\n Begin Macro Definition ($1...$9 to access parameters and type 'end' to end the definition)\n\nconnect app-id\n Connect to an app\n\ndump-properties-file out-file jar-file class-name\n Dump the properties file of an app class\n\necho [arg ...]\n Echo the arguments\n\nexit\n Exit the CLI\n\nget-app-info app-id\n Get the information of an app\n\nget-app-package-info app-package-file\n Get info on the app package file\n\nget-app-package-operator-properties app-package-file operator-class\n Get operator properties within the given app package\n\nget-app-package-operators [options] app-package-file [search-term]\n Get operators within the given app package\n Options:\n -parent Specify the parent class for the operators\n\nget-config-parameter [parameter-name]\n Get the configuration parameter\n\nget-jar-operator-classes [options] jar-files-comma-separated [search-term]\n List operators in a jar list\n Options:\n -parent Specify the parent class for the operators\n\nget-jar-operator-properties jar-files-comma-separated operator-class-name\n List properties in specified operator\n\nhelp [command]\n Show help\n\nkill-app app-id [app-id ...]\n Kill an app\n\n launch [options] jar-file/json-file/properties-file/app-package-file [matching-app-name]\n Launch an app\n Options:\n -apconf \napp package configuration file\n Specify an application\n configuration file\n within the app\n package if launching\n an app package.\n -archives \ncomma separated list of archives\n Specify comma\n separated archives\n to be unarchived on\n the compute machines.\n -conf \nconfiguration file\n Specify an\n application\n configuration file.\n -D \nproperty=value\n Use value for given\n property.\n -exactMatch Only consider\n applications with\n exact app name\n -files \ncomma separated list of files\n Specify comma\n separated files to\n be copied on the\n compute machines.\n -ignorepom Do not run maven to\n find the dependency\n -libjars \ncomma separated list of libjars\n Specify comma\n separated jar files\n or other resource\n files to include in\n the classpath.\n -local Run application in\n local mode.\n -originalAppId \napplication id\n Specify original\n application\n identifier for restart.\n -queue \nqueue name\n Specify the queue to\n launch the application\n\nlist-application-attributes\n Lists the application attributes\nlist-apps [pattern]\n List applications\nlist-operator-attributes\n Lists the operator attributes\nlist-port-attributes\n Lists the port attributes\nset-pager on/off\n Set the pager program for output\nshow-logical-plan [options] jar-file/app-package-file [class-name]\n List apps in a jar or show logical plan of an app class\n Options:\n -exactMatch Only consider exact match\n for app name\n -ignorepom Do not run maven to find\n the dependency\n -libjars \ncomma separated list of jars\n Specify comma separated\n jar/resource files to\n include in the classpath.\nshutdown-app app-id [app-id ...]\n Shutdown an app\nsource file\n Execute the commands in a file\n\n\n\n\nCommands after connecting to an application\n\n\nCOMMANDS WHEN CONNECTED TO AN APP (via connect \nappid\n) EXCEPT WHEN CHANGING LOGICAL PLAN:\n\nbegin-logical-plan-change\n Begin Logical Plan Change\ndump-properties-file out-file [jar-file] [class-name]\n Dump the properties file of an app class\nget-app-attributes [attribute-name]\n Get attributes of the connected app\nget-app-info [app-id]\n Get the information of an app\nget-operator-attributes operator-name [attribute-name]\n Get attributes of an operator\nget-operator-properties operator-name [property-name]\n Get properties of a logical operator\nget-physical-operator-properties [options] operator-id\n Get properties of a physical operator\n Options:\n -propertyName \nproperty name\n The name of the property whose\n value needs to be retrieved\n -waitTime \nwait time\n How long to wait to get the result\nget-port-attributes operator-name port-name [attribute-name]\n Get attributes of a port\nget-recording-info [operator-id] [start-time]\n Get tuple recording info\nkill-app [app-id ...]\n Kill an app\nkill-container container-id [container-id ...]\n Kill a container\nlist-containers\n List containers\nlist-operators [pattern]\n List operators\nset-operator-property operator-name property-name property-value\n Set a property of an operator\nset-physical-operator-property operator-id property-name property-value\n Set a property of an operator\nshow-logical-plan [options] [jar-file/app-package-file] [class-name]\n Show logical plan of an app class\n Options:\n -exactMatch Only consider exact match\n for app name\n -ignorepom Do not run maven to find\n the dependency\n -libjars \ncomma separated list of jars\n Specify comma separated\n jar/resource files to\n include in the classpath.\nshow-physical-plan\n Show physical plan\nshutdown-app [app-id ...]\n Shutdown an app\nstart-recording operator-id [port-name] [num-windows]\n Start recording\nstop-recording operator-id [port-name]\n Stop recording\nwait timeout\n Wait for completion of current application\n\n\n\n\nCommands when changing the logical plan\n\n\nCOMMANDS WHEN CHANGING LOGICAL PLAN (via begin-logical-plan-change):\n\nabort\n Abort the plan change\nadd-stream-sink stream-name to-operator-name to-port-name\n Add a sink to an existing stream\ncreate-operator operator-name class-name\n Create an operator\ncreate-stream stream-name from-operator-name from-port-name to-operator-name to-port-name\n Create a stream\nhelp [command]\n Show help\nremove-operator operator-name\n Remove an operator\nremove-stream stream-name\n Remove a stream\nset-operator-attribute operator-name attr-name attr-value\n Set an attribute of an operator\nset-operator-property operator-name property-name property-value\n Set a property of an operator\nset-port-attribute operator-name port-name attr-name attr-value\n Set an attribute of a port\nset-stream-attribute stream-name attr-name attr-value\n Set an attribute of a stream\nshow-queue\n Show the queue of the plan change\nsubmit\n Submit the plan change\n\n\n\n\nExamples\n\n\nAn example of defining a custom macro. The macro updates a running application by inserting a new operator. It takes three parameters and executes a logical plan changes.\n\n\ndt\n begin-macro add-console-output\nmacro\n begin-logical-plan-change\nmacro\n create-operator $1 com.datatorrent.lib.io.ConsoleOutputOperator\nmacro\n create-stream stream_$1 $2 $3 $1 in\nmacro\n submit\n\n\n\n\nThen execute the \nadd-console-output\n macro like this\n\n\ndt\n add-console-output xyz opername portname\n\n\n\n\nThis macro then expands to run the following command\n\n\nbegin-logical-plan-change\ncreate-operator xyz com.datatorrent.lib.io.ConsoleOutputOperator\ncreate-stream stream_xyz opername portname xyz in\nsubmit\n\n\n\n\nNote\n: To perform runtime logical plan changes, like ability to add new operators,\nthey must be part of the jar files that were deployed at application launch time.",
- "title": "dtCli"
- },
- {
- "location": "/dtcli/#apache-apex-command-line-interface",
- "text": "dtCli, the Apache Apex command line interface, can be used to launch, monitor, and manage Apache Apex applications. It provides a developer friendly way of interacting with Apache Apex platform. Another advantage of dtCli is to provide scope, by connecting and executing commands in a context of specific application. dtCli enables easy integration with existing enterprise toolset for automated application monitoring and management. Currently the following high level tasks are supported. Launch or kill applications View system metrics including load, throughput, latency, etc. Start or stop tuple recording Read operator, stream, port properties and attributes Write to operator properties Dynamically change the application logical plan Create custom macros",
- "title": "Apache Apex Command Line Interface"
- },
- {
- "location": "/dtcli/#dtcli-commands",
- "text": "dtCli can be launched by running following command dtcli Help on all commands is available via \u201chelp\u201d command in the CLI",
- "title": "dtcli Commands"
- },
- {
- "location": "/dtcli/#global-commands",
- "text": "GLOBAL COMMANDS EXCEPT WHEN CHANGING LOGICAL PLAN:\n\nalias alias-name command\n Create a command alias\n\nbegin-macro name\n Begin Macro Definition ($1...$9 to access parameters and type 'end' to end the definition)\n\nconnect app-id\n Connect to an app\n\ndump-properties-file out-file jar-file class-name\n Dump the properties file of an app class\n\necho [arg ...]\n Echo the arguments\n\nexit\n Exit the CLI\n\nget-app-info app-id\n Get the information of an app\n\nget-app-package-info app-package-file\n Get info on the app package file\n\nget-app-package-operator-properties app-package-file operator-class\n Get operator properties within the given app package\n\nget-app-package-operators [options] app-package-file [search-term]\n Get operators within the given app package\n Options:\n -parent Specify the parent class for the operators\n\nget-config-parameter [parameter-name]\n Get the configuration parameter\n\nget-jar-operator-classes [options] jar-files-comma-separated [search-term]\n List operators in a jar list\n Options:\n -parent Specify the parent class for the operators\n\nget-jar-operator-properties jar-files-comma-separated operator-class-name\n List properties in specified operator\n\nhelp [command]\n Show help\n\nkill-app app-id [app-id ...]\n Kill an app\n\n launch [options] jar-file/json-file/properties-file/app-package-file [matching-app-name]\n Launch an app\n Options:\n -apconf app package configuration file Specify an application\n configuration file\n within the app\n package if launching\n an app package.\n -archives comma separated list of archives Specify comma\n separated archives\n to be unarchived on\n the compute machines.\n -conf configuration file Specify an\n application\n configuration file.\n -D property=value Use value for given\n property.\n -exactMatch Only consider\n applications with\n exact app name\n -files comma separated list of files Specify comma\n separated files to\n be copied on the\n compute machines.\n -ignorepom Do not run maven to\n find the dependency\n -libjars comma separated list of libjars Specify comma\n separated jar files\n or other resource\n files to include in\n the classpath.\n -local Run application in\n local mode.\n -originalAppId application id Specify original\n application\n identifier for restart.\n -queue queue name Specify the queue to\n launch the application\n\nlist-application-attributes\n Lists the application attributes\nlist-apps [pattern]\n List applications\nlist-operator-attributes\n Lists the operator attributes\nlist-port-attributes\n Lists the port attributes\nset-pager on/off\n Set the pager program for output\nshow-logical-plan [options] jar-file/app-package-file [class-name]\n List apps in a jar or show logical plan of an app class\n Options:\n -exactMatch Only consider exact match\n for app name\n -ignorepom Do not run maven to find\n the dependency\n -libjars comma separated list of jars Specify comma separated\n jar/resource files to\n include in the classpath.\nshutdown-app app-id [app-id ...]\n Shutdown an app\nsource file\n Execute the commands in a file",
- "title": "Global Commands"
- },
- {
- "location": "/dtcli/#commands-after-connecting-to-an-application",
- "text": "COMMANDS WHEN CONNECTED TO AN APP (via connect appid ) EXCEPT WHEN CHANGING LOGICAL PLAN:\n\nbegin-logical-plan-change\n Begin Logical Plan Change\ndump-properties-file out-file [jar-file] [class-name]\n Dump the properties file of an app class\nget-app-attributes [attribute-name]\n Get attributes of the connected app\nget-app-info [app-id]\n Get the information of an app\nget-operator-attributes operator-name [attribute-name]\n Get attributes of an operator\nget-operator-properties operator-name [property-name]\n Get properties of a logical operator\nget-physical-operator-properties [options] operator-id\n Get properties of a physical operator\n Options:\n -propertyName property name The name of the property whose\n value needs to be retrieved\n -waitTime wait time How long to wait to get the result\nget-port-attributes operator-name port-name [attribute-name]\n Get attributes of a port\nget-recording-info [operator-id] [start-time]\n Get tuple recording info\nkill-app [app-id ...]\n Kill an app\nkill-container container-id [container-id ...]\n Kill a container\nlist-containers\n List containers\nlist-operators [pattern]\n List operators\nset-operator-property operator-name property-name property-value\n Set a property of an operator\nset-physical-operator-property operator-id property-name property-value\n Set a property of an operator\nshow-logical-plan [options] [jar-file/app-package-file] [class-name]\n Show logical plan of an app class\n Options:\n -exactMatch Only consider exact match\n for app name\n -ignorepom Do not run maven to find\n the dependency\n -libjars comma separated list of jars Specify comma separated\n jar/resource files to\n include in the classpath.\nshow-physical-plan\n Show physical plan\nshutdown-app [app-id ...]\n Shutdown an app\nstart-recording operator-id [port-name] [num-windows]\n Start recording\nstop-recording operator-id [port-name]\n Stop recording\nwait timeout\n Wait for completion of current application",
- "title": "Commands after connecting to an application"
- },
- {
- "location": "/dtcli/#commands-when-changing-the-logical-plan",
- "text": "COMMANDS WHEN CHANGING LOGICAL PLAN (via begin-logical-plan-change):\n\nabort\n Abort the plan change\nadd-stream-sink stream-name to-operator-name to-port-name\n Add a sink to an existing stream\ncreate-operator operator-name class-name\n Create an operator\ncreate-stream stream-name from-operator-name from-port-name to-operator-name to-port-name\n Create a stream\nhelp [command]\n Show help\nremove-operator operator-name\n Remove an operator\nremove-stream stream-name\n Remove a stream\nset-operator-attribute operator-name attr-name attr-value\n Set an attribute of an operator\nset-operator-property operator-name property-name property-value\n Set a property of an operator\nset-port-attribute operator-name port-name attr-name attr-value\n Set an attribute of a port\nset-stream-attribute stream-name attr-name attr-value\n Set an attribute of a stream\nshow-queue\n Show the queue of the plan change\nsubmit\n Submit the plan change",
- "title": "Commands when changing the logical plan"
- },
- {
- "location": "/dtcli/#examples",
- "text": "An example of defining a custom macro. The macro updates a running application by inserting a new operator. It takes three parameters and executes a logical plan changes. dt begin-macro add-console-output\nmacro begin-logical-plan-change\nmacro create-operator $1 com.datatorrent.lib.io.ConsoleOutputOperator\nmacro create-stream stream_$1 $2 $3 $1 in\nmacro submit Then execute the add-console-output macro like this dt add-console-output xyz opername portname This macro then expands to run the following command begin-logical-plan-change\ncreate-operator xyz com.datatorrent.lib.io.ConsoleOutputOperator\ncreate-stream stream_xyz opername portname xyz in\nsubmit Note : To perform runtime logical plan changes, like ability to add new operators,\nthey must be part of the jar files that were deployed at application launch time.",
- "title": "Examples"
- }
- ]
-}
\ No newline at end of file
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- <li class="toctree-l3"><a href="#operator-development-guide">Operator Development Guide</a></li>
-
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- <li class="toctree-l3"><a href="#apache-apex-operators">Apache Apex Operators </a></li>
-
- <li><a class="toctree-l4" href="#operators-what-in-a-nutshell">Operators - “What” in a nutshell</a></li>
-
- <li><a class="toctree-l4" href="#operators-how-in-a-nutshell">Operators - “How” in a nutshell</a></li>
-
- <li><a class="toctree-l4" href="#types-of-operators">Types of Operators</a></li>
-
- <li><a class="toctree-l4" href="#operators-position-in-a-dag">Operators Position in a DAG</a></li>
-
- <li><a class="toctree-l4" href="#ports">Ports</a></li>
-
- <li><a class="toctree-l4" href="#how-operator-works">How Operator Works</a></li>
-
-
- <li class="toctree-l3"><a href="#developing-custom-operators">Developing Custom Operators </a></li>
-
- <li><a class="toctree-l4" href="#about-this-tutorial">About this tutorial</a></li>
-
- <li><a class="toctree-l4" href="#introduction">Introduction</a></li>
-
- <li><a class="toctree-l4" href="#design">Design</a></li>
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- <li><a class="toctree-l4" href="#configuration">Configuration</a></li>
-
- <li><a class="toctree-l4" href="#code">Code</a></li>
-
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- <li class="toctree-l3"><a href="#operator-reference">Operator Reference </a></li>
-
- <li><a class="toctree-l4" href="#the-operator-class">The Operator Class</a></li>
-
- <li><a class="toctree-l4" href="#class-operator-properties">Class (Operator) properties</a></li>
-
- <li><a class="toctree-l4" href="#the-constructor">The Constructor</a></li>
-
- <li><a class="toctree-l4" href="#setup-call">Setup call</a></li>
-
- <li><a class="toctree-l4" href="#begin-window-call">Begin Window call</a></li>
-
- <li><a class="toctree-l4" href="#process-tuple-call">Process Tuple call</a></li>
-
- <li><a class="toctree-l4" href="#end-window-call">End Window call</a></li>
-
- <li><a class="toctree-l4" href="#teardown-call">Teardown call</a></li>
-
- <li><a class="toctree-l4" href="#testing-your-operator">Testing your Operator</a></li>
-
-
- <li class="toctree-l3"><a href="#malhar-operator-library">Malhar Operator Library</a></li>
-
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- </ul>
-
- </li>
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- <li class="toctree-l1 ">
- <a class="" href="../autometrics/">AutoMetric API</a>
-
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- <h1 id="operator-development-guide">Operator Development Guide</h1>
-<p>Operators are basic building blocks of an application built to run on
-Apache Apex platform. An application may consist of one or more
-operators each of which define some logical operation to be done on the
-tuples arriving at the operator. These operators are connected together
-using streams forming a Directed Acyclic Graph (DAG). In other words, a streaming
-application is represented by a DAG that consists of operations (called operators) and
-data flow (called streams).</p>
-<p>In this document we will discuss details on how an operator works and
-its internals. This document is intended to serve the following purposes</p>
-<ol>
-<li><strong><a href="#apex_operators">Apache Apex Operators</a></strong> - Introduction to operator terminology and concepts.</li>
-<li><strong><a href="#writing_custom_operators">Writing Custom Operators</a></strong> - Designing, coding and testing new operators from scratch. Includes code examples.</li>
-<li><strong><a href="#operator_reference">Operator Reference</a></strong> - Details of operator internals, lifecycle, and best practices and optimizations.</li>
-</ol>
-<hr />
-<h1 id="apache-apex-operators">Apache Apex Operators <a name="apex_operators"></a></h1>
-<h2 id="operators-what-in-a-nutshell">Operators - “What” in a nutshell</h2>
-<p>Operators are independent units of logical operations which can
-contribute in executing the business logic of a use case. For example,
-in an ETL workflow, a filtering operation can be represented by a single
-operator. This filtering operator will be responsible for doing just one
-task in the ETL pipeline, i.e. filter incoming tuples. Operators do not
-impose any restrictions on what can or cannot be done as part of a
-operator. An operator may as well contain the entire business logic.
-However, it is recommended, that the operators are light weight
-independent tasks, in
-order to take advantage of the distributed framework that Apache Apex
-provides. The structure of a streaming application shares resemblance
-with the way CPU pipelining works. CPU pipelining breaks down the
-computation engine into different stages viz. instruction fetch,
-instruction decode, etc. so that each of them can perform their task on
-different instructions
-parallely. Similarly,
-Apache Apex APIs allow the user to break down their tasks into different
-stages so that all of the tasks can be executed on different tuples
-parallely.</p>
-<p><img alt="" src="../images/operator/image05.png" /></p>
-<h2 id="operators-how-in-a-nutshell">Operators - “How” in a nutshell</h2>
-<p>An Apache Apex application runs as a YARN application. Hence, each of
-the operators that the application DAG contains, runs in one of the
-containers provisioned by YARN. Further, Apache Apex exposes APIs to
-allow the user to request bundling multiple operators in a single node,
-a single container or even a single thread. We shall look at these calls
-in the reference sections [cite reference sections]. For now, consider
-an operator as some piece of code that runs on some machine of a YARN
-cluster.</p>
-<h2 id="types-of-operators">Types of Operators</h2>
-<p>An operator works on one tuple at a time. These tuples may be supplied
-by other operators in the application or by external sources,
-such as a database or a message bus. Similarly, after the tuples are
-processed, these may be passed on to other operators, or stored into an external system.
-Therea are 3 type of operators based on function: </p>
-<ol>
-<li><strong>Input Adapter</strong> - This is one of the starting points in
- the application DAG and is responsible for getting tuples from an
- external system. At the same time, such data may also be generated
- by the operator itself, without interacting with the outside
- world. These input tuples will form the initial universe of
- data that the application works on.</li>
-<li><strong>Generic Operator</strong> - This type of operator accepts input tuples from
- the previous operators and passes them on to the following operators
- in the DAG.</li>
-<li><strong>Output Adapter</strong> - This is one of the ending points in the application
- DAG and is responsible for writing the data out to some external
- system.</li>
-</ol>
-<p>Note: There can be multiple operators of all types in an application
-DAG.</p>
-<h2 id="operators-position-in-a-dag">Operators Position in a DAG</h2>
-<p>We may refer to operators depending on their position with respect to
-one another. For any operator opr (see image below), there are two types of operators.</p>
-<ol>
-<li><strong>Upstream operators</strong> - These are the operators from which there is a
- directed path to opr in the application DAG.</li>
-<li><strong>Downstream operators</strong> - These are the operators to which there is a
- directed path from opr in the application DAG.</li>
-</ol>
-<p>Note that there are no cycles formed in the application DAG.</p>
-<p><img alt="" src="../images/operator/image00.png" /></p>
-<h2 id="ports">Ports</h2>
-<p>Operators in a DAG are connected together via directed flows
-called streams. Each stream has end-points located on the operators
-called ports. Therea are 2 types of ports.</p>
-<ol>
-<li><strong>Input Port</strong> - This is a port through which an operator accepts input
- tuples from an upstream operator.</li>
-<li><strong>Output port</strong> - This is a port through which an operator passes on the
- processed data to downstream operators.</li>
-</ol>
-<p>Looking at the number of input ports, an Input Adapter is an operator
-with no input ports, a Generic operator has both input and output ports,
-while an Output Adapter has no output ports. At the same time, note that
-an operator may act as an Input Adapter while at the same time have an
-input port. In such cases, the operator is getting data from two
-different sources, viz. the input stream from the input port and an
-external source.</p>
-<p><img alt="" src="../images/operator/image02.png" /></p>
-<hr />
-<h2 id="how-operator-works">How Operator Works</h2>
-<p>An operator passes through various stages during its lifetime. Each
-stage is an API call that the Streaming Application Master makes for an
-operator. The following figure illustrates the stages through which an
-operator passes.</p>
-<p><img alt="" src="../images/operator/image01.png" /></p>
-<ul>
-<li>The <em>setup()</em> call initializes the operator and prepares itself to
- start processing tuples.</li>
-<li>The <em>beginWindow()</em> call marks the beginning of an application window
- and allows for any processing to be done before a window starts.</li>
-<li>The <em>process()</em> call belongs to the <em>InputPort</em> and gets triggered when
- any tuple arrives at the Input port of the operator. This call is
- specific only to Generic and Output adapters, since Input Adapters
- do not have an input port. This is made for all the tuples at the
- input port until the end window marker tuple is received on the
- input port.</li>
-<li>The <em>emitTuples()</em> is the counterpart of <em>process()</em> call for Input
- Adapters.
- This call is used by Input adapters to emit any tuples that are
- fetched from the external systems, or generated by the operator.
- This method is called continuously until the pre-configured window
- time is elapsed, at which the end window marker tuple is sent out on
- the output port.</li>
-<li>The <em>endWindow()</em> call marks the end of the window and allows for any
- processing to be done after the window ends.</li>
-<li>The <em>teardown()</em> call is used for gracefully shutting down the
- operator and releasing any resources held by the operator.</li>
-</ul>
-<h1 id="developing-custom-operators">Developing Custom Operators <a name="writing_custom_operators"></a></h1>
-<h2 id="about-this-tutorial">About this tutorial</h2>
-<p>This tutorial will guide the user towards developing a operator from
-scratch. It includes all aspects of writing an operator including
-design, code and unit testing.</p>
-<h2 id="introduction">Introduction</h2>
-<p>In this tutorial, we will design and write, from scratch, an operator
-called Word Count. This operator will accept tuples of type String,
-count the number of occurrences for each word appearing in the tuple and
-send out the updated counts for all the words encountered in the tuple.
-Further, the operator will also accept a file path on HDFS which will
-contain the stop-words which need to be ignored when counting
-occurrences.</p>
-<h2 id="design">Design</h2>
-<p>Design of the operator must be finalized before starting to write an
-operator. Many aspects including the functionality, the data sources,
-the types involved etc. need to be first finalized before writing the
-operator. Let us dive into each of these while considering the Word
-Count operator.</p>
-<h3 id="functionality">Functionality</h3>
-<p>We can define the scope of operator functionality using the following
-tasks:</p>
-<ol>
-<li>Parse the input tuple to identify the words in the tuple</li>
-<li>Identify the stop-words in the tuple by looking up the stop-word
- file as configured</li>
-<li>For each non-stop-word in the tuple, count the occurrences in that
- tuple and add it to a global counts</li>
-</ol>
-<p>Let’s consider an example. Suppose we have the following tuples flow
-into the Word Count operator.</p>
-<ol>
-<li><em>Humpty dumpty sat on a wall</em></li>
-<li><em>Humpty dumpty had a great fall</em></li>
-</ol>
-<p>Initially counts for all words is 0. Once the first tuple is processed,
-the counts that must be emitted are:</p>
-<pre><code class="java">humpty - 1
-dumpty - 1
-sat - 1
-wall - 1
-</code></pre>
-
-<p>Note that we are ignoring the stop-words, “on” and “a” in this case.
-Also note that as a rule, we’ll ignore the case of the words when
-counting occurrences.</p>
-<p>Similarly, after the second tuple is processed, the counts that must be
-emitted are:</p>
-<pre><code class="java">humpty - 2
-dumpty - 2
-great - 1
-fall - 1
-</code></pre>
-
-<p>Again, we ignore the words <em>“had”</em> and <em>“a”</em> since these are stop-words.</p>
-<p>Note that the most recent count for any word is correct count for that
-word. In other words, any new output for a word, invalidated all the
-previous counts for that word.</p>
-<h3 id="inputs">Inputs</h3>
-<p>As seen from the example above, the following inputs are expected for
-the operator:</p>
-<ol>
-<li>Input stream whose tuple type is String</li>
-<li>Input HDFS file path, pointing to a file containing stop-words</li>
-</ol>
-<p>Only one input port is needed. The stop-word file will be small enough
-to be read completely in a single read. In addition this will be a one
-time activity for the lifetime of the operator. This does not need a
-separate input port.</p>
-<p><img alt="" src="../images/operator/image03.png" /></p>
-<h3 id="outputs">Outputs</h3>
-<p>We can define the output for this operator in multiple ways.</p>
-<ol>
-<li>The operator may send out the set of counts for which the counts
- have changed after processing each tuple.</li>
-<li>Some applications might not need an update after every tuple, but
- only after a certain time duration.</li>
-</ol>
-<p>Let us try and implement both these options depending on the
-configuration. Let us define a boolean configuration parameter
-<em>“sendPerTuple”</em>. The value of this parameter will indicate whether the
-updated counts for words need to be emitted after processing each
-tuple (true) or after a certain time duration (false).</p>
-<p>The type of information the operator will be sending out on the output
-port is the same for all the cases. This will be a <em>< key, value ></em> pair,
-where the key is the word while, the value is the latest count for that
-word. This means we just need one output port on which this information
-will go out.</p>
-<p><img alt="" src="../images/operator/image04.png" /></p>
-<h2 id="configuration">Configuration</h2>
-<p>We have the following configuration parameters:</p>
-<ol>
-<li><em>stopWordFilePath</em> - This parameter will store the path to the stop
- word file on HDFS as configured by the user.</li>
-<li><em>sendPerTuple</em> - This parameter decides whether we send out the
- updated counts after processing each tuple or at the end of a
- window. When set to true, the operator will send out the updated
- counts after each tuple, else it will send at the end of
- each window.</li>
-</ol>
-<h2 id="code">Code</h2>
-<p>The source code for the tutorial can be found here:</p>
-<p><a href="https://github.com/DataTorrent/examples/tree/master/tutorials/operatorTutorial">https://github.com/DataTorrent/examples/tree/master/tutorials/operatorTutorial</a></p>
-<h1 id="operator-reference">Operator Reference <a name="operator_reference"></a></h1>
-<h3 id="the-operator-class">The Operator Class</h3>
-<p>The operator will exist physically as a class which implements the
-Operator interface. This interface will require implementations for the
-following method calls:</p>
-<ul>
-<li>setup(OperatorContext context)</li>
-<li>beginWindow(long windowId)</li>
-<li>endWindow()</li>
-<li>tearDown()</li>
-</ul>
-<p>In order to simplify the creation of an operator, Apache Apex
-library also provides a base class “BaseOperator” which has empty
-implementations for these methods. Please refer to the <a href="#apex_operators">Apex Operators</a> section and the
-<a href="#operator_reference">Reference</a> section for details on these.</p>
-<p>We extend the class “BaseOperator” to create our own operator
-“WordCountOperator”.</p>
-<pre><code class="java">public class WordCountOperator extends BaseOperator
-{
-}
-</code></pre>
-
-<h3 id="class-operator-properties">Class (Operator) properties</h3>
-<p>We define the following class variables:</p>
-<ul>
-<li><em>sendPerTuple</em> - Configures the output frequency from the operator</li>
-</ul>
-<pre><code class="java">private boolean sendPerTuple = true; // default
-</code></pre>
-
-<ul>
-<li><em>stopWordFilePath</em> - Stores the path to the stop words file on HDFS</li>
-</ul>
-<pre><code class="java">private String stopWordFilePath; // no default
-</code></pre>
-
-<ul>
-<li><em>stopWords</em> - Stores the stop words read from the configured file</li>
-</ul>
-<pre><code class="java">private transient String[] stopWords;
-</code></pre>
-
-<ul>
-<li><em>globalCounts</em> - A Map which stores the counts of all the words
- encountered so far. Note that this variable is non transient, which
- means that this variable is saved as part of the checkpoint and can be recovered in event of a crash.</li>
-</ul>
-<pre><code class="java">private Map<String, Long> globalCounts;
-</code></pre>
-
-<ul>
-<li><em>updatedCounts</em> - A Map which stores the counts for only the most
- recent tuple(s). sendPerTuple configuration determines whether to store the most recent or the recent
- window worth of tuples.</li>
-</ul>
-<pre><code class="java">private transient Map<String, Long> updatedCounts;
-</code></pre>
-
-<ul>
-<li><em>input</em> - The input port for the operator. The type of this input port
- is String which means it will only accept tuples of type String. The
- definition of an input port requires implementation of a method
- called process(String tuple), which should have the processing logic
- for the input tuple which arrives at this input port. We delegate
- this task to another method called processTuple(String tuple). This
- helps in keeping the operator classes extensible by overriding the
- processing logic for the input tuples.</li>
-</ul>
-<pre><code class="java">public transient DefaultInputPort<String> input = new
-DefaultInputPort<String>()
-{
- @Override
- public void process(String tuple)
- {
- processTuple(tuple);
- }
-};
-</code></pre>
-
-<ul>
-<li>output - The output port for the operator. The type of this port is
- Entry < String, Long >, which means the operator will emit < word,
- count > pairs for the updated counts.</li>
-</ul>
-<pre><code class="java">public transient DefaultOutputPort <Entry<String, Long>> output = new
-DefaultOutputPort<Entry<String,Long>>();
-</code></pre>
-
-<h3 id="the-constructor">The Constructor</h3>
-<p>The constructor is the place where we initialize the non-transient data
-structures, since
-constructor is called just once per activation of an operator. With regards to Word Count operator, we initialize the globalCounts variable in the constructor.</p>
-<pre><code class="java">globalCounts = Maps.newHashMap();
-</code></pre>
-
-<h3 id="setup-call">Setup call</h3>
-<p>The setup method is called only once during an operator lifetime and its purpose is to allow
-the operator to set itself up for processing incoming streams. Transient objects in the operator are
-not serialized and checkpointed. Hence, it is essential that such objects initialized in the setup call.
-In case of operator failure, the operator will be redeployed (most likely on a different container). The setup method called by the Apache Apex engine allows the operator to prepare for execution in the new container.</p>
-<p>The following tasks are executed as part of the setup call:</p>
-<ol>
-<li>Read the stop-word list from HDFS and store it in the
- stopWords array</li>
-<li>Initialize updatedCounts variable. This will store the updated
- counts for words in most recent tuples processed by the operator.
- As a transient variable, the value will be lost when operator fails.</li>
-</ol>
-<h3 id="begin-window-call">Begin Window call</h3>
-<p>The begin window call signals the start of an application window. With
-regards to Word Count Operator, we are expecting updated counts for the most recent window of
-data if the sendPerTuple is set to false. Hence, we clear the updatedCounts variable in the begin window
-call and start accumulating the counts till the end window call.</p>
-<h3 id="process-tuple-call">Process Tuple call</h3>
-<p>The processTuple method is called by the process method of the input
-port, input. This method defines the processing logic for the current
-tuple that is received at the input port. As part of this method, we
-identify the words in the current tuple and update the globalCounts and
-the updatedCounts variables. In addition, if the sendPerTuple variable
-is set to true, we also emit the words and corresponding counts in
-updatedCounts to the output port. Note that in this case (sendPerTuple =
-true), we clear the updatedCounts variable in every call to
-processTuple.</p>
-<h3 id="end-window-call">End Window call</h3>
-<p>This call signals the end of an application window. With regards to Word
-Count Operator, we emit the updatedCounts to the output port if the
-sendPerTuple flag is set to false.</p>
-<h3 id="teardown-call">Teardown call</h3>
-<p>This method allows the operator to gracefully shut down itself after
-releasing the resources that it has acquired. With regards to our operator,
-we call the shutDown method which shuts down the operator along with any
-downstream operators.</p>
-<h2 id="testing-your-operator">Testing your Operator</h2>
-<p>As part of testing our operator, we test the following two facets:</p>
-<ol>
-<li>Test output of the operator after processing a single tuple</li>
-<li>Test output of the operator after processing of a window of tuples</li>
-</ol>
-<p>The unit tests for the WordCount operator are available in the class
-WordCountOperatorTest.java. We simulate the behavior of the engine by
-using the test utilities provided by Apache Apex libraries. We simulate
-the setup, beginWindow, process method of the input port and
-endWindow calls and compare the output received at the simulated output
-ports.</p>
-<ol>
-<li>Invoke constructor; non-transients initialized.</li>
-<li>Copy state from checkpoint -- initialized values from step 1 are
-replaced.</li>
-</ol>
-<h1 id="malhar-operator-library">Malhar Operator Library</h1>
-<p>To see the full list of Apex Malhar operators along with related documentation, visit <a href="https://github.com/apache/incubator-apex-malhar">Apex Malhar on Github</a></p>
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- <li class="toctree-l3"><a href="#capabilities-common-across-malhar-operators">Capabilities common across Malhar operators</a></li>
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- <h1 id="apache-apex-malhar">Apache Apex Malhar</h1>
-<p>Apache Apex Malhar is an open source operator and codec library that can be used with the <a href="http://apex.apache.org/">Apache Apex</a> platform to build real-time streaming applications. Enabling users to extract value quickly, Malhar operators help get data in, analyze it in real-time, and get data out of Hadoop. In addition to the operators, the library contains a number of demos applications, demonstrating operator features and capabilities.</p>
-<p><img alt="MalharDiagram" src="./images/malhar-operators.png" /></p>
-<h1 id="capabilities-common-across-malhar-operators">Capabilities common across Malhar operators</h1>
-<p>For most streaming platforms, connectors are afterthoughts and often end up being simple ‘bolt-ons’ to the platform. As a result they often cause performance issues or data loss when put through failure scenarios and scalability requirements. Malhar operators do not face these issues as they were designed to be integral parts of Apex. Hence, they have following core streaming runtime capabilities</p>
-<ol>
-<li><strong>Fault tolerance</strong> – Malhar operators where applicable have fault tolerance built in. They use the checkpoint capability provided by the framework to ensure that there is no data loss under ANY failure scenario.</li>
-<li><strong>Processing guarantees</strong> – Malhar operators where applicable provide out of the box support for ALL three processing guarantees – exactly once, at-least once, and at-most once WITHOUT requiring the user to write any additional code. Some operators, like MQTT operator, deal with source systems that can not track processed data and hence need the operators to keep track of the data. Malhar has support for a generic operator that uses alternate storage like HDFS to facilitate this. Finally for databases that support transactions or support any sort of atomic batch operations Malhar operators can do exactly once down to the tuple level.</li>
-<li><strong>Dynamic updates</strong> – Based on changing business conditions you often have to tweak several parameters used by the operators in your streaming application without incurring any application downtime. You can also change properties of a Malhar operator at runtime without having to bring down the application.</li>
-<li><strong>Ease of extensibility</strong> – Malhar operators are based on templates that are easy to extend.</li>
-<li><strong>Partitioning support</strong> – In streaming applications the input data stream often needs to be partitioned based on the contents of the stream. Also for operators that ingest data from external systems partitioning needs to be done based on the capabilities of the external system. For example with Kafka, the operator can automatically scale up or down based on the changes in the number of Kafka partitions.</li>
-</ol>
-<h1 id="operator-library-overview">Operator Library Overview</h1>
-<h2 id="inputoutput-connectors">Input/output connectors</h2>
-<p>Below is a summary of the various sub categories of input and output operators. Input operators also have a corresponding output operator</p>
-<ul>
-<li><strong>File Systems</strong> – Most streaming analytics use cases require the data to be stored in HDFS or perhaps S3 if the application is running in AWS. Users often need to re-run their streaming analytical applications against historical data or consume data from upstream processes that are perhaps writing to some NFS share. Apex supports input & output operators for HDFS, S3, NFS & Local Files. There are also File Splitter and Block Reader operators, which can accelecate processing of large files by splitting and paralellizing the work across non-overlapping sets of file blocks.</li>
-<li><strong>Relational Databases</strong> – Most stream processing use cases require some reference data lookups to enrich, tag or filter streaming data. There is also a need to save results of the streaming analytical computation to a database so an operational dashboard can see them. Apex supports a JDBC operator so you can read/write data from any JDBC compliant RDBMS like Oracle, MySQL, Sqlite, etc.</li>
-<li><strong>NoSQL Databases</strong> – NoSQL key-value pair databases like Cassandra & HBase are a common part of streaming analytics application architectures to lookup reference data or store results. Malhar has operators for HBase, Cassandra, Accumulo, Aerospike, MongoDB, and CouchDB.</li>
-<li><strong>Messaging Systems</strong> – Kafka, JMS, and similar systems are the workhorses of messaging infrastructure in most enterprises. Malhar has a robust, industry-tested set of operators to read and write Kafka, JMS, ZeroMQ, and RabbitMQ messages.</li>
-<li><strong>Notification Systems</strong> – Malhar includes an operator for sending notifications via SMTP.</li>
-<li><strong>In-memory Databases & Caching platforms</strong> - Some streaming use cases need instantaneous access to shared state across the application. Caching platforms and in-memory databases serve this purpose really well. To support these use cases, Malhar has operators for memcached and Redis.</li>
-<li><strong>Social Media</strong> - Malhar includes an operator to connect to the popular Twitter stream fire hose.</li>
-<li><strong>Protocols</strong> - Malhar provides connectors that can communicate in HTTP, RSS, Socket, WebSocket, FTP, and MQTT.</li>
-</ul>
-<h2 id="parsers">Parsers</h2>
-<p>There are many industry vertical specific data formats that a streaming application developer might need to parse. Often there are existing parsers available for these that can be directly plugged into an Apache Apex application. For example in the Telco space, a Java based CDR parser can be directly plugged into Apache Apex operator. To further simplify development experience, Malhar also provides some operators for parsing common formats like XML (DOM & SAX), JSON (flat map converter), Apache log files, syslog, etc.</p>
-<h2 id="stream-manipulation">Stream manipulation</h2>
-<p>Streaming data inevitably needs processing to clean, filter, tag, summarize, etc. The goal of Malhar is to enable the application developer to focus on WHAT needs to be done to the stream to get it in the right format and not worry about the HOW. Malhar has several operators to perform the common stream manipulation actions like – GroupBy, Join, Distinct/Unique, Limit, OrderBy, Split, Sample, Inner join, Outer join, Select, Update etc.</p>
-<h2 id="compute">Compute</h2>
-<p>One of the most important promises of a streaming analytics platform like Apache Apex is the ability to do analytics in real-time. However delivering on the promise becomes really difficult when the platform does not provide out of the box operators to support variety of common compute functions as the user then has to worry about making these scalable, fault tolerant, stateful, etc. Malhar takes this responsibility away from the application developer by providing a variety of out of the box computational operators.</p>
-<p>Below is just a snapshot of the compute operators available in Malhar</p>
-<ul>
-<li>Statistics and math - Various mathematical and statistical computations over application defined time windows.</li>
-<li>Filtering and pattern matching</li>
-<li>Sorting, maps, frequency, TopN, BottomN</li>
-<li>Random data generators</li>
-</ul>
-<h2 id="languages-support">Languages Support</h2>
-<p>Migrating to a new platform often requires re-use of the existing code that would be difficult or time-consuming to re-write. With this in mind, Malhar supports invocation of code written in other languages by wrapping them in one of the library operators, and allows execution of software written in:</p>
-<ul>
-<li>JavaScript</li>
-<li>Python</li>
-<li>R</li>
-<li>Ruby</li>
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pmulhuw psadbw pshufw pf2iw pfnacc pfpnacc pi2fw pswapd maskmovdqu clflush movntdq movnti movntpd movdqa movdqu movdq2q movq2dq paddq pmuludq pshufd pshufhw pshuflw pslldq psrldq psubq punpckhqdq punpcklqdq addpd addsd andnpd andpd cmpeqpd cmpeqsd cmplepd cmplesd cmpltpd cmpltsd cmpneqpd cmpneqsd cmpnlepd cmpnlesd cmpnltpd cmpnltsd cmpordpd cmpordsd cmpunordpd cmpunordsd cmppd comisd cvtdq2pd cvtdq2ps cvtpd2dq cvtpd2pi cvtpd2ps cvtpi2pd cvtps2dq cvtps2pd cvtsd2si cvtsd2ss cvtsi2sd cvtss2sd cvttpd2pi cvttpd2dq cvttps2dq cvttsd2si divpd divsd maxpd maxsd minpd minsd movapd movhpd movlpd movmskpd movupd mulpd mulsd orpd shufpd sqrtpd sqrtsd subpd subsd ucomisd unpckhpd unpcklpd xorpd addsubpd addsubps haddpd haddps hsubpd hsubps lddqu movddup movshdup movsldup clgi stgi vmcall vmclear vmfunc vmlaunch vmload vmmcall vmptrld vmptrst vmread vmresume vmrun vmsave vmwrite vmxoff vmxon invept invvpid pabsb pabsw pabsd palignr phaddw phaddd phaddsw phsubw phsubd phsubsw pmaddubsw pmulhrsw pshufb psignb psignw psignd extrq insertq movntsd movntss lzcnt blendpd blendps blendvpd blendvps dppd dpps extractps insertps movntdqa mpsadbw packusdw pblendvb pblendw pcmpeqq pextrb pextrd pextrq phminposuw pinsrb pinsrd pinsrq pmaxsb pmaxsd pmaxud pmaxuw pminsb pminsd pminud pminuw pmovsxbw pmovsxbd pmovsxbq pmovsxwd pmovsxwq pmovsxdq pmovzxbw pmovzxbd pmovzxbq pmovzxwd pmovzxwq pmovzxdq pmuldq pmulld ptest roundpd roundps roundsd roundss crc32 pcmpestri pcmpestrm pcmpistri pcmpistrm pcmpgtq popcnt getsec pfrcpv pfrsqrtv movbe aesenc aesenclast aesdec aesdeclast aesimc aeskeygenassist vaesenc vaesenclast vaesdec vaesdeclast vaesimc vaeskeygenassist vaddpd vaddps vaddsd vaddss vaddsubpd vaddsubps vandpd vandps vandnpd vandnps vblendpd vblendps vblendvpd vblendvps vbroadcastss vbroadcastsd vbroadcastf128 vcmpeq_ospd vcmpeqpd vcmplt_ospd vcmpltpd vcmple_ospd vcmplepd vcmpunord_qpd vcmpunordpd vcmpneq_uqpd vcmpneqpd vcmpnlt_uspd vcmpnltpd vcmpnle_uspd vcmpnlepd vcmpord_qpd vcmpordpd vcmpeq_uqpd vcmpnge_uspd vcmpngepd vcmpngt_uspd vcmpngtpd vcmpfalse_oqpd vcmpfalsepd vcmpneq_oqpd vcmpge_ospd vcmpgepd vcmpgt_ospd vcmpgtpd vcmptrue_uqpd vcmptruepd vcmplt_oqpd vcmple_oqpd vcmpunord_spd vcmpneq_uspd vcmpnlt_uqpd vcmpnle_uqpd vcmpord_spd vcmpeq_uspd vcmpnge_uqpd vcmpngt_uqpd vcmpfalse_ospd vcmpneq_ospd vcmpge_oqpd vcmpgt_oqpd vcmptrue_uspd vcmppd vcmpeq_osps vcmpeqps vcmplt_osps vcmpltps vcmple_osps vcmpleps vcmpunord_qps vcmpunordps vcmpneq_uqps vcmpneqps vcmpnlt_usps vcmpnltps vcmpnle_usps vcmpnleps vcmpord_qps vcmpordps vcmpeq_uqps vcmpnge_usps vcmpngeps vcmpngt_usps vcmpngtps vcmpfalse_oqps vcmpfalseps vcmpneq_oqps vcmpge_osps vcmpgeps vcmpgt_osps vcmpgtps vcmptrue_uqps vcmptrueps vcmplt_oqps vcmple_oqps vcmpunord_sps vcmpneq_usps vcmpnlt_uqps vcmpnle_uqps vcmpord_sps vcmpeq_usps vcmpnge_uqps vcmpngt_uqps vcmpfalse_osps vcmpneq_osps vcmpge_oqps vcmpgt_oqps vcmptrue_usps vcmpps vcmpeq_ossd vcmpeqsd vcmplt_ossd vcmpltsd vcmple_ossd vcmplesd vcmpunord_qsd vcmpunordsd vcmpneq_uqsd vcmpneqsd vcmpnlt_ussd vcmpnltsd vcmpnle_ussd vcmpnlesd vcmpord_qsd vcmpordsd vcmpeq_uqsd vcmpnge_ussd vcmpngesd vcmpngt_ussd vcmpngtsd vcmpfalse_oqsd vcmpfalsesd vcmpneq_oqsd vcmpge_ossd vcmpgesd vcmpgt_ossd vcmpgtsd vcmptrue_uqsd vcmptruesd vcmplt_oqsd vcmple_oqsd vcmpunord_ssd vcmpneq_ussd vcmpnlt_uqsd vcmpnle_uqsd vcmpord_ssd vcmpeq_ussd vcmpnge_uqsd vcmpngt_uqsd vcmpfalse_ossd vcmpneq_ossd vcmpge_oqsd vcmpgt_oqsd vcmptrue_ussd vcmpsd vcmpeq_osss vcmpeqss vcmplt_osss vcmpltss vcmple_osss vcmpless vcmpunord_qss vcmpunordss vcmpneq_uqss vcmpneqss vcmpnlt_usss vcmpnltss vcmpnle_usss vcmpnless vcmpord_qss vcmpordss vcmpeq_uqss vcmpnge_usss vcmpngess vcmpngt_usss vcmpngtss vcmpfalse_oqss vcmpfalsess vcmpneq_oqss vcmpge_osss vcmpgess vcmpgt_osss vcmpgtss vcmptrue_uqss vcmptruess vcmplt_oqss vcmple_oqss vcmpunord_sss vcmpneq_usss vcmpnlt_uqss vcmpnle_uqss vcmpord_sss vcmpeq_usss vcmpnge_uqss vcmpngt_uqss vcmpfalse_osss vcmpneq_osss vcmpge_oqss vcmpgt_oqss vcmptrue_usss vcmpss vcomisd vcomiss vcvtdq2pd vcvtdq2ps vcvtpd2dq vcvtpd2ps vcvtps2dq vcvtps2pd vcvtsd2si vcvtsd2ss vcvtsi2sd vcvtsi2ss vcvtss2sd vcvtss2si vcvttpd2dq vcvttps2dq vcvttsd2si vcvttss2si vdivpd vdivps vdivsd vdivss vdppd vdpps vextractf128 vextractps vhaddpd vhaddps vhsubpd vhsubps vinsertf128 vinsertps vlddqu vldqqu vldmxcsr vmaskmovdqu vmaskmovps vmaskmovpd vmaxpd vmaxps vmaxsd vmaxss vminpd vminps vminsd vminss vmovapd vmovaps vmovd vmovq vmovddup vmovdqa vmovqqa vmovdqu vmovqqu vmovhlps vmovhpd vmovhps vmovlhps vmovlpd vmovlps vmovmskpd vmovmskps vmovntdq vmovntqq vmovntdqa vmovntpd vmovntps vmovsd vmovshdup vmovsldup vmovss vmovupd vmovups vmpsadbw vmulpd vmulps vmulsd vmulss vorpd vorps vpabsb vpabsw vpabsd vpacksswb vpackssdw vpackuswb vpackusdw vpaddb vpaddw vpaddd vpaddq vpaddsb vpaddsw vpaddusb vpaddusw vpalignr vpand vpandn vpavgb vpavgw vpblendvb vpblendw vpcmpestri vpcmpestrm vpcmpistri vpcmpistrm vpcmpeqb vpcmpeqw vpcmpeqd vpcmpeqq vpcmpgtb vpcmpgtw vpcmpgtd vpcmpgtq vpermilpd vpermilps vperm2f128 vpextrb vpextrw vpextrd vpextrq vphaddw vphaddd vphaddsw vphminposuw vphsubw vphsubd vphsubsw vpinsrb vpinsrw vpinsrd vpinsrq vpmaddwd vpmaddubsw vpmaxsb vpmaxsw vpmaxsd vpmaxub vpmaxuw vpmaxud vpminsb vpminsw vpminsd vpminub vpminuw vpminud vpmovmskb vpmovsxbw vpmovsxbd vpmovsxbq vpmovsxwd vpmovsxwq vpmovsxdq vpmovzxbw vpmovzxbd vpmovzxbq vpmovzxwd vpmovzxwq vpmovzxdq vpmulhuw vpmulhrsw vpmulhw vpmullw vpmulld vpmuludq vpmuldq vpor vpsadbw vpshufb vpshufd vpshufhw vpshuflw vpsignb vpsignw vpsignd vpslldq vpsrldq vpsllw vpslld vpsllq vpsraw vpsrad vpsrlw vpsrld vpsrlq vptest vpsubb vpsubw vpsubd vpsubq vpsubsb vpsubsw vpsubusb vpsubusw vpunpckhbw vpunpckhwd vpunpckhdq vpunpckhqdq vpunpcklbw vpunpcklwd vpunpckldq vpunpcklqdq vpxor vrcpps vrcpss vrsqrtps vrsqrtss vroundpd vroundps vroundsd vroundss vshufpd vshufps vsqrtpd vsqrtps vsqrtsd vsqrtss vstmxcsr vsubpd vsubps vsubsd vsubss vtestps vtestpd vucomisd vucomiss vunpckhpd vunpckhps vunpcklpd vunpcklps vxorpd vxorps vzeroall vzeroupper pclmullqlqdq pclmulhqlqdq pclmullqhqdq pclmulhqhqdq pclmulqdq vpclmullqlqdq vpclmulhqlqdq vpclmullqhqdq vpclmulhqhqdq vpclmulqdq vfmadd132ps vfmadd132pd vfmadd312ps vfmadd312pd vfmadd213ps vfmadd213pd vfmadd123ps vfmadd123pd vfmadd231ps vfmadd231pd vfmadd321ps vfmadd321pd vfmaddsub132ps vfmaddsub132pd vfmaddsub312ps vfmaddsub312pd vfmaddsub213ps vfmaddsub213pd vfmaddsub123ps vfmaddsub123pd vfmaddsub231ps vfmaddsub231pd vfmaddsub321ps vfmaddsub321pd vfmsub132ps vfmsub132pd vfmsub312ps vfmsub312pd vfmsub213ps vfmsub213pd vfmsub123ps vfmsub123pd vfmsub231ps vfmsub231pd vfmsub321ps vfmsub321pd vfmsubadd132ps vfmsubadd132pd vfmsubadd312ps vfmsubadd312pd vfmsubadd213ps vfmsubadd213pd vfmsubadd123ps vfmsubadd123pd vfmsubadd231ps vfmsubadd231pd vfmsubadd321ps vfmsubadd321pd vfnmadd132ps vfnmadd132pd vfnmadd312ps vfnmadd312pd vfnmadd213ps vfnmadd213pd vfnmadd123ps vfnmadd123pd vfnmadd231ps vfnmadd231pd vfnmadd321ps vfnmadd321pd vfnmsub132ps vfnmsub132pd vfnmsub312ps vfnmsub312pd vfnmsub213ps vfnmsub213pd vfnmsub123ps vfnmsub123pd vfnmsub231ps vfnmsub231pd vfnmsub321ps vfnmsub321pd vfmadd132ss vfmadd132sd vfmadd312ss vfmadd312sd vfmadd213ss vfmadd213sd vfmadd123ss vfmadd123sd vfmadd231ss vfmadd231sd vfmadd321ss vfmadd321sd vfmsub132ss vfmsub132sd vfmsub312ss vfmsub312sd vfmsub213ss vfmsub213sd vfmsub123ss vfmsub123sd vfmsub231ss vfmsub231sd vfmsub321ss vfmsub321sd vfnmadd132ss vfnmadd132sd vfnmadd312ss vfnmadd312sd vfnmadd213ss vfnmadd213sd vfnmadd123ss vfnmadd123sd vfnmadd231ss vfnmadd231sd vfnmadd321ss vfnmadd321sd vfnmsub132ss vfnmsub132sd vfnmsub312ss vfnmsub312sd vfnmsub213ss vfnmsub213sd vfnmsub123ss vfnmsub123sd vfnmsub231ss vfnmsub231sd vfnmsub321ss vfnmsub321sd rdfsbase rdgsbase rdrand wrfsbase wrgsbase vcvtph2ps vcvtps2ph adcx adox rdseed clac stac xstore xcryptecb xcryptcbc xcryptctr xcryptcfb xcryptofb montmul xsha1 xsha256 llwpcb slwpcb lwpval lwpins vfmaddpd vfmaddps vfmaddsd vfmaddss vfmaddsubpd vfmaddsubps vfmsubaddpd vfmsubaddps vfmsubpd vfmsubps vfmsubsd vfmsubss vfnmaddpd vfnmaddps vfnmaddsd vfnmaddss vfnmsubpd vfnmsubps vfnmsubsd vfnmsubss vfrczpd vfrczps vfrczsd vfrczss vpcmov vpcomb vpcomd vpcomq vpcomub vpcomud vpcomuq vpcomuw vpcomw vphaddbd vphaddbq vphaddbw vphadddq vphaddubd vphaddubq vphaddubw vphaddudq vphadduwd vphadduwq vphaddwd vphaddwq vphsubbw vphsubdq vphsubwd vpmacsdd vpmacsdqh vpmacsdql vpmacssdd vpmacssdqh vpmacssdql vpmacsswd vpmacssww vpmacswd vpmacsww vpmadcsswd vpmadcswd vpperm vprotb vprotd vprotq vprotw vpshab vpshad vpshaq vpshaw vpshlb vpshld vpshlq vpshlw vbroadcasti128 vpblendd vpbroadcastb vpbroadcastw vpbroadcastd vpbroadcastq vpermd vpermpd vpermps vpermq vperm2i128 vextracti128 vinserti128 vpmaskmovd vpmaskmovq vpsllvd vpsllvq vpsravd vpsrlvd vpsrlvq vgatherdpd vgatherqpd vgatherdps vgatherqps vpgatherdd vpgatherqd vpgatherdq vpgatherqq xabort xbegin xend xtest andn bextr blci blcic blsi blsic blcfill blsfill blcmsk blsmsk blsr blcs bzhi mulx pdep pext rorx sarx shlx shrx tzcnt tzmsk t1mskc valignd valignq vblendmpd vblendmps vbroadcastf32x4 vbroadcastf64x4 vbroadcasti32x4 vbroadcasti64x4 vcompresspd vcompressps vcvtpd2udq vcvtps2udq vcvtsd2usi vcvtss2usi vcvttpd2udq vcvttps2udq vcvttsd2usi vcvttss2usi vcvtudq2pd vcvtudq2ps vcvtusi2sd vcvtusi2ss vexpandpd vexpandps vextractf32x4 vextractf64x4 vextracti32x4 vextracti64x4 vfixupimmpd vfixupimmps vfixupimmsd vfixupimmss vgetexppd vgetexpps vgetexpsd vgetexpss vgetmantpd vgetmantps vgetmantsd vgetmantss vinsertf32x4 vinsertf64x4 vinserti32x4 vinserti64x4 vmovdqa32 vmovdqa64 vmovdqu32 vmovdqu64 vpabsq vpandd vpandnd vpandnq vpandq vpblendmd vpblendmq vpcmpltd vpcmpled vpcmpneqd vpcmpnltd vpcmpnled vpcmpd vpcmpltq vpcmpleq vpcmpneqq vpcmpnltq vpcmpnleq vpcmpq vpcmpequd vpcmpltud vpcmpleud vpcmpnequd vpcmpnltud vpcmpnleud vpcmpud vpcmpequq vpcmpltuq vpcmpleuq vpcmpnequq vpcmpnltuq vpcmpnleuq vpcmpuq vpcompressd vpcompressq vpermi2d vpermi2pd vpermi2ps vpermi2q vpermt2d vpermt2pd vpermt2ps vpermt2q vpexpandd vpexpandq vpmaxsq vpmaxuq vpminsq vpminuq vpmovdb vpmovdw vpmovqb vpmovqd vpmovqw vpmovsdb vpmovsdw vpmovsqb vpmovsqd vpmovsqw vpmovusdb vpmovusdw vpmovusqb vpmovusqd vpmovusqw vpord vporq vprold vprolq vprolvd vprolvq vprord vprorq vprorvd vprorvq vpscatterdd vpscatterdq vpscatterqd vpscatterqq vpsraq vpsravq vpternlogd vpternlogq vptestmd vptestmq vptestnmd vptestnmq vpxord vpxorq vrcp14pd vrcp14ps vrcp14sd vrcp14ss vrndscalepd vrndscaleps vrndscalesd vrndscaless vrsqrt14pd vrsqrt14ps vrsqrt14sd vrsqrt14ss vscalefpd vscalefps vscalefsd vscalefss vscatterdpd vscatterdps vscatterqpd vscatterqps vshuff32x4 vshuff64x2 vshufi32x4 vshufi64x2 kandnw kandw kmovw knotw kortestw korw kshiftlw kshiftrw kunpckbw kxnorw kxorw vpbroadcastmb2q vpbroadcastmw2d vpconflictd vpconflictq vplzcntd vplzcntq vexp2pd vexp2ps vrcp28pd vrcp28ps vrcp28sd vrcp28ss vrsqrt28pd vrsqrt28ps vrsqrt28sd vrsqrt28ss vgatherpf0dpd vgatherpf0dps vgatherpf0qpd vgatherpf0qps vgatherpf1dpd vgatherpf1dps vgatherpf1qpd vgatherpf1qps vscatterpf0dpd vscatterpf0dps vscatterpf0qpd vscatterpf0qps vscatterpf1dpd vscatterpf1dps vscatterpf1qpd vscatterpf1qps prefetchwt1 bndmk bndcl bndcu bndcn bndmov bndldx bndstx sha1rnds4 sha1nexte sha1msg1 sha1msg2 sha256rnds2 sha256msg1 sha256msg2 hint_nop0 hint_nop1 hint_nop2 hint_nop3 hint_nop4 hint_nop5 hint_nop6 hint_nop7 hint_nop8 hint_nop9 hint_nop10 hint_nop11 hint_nop12 hint_nop13 hint_nop14 hint_nop15 hint_nop16 hint_nop17 hint_nop18 hint_nop19 hint_nop20 hint_nop21 hint_nop22 hint_nop23 hint_nop24 hint_nop25 hint_nop26 hint_nop27 hint_nop28 hint_nop29 hint_nop30 hint_nop31 hint_nop32 hint_nop33 hint_nop34 hint_nop35 hint_nop36 hint_nop37 hint_nop38 hint_nop39 hint_nop40 hint_nop41 hint_nop42 hint_nop43 hint_nop44 hint_nop45 hint_nop46 hint_nop47 hint_nop48 hint_nop49 hint_nop50 hint_nop51 hint_nop52 hint_nop53 hint_nop54 hint_nop55 hint_nop56 hint_nop57 hint_nop58 hint_nop59 hint_nop60 hint_nop61 hint_nop62 hint_nop63",literal:"ip eip rip al ah bl bh cl ch dl dh sil dil bpl spl r8b r9b r10b r11b r12b r13b r14b r15b ax bx cx dx si di bp sp r8w r9w r10w r11w r12w r13w r14w r15w eax ebx ecx edx esi edi ebp esp eip r8d r9d r10d r11d r12d r13d r14d r15d rax rbx rcx rdx rsi rdi rbp rsp r8 r9 r10 r11 r12 r13 r14 r15 cs ds es fs gs ss st st0 st1 st2 st3 st4 st5 st6 st7 mm0 mm1 mm2 mm3 mm4 mm5 mm6 mm7 xmm0 xmm1 xmm2 xmm3 xmm4 xmm5 xmm6 xmm7 xmm8 xmm9 xmm10 xmm11 xmm12 xmm13 xmm14 xmm15 xmm16 xmm17 xmm18 xmm19 xmm20 xmm21 xmm22 xmm23 xmm24 xmm25 xmm26 xmm27 xmm28 xmm29 xmm30 xmm31 ymm0 ymm1 ymm2 ymm3 ymm4 ymm5 ymm6 ymm7 ymm8 ymm9 ymm10 ymm11 ymm12 ymm13 ymm14 ymm15 ymm16 ymm17 ymm18 ymm19 ymm20 ymm21 ymm22 ymm23 ymm24 ymm25 ymm26 ymm27 ymm28 ymm29 ymm30 ymm31 zmm0 zmm1 zmm2 zmm3 zmm4 zmm5 zmm6 zmm7 zmm8 zmm9 zmm10 zmm11 zmm12 zmm13 zmm14 zmm15 zmm16 zmm17 zmm18 zmm19 zmm20 zmm21 zmm22 zmm23 zmm24 zmm25 zmm26 zmm27 zmm28 zmm29 zmm30 zmm31 k0 k1 k2 k3 k4 k5 k6 k7 bnd0 bnd1 bnd2 bnd3 cr0 cr1 cr2 cr3 cr4 cr8 dr0 dr1 dr2 dr3 dr8 tr3 tr4 tr5 tr6 tr7 r0 r1 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FindMinimumCut FindMinValue FindPermutation FindPostmanTour FindProcessParameters FindRoot FindSequenceFunction FindSettings FindShortestPath FindShortestTour FindThreshold FindVertexCover FindVertexCut Fine FinishDynamic FiniteAbelianGroupCount FiniteGroupCount FiniteGroupData First FirstPassageTimeDistribution FischerGroupFi22 FischerGroupFi23 FischerGroupFi24Prime FisherHypergeometricDistribution FisherRatioTest FisherZDistribution Fit FitAll FittedModel FixedPoint FixedPointList FlashSelection Flat Flatten FlattenAt FlatTopWindow FlipView Floor FlushPrintOutputPacket Fold FoldList Font FontColor FontFamily FontForm FontName FontOpacity FontPostScriptName FontProperties FontReencoding FontSize FontSlant FontSubstitutions FontTracking FontVariations FontWeight For ForAll Format FormatRules FormatType FormatTypeAutoConvert FormatValues FormBox FormBoxOptions FortranForm Forward ForwardBackward Fourier FourierCoefficient FourierCosCoefficient FourierCosSeries FourierCosTransform FourierDCT FourierDCTFilter FourierDCTMatrix FourierDST FourierDSTMatrix FourierMatrix FourierParameters FourierSequenceTransform FourierSeries FourierSinCoefficient FourierSinSeries FourierSinTransform FourierTransform FourierTrigSeries FractionalBrownianMotionProcess FractionalPart FractionBox FractionBoxOptions FractionLine Frame FrameBox FrameBoxOptions Framed FrameInset FrameLabel Frameless FrameMargins FrameStyle FrameTicks FrameTicksStyle FRatioDistribution FrechetDistribution FreeQ FrequencySamplingFilterKernel FresnelC FresnelS Friday FrobeniusNumber FrobeniusSolve FromCharacterCode FromCoefficientRules FromContinuedFraction FromDate FromDigits FromDMS Front FrontEndDynamicExpression FrontEndEventActions FrontEndExecute FrontEndObject FrontEndResource FrontEndResourceString FrontEndStackSize FrontEndToken FrontEndTokenExecute FrontEndValueCache FrontEndVersion FrontFaceColor FrontFaceOpacity Full FullAxes FullDefinition FullForm FullGraphics FullOptions FullSimplify Function FunctionExpand FunctionInterpolation FunctionSpace FussellVeselyImportance GaborFilter GaborMatrix GaborWavelet GainMargins GainPhaseMargins Gamma GammaDistribution GammaRegularized GapPenalty Gather GatherBy GaugeFaceElementFunction GaugeFaceStyle GaugeFrameElementFunction GaugeFrameSize GaugeFrameStyle GaugeLabels GaugeMarkers GaugeStyle GaussianFilter GaussianIntegers GaussianMatrix GaussianWindow GCD GegenbauerC General GeneralizedLinearModelFit GenerateConditions GeneratedCell GeneratedParameters GeneratingFunction Generic GenericCylindricalDecomposition GenomeData GenomeLookup GeodesicClosing GeodesicDilation GeodesicErosion GeodesicOpening GeoDestination GeodesyData GeoDirection GeoDistance GeoGridPosition GeometricBrownianMotionProcess GeometricDistribution GeometricMean GeometricMeanFilter GeometricTransformation GeometricTransformation3DBox GeometricTransformation3DBoxOptions GeometricTransformationBox GeometricTransformationBoxOptions GeoPosition GeoPositionENU GeoPositionXYZ GeoProjectionData GestureHandler GestureHandlerTag Get GetBoundingBoxSizePacket GetContext GetEnvironment GetFileName GetFrontEndOptionsDataPacket GetLinebreakInformationPacket GetMenusPacket GetPageBreakInformationPacket Glaisher GlobalClusteringCoefficient GlobalPreferences GlobalSession Glow GoldenRatio GompertzMakehamDistribution GoodmanKruskalGamma GoodmanKruskalGammaTest Goto Grad Gradient GradientFilter GradientOrientationFilter Graph GraphAssortativity GraphCenter GraphComplement GraphData GraphDensity GraphDiameter GraphDifference GraphDisjointUnion GraphDistance GraphDistanceMatrix GraphElementData GraphEmbedding GraphHighlight GraphHighlightStyle GraphHub Graphics Graphics3D Graphics3DBox Graphics3DBoxOptions GraphicsArray GraphicsBaseline GraphicsBox GraphicsBoxOptions GraphicsColor GraphicsColumn GraphicsComplex GraphicsComplex3DBox GraphicsComplex3DBoxOptions GraphicsComplexBox GraphicsComplexBoxOptions GraphicsContents GraphicsData GraphicsGrid GraphicsGridBox GraphicsGroup GraphicsGroup3DBox GraphicsGroup3DBoxOptions GraphicsGroupBox GraphicsGroupBoxOptions GraphicsGrouping GraphicsHighlightColor GraphicsRow GraphicsSpacing GraphicsStyle GraphIntersection GraphLayout GraphLinkEfficiency GraphPeriphery GraphPlot GraphPlot3D GraphPower GraphPropertyDistribution GraphQ GraphRadius GraphReciprocity GraphRoot GraphStyle GraphUnion Gray GrayLevel GreatCircleDistance Greater GreaterEqual GreaterEqualLess GreaterFullEqual GreaterGreater GreaterLess GreaterSlantEqual GreaterTilde Green Grid GridBaseline GridBox GridBoxAlignment GridBoxBackground GridBoxDividers GridBoxFrame GridBoxItemSize GridBoxItemStyle GridBoxOptions GridBoxSpacings GridCreationSettings GridDefaultElement GridElementStyleOptions GridFrame GridFrameMargins GridGraph GridLines GridLinesStyle GroebnerBasis GroupActionBase GroupCentralizer GroupElementFromWord GroupElementPosition GroupElementQ GroupElements GroupElementToWord GroupGenerators GroupMultiplicationTable GroupOrbits GroupOrder GroupPageBreakWithin GroupSetwiseStabilizer GroupStabilizer GroupStabilizerChain Gudermannian GumbelDistribution HaarWavelet HadamardMatrix HalfNormalDistribution HamiltonianGraphQ HammingDistance HammingWindow HankelH1 HankelH2 HankelMatrix HannPoissonWindow HannWindow HaradaNortonGroupHN HararyGraph HarmonicMean HarmonicMeanFilter HarmonicNumber Hash HashTable Haversine HazardFunction Head HeadCompose Heads HeavisideLambda HeavisidePi HeavisideTheta HeldGroupHe HeldPart HelpBrowserLookup HelpBrowserNotebook HelpBrowserSettings HermiteDecomposition HermiteH HermitianMatrixQ HessenbergDecomposition Hessian HexadecimalCharacter Hexahedron HexahedronBox HexahedronBoxOptions HiddenSurface HighlightGraph HighlightImage HighpassFilter HigmanSimsGroupHS HilbertFilter HilbertMatrix Histogram Histogram3D HistogramDistribution HistogramList HistogramTransform HistogramTransformInterpolation HitMissTransform HITSCentrality HodgeDual HoeffdingD HoeffdingDTest Hold HoldAll HoldAllComplete HoldComplete HoldFirst HoldForm HoldPattern HoldRest HolidayCalendar HomeDirectory HomePage Horizontal HorizontalForm HorizontalGauge HorizontalScrollPosition HornerForm HotellingTSquareDistribution HoytDistribution HTMLSave Hue HumpDownHump HumpEqual HurwitzLerchPhi HurwitzZeta HyperbolicDistribution HypercubeGraph HyperexponentialDistribution Hyperfactorial Hypergeometric0F1 Hypergeometric0F1Regularized Hypergeometric1F1 Hypergeometric1F1Regularized Hypergeometric2F1 Hypergeometric2F1Regularized HypergeometricDistribution HypergeometricPFQ HypergeometricPFQRegularized HypergeometricU Hyperlink HyperlinkCreationSettings Hyphenation HyphenationOptions HypoexponentialDistribution HypothesisTestData I Identity IdentityMatrix If IgnoreCase Im Image Image3D Image3DSlices ImageAccumulate ImageAdd ImageAdjust ImageAlign ImageApply ImageAspectRatio ImageAssemble ImageCache ImageCacheValid ImageCapture ImageChannels ImageClip ImageColorSpace ImageCompose ImageConvolve ImageCooccurrence ImageCorners ImageCorrelate ImageCorrespondingPoints ImageCrop ImageData ImageDataPacket ImageDeconvolve ImageDemosaic ImageDifference ImageDimensions ImageDistance ImageEffect ImageFeatureTrack ImageFileApply ImageFileFilter ImageFileScan ImageFilter ImageForestingComponents ImageForwardTransformation ImageHistogram ImageKeypoints ImageLevels ImageLines ImageMargins ImageMarkers ImageMeasurements ImageMultiply ImageOffset ImagePad ImagePadding ImagePartition ImagePeriodogram ImagePerspectiveTransformation ImageQ ImageRangeCache ImageReflect ImageRegion ImageResize ImageResolution ImageRotate ImageRotated ImageScaled ImageScan ImageSize ImageSizeAction ImageSizeCache ImageSizeMultipliers ImageSizeRaw ImageSubtract ImageTake ImageTransformation ImageTrim ImageType ImageValue ImageValuePositions Implies Import ImportAutoReplacements ImportString ImprovementImportance In IncidenceGraph IncidenceList IncidenceMatrix IncludeConstantBasis IncludeFileExtension IncludePods IncludeSingularTerm Increment Indent IndentingNewlineSpacings IndentMaxFraction IndependenceTest IndependentEdgeSetQ IndependentUnit IndependentVertexSetQ Indeterminate IndexCreationOptions Indexed IndexGraph IndexTag Inequality InexactNumberQ InexactNumbers Infinity Infix Information Inherited InheritScope Initialization InitializationCell InitializationCellEvaluation InitializationCellWarning InlineCounterAssignments InlineCounterIncrements InlineRules Inner Inpaint Input InputAliases InputAssumptions InputAutoReplacements InputField InputFieldBox InputFieldBoxOptions InputForm InputGrouping InputNamePacket InputNotebook InputPacket InputSettings InputStream InputString InputStringPacket InputToBoxFormPacket Insert InsertionPointObject InsertResults Inset Inset3DBox Inset3DBoxOptions InsetBox InsetBoxOptions Install InstallService InString Integer IntegerDigits IntegerExponent IntegerLength IntegerPart IntegerPartitions IntegerQ Integers IntegerString Integral Integrate Interactive InteractiveTradingChart Interlaced Interleaving InternallyBalancedDecomposition InterpolatingFunction InterpolatingPolynomial Interpolation InterpolationOrder InterpolationPoints InterpolationPrecision Interpretation InterpretationBox InterpretationBoxOptions InterpretationFunction InterpretTemplate InterquartileRange Interrupt InterruptSettings Intersection Interval IntervalIntersection IntervalMemberQ IntervalUnion Inverse InverseBetaRegularized InverseCDF InverseChiSquareDistribution InverseContinuousWaveletTransform InverseDistanceTransform InverseEllipticNomeQ InverseErf InverseErfc InverseFourier InverseFourierCosTransform InverseFourierSequenceTransform InverseFourierSinTransform InverseFourierTransform InverseFunction InverseFunctions InverseGammaDistribution InverseGammaRegularized InverseGaussianDistribution InverseGudermannian InverseHaversine InverseJacobiCD InverseJacobiCN InverseJacobiCS InverseJacobiDC InverseJacobiDN InverseJacobiDS InverseJacobiNC InverseJacobiND InverseJacobiNS InverseJacobiSC InverseJacobiSD InverseJacobiSN InverseLaplaceTransform InversePermutation InverseRadon InverseSeries InverseSurvivalFunction InverseWaveletTransform InverseWeierstrassP InverseZTransform Invisible InvisibleApplication InvisibleTimes IrreduciblePolynomialQ IsolatingInterval IsomorphicGraphQ IsotopeData Italic Item ItemBox ItemBoxOptions ItemSize ItemStyle ItoProcess JaccardDissimilarity JacobiAmplitude Jacobian JacobiCD JacobiCN JacobiCS JacobiDC JacobiDN JacobiDS JacobiNC JacobiND JacobiNS JacobiP JacobiSC JacobiSD JacobiSN JacobiSymbol JacobiZeta JankoGroupJ1 JankoGroupJ2 JankoGroupJ3 JankoGroupJ4 JarqueBeraALMTest JohnsonDistribution Join Joined JoinedCurve JoinedCurveBox JoinForm JordanDecomposition JordanModelDecomposition K KagiChart KaiserBesselWindow KaiserWindow KalmanEstimator KalmanFilter KarhunenLoeveDecomposition KaryTree KatzCentrality KCoreComponents KDistribution KelvinBei KelvinBer KelvinKei KelvinKer KendallTau KendallTauTest KernelExecute KernelMixtureDistribution KernelObject Kernels Ket Khinchin KirchhoffGraph KirchhoffMatrix KleinInvariantJ KnightTourGraph KnotData KnownUnitQ KolmogorovSmirnovTest KroneckerDelta KroneckerModelDecomposition KroneckerProduct KroneckerSymbol KuiperTest KumaraswamyDistribution Kurtosis KuwaharaFilter Label Labeled LabeledSlider LabelingFunction LabelStyle LaguerreL LambdaComponents LambertW LanczosWindow LandauDistribution Language LanguageCategory LaplaceDistribution LaplaceTransform Laplacian LaplacianFilter LaplacianGaussianFilter Large Larger Last Latitude LatitudeLongitude LatticeData LatticeReduce Launch LaunchKernels LayeredGraphPlot LayerSizeFunction LayoutInformation LCM LeafCount LeapYearQ LeastSquares LeastSquaresFilterKernel Left LeftArrow LeftArrowBar LeftArrowRightArrow LeftDownTeeVector LeftDownVector LeftDownVectorBar LeftRightArrow LeftRightVector LeftTee LeftTeeArrow LeftTeeVector LeftTriangle LeftTriangleBar LeftTriangleEqual LeftUpDownVector LeftUpTeeVector LeftUpVector LeftUpVectorBar LeftVector LeftVectorBar LegendAppearance Legended LegendFunction LegendLabel LegendLayout LegendMargins LegendMarkers LegendMarkerSize LegendreP LegendreQ LegendreType Length LengthWhile LerchPhi Less LessEqual LessEqualGreater LessFullEqual LessGreater LessLess LessSlantEqual LessTilde LetterCharacter LetterQ Level LeveneTest LeviCivitaTensor LevyDistribution Lexicographic LibraryFunction LibraryFunctionError LibraryFunctionInformation LibraryFunctionLoad LibraryFunctionUnload LibraryLoad LibraryUnload LicenseID LiftingFilterData LiftingWaveletTransform LightBlue LightBrown LightCyan Lighter LightGray LightGreen Lighting LightingAngle LightMagenta LightOrange LightPink LightPurple LightRed LightSources LightYellow Likelihood Limit LimitsPositioning LimitsPositioningTokens LindleyDistribution Line Line3DBox LinearFilter LinearFractionalTransform LinearModelFit LinearOffsetFunction LinearProgramming LinearRecurrence LinearSolve LinearSolveFunction LineBox LineBreak LinebreakAdjustments LineBreakChart LineBreakWithin LineColor LineForm LineGraph LineIndent LineIndentMaxFraction LineIntegralConvolutionPlot LineIntegralConvolutionScale LineLegend LineOpacity LineSpacing LineWrapParts LinkActivate LinkClose LinkConnect LinkConnectedQ LinkCreate LinkError LinkFlush LinkFunction LinkHost LinkInterrupt LinkLaunch LinkMode LinkObject LinkOpen LinkOptions LinkPatterns LinkProtocol LinkRead LinkReadHeld LinkReadyQ Links LinkWrite LinkWriteHeld LiouvilleLambda List Listable ListAnimate ListContourPlot ListContourPlot3D ListConvolve ListCorrelate ListCurvePathPlot ListDeconvolve ListDensityPlot Listen ListFourierSequenceTransform ListInterpolation ListLineIntegralConvolutionPlot ListLinePlot ListLogLinearPlot ListLogLogPlot ListLogPlot ListPicker ListPickerBox ListPickerBoxBackground ListPickerBoxOptions ListPlay ListPlot ListPlot3D ListPointPlot3D ListPolarPlot ListQ ListStreamDensityPlot ListStreamPlot ListSurfacePlot3D ListVectorDensityPlot ListVectorPlot ListVectorPlot3D ListZTransform Literal LiteralSearch LocalClusteringCoefficient LocalizeVariables LocationEquivalenceTest LocationTest Locator LocatorAutoCreate LocatorBox LocatorBoxOptions LocatorCentering LocatorPane LocatorPaneBox LocatorPaneBoxOptions LocatorRegion Locked Log Log10 Log2 LogBarnesG LogGamma LogGammaDistribution LogicalExpand LogIntegral LogisticDistribution LogitModelFit LogLikelihood LogLinearPlot LogLogisticDistribution LogLogPlot LogMultinormalDistribution LogNormalDistribution LogPlot LogRankTest LogSeriesDistribution LongEqual Longest LongestAscendingSequence LongestCommonSequence LongestCommonSequencePositions LongestCommonSubsequence LongestCommonSubsequencePositions LongestMatch LongForm Longitude LongLeftArrow LongLeftRightArrow LongRightArrow Loopback LoopFreeGraphQ LowerCaseQ LowerLeftArrow LowerRightArrow LowerTriangularize LowpassFilter LQEstimatorGains LQGRegulator LQOutputRegulatorGains LQRegulatorGains LUBackSubstitution LucasL LuccioSamiComponents LUDecomposition LyapunovSolve LyonsGroupLy MachineID MachineName MachineNumberQ MachinePrecision MacintoshSystemPageSetup Magenta Magnification Magnify MainSolve MaintainDynamicCaches Majority MakeBoxes MakeExpression MakeRules MangoldtLambda ManhattanDistance Manipulate Manipulator MannWhitneyTest MantissaExponent Manual Map MapAll MapAt MapIndexed MAProcess MapThread MarcumQ MardiaCombinedTest MardiaKurtosisTest MardiaSkewnessTest MarginalDistribution MarkovProcessProperties Masking MatchingDissimilarity MatchLocalNameQ MatchLocalNames MatchQ Material MathematicaNotation MathieuC MathieuCharacteristicA MathieuCharacteristicB MathieuCharacteristicExponent MathieuCPrime MathieuGroupM11 MathieuGroupM12 MathieuGroupM22 MathieuGroupM23 MathieuGroupM24 MathieuS MathieuSPrime MathMLForm MathMLText Matrices MatrixExp MatrixForm MatrixFunction MatrixLog MatrixPlot MatrixPower MatrixQ MatrixRank Max MaxBend MaxDetect MaxExtraBandwidths MaxExtraConditions MaxFeatures MaxFilter Maximize MaxIterations MaxMemoryUsed MaxMixtureKernels MaxPlotPoints MaxPoints MaxRecursion MaxStableDistribution MaxStepFraction MaxSteps MaxStepSize MaxValue MaxwellDistribution McLaughlinGroupMcL Mean MeanClusteringCoefficient MeanDegreeConnectivity MeanDeviation MeanFilter MeanGraphDistance MeanNeighborDegree MeanShift MeanShiftFilter Median MedianDeviation MedianFilter Medium MeijerG MeixnerDistribution MemberQ MemoryConstrained MemoryInUse Menu MenuAppearance MenuCommandKey MenuEvaluator MenuItem MenuPacket MenuSortingValue MenuStyle MenuView MergeDifferences Mesh MeshFunctions MeshRange MeshShading MeshStyle Message MessageDialog MessageList MessageName MessageOptions MessagePacket Messages MessagesNotebook MetaCharacters MetaInformation Method MethodOptions MexicanHatWavelet MeyerWavelet Min MinDetect MinFilter MinimalPolynomial MinimalStateSpaceModel Minimize Minors MinRecursion MinSize MinStableDistribution Minus MinusPlus MinValue Missing MissingDataMethod MittagLefflerE MixedRadix MixedRadixQuantity MixtureDistribution Mod Modal Mode Modular ModularLambda Module Modulus MoebiusMu Moment Momentary MomentConvert MomentEvaluate MomentGeneratingFunction Monday Monitor MonomialList MonomialOrder MonsterGroupM MorletWavelet MorphologicalBinarize MorphologicalBranchPoints MorphologicalComponents MorphologicalEulerNumber MorphologicalGraph MorphologicalPerimeter MorphologicalTransform Most MouseAnnotation MouseAppearance MouseAppearanceTag MouseButtons Mouseover MousePointerNote MousePosition MovingAverage MovingMedian MoyalDistribution MultiedgeStyle MultilaunchWarning MultiLetterItalics MultiLetterStyle MultilineFunction Multinomial MultinomialDistribution MultinormalDistribution MultiplicativeOrder Multiplicity Multiselection MultivariateHypergeometricDistribution MultivariatePoissonDistribution MultivariateTDistribution N NakagamiDistribution NameQ Names NamespaceBox Nand NArgMax NArgMin NBernoulliB NCache NDSolve NDSolveValue Nearest NearestFunction NeedCurrentFrontEndPackagePacket NeedCurrentFrontEndSymbolsPacket NeedlemanWunschSimilarity Needs Negative NegativeBinomialDistribution NegativeMultinomialDistribution NeighborhoodGraph Nest NestedGreaterGreater NestedLessLess NestedScriptRules NestList NestWhile NestWhileList NevilleThetaC NevilleThetaD NevilleThetaN NevilleThetaS NewPrimitiveStyle NExpectation Next NextPrime NHoldAll NHoldFirst NHoldRest NicholsGridLines NicholsPlot NIntegrate NMaximize NMaxValue NMinimize NMinValue NominalVariables NonAssociative NoncentralBetaDistribution NoncentralChiSquareDistribution NoncentralFRatioDistribution NoncentralStudentTDistribution NonCommutativeMultiply NonConstants None NonlinearModelFit NonlocalMeansFilter NonNegative NonPositive Nor NorlundB Norm Normal NormalDistribution NormalGrouping Normalize NormalizedSquaredEuclideanDistance NormalsFunction NormFunction Not NotCongruent NotCupCap NotDoubleVerticalBar Notebook NotebookApply NotebookAutoSave NotebookClose NotebookConvertSettings NotebookCreate NotebookCreateReturnObject NotebookDefault NotebookDelete NotebookDirectory NotebookDynamicExpression NotebookEvaluate NotebookEventActions NotebookFileName NotebookFind NotebookFindReturnObject NotebookGet NotebookGetLayoutInformationPacket NotebookGetMisspellingsPacket NotebookInformation NotebookInterfaceObject NotebookLocate NotebookObject NotebookOpen NotebookOpenReturnObject NotebookPath NotebookPrint NotebookPut NotebookPutReturnObject NotebookRead NotebookResetGeneratedCells Notebooks NotebookSave NotebookSaveAs NotebookSelection NotebookSetupLayoutInformationPacket NotebooksMenu NotebookWrite NotElement NotEqualTilde NotExists NotGreater NotGreaterEqual NotGreaterFullEqual NotGreaterGreater NotGreaterLess NotGreaterSlantEqual NotGreaterTilde NotHumpDownHump NotHumpEqual NotLeftTriangle NotLeftTriangleBar NotLeftTriangleEqual NotLess NotLessEqual NotLessFullEqual NotLessGreater NotLessLess NotLessSlantEqual NotLessTilde NotNestedGreaterGreater NotNestedLessLess NotPrecedes NotPrecedesEqual NotPrecedesSlantEqual NotPrecedesTilde NotReverseElement NotRightTriangle NotRightTriangleBar NotRightTriangleEqual NotSquareSubset NotSquareSubsetEqual NotSquareSuperset NotSquareSupersetEqual NotSubset NotSubsetEqual NotSucceeds NotSucceedsEqual NotSucceedsSlantEqual NotSucceedsTilde NotSuperset NotSupersetEqual NotTilde NotTildeEqual NotTildeFullEqual NotTildeTilde NotVerticalBar NProbability NProduct NProductFactors NRoots NSolve NSum NSumTerms Null NullRecords NullSpace NullWords Number NumberFieldClassNumber NumberFieldDiscriminant NumberFieldFundamentalUnits NumberFieldIntegralBasis NumberFieldNormRepresentatives NumberFieldRegulator NumberFieldRootsOfUnity NumberFieldSignature NumberForm NumberFormat NumberMarks NumberMultiplier NumberPadding NumberPoint NumberQ NumberSeparator NumberSigns NumberString Numerator NumericFunction NumericQ NuttallWindow NValues NyquistGridLines NyquistPlot O ObservabilityGramian ObservabilityMatrix ObservableDecomposition ObservableModelQ OddQ Off Offset OLEData On ONanGroupON OneIdentity Opacity Open OpenAppend Opener OpenerBox OpenerBoxOptions OpenerView OpenFunctionInspectorPacket Opening OpenRead OpenSpecialOptions OpenTemporary OpenWrite Operate OperatingSystem OptimumFlowData Optional OptionInspectorSettings OptionQ Options OptionsPacket OptionsPattern OptionValue OptionValueBox OptionValueBoxOptions Or Orange Order OrderDistribution OrderedQ Ordering Orderless OrnsteinUhlenbeckProcess Orthogonalize Out Outer OutputAutoOverwrite OutputControllabilityMatrix OutputControllableModelQ OutputForm OutputFormData OutputGrouping OutputMathEditExpression OutputNamePacket OutputResponse OutputSizeLimit OutputStream Over OverBar OverDot Overflow OverHat Overlaps Overlay OverlayBox OverlayBoxOptions Overscript OverscriptBox OverscriptBoxOptions OverTilde OverVector OwenT OwnValues PackingMethod PaddedForm Padding PadeApproximant PadLeft PadRight PageBreakAbove PageBreakBelow PageBreakWithin PageFooterLines PageFooters PageHeaderLines PageHeaders PageHeight PageRankCentrality PageWidth PairedBarChart PairedHistogram PairedSmoothHistogram PairedTTest PairedZTest PaletteNotebook PalettePath Pane PaneBox PaneBoxOptions Panel PanelBox PanelBoxOptions Paneled PaneSelector PaneSelectorBox PaneSelectorBoxOptions PaperWidth ParabolicCylinderD ParagraphIndent ParagraphSpacing ParallelArray ParallelCombine ParallelDo ParallelEvaluate Parallelization Parallelize ParallelMap ParallelNeeds ParallelProduct ParallelSubmit ParallelSum ParallelTable ParallelTry Parameter ParameterEstimator ParameterMixtureDistribution ParameterVariables ParametricFunction ParametricNDSolve ParametricNDSolveValue ParametricPlot ParametricPlot3D ParentConnect ParentDirectory ParentForm Parenthesize ParentList ParetoDistribution Part PartialCorrelationFunction PartialD ParticleData Partition PartitionsP PartitionsQ ParzenWindow PascalDistribution PassEventsDown PassEventsUp Paste PasteBoxFormInlineCells PasteButton Path PathGraph PathGraphQ Pattern PatternSequence PatternTest PauliMatrix PaulWavelet Pause PausedTime PDF PearsonChiSquareTest PearsonCorrelationTest PearsonDistribution PerformanceGoal PeriodicInterpolation Periodogram PeriodogramArray PermutationCycles PermutationCyclesQ PermutationGroup PermutationLength PermutationList PermutationListQ PermutationMax PermutationMin PermutationOrder PermutationPower PermutationProduct PermutationReplace Permutations PermutationSupport Permute PeronaMalikFilter Perpendicular PERTDistribution PetersenGraph PhaseMargins Pi Pick PIDData PIDDerivativeFilter PIDFeedforward PIDTune Piecewise PiecewiseExpand PieChart PieChart3D PillaiTrace PillaiTraceTest Pink Pivoting PixelConstrained PixelValue PixelValuePositions Placed Placeholder PlaceholderReplace Plain PlanarGraphQ Play PlayRange Plot Plot3D Plot3Matrix PlotDivision PlotJoined PlotLabel PlotLayout PlotLegends PlotMarkers PlotPoints PlotRange PlotRangeClipping PlotRangePadding PlotRegion PlotStyle Plus PlusMinus Pochhammer PodStates PodWidth Point Point3DBox PointBox PointFigureChart PointForm PointLegend PointSize PoissonConsulDistribution PoissonDistribution PoissonProcess PoissonWindow PolarAxes PolarAxesOrigin PolarGridLines PolarPlot PolarTicks PoleZeroMarkers PolyaAeppliDistribution PolyGamma Polygon Polygon3DBox Polygon3DBoxOptions PolygonBox PolygonBoxOptions PolygonHoleScale PolygonIntersections PolygonScale PolyhedronData PolyLog PolynomialExtendedGCD PolynomialForm PolynomialGCD PolynomialLCM PolynomialMod PolynomialQ PolynomialQuotient PolynomialQuotientRemainder PolynomialReduce PolynomialRemainder Polynomials PopupMenu PopupMenuBox PopupMenuBoxOptions PopupView PopupWindow Position Positive PositiveDefiniteMatrixQ PossibleZeroQ Postfix PostScript Power PowerDistribution PowerExpand PowerMod PowerModList PowerSpectralDensity PowersRepresentations PowerSymmetricPolynomial Precedence PrecedenceForm Precedes PrecedesEqual PrecedesSlantEqual PrecedesTilde Precision PrecisionGoal PreDecrement PredictionRoot PreemptProtect PreferencesPath Prefix PreIncrement Prepend PrependTo PreserveImageOptions Previous PriceGraphDistribution PrimaryPlaceholder Prime PrimeNu PrimeOmega PrimePi PrimePowerQ PrimeQ Primes PrimeZetaP PrimitiveRoot PrincipalComponents PrincipalValue Print PrintAction PrintForm PrintingCopies PrintingOptions PrintingPageRange PrintingStartingPageNumber PrintingStyleEnvironment PrintPrecision PrintTemporary Prism PrismBox PrismBoxOptions PrivateCellOptions PrivateEvaluationOptions PrivateFontOptions PrivateFrontEndOptions PrivateNotebookOptions PrivatePaths Probability ProbabilityDistribution ProbabilityPlot ProbabilityPr ProbabilityScalePlot ProbitModelFit ProcessEstimator ProcessParameterAssumptions ProcessParameterQ ProcessStateDomain ProcessTimeDomain Product ProductDistribution ProductLog ProgressIndicator ProgressIndicatorBox ProgressIndicatorBoxOptions Projection Prolog PromptForm Properties Property PropertyList PropertyValue Proportion Proportional Protect Protected ProteinData Pruning PseudoInverse Purple Put PutAppend Pyramid PyramidBox PyramidBoxOptions QBinomial QFactorial QGamma QHypergeometricPFQ QPochhammer QPolyGamma QRDecomposition QuadraticIrrationalQ Quantile QuantilePlot Quantity QuantityForm QuantityMagnitude QuantityQ QuantityUnit Quartics QuartileDeviation Quartiles QuartileSkewness QueueingNetworkProcess QueueingProcess QueueProperties Quiet Quit Quotient QuotientRemainder RadialityCentrality RadicalBox RadicalBoxOptions RadioButton RadioButtonBar RadioButtonBox RadioButtonBoxOptions Radon RamanujanTau RamanujanTauL RamanujanTauTheta RamanujanTauZ Random RandomChoice RandomComplex RandomFunction RandomGraph RandomImage RandomInteger RandomPermutation RandomPrime RandomReal RandomSample RandomSeed RandomVariate RandomWalkProcess Range RangeFilter RangeSpecification RankedMax RankedMin Raster Raster3D Raster3DBox Raster3DBoxOptions RasterArray RasterBox RasterBoxOptions Rasterize RasterSize Rational RationalFunctions Rationalize Rationals Ratios Raw RawArray RawBoxes RawData RawMedium RayleighDistribution Re Read ReadList ReadProtected Real RealBlockDiagonalForm RealDigits RealExponent Reals Reap Record RecordLists RecordSeparators Rectangle RectangleBox RectangleBoxOptions RectangleChart RectangleChart3D RecurrenceFilter RecurrenceTable RecurringDigitsForm Red Reduce RefBox ReferenceLineStyle ReferenceMarkers ReferenceMarkerStyle Refine ReflectionMatrix ReflectionTransform Refresh RefreshRate RegionBinarize RegionFunction RegionPlot RegionPlot3D RegularExpression Regularization Reinstall Release ReleaseHold ReliabilityDistribution ReliefImage ReliefPlot Remove RemoveAlphaChannel RemoveAsynchronousTask Removed RemoveInputStreamMethod RemoveOutputStreamMethod RemoveProperty RemoveScheduledTask RenameDirectory RenameFile RenderAll RenderingOptions RenewalProcess RenkoChart Repeated RepeatedNull RepeatedString Replace ReplaceAll ReplaceHeldPart ReplaceImageValue ReplaceList ReplacePart ReplacePixelValue ReplaceRepeated Resampling Rescale RescalingTransform ResetDirectory ResetMenusPacket ResetScheduledTask Residue Resolve Rest Resultant ResumePacket Return ReturnExpressionPacket ReturnInputFormPacket ReturnPacket ReturnTextPacket Reverse ReverseBiorthogonalSplineWavelet ReverseElement ReverseEquilibrium ReverseGraph ReverseUpEquilibrium RevolutionAxis RevolutionPlot3D RGBColor RiccatiSolve RiceDistribution RidgeFilter RiemannR RiemannSiegelTheta RiemannSiegelZ Riffle Right RightArrow RightArrowBar RightArrowLeftArrow RightCosetRepresentative RightDownTeeVector RightDownVector RightDownVectorBar RightTee RightTeeArrow RightTeeVector RightTriangle RightTriangleBar RightTriangleEqual RightUpDownVector RightUpTeeVector RightUpVector RightUpVectorBar RightVector RightVectorBar RiskAchievementImportance RiskReductionImportance RogersTanimotoDissimilarity Root RootApproximant RootIntervals RootLocusPlot RootMeanSquare RootOfUnityQ RootReduce Roots RootSum Rotate RotateLabel RotateLeft RotateRight RotationAction RotationBox RotationBoxOptions RotationMatrix RotationTransform Round RoundImplies RoundingRadius Row RowAlignments RowBackgrounds RowBox RowHeights RowLines RowMinHeight RowReduce RowsEqual RowSpacings RSolve RudvalisGroupRu Rule RuleCondition RuleDelayed RuleForm RulerUnits Run RunScheduledTask RunThrough RuntimeAttributes RuntimeOptions RussellRaoDissimilarity SameQ SameTest SampleDepth SampledSoundFunction SampledSoundList SampleRate SamplingPeriod SARIMAProcess SARMAProcess SatisfiabilityCount SatisfiabilityInstances SatisfiableQ Saturday Save Saveable SaveAutoDelete SaveDefinitions SawtoothWave Scale Scaled ScaleDivisions ScaledMousePosition ScaleOrigin ScalePadding ScaleRanges ScaleRangeStyle ScalingFunctions ScalingMatrix ScalingTransform Scan ScheduledTaskActiveQ ScheduledTaskData ScheduledTaskObject ScheduledTasks SchurDecomposition ScientificForm ScreenRectangle ScreenStyleEnvironment ScriptBaselineShifts ScriptLevel ScriptMinSize ScriptRules ScriptSizeMultipliers Scrollbars ScrollingOptions ScrollPosition Sec Sech SechDistribution SectionGrouping SectorChart SectorChart3D SectorOrigin SectorSpacing SeedRandom Select Selectable SelectComponents SelectedCells SelectedNotebook Selection SelectionAnimate SelectionCell SelectionCellCreateCell SelectionCellDefaultStyle SelectionCellParentStyle SelectionCreateCell SelectionDebuggerTag SelectionDuplicateCell SelectionEvaluate SelectionEvaluateCreateCell SelectionMove SelectionPlaceholder SelectionSetStyle SelectWithContents SelfLoops SelfLoopStyle SemialgebraicComponentInstances SendMail Sequence SequenceAlignment SequenceForm SequenceHold SequenceLimit Series SeriesCoefficient SeriesData SessionTime Set SetAccuracy SetAlphaChannel SetAttributes Setbacks SetBoxFormNamesPacket SetDelayed SetDirectory SetEnvironment SetEvaluationNotebook SetFileDate SetFileLoadingContext SetNotebookStatusLine SetOptions SetOptionsPacket SetPrecision SetProperty SetSelectedNotebook SetSharedFunction SetSharedVariable SetSpeechParametersPacket SetStreamPosition SetSystemOptions Setter SetterBar SetterBox SetterBoxOptions Setting SetValue Shading Shallow ShannonWavelet ShapiroWilkTest Share Sharpen ShearingMatrix ShearingTransform ShenCastanMatrix Short ShortDownArrow Shortest ShortestMatch ShortestPathFunction ShortLeftArrow ShortRightArrow ShortUpArrow Show ShowAutoStyles ShowCellBracket ShowCellLabel ShowCellTags ShowClosedCellArea ShowContents ShowControls ShowCursorTracker ShowGroupOpenCloseIcon ShowGroupOpener ShowInvisibleCharacters ShowPageBreaks ShowPredictiveInterface ShowSelection ShowShortBoxForm ShowSpecialCharacters ShowStringCharacters ShowSyntaxStyles ShrinkingDelay ShrinkWrapBoundingBox SiegelTheta SiegelTukeyTest Sign Signature SignedRankTest SignificanceLevel SignPadding SignTest SimilarityRules SimpleGraph SimpleGraphQ Simplify Sin Sinc SinghMaddalaDistribution SingleEvaluation SingleLetterItalics SingleLetterStyle SingularValueDecomposition SingularValueList SingularValuePlot SingularValues Sinh SinhIntegral SinIntegral SixJSymbol Skeleton SkeletonTransform SkellamDistribution Skewness SkewNormalDistribution Skip SliceDistribution Slider Slider2D Slider2DBox Slider2DBoxOptions SliderBox SliderBoxOptions SlideView Slot SlotSequence Small SmallCircle Smaller SmithDelayCompensator SmithWatermanSimilarity SmoothDensityHistogram SmoothHistogram SmoothHistogram3D SmoothKernelDistribution SocialMediaData Socket SokalSneathDissimilarity Solve SolveAlways SolveDelayed Sort SortBy Sound SoundAndGraphics SoundNote SoundVolume Sow Space SpaceForm Spacer Spacings Span SpanAdjustments SpanCharacterRounding SpanFromAbove SpanFromBoth SpanFromLeft SpanLineThickness SpanMaxSize SpanMinSize SpanningCharacters SpanSymmetric SparseArray SpatialGraphDistribution Speak SpeakTextPacket SpearmanRankTest SpearmanRho Spectrogram SpectrogramArray Specularity SpellingCorrection SpellingDictionaries SpellingDictionariesPath SpellingOptions SpellingSuggestionsPacket Sphere SphereBox SphericalBesselJ SphericalBesselY SphericalHankelH1 SphericalHankelH2 SphericalHarmonicY SphericalPlot3D SphericalRegion SpheroidalEigenvalue SpheroidalJoiningFactor SpheroidalPS SpheroidalPSPrime SpheroidalQS SpheroidalQSPrime SpheroidalRadialFactor SpheroidalS1 SpheroidalS1Prime SpheroidalS2 SpheroidalS2Prime Splice SplicedDistribution SplineClosed SplineDegree SplineKnots SplineWeights Split SplitBy SpokenString Sqrt SqrtBox SqrtBoxOptions Square SquaredEuclideanDistance SquareFreeQ SquareIntersection SquaresR SquareSubset SquareSubsetEqual SquareSuperset SquareSupersetEqual SquareUnion SquareWave StabilityMargins StabilityMarginsStyle StableDistribution Stack StackBegin StackComplete StackInhibit StandardDeviation StandardDeviationFilter StandardForm Standardize StandbyDistribution Star StarGraph StartAsynchronousTask StartingStepSize StartOfLine StartOfString StartScheduledTask StartupSound StateDimensions StateFeedbackGains StateOutputEstimator StateResponse StateSpaceModel StateSpaceRealization StateSpaceTransform StationaryDistribution StationaryWaveletPacketTransform StationaryWaveletTransform StatusArea StatusCentrality StepMonitor StieltjesGamma StirlingS1 StirlingS2 StopAsynchronousTask StopScheduledTask StrataVariables StratonovichProcess StreamColorFunction StreamColorFunctionScaling StreamDensityPlot StreamPlot StreamPoints StreamPosition Streams StreamScale StreamStyle String StringBreak StringByteCount StringCases StringCount StringDrop StringExpression StringForm StringFormat StringFreeQ StringInsert StringJoin StringLength StringMatchQ StringPosition StringQ StringReplace StringReplaceList StringReplacePart StringReverse StringRotateLeft StringRotateRight StringSkeleton StringSplit StringTake StringToStream StringTrim StripBoxes StripOnInput StripWrapperBoxes StrokeForm StructuralImportance StructuredArray StructuredSelection StruveH StruveL Stub StudentTDistribution Style StyleBox StyleBoxAutoDelete StyleBoxOptions StyleData StyleDefinitions StyleForm StyleKeyMapping StyleMenuListing StyleNameDialogSettings StyleNames StylePrint StyleSheetPath Subfactorial Subgraph SubMinus SubPlus SubresultantPolynomialRemainders SubresultantPolynomials Subresultants Subscript SubscriptBox SubscriptBoxOptions Subscripted Subset SubsetEqual Subsets SubStar Subsuperscript SubsuperscriptBox SubsuperscriptBoxOptions Subtract SubtractFrom SubValues Succeeds SucceedsEqual SucceedsSlantEqual SucceedsTilde SuchThat Sum SumConvergence Sunday SuperDagger SuperMinus SuperPlus Superscript SuperscriptBox SuperscriptBoxOptions Superset SupersetEqual SuperStar Surd SurdForm SurfaceColor SurfaceGraphics SurvivalDistribution SurvivalFunction SurvivalModel SurvivalModelFit SuspendPacket SuzukiDistribution SuzukiGroupSuz SwatchLegend Switch Symbol SymbolName SymletWavelet Symmetric SymmetricGroup SymmetricMatrixQ SymmetricPolynomial SymmetricReduction Symmetrize SymmetrizedArray SymmetrizedArrayRules SymmetrizedDependentComponents SymmetrizedIndependentComponents SymmetrizedReplacePart SynchronousInitialization SynchronousUpdating Syntax SyntaxForm SyntaxInformation SyntaxLength SyntaxPacket SyntaxQ SystemDialogInput SystemException SystemHelpPath SystemInformation SystemInformationData SystemOpen SystemOptions SystemsModelDelay SystemsModelDelayApproximate SystemsModelDelete SystemsModelDimensions SystemsModelExtract SystemsModelFeedbackConnect SystemsModelLabels SystemsModelOrder SystemsModelParallelConnect SystemsModelSeriesConnect SystemsModelStateFeedbackConnect SystemStub Tab TabFilling Table TableAlignments TableDepth TableDirections TableForm TableHeadings TableSpacing TableView TableViewBox TabSpacings TabView TabViewBox TabViewBoxOptions TagBox TagBoxNote TagBoxOptions TaggingRules TagSet TagSetDelayed TagStyle 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diff --git a/docs/malhar-3.3/license/highlight.js/LICENSE b/docs/malhar-3.3/license/highlight.js/LICENSE
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diff --git a/docs/malhar-3.3/mkdocs/js/lunr.min.js b/docs/malhar-3.3/mkdocs/js/lunr.min.js
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diff --git a/docs/malhar-3.3/mkdocs/js/mustache.min.js b/docs/malhar-3.3/mkdocs/js/mustache.min.js
deleted file mode 100644
index 7fc6da8..0000000
--- a/docs/malhar-3.3/mkdocs/js/mustache.min.js
+++ /dev/null
@@ -1 +0,0 @@
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diff --git a/docs/malhar-3.3/mkdocs/js/require.js b/docs/malhar-3.3/mkdocs/js/require.js
deleted file mode 100644
index 8638a31..0000000
--- a/docs/malhar-3.3/mkdocs/js/require.js
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- RequireJS 2.1.16 Copyright (c) 2010-2015, The Dojo Foundation All Rights Reserved.
- Available via the MIT or new BSD license.
- see: http://github.com/jrburke/requirejs for details
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diff --git a/docs/malhar-3.3/mkdocs/js/search-results-template.mustache b/docs/malhar-3.3/mkdocs/js/search-results-template.mustache
deleted file mode 100644
index a8b3862..0000000
--- a/docs/malhar-3.3/mkdocs/js/search-results-template.mustache
+++ /dev/null
@@ -1,4 +0,0 @@
-<article>
- <h3><a href="{{location}}">{{title}}</a></h3>
- <p>{{summary}}</p>
-</article>
diff --git a/docs/malhar-3.3/mkdocs/js/search.js b/docs/malhar-3.3/mkdocs/js/search.js
deleted file mode 100644
index 88d563a..0000000
--- a/docs/malhar-3.3/mkdocs/js/search.js
+++ /dev/null
@@ -1,88 +0,0 @@
-require([
- base_url + '/mkdocs/js/mustache.min.js',
- base_url + '/mkdocs/js/lunr.min.js',
- 'text!search-results-template.mustache',
- 'text!../search_index.json',
-], function (Mustache, lunr, results_template, data) {
- "use strict";
-
- function getSearchTerm()
- {
- var sPageURL = window.location.search.substring(1);
- var sURLVariables = sPageURL.split('&');
- for (var i = 0; i < sURLVariables.length; i++)
- {
- var sParameterName = sURLVariables[i].split('=');
- if (sParameterName[0] == 'q')
- {
- return decodeURIComponent(sParameterName[1].replace(/\+/g, '%20'));
- }
- }
- }
-
- var index = lunr(function () {
- this.field('title', {boost: 10});
- this.field('text');
- this.ref('location');
- });
-
- data = JSON.parse(data);
- var documents = {};
-
- for (var i=0; i < data.docs.length; i++){
- var doc = data.docs[i];
- doc.location = base_url + doc.location;
- index.add(doc);
- documents[doc.location] = doc;
- }
-
- var search = function(){
-
- var query = document.getElementById('mkdocs-search-query').value;
- var search_results = document.getElementById("mkdocs-search-results");
- while (search_results.firstChild) {
- search_results.removeChild(search_results.firstChild);
- }
-
- if(query === ''){
- return;
- }
-
- var results = index.search(query);
-
- if (results.length > 0){
- for (var i=0; i < results.length; i++){
- var result = results[i];
- doc = documents[result.ref];
- doc.base_url = base_url;
- doc.summary = doc.text.substring(0, 200);
- var html = Mustache.to_html(results_template, doc);
- search_results.insertAdjacentHTML('beforeend', html);
- }
- } else {
- search_results.insertAdjacentHTML('beforeend', "<p>No results found</p>");
- }
-
- if(jQuery){
- /*
- * We currently only automatically hide bootstrap models. This
- * requires jQuery to work.
- */
- jQuery('#mkdocs_search_modal a').click(function(){
- jQuery('#mkdocs_search_modal').modal('hide');
- })
- }
-
- };
-
- var search_input = document.getElementById('mkdocs-search-query');
-
- var term = getSearchTerm();
- if (term){
- search_input.value = term;
- search();
- }
-
- search_input.addEventListener("keyup", search);
-
-});
diff --git a/docs/malhar-3.3/mkdocs/js/text.js b/docs/malhar-3.3/mkdocs/js/text.js
deleted file mode 100644
index 17921b6..0000000
--- a/docs/malhar-3.3/mkdocs/js/text.js
+++ /dev/null
@@ -1,390 +0,0 @@
-/**
- * @license RequireJS text 2.0.12 Copyright (c) 2010-2014, The Dojo Foundation All Rights Reserved.
- * Available via the MIT or new BSD license.
- * see: http://github.com/requirejs/text for details
- */
-/*jslint regexp: true */
-/*global require, XMLHttpRequest, ActiveXObject,
- define, window, process, Packages,
- java, location, Components, FileUtils */
-
-define(['module'], function (module) {
- 'use strict';
-
- var text, fs, Cc, Ci, xpcIsWindows,
- progIds = ['Msxml2.XMLHTTP', 'Microsoft.XMLHTTP', 'Msxml2.XMLHTTP.4.0'],
- xmlRegExp = /^\s*<\?xml(\s)+version=[\'\"](\d)*.(\d)*[\'\"](\s)*\?>/im,
- bodyRegExp = /<body[^>]*>\s*([\s\S]+)\s*<\/body>/im,
- hasLocation = typeof location !== 'undefined' && location.href,
- defaultProtocol = hasLocation && location.protocol && location.protocol.replace(/\:/, ''),
- defaultHostName = hasLocation && location.hostname,
- defaultPort = hasLocation && (location.port || undefined),
- buildMap = {},
- masterConfig = (module.config && module.config()) || {};
-
- text = {
- version: '2.0.12',
-
- strip: function (content) {
- //Strips <?xml ...?> declarations so that external SVG and XML
- //documents can be added to a document without worry. Also, if the string
- //is an HTML document, only the part inside the body tag is returned.
- if (content) {
- content = content.replace(xmlRegExp, "");
- var matches = content.match(bodyRegExp);
- if (matches) {
- content = matches[1];
- }
- } else {
- content = "";
- }
- return content;
- },
-
- jsEscape: function (content) {
- return content.replace(/(['\\])/g, '\\$1')
- .replace(/[\f]/g, "\\f")
- .replace(/[\b]/g, "\\b")
- .replace(/[\n]/g, "\\n")
- .replace(/[\t]/g, "\\t")
- .replace(/[\r]/g, "\\r")
- .replace(/[\u2028]/g, "\\u2028")
- .replace(/[\u2029]/g, "\\u2029");
- },
-
- createXhr: masterConfig.createXhr || function () {
- //Would love to dump the ActiveX crap in here. Need IE 6 to die first.
- var xhr, i, progId;
- if (typeof XMLHttpRequest !== "undefined") {
- return new XMLHttpRequest();
- } else if (typeof ActiveXObject !== "undefined") {
- for (i = 0; i < 3; i += 1) {
- progId = progIds[i];
- try {
- xhr = new ActiveXObject(progId);
- } catch (e) {}
-
- if (xhr) {
- progIds = [progId]; // so faster next time
- break;
- }
- }
- }
-
- return xhr;
- },
-
- /**
- * Parses a resource name into its component parts. Resource names
- * look like: module/name.ext!strip, where the !strip part is
- * optional.
- * @param {String} name the resource name
- * @returns {Object} with properties "moduleName", "ext" and "strip"
- * where strip is a boolean.
- */
- parseName: function (name) {
- var modName, ext, temp,
- strip = false,
- index = name.indexOf("."),
- isRelative = name.indexOf('./') === 0 ||
- name.indexOf('../') === 0;
-
- if (index !== -1 && (!isRelative || index > 1)) {
- modName = name.substring(0, index);
- ext = name.substring(index + 1, name.length);
- } else {
- modName = name;
- }
-
- temp = ext || modName;
- index = temp.indexOf("!");
- if (index !== -1) {
- //Pull off the strip arg.
- strip = temp.substring(index + 1) === "strip";
- temp = temp.substring(0, index);
- if (ext) {
- ext = temp;
- } else {
- modName = temp;
- }
- }
-
- return {
- moduleName: modName,
- ext: ext,
- strip: strip
- };
- },
-
- xdRegExp: /^((\w+)\:)?\/\/([^\/\\]+)/,
-
- /**
- * Is an URL on another domain. Only works for browser use, returns
- * false in non-browser environments. Only used to know if an
- * optimized .js version of a text resource should be loaded
- * instead.
- * @param {String} url
- * @returns Boolean
- */
- useXhr: function (url, protocol, hostname, port) {
- var uProtocol, uHostName, uPort,
- match = text.xdRegExp.exec(url);
- if (!match) {
- return true;
- }
- uProtocol = match[2];
- uHostName = match[3];
-
- uHostName = uHostName.split(':');
- uPort = uHostName[1];
- uHostName = uHostName[0];
-
- return (!uProtocol || uProtocol === protocol) &&
- (!uHostName || uHostName.toLowerCase() === hostname.toLowerCase()) &&
- ((!uPort && !uHostName) || uPort === port);
- },
-
- finishLoad: function (name, strip, content, onLoad) {
- content = strip ? text.strip(content) : content;
- if (masterConfig.isBuild) {
- buildMap[name] = content;
- }
- onLoad(content);
- },
-
- load: function (name, req, onLoad, config) {
- //Name has format: some.module.filext!strip
- //The strip part is optional.
- //if strip is present, then that means only get the string contents
- //inside a body tag in an HTML string. For XML/SVG content it means
- //removing the <?xml ...?> declarations so the content can be inserted
- //into the current doc without problems.
-
- // Do not bother with the work if a build and text will
- // not be inlined.
- if (config && config.isBuild && !config.inlineText) {
- onLoad();
- return;
- }
-
- masterConfig.isBuild = config && config.isBuild;
-
- var parsed = text.parseName(name),
- nonStripName = parsed.moduleName +
- (parsed.ext ? '.' + parsed.ext : ''),
- url = req.toUrl(nonStripName),
- useXhr = (masterConfig.useXhr) ||
- text.useXhr;
-
- // Do not load if it is an empty: url
- if (url.indexOf('empty:') === 0) {
- onLoad();
- return;
- }
-
- //Load the text. Use XHR if possible and in a browser.
- if (!hasLocation || useXhr(url, defaultProtocol, defaultHostName, defaultPort)) {
- text.get(url, function (content) {
- text.finishLoad(name, parsed.strip, content, onLoad);
- }, function (err) {
- if (onLoad.error) {
- onLoad.error(err);
- }
- });
- } else {
- //Need to fetch the resource across domains. Assume
- //the resource has been optimized into a JS module. Fetch
- //by the module name + extension, but do not include the
- //!strip part to avoid file system issues.
- req([nonStripName], function (content) {
- text.finishLoad(parsed.moduleName + '.' + parsed.ext,
- parsed.strip, content, onLoad);
- });
- }
- },
-
- write: function (pluginName, moduleName, write, config) {
- if (buildMap.hasOwnProperty(moduleName)) {
- var content = text.jsEscape(buildMap[moduleName]);
- write.asModule(pluginName + "!" + moduleName,
- "define(function () { return '" +
- content +
- "';});\n");
- }
- },
-
- writeFile: function (pluginName, moduleName, req, write, config) {
- var parsed = text.parseName(moduleName),
- extPart = parsed.ext ? '.' + parsed.ext : '',
- nonStripName = parsed.moduleName + extPart,
- //Use a '.js' file name so that it indicates it is a
- //script that can be loaded across domains.
- fileName = req.toUrl(parsed.moduleName + extPart) + '.js';
-
- //Leverage own load() method to load plugin value, but only
- //write out values that do not have the strip argument,
- //to avoid any potential issues with ! in file names.
- text.load(nonStripName, req, function (value) {
- //Use own write() method to construct full module value.
- //But need to create shell that translates writeFile's
- //write() to the right interface.
- var textWrite = function (contents) {
- return write(fileName, contents);
- };
- textWrite.asModule = function (moduleName, contents) {
- return write.asModule(moduleName, fileName, contents);
- };
-
- text.write(pluginName, nonStripName, textWrite, config);
- }, config);
- }
- };
-
- if (masterConfig.env === 'node' || (!masterConfig.env &&
- typeof process !== "undefined" &&
- process.versions &&
- !!process.versions.node &&
- !process.versions['node-webkit'])) {
- //Using special require.nodeRequire, something added by r.js.
- fs = require.nodeRequire('fs');
-
- text.get = function (url, callback, errback) {
- try {
- var file = fs.readFileSync(url, 'utf8');
- //Remove BOM (Byte Mark Order) from utf8 files if it is there.
- if (file.indexOf('\uFEFF') === 0) {
- file = file.substring(1);
- }
- callback(file);
- } catch (e) {
- if (errback) {
- errback(e);
- }
- }
- };
- } else if (masterConfig.env === 'xhr' || (!masterConfig.env &&
- text.createXhr())) {
- text.get = function (url, callback, errback, headers) {
- var xhr = text.createXhr(), header;
- xhr.open('GET', url, true);
-
- //Allow plugins direct access to xhr headers
- if (headers) {
- for (header in headers) {
- if (headers.hasOwnProperty(header)) {
- xhr.setRequestHeader(header.toLowerCase(), headers[header]);
- }
- }
- }
-
- //Allow overrides specified in config
- if (masterConfig.onXhr) {
- masterConfig.onXhr(xhr, url);
- }
-
- xhr.onreadystatechange = function (evt) {
- var status, err;
- //Do not explicitly handle errors, those should be
- //visible via console output in the browser.
- if (xhr.readyState === 4) {
- status = xhr.status || 0;
- if (status > 399 && status < 600) {
- //An http 4xx or 5xx error. Signal an error.
- err = new Error(url + ' HTTP status: ' + status);
- err.xhr = xhr;
- if (errback) {
- errback(err);
- }
- } else {
- callback(xhr.responseText);
- }
-
- if (masterConfig.onXhrComplete) {
- masterConfig.onXhrComplete(xhr, url);
- }
- }
- };
- xhr.send(null);
- };
- } else if (masterConfig.env === 'rhino' || (!masterConfig.env &&
- typeof Packages !== 'undefined' && typeof java !== 'undefined')) {
- //Why Java, why is this so awkward?
- text.get = function (url, callback) {
- var stringBuffer, line,
- encoding = "utf-8",
- file = new java.io.File(url),
- lineSeparator = java.lang.System.getProperty("line.separator"),
- input = new java.io.BufferedReader(new java.io.InputStreamReader(new java.io.FileInputStream(file), encoding)),
- content = '';
- try {
- stringBuffer = new java.lang.StringBuffer();
- line = input.readLine();
-
- // Byte Order Mark (BOM) - The Unicode Standard, version 3.0, page 324
- // http://www.unicode.org/faq/utf_bom.html
-
- // Note that when we use utf-8, the BOM should appear as "EF BB BF", but it doesn't due to this bug in the JDK:
- // http://bugs.sun.com/bugdatabase/view_bug.do?bug_id=4508058
- if (line && line.length() && line.charAt(0) === 0xfeff) {
- // Eat the BOM, since we've already found the encoding on this file,
- // and we plan to concatenating this buffer with others; the BOM should
- // only appear at the top of a file.
- line = line.substring(1);
- }
-
- if (line !== null) {
- stringBuffer.append(line);
- }
-
- while ((line = input.readLine()) !== null) {
- stringBuffer.append(lineSeparator);
- stringBuffer.append(line);
- }
- //Make sure we return a JavaScript string and not a Java string.
- content = String(stringBuffer.toString()); //String
- } finally {
- input.close();
- }
- callback(content);
- };
- } else if (masterConfig.env === 'xpconnect' || (!masterConfig.env &&
- typeof Components !== 'undefined' && Components.classes &&
- Components.interfaces)) {
- //Avert your gaze!
- Cc = Components.classes;
- Ci = Components.interfaces;
- Components.utils['import']('resource://gre/modules/FileUtils.jsm');
- xpcIsWindows = ('@mozilla.org/windows-registry-key;1' in Cc);
-
- text.get = function (url, callback) {
- var inStream, convertStream, fileObj,
- readData = {};
-
- if (xpcIsWindows) {
- url = url.replace(/\//g, '\\');
- }
-
- fileObj = new FileUtils.File(url);
-
- //XPCOM, you so crazy
- try {
- inStream = Cc['@mozilla.org/network/file-input-stream;1']
- .createInstance(Ci.nsIFileInputStream);
- inStream.init(fileObj, 1, 0, false);
-
- convertStream = Cc['@mozilla.org/intl/converter-input-stream;1']
- .createInstance(Ci.nsIConverterInputStream);
- convertStream.init(inStream, "utf-8", inStream.available(),
- Ci.nsIConverterInputStream.DEFAULT_REPLACEMENT_CHARACTER);
-
- convertStream.readString(inStream.available(), readData);
- convertStream.close();
- inStream.close();
- callback(readData.value);
- } catch (e) {
- throw new Error((fileObj && fileObj.path || '') + ': ' + e);
- }
- };
- }
- return text;
-});
diff --git a/docs/malhar-3.3/mkdocs/search_index.json b/docs/malhar-3.3/mkdocs/search_index.json
deleted file mode 100644
index d782b9e..0000000
--- a/docs/malhar-3.3/mkdocs/search_index.json
+++ /dev/null
@@ -1,434 +0,0 @@
-{
- "docs": [
- {
- "location": "/",
- "text": "Apache Apex Malhar\n\n\nApache Apex Malhar is an open source operator and codec library that can be used with the \nApache Apex\n platform to build real-time streaming applications. Enabling users to extract value quickly, Malhar operators help get data in, analyze it in real-time, and get data out of Hadoop. In addition to the operators, the library contains a number of demos applications, demonstrating operator features and capabilities.\n\n\n\n\nCapabilities common across Malhar operators\n\n\nFor most streaming platforms, connectors are afterthoughts and often end up being simple \u2018bolt-ons\u2019 to the platform. As a result they often cause performance issues or data loss when put through failure scenarios and scalability requirements. Malhar operators do not face these issues as they were designed to be integral parts of Apex. Hence, they have following core streaming runtime capabilities\n\n\n\n\nFault tolerance\n \u2013 Malhar operators where applicable have fault tolerance built in. They use the checkpoint capability provided by the framework to ensure that there is no data loss under ANY failure scenario.\n\n\nProcessing guarantees\n \u2013 Malhar operators where applicable provide out of the box support for ALL three processing guarantees \u2013 exactly once, at-least once, and at-most once WITHOUT requiring the user to write any additional code. Some operators, like MQTT operator, deal with source systems that can not track processed data and hence need the operators to keep track of the data. Malhar has support for a generic operator that uses alternate storage like HDFS to facilitate this. Finally for databases that support transactions or support any sort of atomic batch operations Malhar operators can do exactly once down to the tuple level.\n\n\nDynamic updates\n \u2013 Based on changing business conditions you often have to tweak several parameters used by the operators in your streaming application without incurring any application downtime. You can also change properties of a Malhar operator at runtime without having to bring down the application.\n\n\nEase of extensibility\n \u2013 Malhar operators are based on templates that are easy to extend.\n\n\nPartitioning support\n \u2013 In streaming applications the input data stream often needs to be partitioned based on the contents of the stream. Also for operators that ingest data from external systems partitioning needs to be done based on the capabilities of the external system. For example with Kafka, the operator can automatically scale up or down based on the changes in the number of Kafka partitions.\n\n\n\n\nOperator Library Overview\n\n\nInput/output connectors\n\n\nBelow is a summary of the various sub categories of input and output operators. Input operators also have a corresponding output operator\n\n\n\n\nFile Systems\n \u2013 Most streaming analytics use cases require the data to be stored in HDFS or perhaps S3 if the application is running in AWS. Users often need to re-run their streaming analytical applications against historical data or consume data from upstream processes that are perhaps writing to some NFS share. Apex supports input \n output operators for HDFS, S3, NFS \n Local Files. There are also File Splitter and Block Reader operators, which can accelecate processing of large files by splitting and paralellizing the work across non-overlapping sets of file blocks.\n\n\nRelational Databases\n \u2013 Most stream processing use cases require some reference data lookups to enrich, tag or filter streaming data. There is also a need to save results of the streaming analytical computation to a database so an operational dashboard can see them. Apex supports a JDBC operator so you can read/write data from any JDBC compliant RDBMS like Oracle, MySQL, Sqlite, etc.\n\n\nNoSQL Databases\n \u2013 NoSQL key-value pair databases like Cassandra \n HBase are a common part of streaming analytics application architectures to lookup reference data or store results. Malhar has operators for HBase, Cassandra, Accumulo, Aerospike, MongoDB, and CouchDB.\n\n\nMessaging Systems\n \u2013 Kafka, JMS, and similar systems are the workhorses of messaging infrastructure in most enterprises. Malhar has a robust, industry-tested set of operators to read and write Kafka, JMS, ZeroMQ, and RabbitMQ messages.\n\n\nNotification Systems\n \u2013 Malhar includes an operator for sending notifications via SMTP.\n\n\nIn-memory Databases \n Caching platforms\n - Some streaming use cases need instantaneous access to shared state across the application. Caching platforms and in-memory databases serve this purpose really well. To support these use cases, Malhar has operators for memcached and Redis.\n\n\nSocial Media\n - Malhar includes an operator to connect to the popular Twitter stream fire hose.\n\n\nProtocols\n - Malhar provides connectors that can communicate in HTTP, RSS, Socket, WebSocket, FTP, and MQTT.\n\n\n\n\nParsers\n\n\nThere are many industry vertical specific data formats that a streaming application developer might need to parse. Often there are existing parsers available for these that can be directly plugged into an Apache Apex application. For example in the Telco space, a Java based CDR parser can be directly plugged into Apache Apex operator. To further simplify development experience, Malhar also provides some operators for parsing common formats like XML (DOM \n SAX), JSON (flat map converter), Apache log files, syslog, etc.\n\n\nStream manipulation\n\n\nStreaming data inevitably needs processing to clean, filter, tag, summarize, etc. The goal of Malhar is to enable the application developer to focus on WHAT needs to be done to the stream to get it in the right format and not worry about the HOW. Malhar has several operators to perform the common stream manipulation actions like \u2013 GroupBy, Join, Distinct/Unique, Limit, OrderBy, Split, Sample, Inner join, Outer join, Select, Update etc.\n\n\nCompute\n\n\nOne of the most important promises of a streaming analytics platform like Apache Apex is the ability to do analytics in real-time. However delivering on the promise becomes really difficult when the platform does not provide out of the box operators to support variety of common compute functions as the user then has to worry about making these scalable, fault tolerant, stateful, etc. Malhar takes this responsibility away from the application developer by providing a variety of out of the box computational operators.\n\n\nBelow is just a snapshot of the compute operators available in Malhar\n\n\n\n\nStatistics and math - Various mathematical and statistical computations over application defined time windows.\n\n\nFiltering and pattern matching\n\n\nSorting, maps, frequency, TopN, BottomN\n\n\nRandom data generators\n\n\n\n\nLanguages Support\n\n\nMigrating to a new platform often requires re-use of the existing code that would be difficult or time-consuming to re-write. With this in mind, Malhar supports invocation of code written in other languages by wrapping them in one of the library operators, and allows execution of software written in:\n\n\n\n\nJavaScript\n\n\nPython\n\n\nR\n\n\nRuby",
- "title": "Apache Apex Malhar"
- },
- {
- "location": "/#apache-apex-malhar",
- "text": "Apache Apex Malhar is an open source operator and codec library that can be used with the Apache Apex platform to build real-time streaming applications. Enabling users to extract value quickly, Malhar operators help get data in, analyze it in real-time, and get data out of Hadoop. In addition to the operators, the library contains a number of demos applications, demonstrating operator features and capabilities.",
- "title": "Apache Apex Malhar"
- },
- {
- "location": "/#capabilities-common-across-malhar-operators",
- "text": "For most streaming platforms, connectors are afterthoughts and often end up being simple \u2018bolt-ons\u2019 to the platform. As a result they often cause performance issues or data loss when put through failure scenarios and scalability requirements. Malhar operators do not face these issues as they were designed to be integral parts of Apex. Hence, they have following core streaming runtime capabilities Fault tolerance \u2013 Malhar operators where applicable have fault tolerance built in. They use the checkpoint capability provided by the framework to ensure that there is no data loss under ANY failure scenario. Processing guarantees \u2013 Malhar operators where applicable provide out of the box support for ALL three processing guarantees \u2013 exactly once, at-least once, and at-most once WITHOUT requiring the user to write any additional code. Some operators, like MQTT operator, deal with source systems that can not track processed data and hence need the operators to keep track of the data. Malhar has support for a generic operator that uses alternate storage like HDFS to facilitate this. Finally for databases that support transactions or support any sort of atomic batch operations Malhar operators can do exactly once down to the tuple level. Dynamic updates \u2013 Based on changing business conditions you often have to tweak several parameters used by the operators in your streaming application without incurring any application downtime. You can also change properties of a Malhar operator at runtime without having to bring down the application. Ease of extensibility \u2013 Malhar operators are based on templates that are easy to extend. Partitioning support \u2013 In streaming applications the input data stream often needs to be partitioned based on the contents of the stream. Also for operators that ingest data from external systems partitioning needs to be done based on the capabilities of the external system. For example with Kafka, the operator can automatically scale up or down based on the changes in the number of Kafka partitions.",
- "title": "Capabilities common across Malhar operators"
- },
- {
- "location": "/#operator-library-overview",
- "text": "",
- "title": "Operator Library Overview"
- },
- {
- "location": "/#inputoutput-connectors",
- "text": "Below is a summary of the various sub categories of input and output operators. Input operators also have a corresponding output operator File Systems \u2013 Most streaming analytics use cases require the data to be stored in HDFS or perhaps S3 if the application is running in AWS. Users often need to re-run their streaming analytical applications against historical data or consume data from upstream processes that are perhaps writing to some NFS share. Apex supports input output operators for HDFS, S3, NFS Local Files. There are also File Splitter and Block Reader operators, which can accelecate processing of large files by splitting and paralellizing the work across non-overlapping sets of file blocks. Relational Databases \u2013 Most stream processing use cases require some reference data lookups to enrich, tag or filter streaming data. There is also a need to save results of the streaming analytical computation to a database so an operational dashboard can see them. Apex supports a JDBC operator so you can read/write data from any JDBC compliant RDBMS like Oracle, MySQL, Sqlite, etc. NoSQL Databases \u2013 NoSQL key-value pair databases like Cassandra HBase are a common part of streaming analytics application architectures to lookup reference data or store results. Malhar has operators for HBase, Cassandra, Accumulo, Aerospike, MongoDB, and CouchDB. Messaging Systems \u2013 Kafka, JMS, and similar systems are the workhorses of messaging infrastructure in most enterprises. Malhar has a robust, industry-tested set of operators to read and write Kafka, JMS, ZeroMQ, and RabbitMQ messages. Notification Systems \u2013 Malhar includes an operator for sending notifications via SMTP. In-memory Databases Caching platforms - Some streaming use cases need instantaneous access to shared state across the application. Caching platforms and in-memory databases serve this purpose really well. To support these use cases, Malhar has operators for memcached and Redis. Social Media - Malhar includes an operator to connect to the popular Twitter stream fire hose. Protocols - Malhar provides connectors that can communicate in HTTP, RSS, Socket, WebSocket, FTP, and MQTT.",
- "title": "Input/output connectors"
- },
- {
- "location": "/#parsers",
- "text": "There are many industry vertical specific data formats that a streaming application developer might need to parse. Often there are existing parsers available for these that can be directly plugged into an Apache Apex application. For example in the Telco space, a Java based CDR parser can be directly plugged into Apache Apex operator. To further simplify development experience, Malhar also provides some operators for parsing common formats like XML (DOM SAX), JSON (flat map converter), Apache log files, syslog, etc.",
- "title": "Parsers"
- },
- {
- "location": "/#stream-manipulation",
- "text": "Streaming data inevitably needs processing to clean, filter, tag, summarize, etc. The goal of Malhar is to enable the application developer to focus on WHAT needs to be done to the stream to get it in the right format and not worry about the HOW. Malhar has several operators to perform the common stream manipulation actions like \u2013 GroupBy, Join, Distinct/Unique, Limit, OrderBy, Split, Sample, Inner join, Outer join, Select, Update etc.",
- "title": "Stream manipulation"
- },
- {
- "location": "/#compute",
- "text": "One of the most important promises of a streaming analytics platform like Apache Apex is the ability to do analytics in real-time. However delivering on the promise becomes really difficult when the platform does not provide out of the box operators to support variety of common compute functions as the user then has to worry about making these scalable, fault tolerant, stateful, etc. Malhar takes this responsibility away from the application developer by providing a variety of out of the box computational operators. Below is just a snapshot of the compute operators available in Malhar Statistics and math - Various mathematical and statistical computations over application defined time windows. Filtering and pattern matching Sorting, maps, frequency, TopN, BottomN Random data generators",
- "title": "Compute"
- },
- {
- "location": "/#languages-support",
- "text": "Migrating to a new platform often requires re-use of the existing code that would be difficult or time-consuming to re-write. With this in mind, Malhar supports invocation of code written in other languages by wrapping them in one of the library operators, and allows execution of software written in: JavaScript Python R Ruby",
- "title": "Languages Support"
- },
- {
- "location": "/operators/kafkaInputOperator/",
- "text": "KAFKA INPUT OPERATOR\n\n\nIntroduction: About Kafka Input Operator\n\n\nThis is an input operator that consumes data from Kafka messaging system for further processing in Apex. Kafka Input Operator is an fault-tolerant and scalable Malhar Operator.\n\n\nWhy is it needed ?\n\n\nKafka is a pull-based and distributed publish subscribe messaging system, topics are partitioned and replicated across\nnodes. Kafka input operator is needed when you want to read data from multiple\npartitions of a Kafka topic in parallel in an Apex application.\n\n\nAbstractKafkaInputOperator\n\n\nThis is the abstract implementation that serves as base class for consuming messages from Kafka messaging system. This class doesn\u2019t have any ports.\n\n\n\n\nConfiguration Parameters\n\n\n\n\n\n\n\n\n\n\n\n\nParameter\n\n\nDescription\n\n\n\n\n\n\nmaxTuplesPerWindow\n\n\nControls the maximum number of messages emitted in each streaming window from this operator. Minimum value is 1. Default value = MAX_VALUE \n\n\n\n\n\n\nidempotentStorageManager\n\n\nThis is an instance of IdempotentStorageManager. Idempotency ensures that the operator will process the same set of messages in a window before and after a failure. For example, let's say the operator completed window 10 and failed somewhere between window 11. If the operator gets restored at window 10 then it will process the same messages again in window 10 which it did in the previous run before the failure. Idempotency is important but comes with higher cost because at the end of each window the operator needs to persist some state with respect to that window. Default Value = com.datatorrent.lib.io.IdempotentStorageManager.\nNoopIdempotentStorageManager\n\n\n\n\n\n\nstrategy\n\n\nOperator supports two types of partitioning strategies, ONE_TO_ONE and ONE_TO_MANY.\n\n\nONE_TO_ONE: If this is enabled, the AppMaster creates one input operator instance per Kafka topic partition. So the number of Kafka topic partitions equals the number of operator instances.\n\n\nONE_TO_MANY: The AppMaster creates K = min(initialPartitionCount, N) Kafka input operator instances where N is the number of Kafka topic partitions. If K is less than N, the remaining topic partitions are assigned to the K operator instances in round-robin fashion. If K is less than initialPartitionCount, the AppMaster creates one input operator instance per Kafka topic partition. For example, if initialPartitionCount = 5 and number of Kafka partitions(N) = 2 then AppMaster creates 2 Kafka input operator instances.\nDefault Value = ONE_TO_ONE\n\n\n\n\n\n\nmsgRateUpperBound\n\n\nMaximum messages upper bound. Operator repartitions when the \nmsgProcessedPS\n exceeds this bound. \nmsgProcessedPS\n is the average number of messages processed per second by this operator.\n\n\n\n\n\n\nbyteRateUpperBound\n\n\nMaximum bytes upper bound. Operator repartitions when the \nbytesPS\n exceeds this bound. \nbytesPS\n is the average number of bytes processed per second by this operator.\n\n\n\n\n\n\n\n\noffsetManager\n\n\nThis is an optional parameter that is useful when the application restarts or start at specific offsets (offsets are explained below)\n\n\n\n\n\n\nrepartitionInterval\n\n\nInterval specified in milliseconds. This value specifies the minimum time required between two repartition actions. Default Value = 30 Seconds\n\n\n\n\n\n\nrepartitionCheckInterval\n\n\nInterval specified in milliseconds. This value specifies the minimum interval between two offset updates. Default Value = 5 Seconds\n\n\n\n\n\n\ninitialPartitionCount\n\n\nWhen the ONE_TO_MANY partition strategy is enabled, this value indicates the number of Kafka input operator instances. Default Value = 1\n\n\n\n\n\n\nconsumer\n\n\nThis is an instance of com.datatorrent.contrib.kafka.KafkaConsumer. Default Value = Instance of SimpleKafkaConsumer.\n\n\n\n\n\n\n\n\nAbstract Methods\n\n\nvoid emitTuple(Message message): Abstract method that emits tuples\nextracted from Kafka message.\n\n\nKafkaConsumer\n\n\nThis is an abstract implementation of Kafka consumer. It sends the fetch\nrequests to the leading brokers of Kafka partitions. For each request,\nit receives the set of messages and stores them into the buffer which is\nArrayBlockingQueue. SimpleKafkaConsumer\u00a0which extends\nKafkaConsumer and serves the functionality of Simple Consumer API and\nHighLevelKafkaConsumer which extends KafkaConsumer and \u00a0serves the\nfunctionality of High Level Consumer API.\n\n\nPre-requisites\n\n\nThis operator referred the Kafka Consumer API of version\n0.8.1.1. So, this operator will work with any 0.8.x and 0.7.x version of Apache Kafka.\n\n\nConfiguration Parameters\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nParameter\n\n\nType\n\n\nDefault\n\n\nDescription\n\n\n\n\n\n\nzookeeper\n\n\nString\n\n\n\n\nSpecifies the zookeeper quorum of Kafka clusters that you want to consume messages from. zookeeper \u00a0is a string in the form of hostname1:port1,hostname2:port2,hostname3:port3 \u00a0where hostname1,hostname2,hostname3 are hosts and port1,port2,port3 are ports of zookeeper server. \u00a0If the topic name is the same across the Kafka clusters and want to consume data from these clusters, then configure the zookeeper as follows: c1::hs1:p1,hs2:p2,hs3:p3;c2::hs4:p4,hs5:p5,c3::hs6:p6\n\n\nwhere\n\n\nc1,c2,c3 indicates the cluster names, hs1,hs2,hs3,hs4,hs5,hs6 are zookeeper hosts and p1,p2,p3,p4,p5,p6 are corresponding ports. Here, cluster name is optional in case of single cluster\n\n\n\n\n\n\ncacheSize\n\n\nint\n\n\n1024\n\n\nMaximum of buffered messages hold in memory.\n\n\n\n\n\n\ntopic\n\n\nString\n\n\ndefault_topic\n\n\nIndicates the name of the topic.\n\n\n\n\n\n\ninitialOffset\n\n\nString\n\n\nlatest\n\n\nIndicates the type of offset i.e, \u201cearliest or latest\u201d. If initialOffset is \u201clatest\u201d, then the operator consumes messages from latest point of Kafka queue. If initialOffset is \u201cearliest\u201d, then the operator consumes messages starting from message queue. This can be overridden by OffsetManager.\n\n\n\n\n\n\n\n\n\nAbstract Methods\n\n\n\n\nvoid commitOffset(): Commit the offsets at checkpoint.\n\n\nMap \nKafkaPartition, Long\n getCurrentOffsets(): Return the current\n offset status.\n\n\nresetPartitionsAndOffset(Set \nKafkaPartition\n partitionIds,\n Map \nKafkaPartition, Long\n startOffset): Reset the partitions with\n parittionIds and offsets with startOffset.\n\n\n\n\nConfiguration Parameters\u00a0for SimpleKafkaConsumer\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nParameter\n\n\nType\n\n\nDefault\n\n\nDescription\n\n\n\n\n\n\nbufferSize\n\n\nint\n\n\n1 MB\n\n\nSpecifies the maximum total size of messages for each fetch request.\n\n\n\n\n\n\nmetadataRefreshInterval\n\n\nint\n\n\n30 Seconds\n\n\nInterval in between refresh the metadata change(broker change) in milliseconds. Enabling metadata refresh guarantees an automatic reconnect when a new broker is elected as the host. A value of -1 disables this feature.\n\n\n\n\n\n\nmetadataRefreshRetryLimit\n\n\nint\n\n\n-1\n\n\nSpecifies the maximum brokers' metadata refresh retry limit. -1 means unlimited retry.\n\n\n\n\n\n\n\n\n\nOffsetManager\n\n\nThis is an interface for offset management and is useful when consuming data\nfrom specified offsets. Updates the offsets for all the Kafka partitions\nperiodically. Below is the code snippet:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\n\n\npublic interface OffsetManager\n{\n public Map\nKafkaPartition, Long\n loadInitialOffsets();\n public void updateOffsets(Map\nKafkaPartition, Long\n offsetsOfPartitions);\n}\n\n\n\n\nAbstract Methods\n\n\nMap \nKafkaPartition, Long\n loadInitialOffsets(): Specifies the initial offset for consuming messages; called at the activation stage.\n\n\nupdateOffsets(Map \nKafkaPartition, Long\n offsetsOfPartitions): \u00a0This\nmethod is called at every repartitionCheckInterval to update offsets.\n\n\nPartitioning\n\n\nThe logical instance of the KafkaInputOperator acts as the Partitioner\nas well as a StatsListener. This is because the\nAbstractKafkaInputOperator implements both the\ncom.datatorrent.api.Partitioner and com.datatorrent.api.StatsListener\ninterfaces and provides an implementation of definePartitions(...) and\nprocessStats(...) which makes it auto-scalable.\n\n\nResponse processStats(BatchedOperatorStats stats)\n\n\nThe application master invokes this method on the logical instance with\nthe stats (tuplesProcessedPS, bytesPS, etc.) of each partition.\nRe-partitioning happens based on whether any new Kafka partitions added for\nthe topic or bytesPS and msgPS cross their respective upper bounds.\n\n\nDefinePartitions\n\n\nBased on the repartitionRequired field of the Response object which is\nreturned by processStats(...) method, the application master invokes\ndefinePartitions(...) on the logical instance which is also the\npartitioner instance. Dynamic partition can be disabled by setting the\nparameter repartitionInterval value to a negative value.\n\n\nAbstractSinglePortKafkaInputOperator\n\n\nThis class extends AbstractKafkaInputOperator and having single output\nport, will emit the messages through this port.\n\n\nPorts\n\n\noutputPort \nT\n: Tuples extracted from Kafka messages are emitted through\nthis port.\n\n\nAbstract Methods\n\n\nT getTuple(Message msg) : Converts the Kafka message to tuple.\n\n\nConcrete Classes\n\n\n\n\n\n\nKafkaSinglePortStringInputOperator :\nThis class extends AbstractSinglePortKafkaInputOperator and getTuple() method extracts string from Kafka message.\n\n\n\n\n\n\nKafkaSinglePortByteArrayInputOperator:\nThis class extends AbstractSinglePortKafkaInputOperator and getTuple() method extracts byte array from Kafka message.\n\n\n\n\n\n\nApplication Example\n\n\nThis section builds an Apex application using Kafka input operator.\nBelow is the code snippet:\n\n\n@ApplicationAnnotation(name = \nKafkaApp\n)\npublic class ExampleKafkaApplication implements StreamingApplication\n{\n@Override\npublic void populateDAG(DAG dag, Configuration entries)\n{\n KafkaSinglePortByteArrayInputOperator input = dag.addOperator(\nMessageReader\n, new KafkaSinglePortByteArrayInputOperator());\n\n ConsoleOutputOperator output = dag.addOperator(\nOutput\n, new ConsoleOutputOperator());\n\n dag.addStream(\nMessageData\n, input.outputPort, output.input);\n}\n}\n\n\n\n\nBelow is the configuration for \u201ctest\u201d Kafka topic name and\n\u201clocalhost:2181\u201d is the zookeeper forum:\n\n\nproperty\n\n\nname\ndt.operator.MessageReader.prop.topic\n/name\n\n\nvalue\ntest\n/value\n\n\n/property\n\n\n\nproperty\n\n\nname\ndt.operator.KafkaInputOperator.prop.zookeeper\n/nam\n\n\nvalue\nlocalhost:2181\n/value\n\n\n/property",
- "title": "Kafka Input"
- },
- {
- "location": "/operators/kafkaInputOperator/#kafka-input-operator",
- "text": "",
- "title": "KAFKA INPUT OPERATOR"
- },
- {
- "location": "/operators/kafkaInputOperator/#introduction-about-kafka-input-operator",
- "text": "This is an input operator that consumes data from Kafka messaging system for further processing in Apex. Kafka Input Operator is an fault-tolerant and scalable Malhar Operator.",
- "title": "Introduction: About Kafka Input Operator"
- },
- {
- "location": "/operators/kafkaInputOperator/#why-is-it-needed",
- "text": "Kafka is a pull-based and distributed publish subscribe messaging system, topics are partitioned and replicated across\nnodes. Kafka input operator is needed when you want to read data from multiple\npartitions of a Kafka topic in parallel in an Apex application.",
- "title": "Why is it needed ?"
- },
- {
- "location": "/operators/kafkaInputOperator/#abstractkafkainputoperator",
- "text": "This is the abstract implementation that serves as base class for consuming messages from Kafka messaging system. This class doesn\u2019t have any ports.",
- "title": "AbstractKafkaInputOperator"
- },
- {
- "location": "/operators/kafkaInputOperator/#configuration-parameters",
- "text": "Parameter Description maxTuplesPerWindow Controls the maximum number of messages emitted in each streaming window from this operator. Minimum value is 1. Default value = MAX_VALUE idempotentStorageManager This is an instance of IdempotentStorageManager. Idempotency ensures that the operator will process the same set of messages in a window before and after a failure. For example, let's say the operator completed window 10 and failed somewhere between window 11. If the operator gets restored at window 10 then it will process the same messages again in window 10 which it did in the previous run before the failure. Idempotency is important but comes with higher cost because at the end of each window the operator needs to persist some state with respect to that window. Default Value = com.datatorrent.lib.io.IdempotentStorageManager. NoopIdempotentStorageManager strategy Operator supports two types of partitioning strategies, ONE_TO_ONE and ONE_TO_MANY. ONE_TO_ONE: If this is enabled, the AppMaster creates one input operator instance per Kafka topic partition. So the number of Kafka topic partitions equals the number of operator instances. ONE_TO_MANY: The AppMaster creates K = min(initialPartitionCount, N) Kafka input operator instances where N is the number of Kafka topic partitions. If K is less than N, the remaining topic partitions are assigned to the K operator instances in round-robin fashion. If K is less than initialPartitionCount, the AppMaster creates one input operator instance per Kafka topic partition. For example, if initialPartitionCount = 5 and number of Kafka partitions(N) = 2 then AppMaster creates 2 Kafka input operator instances.\nDefault Value = ONE_TO_ONE msgRateUpperBound Maximum messages upper bound. Operator repartitions when the msgProcessedPS exceeds this bound. msgProcessedPS is the average number of messages processed per second by this operator. byteRateUpperBound Maximum bytes upper bound. Operator repartitions when the bytesPS exceeds this bound. bytesPS is the average number of bytes processed per second by this operator. offsetManager This is an optional parameter that is useful when the application restarts or start at specific offsets (offsets are explained below) repartitionInterval Interval specified in milliseconds. This value specifies the minimum time required between two repartition actions. Default Value = 30 Seconds repartitionCheckInterval Interval specified in milliseconds. This value specifies the minimum interval between two offset updates. Default Value = 5 Seconds initialPartitionCount When the ONE_TO_MANY partition strategy is enabled, this value indicates the number of Kafka input operator instances. Default Value = 1 consumer This is an instance of com.datatorrent.contrib.kafka.KafkaConsumer. Default Value = Instance of SimpleKafkaConsumer.",
- "title": "Configuration Parameters"
- },
- {
- "location": "/operators/kafkaInputOperator/#abstract-methods",
- "text": "void emitTuple(Message message): Abstract method that emits tuples\nextracted from Kafka message.",
- "title": "Abstract Methods"
- },
- {
- "location": "/operators/kafkaInputOperator/#kafkaconsumer",
- "text": "This is an abstract implementation of Kafka consumer. It sends the fetch\nrequests to the leading brokers of Kafka partitions. For each request,\nit receives the set of messages and stores them into the buffer which is\nArrayBlockingQueue. SimpleKafkaConsumer\u00a0which extends\nKafkaConsumer and serves the functionality of Simple Consumer API and\nHighLevelKafkaConsumer which extends KafkaConsumer and \u00a0serves the\nfunctionality of High Level Consumer API.",
- "title": "KafkaConsumer"
- },
- {
- "location": "/operators/kafkaInputOperator/#pre-requisites",
- "text": "This operator referred the Kafka Consumer API of version\n0.8.1.1. So, this operator will work with any 0.8.x and 0.7.x version of Apache Kafka.",
- "title": "Pre-requisites"
- },
- {
- "location": "/operators/kafkaInputOperator/#configuration-parameters_1",
- "text": "Parameter Type Default Description zookeeper String Specifies the zookeeper quorum of Kafka clusters that you want to consume messages from. zookeeper \u00a0is a string in the form of hostname1:port1,hostname2:port2,hostname3:port3 \u00a0where hostname1,hostname2,hostname3 are hosts and port1,port2,port3 are ports of zookeeper server. \u00a0If the topic name is the same across the Kafka clusters and want to consume data from these clusters, then configure the zookeeper as follows: c1::hs1:p1,hs2:p2,hs3:p3;c2::hs4:p4,hs5:p5,c3::hs6:p6 where c1,c2,c3 indicates the cluster names, hs1,hs2,hs3,hs4,hs5,hs6 are zookeeper hosts and p1,p2,p3,p4,p5,p6 are corresponding ports. Here, cluster name is optional in case of single cluster cacheSize int 1024 Maximum of buffered messages hold in memory. topic String default_topic Indicates the name of the topic. initialOffset String latest Indicates the type of offset i.e, \u201cearliest or latest\u201d. If initialOffset is \u201clatest\u201d, then the operator consumes messages from latest point of Kafka queue. If initialOffset is \u201cearliest\u201d, then the operator consumes messages starting from message queue. This can be overridden by OffsetManager.",
- "title": "Configuration Parameters"
- },
- {
- "location": "/operators/kafkaInputOperator/#abstract-methods_1",
- "text": "void commitOffset(): Commit the offsets at checkpoint. Map KafkaPartition, Long getCurrentOffsets(): Return the current\n offset status. resetPartitionsAndOffset(Set KafkaPartition partitionIds,\n Map KafkaPartition, Long startOffset): Reset the partitions with\n parittionIds and offsets with startOffset.",
- "title": "Abstract Methods"
- },
- {
- "location": "/operators/kafkaInputOperator/#configuration-parameters-for-simplekafkaconsumer",
- "text": "Parameter Type Default Description bufferSize int 1 MB Specifies the maximum total size of messages for each fetch request. metadataRefreshInterval int 30 Seconds Interval in between refresh the metadata change(broker change) in milliseconds. Enabling metadata refresh guarantees an automatic reconnect when a new broker is elected as the host. A value of -1 disables this feature. metadataRefreshRetryLimit int -1 Specifies the maximum brokers' metadata refresh retry limit. -1 means unlimited retry.",
- "title": "Configuration Parameters\u00a0for SimpleKafkaConsumer"
- },
- {
- "location": "/operators/kafkaInputOperator/#offsetmanager",
- "text": "This is an interface for offset management and is useful when consuming data\nfrom specified offsets. Updates the offsets for all the Kafka partitions\nperiodically. Below is the code snippet:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 public interface OffsetManager\n{\n public Map KafkaPartition, Long loadInitialOffsets();\n public void updateOffsets(Map KafkaPartition, Long offsetsOfPartitions);\n}",
- "title": "OffsetManager"
- },
- {
- "location": "/operators/kafkaInputOperator/#abstract-methods_2",
- "text": "Map KafkaPartition, Long loadInitialOffsets(): Specifies the initial offset for consuming messages; called at the activation stage. updateOffsets(Map KafkaPartition, Long offsetsOfPartitions): \u00a0This\nmethod is called at every repartitionCheckInterval to update offsets.",
- "title": "Abstract Methods"
- },
- {
- "location": "/operators/kafkaInputOperator/#partitioning",
- "text": "The logical instance of the KafkaInputOperator acts as the Partitioner\nas well as a StatsListener. This is because the\nAbstractKafkaInputOperator implements both the\ncom.datatorrent.api.Partitioner and com.datatorrent.api.StatsListener\ninterfaces and provides an implementation of definePartitions(...) and\nprocessStats(...) which makes it auto-scalable.",
- "title": "Partitioning"
- },
- {
- "location": "/operators/kafkaInputOperator/#response-processstatsbatchedoperatorstats-stats",
- "text": "The application master invokes this method on the logical instance with\nthe stats (tuplesProcessedPS, bytesPS, etc.) of each partition.\nRe-partitioning happens based on whether any new Kafka partitions added for\nthe topic or bytesPS and msgPS cross their respective upper bounds.",
- "title": "Response processStats(BatchedOperatorStats stats)"
- },
- {
- "location": "/operators/kafkaInputOperator/#definepartitions",
- "text": "Based on the repartitionRequired field of the Response object which is\nreturned by processStats(...) method, the application master invokes\ndefinePartitions(...) on the logical instance which is also the\npartitioner instance. Dynamic partition can be disabled by setting the\nparameter repartitionInterval value to a negative value.",
- "title": "DefinePartitions"
- },
- {
- "location": "/operators/kafkaInputOperator/#abstractsingleportkafkainputoperator",
- "text": "This class extends AbstractKafkaInputOperator and having single output\nport, will emit the messages through this port.",
- "title": "AbstractSinglePortKafkaInputOperator"
- },
- {
- "location": "/operators/kafkaInputOperator/#ports",
- "text": "outputPort T : Tuples extracted from Kafka messages are emitted through\nthis port.",
- "title": "Ports"
- },
- {
- "location": "/operators/kafkaInputOperator/#abstract-methods_3",
- "text": "T getTuple(Message msg) : Converts the Kafka message to tuple.",
- "title": "Abstract Methods"
- },
- {
- "location": "/operators/kafkaInputOperator/#concrete-classes",
- "text": "KafkaSinglePortStringInputOperator :\nThis class extends AbstractSinglePortKafkaInputOperator and getTuple() method extracts string from Kafka message. KafkaSinglePortByteArrayInputOperator:\nThis class extends AbstractSinglePortKafkaInputOperator and getTuple() method extracts byte array from Kafka message.",
- "title": "Concrete Classes"
- },
- {
- "location": "/operators/kafkaInputOperator/#application-example",
- "text": "This section builds an Apex application using Kafka input operator.\nBelow is the code snippet: @ApplicationAnnotation(name = KafkaApp )\npublic class ExampleKafkaApplication implements StreamingApplication\n{\n@Override\npublic void populateDAG(DAG dag, Configuration entries)\n{\n KafkaSinglePortByteArrayInputOperator input = dag.addOperator( MessageReader , new KafkaSinglePortByteArrayInputOperator());\n\n ConsoleOutputOperator output = dag.addOperator( Output , new ConsoleOutputOperator());\n\n dag.addStream( MessageData , input.outputPort, output.input);\n}\n} Below is the configuration for \u201ctest\u201d Kafka topic name and\n\u201clocalhost:2181\u201d is the zookeeper forum: property name dt.operator.MessageReader.prop.topic /name value test /value /property property name dt.operator.KafkaInputOperator.prop.zookeeper /nam value localhost:2181 /value /property",
- "title": "Application Example"
- },
- {
- "location": "/operators/file_splitter/",
- "text": "File Splitter\n\n\nThis is a simple operator whose main function is to split a file virtually and create metadata describing the files and the splits. \n\n\nWhy is it needed?\n\n\nIt is a common operation to read a file and parse it. This operation can be parallelized by having multiple partitions of such operators and each partition operating on different files. However, at times when a file is large then a single partition reading it can become a bottleneck.\nIn these cases, throughput can be increased if instances of the partitioned operator can read and parse non-overlapping sets of file blocks. This is where file splitter comes in handy. It creates metadata of blocks of file which serves as tasks handed out to downstream operator partitions. \nThe downstream partitions can read/parse the block without the need of interacting with other partitions.\n\n\nClass Diagram\n\n\n\n\nAbstractFileSplitter\n\n\nThe abstract implementation defines the logic of processing \nFileInfo\n. This comprises the following tasks - \n\n\n\n\n\n\nbuilding \nFileMetadata\n per file and emitting it. This metadata contains the file information such as filepath, no. of blocks in it, length of the file, all the block ids, etc.\n\n\n\n\n\n\ncreating \nBlockMetadataIterator\n from \nFileMetadata\n. The iterator lazy-loads the block metadata when needed. We use an iterator because the no. of blocks in a file can be huge if the block size is small and loading all of them at once in memory may cause out of memory errors.\n\n\n\n\n\n\nretrieving \nBlockMetadata.FileBlockMetadata\n from the block metadata iterator and emitting it. The FileBlockMetadata contains the block id, start offset of the block, length of file in the block, etc. The number of block metadata emitted per window are controlled by \nblocksThreshold\n setting which by default is 1. \n\n\n\n\n\n\nThe main utility method that performs all the above tasks is the \nprocess()\n method. Concrete implementations can invoke this method whenever they have data to process.\n\n\nPorts\n\n\nDeclares only output ports on which file metadata and block metadata are emitted.\n\n\n\n\nfilesMetadataOutput: metadata for each file is emitted on this port. \n\n\nblocksMetadataOutput: metadata for each block is emitted on this port. \n\n\n\n\nprocess()\n method\n\n\nWhen process() is invoked, any pending blocks from the current file are emitted on the 'blocksMetadataOutput' port. If the threshold for blocks per window is still not met then a new input file is processed - corresponding metadata is emitted on 'filesMetadataOutput' and more of its blocks are emitted. This operation is repeated until the \nblocksThreshold\n is reached or there are no more new files.\n\n\n protected void process()\n {\n if (blockMetadataIterator != null \n blockCount \n blocksThreshold) {\n emitBlockMetadata();\n }\n\n FileInfo fileInfo;\n while (blockCount \n blocksThreshold \n (fileInfo = getFileInfo()) != null) {\n if (!processFileInfo(fileInfo)) {\n break;\n }\n }\n }\n\n\n\n\nAbstract methods\n\n\n\n\n\n\nFileInfo getFileInfo()\n: called from within the \nprocess()\n and provides the next file to process.\n\n\n\n\n\n\nlong getDefaultBlockSize()\n: provides the block size which is used when user hasn't configured the size.\n\n\n\n\n\n\nFileStatus getFileStatus(Path path)\n: provides the \norg.apache.hadoop.fs.FileStatus\n instance for a path. \n\n\n\n\n\n\nConfiguration\n\n\n\n\nblockSize\n: size of a block.\n\n\nblocksThreshold\n: threshold on the number of blocks emitted by file splitter every window. This setting is used for throttling the work for downstream operators.\n\n\n\n\nFileSplitterBase\n\n\nSimple operator that receives tuples of type \nFileInfo\n on its \ninput\n port. \nFileInfo\n contains the information (currently just the file path) about the file which this operator uses to create file metadata and block metadata.\n\n\nExample application\n\n\nThis is a simple sub-dag that demonstrates how FileSplitterBase can be plugged into an application.\n\n\n\nThe upstream operator emits tuples of type \nFileInfo\n on its output port which is connected to splitter input port. The downstream receives tuples of type \nBlockMetadata.FileBlockMetadata\n from the splitter's block metadata output port.\n\n\npublic class ApplicationWithBaseSplitter implements StreamingApplication\n{\n @Override\n public void populateDAG(DAG dag, Configuration configuration)\n {\n JMSInput input = dag.addOperator(\nInput\n, new JMSInput());\n FileSplitterBase splitter = dag.addOperator(\nSplitter\n, new FileSplitterBase());\n FSSliceReader blockReader = dag.addOperator(\nBlockReader\n, new FSSliceReader());\n ...\n dag.addStream(\nfile-info\n, input.output, splitter.input);\n dag.addStream(\nblock-metadata\n, splitter.blocksMetadataOutput, blockReader.blocksMetadataInput);\n ...\n }\n\n public static class JMSInput extends AbstractJMSInputOperator\nAbstractFileSplitter.FileInfo\n\n {\n\n public final transient DefaultOutputPort\nAbstractFileSplitter.FileInfo\n output = new DefaultOutputPort\n();\n\n @Override\n protected AbstractFileSplitter.FileInfo convert(Message message) throws JMSException\n {\n //assuming the message is a text message containing the absolute path of the file.\n return new AbstractFileSplitter.FileInfo(null, ((TextMessage)message).getText());\n }\n\n @Override\n protected void emit(AbstractFileSplitter.FileInfo payload)\n {\n output.emit(payload);\n }\n }\n}\n\n\n\n\nPorts\n\n\nDeclares an input port on which it receives tuples from the upstream operator. Output ports are inherited from AbstractFileSplitter.\n\n\n\n\ninput: non optional port on which tuples of type \nFileInfo\n are received.\n\n\n\n\nConfiguration\n\n\n\n\nfile\n: path of the file from which the filesystem is inferred. FileSplitter creates an instance of \norg.apache.hadoop.fs.FileSystem\n which is why this path is needed. \n\n\n\n\nFileSystem.newInstance(new Path(file).toUri(), new Configuration());\n\n\n\n\nThe fs instance is then used to fetch the default block size and \norg.apache.hadoop.fs.FileStatus\n for each file path.\n\n\nFileSplitterInput\n\n\nThis is an input operator that discovers files itself. The scanning of the directories for new files is asynchronous which is handled by \nTimeBasedDirectoryScanner\n. The function of TimeBasedDirectoryScanner is to periodically scan specified directories and find files which were newly added or modified. The interaction between the operator and the scanner is depicted in the diagram below.\n\n\n\n\nExample application\n\n\nThis is a simple sub-dag that demonstrates how FileSplitterInput can be plugged into an application.\n\n\n\n\nSplitter is the input operator here that sends block metadata to the downstream BlockReader.\n\n\n @Override\n public void populateDAG(DAG dag, Configuration configuration)\n {\n FileSplitterInput input = dag.addOperator(\nInput\n, new FileSplitterInput());\n FSSliceReader reader = dag.addOperator(\nBlock Reader\n, new FSSliceReader());\n ...\n dag.addStream(\nblock-metadata\n, input.blocksMetadataOutput, reader.blocksMetadataInput);\n ...\n }\n\n\n\n\n\nPorts\n\n\nSince it is an input operator there are no input ports and output ports are inherited from AbstractFileSplitter.\n\n\nConfiguration\n\n\n\n\nscanner\n: the component that scans directories asynchronously. It is of type \ncom.datatorrent.lib.io.fs.FileSplitter.TimeBasedDirectoryScanner\n. The basic implementation of TimeBasedDirectoryScanner can be customized by users. \n\n\n\n\na. \nfiles\n: comma separated list of directories to scan. \n\n\nb. \nrecursive\n: flag that controls whether the directories should be scanned recursively. \n\n\nc. \nscanIntervalMillis\n: interval specified in milliseconds after which another scan iteration is triggered. \n\n\nd. \nfilePatternRegularExp\n: regular expression for accepted file names. \n\n\ne. \ntrigger\n: a flag that triggers a scan iteration instantly. If the scanner thread is idling then it will initiate a scan immediately otherwise if a scan is in progress, then the new iteration will be triggered immediately after the completion of current one.\n2. \nidempotentStorageManager\n: by default FileSplitterInput is idempotent. \nIdempotency ensures that the operator will process the same set of files/blocks in a window if it has seen that window previously, i.e., before a failure. For example, let's say the operator completed window 10 and failed somewhere between window 11. If the operator gets restored at window 10 then it will process the same file/block again in window 10 which it did in the previous run before the failure. Idempotency is important but comes with higher cost because at the end of each window the operator needs to persist some state with respect to that window. Therefore, if one doesn't care about idempotency then they can set this property to be an instance of \ncom.datatorrent.lib.io.IdempotentStorageManager.NoopIdempotentStorageManager\n.\n\n\nHandling of split records\n\n\nSplitting of files to create tasks for downstream operator needs to be a simple operation that doesn't consume a lot of resources and is fast. This is why the file splitter doesn't open files to read. The downside of that is if the file contains records then a record may split across adjacent blocks. Handling of this is left to the downstream operator.\n\n\nWe have created Block readers in Apex-malhar library that handle line splits efficiently. The 2 line readers- \nAbstractFSLineReader\n and \nAbstractFSReadAheadLineReader\n can be found here \nAbstractFSBlockReader\n.",
- "title": "File Splitter"
- },
- {
- "location": "/operators/file_splitter/#file-splitter",
- "text": "This is a simple operator whose main function is to split a file virtually and create metadata describing the files and the splits.",
- "title": "File Splitter"
- },
- {
- "location": "/operators/file_splitter/#why-is-it-needed",
- "text": "It is a common operation to read a file and parse it. This operation can be parallelized by having multiple partitions of such operators and each partition operating on different files. However, at times when a file is large then a single partition reading it can become a bottleneck.\nIn these cases, throughput can be increased if instances of the partitioned operator can read and parse non-overlapping sets of file blocks. This is where file splitter comes in handy. It creates metadata of blocks of file which serves as tasks handed out to downstream operator partitions. \nThe downstream partitions can read/parse the block without the need of interacting with other partitions.",
- "title": "Why is it needed?"
- },
- {
- "location": "/operators/file_splitter/#class-diagram",
- "text": "",
- "title": "Class Diagram"
- },
- {
- "location": "/operators/file_splitter/#abstractfilesplitter",
- "text": "The abstract implementation defines the logic of processing FileInfo . This comprises the following tasks - building FileMetadata per file and emitting it. This metadata contains the file information such as filepath, no. of blocks in it, length of the file, all the block ids, etc. creating BlockMetadataIterator from FileMetadata . The iterator lazy-loads the block metadata when needed. We use an iterator because the no. of blocks in a file can be huge if the block size is small and loading all of them at once in memory may cause out of memory errors. retrieving BlockMetadata.FileBlockMetadata from the block metadata iterator and emitting it. The FileBlockMetadata contains the block id, start offset of the block, length of file in the block, etc. The number of block metadata emitted per window are controlled by blocksThreshold setting which by default is 1. The main utility method that performs all the above tasks is the process() method. Concrete implementations can invoke this method whenever they have data to process.",
- "title": "AbstractFileSplitter"
- },
- {
- "location": "/operators/file_splitter/#ports",
- "text": "Declares only output ports on which file metadata and block metadata are emitted. filesMetadataOutput: metadata for each file is emitted on this port. blocksMetadataOutput: metadata for each block is emitted on this port.",
- "title": "Ports"
- },
- {
- "location": "/operators/file_splitter/#abstract-methods",
- "text": "FileInfo getFileInfo() : called from within the process() and provides the next file to process. long getDefaultBlockSize() : provides the block size which is used when user hasn't configured the size. FileStatus getFileStatus(Path path) : provides the org.apache.hadoop.fs.FileStatus instance for a path.",
- "title": "Abstract methods"
- },
- {
- "location": "/operators/file_splitter/#configuration",
- "text": "blockSize : size of a block. blocksThreshold : threshold on the number of blocks emitted by file splitter every window. This setting is used for throttling the work for downstream operators.",
- "title": "Configuration"
- },
- {
- "location": "/operators/file_splitter/#filesplitterbase",
- "text": "Simple operator that receives tuples of type FileInfo on its input port. FileInfo contains the information (currently just the file path) about the file which this operator uses to create file metadata and block metadata.",
- "title": "FileSplitterBase"
- },
- {
- "location": "/operators/file_splitter/#example-application",
- "text": "This is a simple sub-dag that demonstrates how FileSplitterBase can be plugged into an application. The upstream operator emits tuples of type FileInfo on its output port which is connected to splitter input port. The downstream receives tuples of type BlockMetadata.FileBlockMetadata from the splitter's block metadata output port. public class ApplicationWithBaseSplitter implements StreamingApplication\n{\n @Override\n public void populateDAG(DAG dag, Configuration configuration)\n {\n JMSInput input = dag.addOperator( Input , new JMSInput());\n FileSplitterBase splitter = dag.addOperator( Splitter , new FileSplitterBase());\n FSSliceReader blockReader = dag.addOperator( BlockReader , new FSSliceReader());\n ...\n dag.addStream( file-info , input.output, splitter.input);\n dag.addStream( block-metadata , splitter.blocksMetadataOutput, blockReader.blocksMetadataInput);\n ...\n }\n\n public static class JMSInput extends AbstractJMSInputOperator AbstractFileSplitter.FileInfo \n {\n\n public final transient DefaultOutputPort AbstractFileSplitter.FileInfo output = new DefaultOutputPort ();\n\n @Override\n protected AbstractFileSplitter.FileInfo convert(Message message) throws JMSException\n {\n //assuming the message is a text message containing the absolute path of the file.\n return new AbstractFileSplitter.FileInfo(null, ((TextMessage)message).getText());\n }\n\n @Override\n protected void emit(AbstractFileSplitter.FileInfo payload)\n {\n output.emit(payload);\n }\n }\n}",
- "title": "Example application"
- },
- {
- "location": "/operators/file_splitter/#ports_1",
- "text": "Declares an input port on which it receives tuples from the upstream operator. Output ports are inherited from AbstractFileSplitter. input: non optional port on which tuples of type FileInfo are received.",
- "title": "Ports"
- },
- {
- "location": "/operators/file_splitter/#configuration_1",
- "text": "file : path of the file from which the filesystem is inferred. FileSplitter creates an instance of org.apache.hadoop.fs.FileSystem which is why this path is needed. FileSystem.newInstance(new Path(file).toUri(), new Configuration()); The fs instance is then used to fetch the default block size and org.apache.hadoop.fs.FileStatus for each file path.",
- "title": "Configuration"
- },
- {
- "location": "/operators/file_splitter/#filesplitterinput",
- "text": "This is an input operator that discovers files itself. The scanning of the directories for new files is asynchronous which is handled by TimeBasedDirectoryScanner . The function of TimeBasedDirectoryScanner is to periodically scan specified directories and find files which were newly added or modified. The interaction between the operator and the scanner is depicted in the diagram below.",
- "title": "FileSplitterInput"
- },
- {
- "location": "/operators/file_splitter/#example-application_1",
- "text": "This is a simple sub-dag that demonstrates how FileSplitterInput can be plugged into an application. Splitter is the input operator here that sends block metadata to the downstream BlockReader. @Override\n public void populateDAG(DAG dag, Configuration configuration)\n {\n FileSplitterInput input = dag.addOperator( Input , new FileSplitterInput());\n FSSliceReader reader = dag.addOperator( Block Reader , new FSSliceReader());\n ...\n dag.addStream( block-metadata , input.blocksMetadataOutput, reader.blocksMetadataInput);\n ...\n }",
- "title": "Example application"
- },
- {
- "location": "/operators/file_splitter/#ports_2",
- "text": "Since it is an input operator there are no input ports and output ports are inherited from AbstractFileSplitter.",
- "title": "Ports"
- },
- {
- "location": "/operators/file_splitter/#configuration_2",
- "text": "scanner : the component that scans directories asynchronously. It is of type com.datatorrent.lib.io.fs.FileSplitter.TimeBasedDirectoryScanner . The basic implementation of TimeBasedDirectoryScanner can be customized by users. a. files : comma separated list of directories to scan. b. recursive : flag that controls whether the directories should be scanned recursively. c. scanIntervalMillis : interval specified in milliseconds after which another scan iteration is triggered. d. filePatternRegularExp : regular expression for accepted file names. e. trigger : a flag that triggers a scan iteration instantly. If the scanner thread is idling then it will initiate a scan immediately otherwise if a scan is in progress, then the new iteration will be triggered immediately after the completion of current one.\n2. idempotentStorageManager : by default FileSplitterInput is idempotent. \nIdempotency ensures that the operator will process the same set of files/blocks in a window if it has seen that window previously, i.e., before a failure. For example, let's say the operator completed window 10 and failed somewhere between window 11. If the operator gets restored at window 10 then it will process the same file/block again in window 10 which it did in the previous run before the failure. Idempotency is important but comes with higher cost because at the end of each window the operator needs to persist some state with respect to that window. Therefore, if one doesn't care about idempotency then they can set this property to be an instance of com.datatorrent.lib.io.IdempotentStorageManager.NoopIdempotentStorageManager .",
- "title": "Configuration"
- },
- {
- "location": "/operators/file_splitter/#handling-of-split-records",
- "text": "Splitting of files to create tasks for downstream operator needs to be a simple operation that doesn't consume a lot of resources and is fast. This is why the file splitter doesn't open files to read. The downside of that is if the file contains records then a record may split across adjacent blocks. Handling of this is left to the downstream operator. We have created Block readers in Apex-malhar library that handle line splits efficiently. The 2 line readers- AbstractFSLineReader and AbstractFSReadAheadLineReader can be found here AbstractFSBlockReader .",
- "title": "Handling of split records"
- },
- {
- "location": "/operators/block_reader/",
- "text": "Block Reader\n\n\nThis is a scalable operator that reads and parses blocks of data sources into records. A data source can be a file or a message bus that contains records and a block defines a chunk of data in the source by specifying the block offset and the length of the source belonging to the block. \n\n\nWhy is it needed?\n\n\nA Block Reader is needed to parallelize reading and parsing of a single data source, for example a file. Simple parallelism of reading data sources can be achieved by multiple partitions reading different source of same type (for files see \nAbstractFileInputOperator\n) but Block Reader partitions can read blocks of same source in parallel and parse them for records ensuring that no record is duplicated or missed.\n\n\nClass Diagram\n\n\n\n\nAbstractBlockReader\n\n\nThis is the abstract implementation that serves as the base for different types of data sources. It defines how a block metadata is processed. The flow diagram below describes the processing of a block metadata.\n\n\n\n\nPorts\n\n\n\n\n\n\nblocksMetadataInput: input port on which block metadata are received.\n\n\n\n\n\n\nblocksMetadataOutput: output port on which block metadata are emitted if the port is connected. This port is useful when a downstream operator that receives records from block reader may also be interested to know the details of the corresponding blocks.\n\n\n\n\n\n\nmessages: output port on which tuples of type \ncom.datatorrent.lib.io.block.AbstractBlockReader.ReaderRecord\n are emitted. This class encapsulates a \nrecord\n and the \nblockId\n of the corresponding block.\n\n\n\n\n\n\nreaderContext\n\n\nThis is one of the most important fields in the block reader. It is of type \ncom.datatorrent.lib.io.block.ReaderContext\n and is responsible for fetching bytes that make a record. It also lets the reader know how many total bytes were consumed which may not be equal to the total bytes in a record because consumed bytes also include bytes for the record delimiter which may not be a part of the actual record.\n\n\nOnce the reader creates an input stream for the block (or uses the previous opened stream if the current block is successor of the previous block) it initializes the reader context by invoking \nreaderContext.initialize(stream, blockMetadata, consecutiveBlock);\n. Initialize method is where any implementation of \nReaderContext\n can perform all the operations which have to be executed just before reading the block or create states which are used during the lifetime of reading the block.\n\n\nOnce the initialization is done, \nreaderContext.next()\n is called repeatedly until it returns \nnull\n. It is left to the \nReaderContext\n implementations to decide when a block is completely processed. In cases when a record is split across adjacent blocks, reader context may decide to read ahead of the current block boundary to completely fetch the split record (examples- \nLineReaderContext\n and \nReadAheadLineReaderContext\n). In other cases when there isn't a possibility of split record (example- \nFixedBytesReaderContext\n), it returns \nnull\n immediately when the block boundary is reached. The return type of \nreaderContext.next()\n is of type \ncom.datatorrent.lib.io.block.ReaderContext.Entity\n which is just a wrapper for a \nbyte[]\n that represents the record and total bytes used in fetching the record.\n\n\nAbstract methods\n\n\n\n\n\n\nSTREAM setupStream(B block)\n: creating a stream for a block is dependent on the type of source which is not known to AbstractBlockReader. Sub-classes which deal with a specific data source provide this implementation.\n\n\n\n\n\n\nR convertToRecord(byte[] bytes)\n: this converts the array of bytes into the actual instance of record type.\n\n\n\n\n\n\nAuto-scalability\n\n\nBlock reader can auto-scale, that is, depending on the backlog (total number of all the blocks which are waiting in the \nblocksMetadataInput\n port queue of all partitions) it can create more partitions or reduce them. Details are discussed in the last section which covers the \npartitioner and stats-listener\n.\n\n\nConfiguration\n\n\n\n\nmaxReaders\n: when auto-scaling is enabled, this controls the maximum number of block reader partitions that can be created.\n\n\nminReaders\n: when auto-scaling is enabled, this controls the minimum number of block reader partitions that should always exist.\n\n\ncollectStats\n: this enables or disables auto-scaling. When it is set to \ntrue\n the stats (number of blocks in the queue) are collected and this triggers partitioning; otherwise auto-scaling is disabled.\n\n\nintervalMillis\n: when auto-scaling is enabled, this specifies the interval at which the reader will trigger the logic of computing the backlog and auto-scale.\n\n\n\n\n AbstractFSBlockReader\n\n\nThis abstract implementation deals with files. Different types of file systems that are implementations of \norg.apache.hadoop.fs.FileSystem\n are supported. The user can override \ngetFSInstance()\n method to create an instance of a specific \nFileSystem\n. By default, filesystem instance is created from the filesytem URI that comes from the default hadoop configuration.\n\n\nprotected FileSystem getFSInstance() throws IOException\n{\n return FileSystem.newInstance(configuration);\n}\n\n\n\n\nIt uses this filesystem instance to setup a stream of type \norg.apache.hadoop.fs.FSDataInputStream\n to read the block.\n\n\n@Override\nprotected FSDataInputStream setupStream(BlockMetadata.FileBlockMetadata block) throws IOException\n{\n return fs.open(new Path(block.getFilePath()));\n}\n\n\n\n\nAll the ports and configurations are derived from the super class. It doesn't provide an implementation of \nconvertToRecord(byte[] bytes)\n method which is delegated to concrete sub-classes.\n\n\nExample Application\n\n\nThis simple dag demonstrates how any concrete implementation of \nAbstractFSBlockReader\n can be plugged into an application. \n\n\n\n\nIn the above application, file splitter creates block metadata for files which are sent to block reader. Partitions of the block reader parses the file blocks for records which are filtered, transformed and then persisted to a file (created per block). Therefore block reader is parallel partitioned with the 2 downstream operators - filter/converter and record output operator. The code which implements this dag is below.\n\n\npublic class ExampleApplication implements StreamingApplication\n{\n @Override\n public void populateDAG(DAG dag, Configuration configuration)\n {\n FileSplitterInput input = dag.addOperator(\nFile-splitter\n, new FileSplitterInput());\n //any concrete implementation of AbstractFSBlockReader based on the use-case can be added here.\n LineReader blockReader = dag.addOperator(\nBlock-reader\n, new LineReader());\n Filter filter = dag.addOperator(\nFilter\n, new Filter());\n RecordOutputOperator recordOutputOperator = dag.addOperator(\nRecord-writer\n, new RecordOutputOperator());\n\n dag.addStream(\nfile-block metadata\n, input.blocksMetadataOutput, blockReader.blocksMetadataInput);\n dag.addStream(\nrecords\n, blockReader.messages, filter.input);\n dag.addStream(\nfiltered-records\n, filter.output, recordOutputOperator.input);\n }\n\n /**\n * Concrete implementation of {@link AbstractFSBlockReader} for which a record is a line in the file.\n */\n public static class LineReader extends AbstractFSBlockReader.AbstractFSReadAheadLineReader\nString\n\n {\n\n @Override\n protected String convertToRecord(byte[] bytes)\n {\n return new String(bytes);\n }\n }\n\n /**\n * Considers any line starting with a '.' as invalid. Emits the valid records.\n */\n public static class Filter extends BaseOperator\n {\n public final transient DefaultOutputPort\nAbstractBlockReader.ReaderRecord\nString\n output = new DefaultOutputPort\n();\n public final transient DefaultInputPort\nAbstractBlockReader.ReaderRecord\nString\n input = new DefaultInputPort\nAbstractBlockReader.ReaderRecord\nString\n()\n {\n @Override\n public void process(AbstractBlockReader.ReaderRecord\nString\n stringRecord)\n {\n //filter records and transform\n //if the string starts with a '.' ignore the string.\n if (!StringUtils.startsWith(stringRecord.getRecord(), \n.\n)) {\n output.emit(stringRecord);\n }\n }\n };\n }\n\n /**\n * Persists the valid records to corresponding block files.\n */\n public static class RecordOutputOperator extends AbstractFileOutputOperator\nAbstractBlockReader.ReaderRecord\nString\n\n {\n @Override\n protected String getFileName(AbstractBlockReader.ReaderRecord\nString\n tuple)\n {\n return Long.toHexString(tuple.getBlockId());\n }\n\n @Override\n protected byte[] getBytesForTuple(AbstractBlockReader.ReaderRecord\nString\n tuple)\n {\n return tuple.getRecord().getBytes();\n }\n }\n}\n\n\n\n\nConfiguration to parallel partition block reader with its downstream operators.\n\n\n \nproperty\n\n \nname\ndt.operator.Filter.port.input.attr.PARTITION_PARALLEL\n/name\n\n \nvalue\ntrue\n/value\n\n \n/property\n\n \nproperty\n\n \nname\ndt.operator.Record-writer.port.input.attr.PARTITION_PARALLEL\n/name\n\n \nvalue\ntrue\n/value\n\n \n/property\n\n\n\n\n\nAbstractFSReadAheadLineReader\n\n\nThis extension of \nAbstractFSBlockReader\n parses lines from a block and binds the \nreaderContext\n field to an instance of \nReaderContext.ReadAheadLineReaderContext\n.\n\n\nIt is abstract because it doesn't provide an implementation of \nconvertToRecord(byte[] bytes)\n since the user may want to convert the bytes that make a line into some other type. \n\n\nReadAheadLineReaderContext\n\n\nIn order to handle a line split across adjacent blocks, ReadAheadLineReaderContext always reads beyond the block boundary and ignores the bytes till the first end-of-line character of all the blocks except the first block of the file. This ensures that no line is missed or incomplete.\n\n\nThis is one of the most common ways of handling a split record. It doesn't require any further information to decide if a line is complete. However, the cost of this consistent way to handle a line split is that it always reads from the next block.\n\n\nAbstractFSLineReader\n\n\nSimilar to \nAbstractFSReadAheadLineReader\n, even this parses lines from a block. However, it binds the \nreaderContext\n field to an instance of \nReaderContext.LineReaderContext\n.\n\n\nLineReaderContext\n\n\nThis handles the line split differently from \nReadAheadLineReaderContext\n. It doesn't always read from the next block. If the end of the last line is aligned with the block boundary then it stops processing the block. It does read from the next block when the boundaries are not aligned, that is, last line extends beyond the block boundary. The result of this is an inconsistency in reading the next block.\n\n\nWhen the boundary of the last line of the previous block was aligned with its block, then the first line of the current block is a valid line. However, in the other case the bytes from the block start offset to the first end-of-line character should be ignored. Therefore, this means that any record formed by this reader context has to be validated. For example, if the lines are of fixed size then size of each record can be validated or if each line begins with a special field then that knowledge can be used to check if a record is complete.\n\n\nIf the validations of completeness fails for a line then \nconvertToRecord(byte[] bytes)\n should return null.\n\n\nFSSliceReader\n\n\nA concrete extension of \nAbstractFSBlockReader\n that reads fixed-size \nbyte[]\n from a block and emits the byte array wrapped in \ncom.datatorrent.netlet.util.Slice\n.\n\n\nThis operator binds the \nreaderContext\n to an instance of \nReaderContext.FixedBytesReaderContext\n.\n\n\nFixedBytesReaderContext\n\n\nThis implementation of \nReaderContext\n never reads beyond a block boundary which can result in the last \nbyte[]\n of a block to be of a shorter length than the rest of the records.\n\n\nConfiguration\n\n\nreaderContext.length\n: length of each record. By default, this is initialized to the default hdfs block size.\n\n\nPartitioner and StatsListener\n\n\nThe logical instance of the block reader acts as the Partitioner (unless a custom partitioner is set using the operator attribute - \nPARTITIONER\n) as well as a StatsListener. This is because the \n\nAbstractBlockReader\n implements both the \ncom.datatorrent.api.Partitioner\n and \ncom.datatorrent.api.StatsListener\n interfaces and provides an implementation of \ndefinePartitions(...)\n and \nprocessStats(...)\n which make it auto-scalable.\n\n\nprocessStats \n\n\nThe application master invokes \nResponse processStats(BatchedOperatorStats stats)\n method on the logical instance with the stats (\ntuplesProcessedPSMA\n, \ntuplesEmittedPSMA\n, \nlatencyMA\n, etc.) of each partition. The data which this operator is interested in is the \nqueueSize\n of the input port \nblocksMetadataInput\n.\n\n\nUsually the \nqueueSize\n of an input port gives the count of waiting control tuples plus data tuples. However, if a stats listener is interested only in the count of data tuples then that can be expressed by annotating the class with \n@DataQueueSize\n. In this case \nAbstractBlockReader\n itself is the \nStatsListener\n which is why it is annotated with \n@DataQueueSize\n.\n\n\nThe logical instance caches the queue size per partition and at regular intervals (configured by \nintervalMillis\n) sums these values to find the total backlog which is then used to decide whether re-partitioning is needed. The flow-diagram below describes this logic.\n\n\n\n\nThe goal of this logic is to create as many partitions within bounds (see \nmaxReaders\n and \nminReaders\n above) to quickly reduce this backlog or if the backlog is small then remove any idle partitions.\n\n\ndefinePartitions\n\n\nBased on the \nrepartitionRequired\n field of the \nResponse\n object which is returned by \nprocessStats\n method, the application master invokes \n\n\nCollection\nPartition\nAbstractBlockReader\n...\n definePartitions(Collection\nPartition\nAbstractBlockReader\n...\n partitions, PartitioningContext context)\n\n\n\n\non the logical instance which is also the partitioner instance. The implementation calculates the difference between required partitions and the existing count of partitions. If this difference is negative, then equivalent number of partitions are removed otherwise new partitions are created. \n\n\nPlease note auto-scaling can be disabled by setting \ncollectStats\n to \nfalse\n. If the use-case requires only static partitioning, then that can be achieved by setting \nStatelessPartitioner\n as the operator attribute- \nPARTITIONER\n on the block reader.",
- "title": "Block Reader"
- },
- {
- "location": "/operators/block_reader/#block-reader",
- "text": "This is a scalable operator that reads and parses blocks of data sources into records. A data source can be a file or a message bus that contains records and a block defines a chunk of data in the source by specifying the block offset and the length of the source belonging to the block.",
- "title": "Block Reader"
- },
- {
- "location": "/operators/block_reader/#why-is-it-needed",
- "text": "A Block Reader is needed to parallelize reading and parsing of a single data source, for example a file. Simple parallelism of reading data sources can be achieved by multiple partitions reading different source of same type (for files see AbstractFileInputOperator ) but Block Reader partitions can read blocks of same source in parallel and parse them for records ensuring that no record is duplicated or missed.",
- "title": "Why is it needed?"
- },
- {
- "location": "/operators/block_reader/#class-diagram",
- "text": "",
- "title": "Class Diagram"
- },
- {
- "location": "/operators/block_reader/#abstractblockreader",
- "text": "This is the abstract implementation that serves as the base for different types of data sources. It defines how a block metadata is processed. The flow diagram below describes the processing of a block metadata.",
- "title": "AbstractBlockReader"
- },
- {
- "location": "/operators/block_reader/#ports",
- "text": "blocksMetadataInput: input port on which block metadata are received. blocksMetadataOutput: output port on which block metadata are emitted if the port is connected. This port is useful when a downstream operator that receives records from block reader may also be interested to know the details of the corresponding blocks. messages: output port on which tuples of type com.datatorrent.lib.io.block.AbstractBlockReader.ReaderRecord are emitted. This class encapsulates a record and the blockId of the corresponding block.",
- "title": "Ports"
- },
- {
- "location": "/operators/block_reader/#readercontext",
- "text": "This is one of the most important fields in the block reader. It is of type com.datatorrent.lib.io.block.ReaderContext and is responsible for fetching bytes that make a record. It also lets the reader know how many total bytes were consumed which may not be equal to the total bytes in a record because consumed bytes also include bytes for the record delimiter which may not be a part of the actual record. Once the reader creates an input stream for the block (or uses the previous opened stream if the current block is successor of the previous block) it initializes the reader context by invoking readerContext.initialize(stream, blockMetadata, consecutiveBlock); . Initialize method is where any implementation of ReaderContext can perform all the operations which have to be executed just before reading the block or create states which are used during the lifetime of reading the block. Once the initialization is done, readerContext.next() is called repeatedly until it returns null . It is left to the ReaderContext implementations to decide when a block is completely processed. In cases when a record is split across adjacent blocks, reader context may decide to read ahead of the current block boundary to completely fetch the split record (examples- LineReaderContext and ReadAheadLineReaderContext ). In other cases when there isn't a possibility of split record (example- FixedBytesReaderContext ), it returns null immediately when the block boundary is reached. The return type of readerContext.next() is of type com.datatorrent.lib.io.block.ReaderContext.Entity which is just a wrapper for a byte[] that represents the record and total bytes used in fetching the record.",
- "title": "readerContext"
- },
- {
- "location": "/operators/block_reader/#abstract-methods",
- "text": "STREAM setupStream(B block) : creating a stream for a block is dependent on the type of source which is not known to AbstractBlockReader. Sub-classes which deal with a specific data source provide this implementation. R convertToRecord(byte[] bytes) : this converts the array of bytes into the actual instance of record type.",
- "title": "Abstract methods"
- },
- {
- "location": "/operators/block_reader/#auto-scalability",
- "text": "Block reader can auto-scale, that is, depending on the backlog (total number of all the blocks which are waiting in the blocksMetadataInput port queue of all partitions) it can create more partitions or reduce them. Details are discussed in the last section which covers the partitioner and stats-listener .",
- "title": "Auto-scalability"
- },
- {
- "location": "/operators/block_reader/#configuration",
- "text": "maxReaders : when auto-scaling is enabled, this controls the maximum number of block reader partitions that can be created. minReaders : when auto-scaling is enabled, this controls the minimum number of block reader partitions that should always exist. collectStats : this enables or disables auto-scaling. When it is set to true the stats (number of blocks in the queue) are collected and this triggers partitioning; otherwise auto-scaling is disabled. intervalMillis : when auto-scaling is enabled, this specifies the interval at which the reader will trigger the logic of computing the backlog and auto-scale.",
- "title": "Configuration"
- },
- {
- "location": "/operators/block_reader/#example-application",
- "text": "This simple dag demonstrates how any concrete implementation of AbstractFSBlockReader can be plugged into an application. In the above application, file splitter creates block metadata for files which are sent to block reader. Partitions of the block reader parses the file blocks for records which are filtered, transformed and then persisted to a file (created per block). Therefore block reader is parallel partitioned with the 2 downstream operators - filter/converter and record output operator. The code which implements this dag is below. public class ExampleApplication implements StreamingApplication\n{\n @Override\n public void populateDAG(DAG dag, Configuration configuration)\n {\n FileSplitterInput input = dag.addOperator( File-splitter , new FileSplitterInput());\n //any concrete implementation of AbstractFSBlockReader based on the use-case can be added here.\n LineReader blockReader = dag.addOperator( Block-reader , new LineReader());\n Filter filter = dag.addOperator( Filter , new Filter());\n RecordOutputOperator recordOutputOperator = dag.addOperator( Record-writer , new RecordOutputOperator());\n\n dag.addStream( file-block metadata , input.blocksMetadataOutput, blockReader.blocksMetadataInput);\n dag.addStream( records , blockReader.messages, filter.input);\n dag.addStream( filtered-records , filter.output, recordOutputOperator.input);\n }\n\n /**\n * Concrete implementation of {@link AbstractFSBlockReader} for which a record is a line in the file.\n */\n public static class LineReader extends AbstractFSBlockReader.AbstractFSReadAheadLineReader String \n {\n\n @Override\n protected String convertToRecord(byte[] bytes)\n {\n return new String(bytes);\n }\n }\n\n /**\n * Considers any line starting with a '.' as invalid. Emits the valid records.\n */\n public static class Filter extends BaseOperator\n {\n public final transient DefaultOutputPort AbstractBlockReader.ReaderRecord String output = new DefaultOutputPort ();\n public final transient DefaultInputPort AbstractBlockReader.ReaderRecord String input = new DefaultInputPort AbstractBlockReader.ReaderRecord String ()\n {\n @Override\n public void process(AbstractBlockReader.ReaderRecord String stringRecord)\n {\n //filter records and transform\n //if the string starts with a '.' ignore the string.\n if (!StringUtils.startsWith(stringRecord.getRecord(), . )) {\n output.emit(stringRecord);\n }\n }\n };\n }\n\n /**\n * Persists the valid records to corresponding block files.\n */\n public static class RecordOutputOperator extends AbstractFileOutputOperator AbstractBlockReader.ReaderRecord String \n {\n @Override\n protected String getFileName(AbstractBlockReader.ReaderRecord String tuple)\n {\n return Long.toHexString(tuple.getBlockId());\n }\n\n @Override\n protected byte[] getBytesForTuple(AbstractBlockReader.ReaderRecord String tuple)\n {\n return tuple.getRecord().getBytes();\n }\n }\n} Configuration to parallel partition block reader with its downstream operators. property \n name dt.operator.Filter.port.input.attr.PARTITION_PARALLEL /name \n value true /value \n /property \n property \n name dt.operator.Record-writer.port.input.attr.PARTITION_PARALLEL /name \n value true /value \n /property",
- "title": "Example Application"
- },
- {
- "location": "/operators/block_reader/#abstractfsreadaheadlinereader",
- "text": "This extension of AbstractFSBlockReader parses lines from a block and binds the readerContext field to an instance of ReaderContext.ReadAheadLineReaderContext . It is abstract because it doesn't provide an implementation of convertToRecord(byte[] bytes) since the user may want to convert the bytes that make a line into some other type.",
- "title": "AbstractFSReadAheadLineReader"
- },
- {
- "location": "/operators/block_reader/#readaheadlinereadercontext",
- "text": "In order to handle a line split across adjacent blocks, ReadAheadLineReaderContext always reads beyond the block boundary and ignores the bytes till the first end-of-line character of all the blocks except the first block of the file. This ensures that no line is missed or incomplete. This is one of the most common ways of handling a split record. It doesn't require any further information to decide if a line is complete. However, the cost of this consistent way to handle a line split is that it always reads from the next block.",
- "title": "ReadAheadLineReaderContext"
- },
- {
- "location": "/operators/block_reader/#abstractfslinereader",
- "text": "Similar to AbstractFSReadAheadLineReader , even this parses lines from a block. However, it binds the readerContext field to an instance of ReaderContext.LineReaderContext .",
- "title": "AbstractFSLineReader"
- },
- {
- "location": "/operators/block_reader/#linereadercontext",
- "text": "This handles the line split differently from ReadAheadLineReaderContext . It doesn't always read from the next block. If the end of the last line is aligned with the block boundary then it stops processing the block. It does read from the next block when the boundaries are not aligned, that is, last line extends beyond the block boundary. The result of this is an inconsistency in reading the next block. When the boundary of the last line of the previous block was aligned with its block, then the first line of the current block is a valid line. However, in the other case the bytes from the block start offset to the first end-of-line character should be ignored. Therefore, this means that any record formed by this reader context has to be validated. For example, if the lines are of fixed size then size of each record can be validated or if each line begins with a special field then that knowledge can be used to check if a record is complete. If the validations of completeness fails for a line then convertToRecord(byte[] bytes) should return null.",
- "title": "LineReaderContext"
- },
- {
- "location": "/operators/block_reader/#fsslicereader",
- "text": "A concrete extension of AbstractFSBlockReader that reads fixed-size byte[] from a block and emits the byte array wrapped in com.datatorrent.netlet.util.Slice . This operator binds the readerContext to an instance of ReaderContext.FixedBytesReaderContext .",
- "title": "FSSliceReader"
- },
- {
- "location": "/operators/block_reader/#fixedbytesreadercontext",
- "text": "This implementation of ReaderContext never reads beyond a block boundary which can result in the last byte[] of a block to be of a shorter length than the rest of the records.",
- "title": "FixedBytesReaderContext"
- },
- {
- "location": "/operators/block_reader/#configuration_1",
- "text": "readerContext.length : length of each record. By default, this is initialized to the default hdfs block size.",
- "title": "Configuration"
- },
- {
- "location": "/operators/block_reader/#partitioner-and-statslistener",
- "text": "The logical instance of the block reader acts as the Partitioner (unless a custom partitioner is set using the operator attribute - PARTITIONER ) as well as a StatsListener. This is because the AbstractBlockReader implements both the com.datatorrent.api.Partitioner and com.datatorrent.api.StatsListener interfaces and provides an implementation of definePartitions(...) and processStats(...) which make it auto-scalable.",
- "title": "Partitioner and StatsListener"
- },
- {
- "location": "/operators/block_reader/#processstats",
- "text": "The application master invokes Response processStats(BatchedOperatorStats stats) method on the logical instance with the stats ( tuplesProcessedPSMA , tuplesEmittedPSMA , latencyMA , etc.) of each partition. The data which this operator is interested in is the queueSize of the input port blocksMetadataInput . Usually the queueSize of an input port gives the count of waiting control tuples plus data tuples. However, if a stats listener is interested only in the count of data tuples then that can be expressed by annotating the class with @DataQueueSize . In this case AbstractBlockReader itself is the StatsListener which is why it is annotated with @DataQueueSize . The logical instance caches the queue size per partition and at regular intervals (configured by intervalMillis ) sums these values to find the total backlog which is then used to decide whether re-partitioning is needed. The flow-diagram below describes this logic. The goal of this logic is to create as many partitions within bounds (see maxReaders and minReaders above) to quickly reduce this backlog or if the backlog is small then remove any idle partitions.",
- "title": "processStats "
- },
- {
- "location": "/operators/block_reader/#definepartitions",
- "text": "Based on the repartitionRequired field of the Response object which is returned by processStats method, the application master invokes Collection Partition AbstractBlockReader ... definePartitions(Collection Partition AbstractBlockReader ... partitions, PartitioningContext context) on the logical instance which is also the partitioner instance. The implementation calculates the difference between required partitions and the existing count of partitions. If this difference is negative, then equivalent number of partitions are removed otherwise new partitions are created. Please note auto-scaling can be disabled by setting collectStats to false . If the use-case requires only static partitioning, then that can be achieved by setting StatelessPartitioner as the operator attribute- PARTITIONER on the block reader.",
- "title": "definePartitions"
- },
- {
- "location": "/operators/file_output/",
- "text": "AbstractFileOutputOperator\n\n\nThe abstract file output operator in Apache Apex Malhar library \n \nAbstractFileOutputOperator\n writes streaming data to files. The main features of this operator are:\n\n\n\n\nPersisting data to files.\n\n\nAutomatic rotation of files based on:\n\n a. maximum length of a file.\n\n b. time-based rotation where time is specified using a count of application windows.\n\n\nFault-tolerance.\n\n\nCompression and encryption of data before it is persisted.\n\n\n\n\nIn this tutorial we will cover the details of the basic structure and implementation of all the above features in \nAbstractFileOutputOperator\n. Configuration items related to each feature are discussed as they are introduced in the section of that feature.\n\n\nPersisting data to files\n\n\nThe principal function of this operator is to persist tuples to files efficiently. These files are created under a specific directory on the file system. The relevant configuration item is:\n\n\nfilePath\n: path specifying the directory where files are written.\n\n\nDifferent types of file system that are implementations of \norg.apache.hadoop.fs.FileSystem\n are supported. The file system instance which is used for creating streams is constructed from the \nfilePath\n URI.\n\n\nFileSystem.newInstance(new Path(filePath).toUri(), new Configuration())\n\n\n\n\nTuples may belong to different files therefore expensive IO operations like creating multiple output streams, flushing of data to disk, and closing streams are handled carefully.\n\n\nPorts\n\n\n\n\ninput\n: the input port on which tuples to be persisted are received.\n\n\n\n\nstreamsCache\n\n\nThis transient state caches output streams per file in memory. The file to which the data is appended may change with incoming tuples. It will be highly inefficient to keep re-opening streams for a file just because tuples for that file are interleaved with tuples for another file. Therefore, the operator maintains a cache of limited size with open output streams.\n\n\nstreamsCache\n is of type \ncom.google.common.cache.LoadingCache\n. A \nLoadingCache\n has an attached \nCacheLoader\n which is responsible to load value of a key when the key is not present in the cache. Details are explained here- \nCachesExplained\n.\n\n\nThe operator constructs this cache in \nsetup(...)\n. It is built with the following configuration items:\n\n\n\n\nmaxOpenFiles\n: maximum size of the cache. The cache evicts entries that haven't been used recently when the cache size is approaching this limit. \nDefault\n: 100\n\n\nexpireStreamAfterAcessMillis\n: expires streams after the specified duration has passed since the stream was last accessed. \nDefault\n: value of attribute- \nOperatorContext.SPIN_MILLIS\n.\n\n\n\n\nAn important point to note here is that the guava cache does not perform cleanup and evict values asynchronously, that is, instantly after a value expires. Instead, it performs small amounts of maintenance during write operations, or during occasional read operations if writes are rare.\n\n\nCacheLoader\n\n\nstreamsCache\n is created with a \nCacheLoader\n that opens an \nFSDataOutputStream\n for a file which is not in the cache. The output stream is opened in either \nappend\n or \ncreate\n mode and the basic logic to determine this is explained by the simple diagram below.\n\n\n\n\nThis process gets complicated when fault-tolerance (writing to temporary files) and rotation is added.\n\n\nFollowing are few configuration items used for opening the streams:\n\n\n\n\nreplication\n: specifies the replication factor of the output files. \nDefault\n: \nfs.getDefaultReplication(new Path(filePath))\n\n\nfilePermission\n: specifies the permission of the output files. The permission is an octal number similar to that used by the Unix chmod command. \nDefault\n: 0777\n\n\n\n\nRemovalListener\n\n\nA \nGuava\n cache also allows specification of removal listener which can perform some operation when an entry is removed from the cache. Since \nstreamsCache\n is of limited size and also has time-based expiry enabled, it is imperative that when a stream is evicted from the cache it is closed properly. Therefore, we attach a removal listener to \nstreamsCache\n which closes the stream when it is evicted.\n\n\nsetup(OperatorContext context)\n\n\nDuring setup the following main tasks are performed:\n\n\n\n\nFileSystem instance is created.\n\n\nThe cache of streams is created.\n\n\nFiles are recovered (see Fault-tolerance section).\n\n\nStray part files are cleaned (see Automatic rotation section).\n\n\n\n\nprocessTuple(INPUT tuple)\n\n\nThe code snippet below highlights the basic steps of processing a tuple.\n\n\nprotected void processTuple(INPUT tuple)\n{ \n //which file to write to is derived from the tuple.\n String fileName = getFileName(tuple); \n\n //streamsCache is queried for the output stream. If the stream is already opened then it is returned immediately otherwise the cache loader creates one.\n FilterOutputStream fsOutput = streamsCache.get(fileName).getFilterStream();\n\n byte[] tupleBytes = getBytesForTuple(tuple);\n\n fsOutput.write(tupleBytes);\n}\n\n\n\n\nendWindow()\n\n\nIt should be noted that while processing a tuple we do not flush the stream after every write. Since flushing is expensive it is done periodically for all the open streams in the operator's \nendWindow()\n.\n\n\nMap\nString, FSFilterStreamContext\n openStreams = streamsCache.asMap();\nfor (FSFilterStreamContext streamContext: openStreams.values()) {\n ...\n //this flushes the stream\n streamContext.finalizeContext();\n ...\n}\n\n\n\n\nFSFilterStreamContext\n will be explained with compression and encryption.\n\n\nteardown()\n\n\nWhen any operator in a DAG fails then the application master invokes \nteardown()\n for that operator and its downstream operators. In \nAbstractFileOutputOperator\n we have a bunch of open streams in the cache and the operator (acting as HDFS client) holds leases for all the corresponding files. It is important to release these leases for clean re-deployment. Therefore, we try to close all the open streams in \nteardown()\n.\n\n\nAutomatic rotation\n\n\nIn a streaming application where data is being continuously processed, when this output operator is used, data will be continuously written to an output file. The users may want to be able to take the data from time to time to use it, copy it out of Hadoop or do some other processing. Having all the data in a single file makes it difficult as the user needs to keep track of how much data has been read from the file each time so that the same data is not read again. Also users may already have processes and scripts in place that work with full files and not partial data from a file.\n\n\nTo help solve these problems the operator supports creating many smaller files instead of writing to just one big file. Data is written to a file and when some condition is met the file is finalized and data is written to a new file. This is called file rotation. The user can determine when the file gets rotated. Each of these files is called a part file as they contain portion of the data.\n\n\nPart filename\n\n\nThe filename for a part file is formed by using the original file name and the part number. The part number starts from 0 and is incremented each time a new part file created. The default filename has the format, assuming origfile represents the original filename and partnum represents the part number,\n\n\norigfile.partnum\n\n\nThis naming scheme can be changed by the user. It can be done so by overriding the following method\n\n\nprotected String getPartFileName(String fileName, int part)\n\n\n\n\nThis method is passed the original filename and part number as arguments and should return the part filename.\n\n\nMechanisms\n\n\nThe user has a couple of ways to specify when a file gets rotated. First is based on size and second on time. In the first case the files are limited by size and in the second they are rotated by time.\n\n\nSize Based\n\n\nWith size based rotation the user specifies a size limit. Once the size of the currently file reaches this limit the file is rotated. The size limit can be specified by setting the following property\n\n\nmaxLength\n\n\nLike any other property this can be set in Java application code or in the property file.\n\n\nTime Based\n\n\nIn time based rotation user specifies a time interval. This interval is specified as number of application windows. The files are rotated periodically once the specified number of application windows have elapsed. Since the interval is application window based it is not always exactly constant time. The interval can be specified using the following property\n\n\nrotationWindows\n\n\nsetup(OperatorContext context)\n\n\nWhen an operator is being started there may be stray part files and they need to be cleaned up. One common scenario, when these could be present, is in the case of failure, where a node running the operator failed and a previous instance of the operator was killed. This cleanup and other initial processing for the part files happens in the operator setup. The following diagram describes this process\n\n\n\n\nFault-tolerance\n\n\nThere are two issues that should be addressed in order to make the operator fault-tolerant:\n\n\n\n\n\n\nThe operator flushes data to the filesystem every application window. This implies that after a failure when the operator is re-deployed and tuples of a window are replayed, then duplicate data will be saved to the files. This is handled by recording how much the operator has written to each file every window in a state that is checkpointed and truncating files back to the recovery checkpoint after re-deployment.\n\n\n\n\n\n\nWhile writing to HDFS, if the operator gets killed and didn't have the opportunity to close a file, then later when it is redeployed it will attempt to truncate/restore that file. Restoring a file may fail because the lease that the previous process (operator instance before failure) had acquired from namenode to write to a file may still linger and therefore there can be exceptions in acquiring the lease again by the new process (operator instance after failure). This is handled by always writing data to temporary files and renaming these files to actual files when a file is finalized (closed) for writing, that is, we are sure that no more data will be written to it. The relevant configuration item is: \n\n\n\n\nalwaysWriteToTmp\n: enables/disables writing to a temporary file. \nDefault\n: true.\n\n\n\n\nMost of the complexity in the code comes from making this operator fault-tolerant.\n\n\nCheckpointed states needed for fault-tolerance\n\n\n\n\n\n\nendOffsets\n: contains the size of each file as it is being updated by the operator. It helps the operator to restore a file during recovery in operator \nsetup(...)\n and is also used while loading a stream to find out if the operator has seen a file before.\n\n\n\n\n\n\nfileNameToTmpName\n: contains the name of the temporary file per actual file. It is needed because the name of a temporary file is random. They are named based on the timestamp when the stream is created. During recovery the operator needs to know the temp file which it was writing to and if it needs restoration then it creates a new temp file and updates this mapping.\n\n\n\n\n\n\nfinalizedFiles\n: contains set of files which were requested to be finalized per window id.\n\n\n\n\n\n\nfinalizedPart\n: contains the latest \npart\n of each file which was requested to be finalized.\n\n\n\n\n\n\nThe use of \nfinalizedFiles\n and \nfinalizedPart\n are explained in detail under \nrequestFinalize(...)\n method.\n\n\nRecovering files\n\n\nWhen the operator is re-deployed, it checks in its \nsetup(...)\n method if the state of a file which it has seen before the failure is consistent with the file's state on the file system, that is, the size of the file on the file system should match the size in the \nendOffsets\n. When it doesn't the operator truncates the file.\n\n\nFor example, let's say the operator wrote 100 bytes to test1.txt by the end of window 10. It wrote another 20 bytes by the end of window 12 but failed in window 13. When the operator gets re-deployed it is restored with window 10 (recovery checkpoint) state. In the previous run, by the end of window 10, the size of file on the filesystem was 100 bytes but now it is 120 bytes. Tuples for windows 11 and 12 are going to be replayed. Therefore, in order to avoid writing duplicates to test1.txt, the operator truncates the file to 100 bytes (size at the end of window 10) discarding the last 20 bytes.\n\n\nrequestFinalize(String fileName)\n\n\nWhen the operator is always writing to temporary files (in order to avoid HDFS Lease exceptions), then it is necessary to rename the temporary files to the actual files once it has been determined that the files are closed. This is refered to as \nfinalization\n of files and the method allows the user code to specify when a file is ready for finalization.\n\n\nIn this method, the requested file (or in the case of rotation \n all the file parts including the latest open part which have not yet been requested for finalization) are registered for finalization. Registration is basically adding the file names to \nfinalizedFiles\n state and updating \nfinalizedPart\n.\n\n\nThe process of \nfinalization\n of all the files which were requested till the window \nw\n is deferred till window \nw\n is committed. This is because until a window is committed it can be replayed after a failure which means that a file can be open for writing even after it was requested for finalization.\n\n\nWhen rotation is enabled, part files as and when they get completed are requested for finalization. However, when rotation is not enabled user code needs to invoke this method as the knowledge that when a file is closed is unknown to this abstract operator.",
- "title": "File Output"
- },
- {
- "location": "/operators/file_output/#abstractfileoutputoperator",
- "text": "The abstract file output operator in Apache Apex Malhar library AbstractFileOutputOperator writes streaming data to files. The main features of this operator are: Persisting data to files. Automatic rotation of files based on: \n a. maximum length of a file. \n b. time-based rotation where time is specified using a count of application windows. Fault-tolerance. Compression and encryption of data before it is persisted. In this tutorial we will cover the details of the basic structure and implementation of all the above features in AbstractFileOutputOperator . Configuration items related to each feature are discussed as they are introduced in the section of that feature.",
- "title": "AbstractFileOutputOperator"
- },
- {
- "location": "/operators/file_output/#persisting-data-to-files",
- "text": "The principal function of this operator is to persist tuples to files efficiently. These files are created under a specific directory on the file system. The relevant configuration item is: filePath : path specifying the directory where files are written. Different types of file system that are implementations of org.apache.hadoop.fs.FileSystem are supported. The file system instance which is used for creating streams is constructed from the filePath URI. FileSystem.newInstance(new Path(filePath).toUri(), new Configuration()) Tuples may belong to different files therefore expensive IO operations like creating multiple output streams, flushing of data to disk, and closing streams are handled carefully.",
- "title": "Persisting data to files"
- },
- {
- "location": "/operators/file_output/#ports",
- "text": "input : the input port on which tuples to be persisted are received.",
- "title": "Ports"
- },
- {
- "location": "/operators/file_output/#streamscache",
- "text": "This transient state caches output streams per file in memory. The file to which the data is appended may change with incoming tuples. It will be highly inefficient to keep re-opening streams for a file just because tuples for that file are interleaved with tuples for another file. Therefore, the operator maintains a cache of limited size with open output streams. streamsCache is of type com.google.common.cache.LoadingCache . A LoadingCache has an attached CacheLoader which is responsible to load value of a key when the key is not present in the cache. Details are explained here- CachesExplained . The operator constructs this cache in setup(...) . It is built with the following configuration items: maxOpenFiles : maximum size of the cache. The cache evicts entries that haven't been used recently when the cache size is approaching this limit. Default : 100 expireStreamAfterAcessMillis : expires streams after the specified duration has passed since the stream was last accessed. Default : value of attribute- OperatorContext.SPIN_MILLIS . An important point to note here is that the guava cache does not perform cleanup and evict values asynchronously, that is, instantly after a value expires. Instead, it performs small amounts of maintenance during write operations, or during occasional read operations if writes are rare.",
- "title": "streamsCache"
- },
- {
- "location": "/operators/file_output/#cacheloader",
- "text": "streamsCache is created with a CacheLoader that opens an FSDataOutputStream for a file which is not in the cache. The output stream is opened in either append or create mode and the basic logic to determine this is explained by the simple diagram below. This process gets complicated when fault-tolerance (writing to temporary files) and rotation is added. Following are few configuration items used for opening the streams: replication : specifies the replication factor of the output files. Default : fs.getDefaultReplication(new Path(filePath)) filePermission : specifies the permission of the output files. The permission is an octal number similar to that used by the Unix chmod command. Default : 0777",
- "title": "CacheLoader"
- },
- {
- "location": "/operators/file_output/#removallistener",
- "text": "A Guava cache also allows specification of removal listener which can perform some operation when an entry is removed from the cache. Since streamsCache is of limited size and also has time-based expiry enabled, it is imperative that when a stream is evicted from the cache it is closed properly. Therefore, we attach a removal listener to streamsCache which closes the stream when it is evicted.",
- "title": "RemovalListener"
- },
- {
- "location": "/operators/file_output/#setupoperatorcontext-context",
- "text": "During setup the following main tasks are performed: FileSystem instance is created. The cache of streams is created. Files are recovered (see Fault-tolerance section). Stray part files are cleaned (see Automatic rotation section).",
- "title": "setup(OperatorContext context)"
- },
- {
- "location": "/operators/file_output/#automatic-rotation",
- "text": "In a streaming application where data is being continuously processed, when this output operator is used, data will be continuously written to an output file. The users may want to be able to take the data from time to time to use it, copy it out of Hadoop or do some other processing. Having all the data in a single file makes it difficult as the user needs to keep track of how much data has been read from the file each time so that the same data is not read again. Also users may already have processes and scripts in place that work with full files and not partial data from a file. To help solve these problems the operator supports creating many smaller files instead of writing to just one big file. Data is written to a file and when some condition is met the file is finalized and data is written to a new file. This is called file rotation. The user can determine when the file gets rotated. Each of these files is called a part file as they contain portion of the data.",
- "title": "Automatic rotation"
- },
- {
- "location": "/operators/file_output/#part-filename",
- "text": "The filename for a part file is formed by using the original file name and the part number. The part number starts from 0 and is incremented each time a new part file created. The default filename has the format, assuming origfile represents the original filename and partnum represents the part number, origfile.partnum This naming scheme can be changed by the user. It can be done so by overriding the following method protected String getPartFileName(String fileName, int part) This method is passed the original filename and part number as arguments and should return the part filename.",
- "title": "Part filename"
- },
- {
- "location": "/operators/file_output/#mechanisms",
- "text": "The user has a couple of ways to specify when a file gets rotated. First is based on size and second on time. In the first case the files are limited by size and in the second they are rotated by time.",
- "title": "Mechanisms"
- },
- {
- "location": "/operators/file_output/#size-based",
- "text": "With size based rotation the user specifies a size limit. Once the size of the currently file reaches this limit the file is rotated. The size limit can be specified by setting the following property maxLength Like any other property this can be set in Java application code or in the property file.",
- "title": "Size Based"
- },
- {
- "location": "/operators/file_output/#time-based",
- "text": "In time based rotation user specifies a time interval. This interval is specified as number of application windows. The files are rotated periodically once the specified number of application windows have elapsed. Since the interval is application window based it is not always exactly constant time. The interval can be specified using the following property rotationWindows",
- "title": "Time Based"
- },
- {
- "location": "/operators/file_output/#setupoperatorcontext-context_1",
- "text": "When an operator is being started there may be stray part files and they need to be cleaned up. One common scenario, when these could be present, is in the case of failure, where a node running the operator failed and a previous instance of the operator was killed. This cleanup and other initial processing for the part files happens in the operator setup. The following diagram describes this process",
- "title": "setup(OperatorContext context)"
- },
- {
- "location": "/operators/file_output/#fault-tolerance",
- "text": "There are two issues that should be addressed in order to make the operator fault-tolerant: The operator flushes data to the filesystem every application window. This implies that after a failure when the operator is re-deployed and tuples of a window are replayed, then duplicate data will be saved to the files. This is handled by recording how much the operator has written to each file every window in a state that is checkpointed and truncating files back to the recovery checkpoint after re-deployment. While writing to HDFS, if the operator gets killed and didn't have the opportunity to close a file, then later when it is redeployed it will attempt to truncate/restore that file. Restoring a file may fail because the lease that the previous process (operator instance before failure) had acquired from namenode to write to a file may still linger and therefore there can be exceptions in acquiring the lease again by the new process (operator instance after failure). This is handled by always writing data to temporary files and renaming these files to actual files when a file is finalized (closed) for writing, that is, we are sure that no more data will be written to it. The relevant configuration item is: alwaysWriteToTmp : enables/disables writing to a temporary file. Default : true. Most of the complexity in the code comes from making this operator fault-tolerant.",
- "title": "Fault-tolerance"
- },
- {
- "location": "/operators/file_output/#checkpointed-states-needed-for-fault-tolerance",
- "text": "endOffsets : contains the size of each file as it is being updated by the operator. It helps the operator to restore a file during recovery in operator setup(...) and is also used while loading a stream to find out if the operator has seen a file before. fileNameToTmpName : contains the name of the temporary file per actual file. It is needed because the name of a temporary file is random. They are named based on the timestamp when the stream is created. During recovery the operator needs to know the temp file which it was writing to and if it needs restoration then it creates a new temp file and updates this mapping. finalizedFiles : contains set of files which were requested to be finalized per window id. finalizedPart : contains the latest part of each file which was requested to be finalized. The use of finalizedFiles and finalizedPart are explained in detail under requestFinalize(...) method.",
- "title": "Checkpointed states needed for fault-tolerance"
- },
- {
- "location": "/operators/file_output/#recovering-files",
- "text": "When the operator is re-deployed, it checks in its setup(...) method if the state of a file which it has seen before the failure is consistent with the file's state on the file system, that is, the size of the file on the file system should match the size in the endOffsets . When it doesn't the operator truncates the file. For example, let's say the operator wrote 100 bytes to test1.txt by the end of window 10. It wrote another 20 bytes by the end of window 12 but failed in window 13. When the operator gets re-deployed it is restored with window 10 (recovery checkpoint) state. In the previous run, by the end of window 10, the size of file on the filesystem was 100 bytes but now it is 120 bytes. Tuples for windows 11 and 12 are going to be replayed. Therefore, in order to avoid writing duplicates to test1.txt, the operator truncates the file to 100 bytes (size at the end of window 10) discarding the last 20 bytes.",
- "title": "Recovering files"
- }
- ]
-}
\ No newline at end of file
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- <li class="toctree-l3"><a href="#block-reader">Block Reader</a></li>
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- <li><a class="toctree-l4" href="#why-is-it-needed">Why is it needed?</a></li>
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- <li><a class="toctree-l4" href="#class-diagram">Class Diagram</a></li>
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- <li><a class="toctree-l4" href="#abstractblockreader">AbstractBlockReader</a></li>
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- <li><a class="toctree-l4" href="#linereadercontext">LineReaderContext</a></li>
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- <li class="toctree-l3"><a href="#fsslicereader">FSSliceReader</a></li>
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- <li><a class="toctree-l4" href="#fixedbytesreadercontext">FixedBytesReaderContext</a></li>
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-
- <h1 id="block-reader">Block Reader</h1>
-<p>This is a scalable operator that reads and parses blocks of data sources into records. A data source can be a file or a message bus that contains records and a block defines a chunk of data in the source by specifying the block offset and the length of the source belonging to the block. </p>
-<h2 id="why-is-it-needed">Why is it needed?</h2>
-<p>A Block Reader is needed to parallelize reading and parsing of a single data source, for example a file. Simple parallelism of reading data sources can be achieved by multiple partitions reading different source of same type (for files see <a href="https://github.com/apache/incubator-apex-malhar/blob/devel-3/library/src/main/java/com/datatorrent/lib/io/fs/AbstractFileInputOperator.java">AbstractFileInputOperator</a>) but Block Reader partitions can read blocks of same source in parallel and parse them for records ensuring that no record is duplicated or missed.</p>
-<h2 id="class-diagram">Class Diagram</h2>
-<p><img alt="BlockReader class diagram" src="../images/blockreader/classdiagram.png" /></p>
-<h2 id="abstractblockreader">AbstractBlockReader</h2>
-<p>This is the abstract implementation that serves as the base for different types of data sources. It defines how a block metadata is processed. The flow diagram below describes the processing of a block metadata.</p>
-<p><img alt="BlockReader flow diagram" src="../images/blockreader/flowdiagram.png" /></p>
-<h3 id="ports">Ports</h3>
-<ul>
-<li>
-<p>blocksMetadataInput: input port on which block metadata are received.</p>
-</li>
-<li>
-<p>blocksMetadataOutput: output port on which block metadata are emitted if the port is connected. This port is useful when a downstream operator that receives records from block reader may also be interested to know the details of the corresponding blocks.</p>
-</li>
-<li>
-<p>messages: output port on which tuples of type <code>com.datatorrent.lib.io.block.AbstractBlockReader.ReaderRecord</code> are emitted. This class encapsulates a <code>record</code> and the <code>blockId</code> of the corresponding block.</p>
-</li>
-</ul>
-<h3 id="readercontext">readerContext</h3>
-<p>This is one of the most important fields in the block reader. It is of type <code>com.datatorrent.lib.io.block.ReaderContext</code> and is responsible for fetching bytes that make a record. It also lets the reader know how many total bytes were consumed which may not be equal to the total bytes in a record because consumed bytes also include bytes for the record delimiter which may not be a part of the actual record.</p>
-<p>Once the reader creates an input stream for the block (or uses the previous opened stream if the current block is successor of the previous block) it initializes the reader context by invoking <code>readerContext.initialize(stream, blockMetadata, consecutiveBlock);</code>. Initialize method is where any implementation of <code>ReaderContext</code> can perform all the operations which have to be executed just before reading the block or create states which are used during the lifetime of reading the block.</p>
-<p>Once the initialization is done, <code>readerContext.next()</code> is called repeatedly until it returns <code>null</code>. It is left to the <code>ReaderContext</code> implementations to decide when a block is completely processed. In cases when a record is split across adjacent blocks, reader context may decide to read ahead of the current block boundary to completely fetch the split record (examples- <code>LineReaderContext</code> and <code>ReadAheadLineReaderContext</code>). In other cases when there isn't a possibility of split record (example- <code>FixedBytesReaderContext</code>), it returns <code>null</code> immediately when the block boundary is reached. The return type of <code>readerContext.next()</code> is of type <code>com.datatorrent.lib.io.block.ReaderContext.Entity</code> which is just a wrapper for a <code>byte[]</code> that represents the record and total bytes used in fetching the record.</p>
-<h3 id="abstract-methods">Abstract methods</h3>
-<ul>
-<li>
-<p><code>STREAM setupStream(B block)</code>: creating a stream for a block is dependent on the type of source which is not known to AbstractBlockReader. Sub-classes which deal with a specific data source provide this implementation.</p>
-</li>
-<li>
-<p><code>R convertToRecord(byte[] bytes)</code><a name="convertToRecord"></a>: this converts the array of bytes into the actual instance of record type.</p>
-</li>
-</ul>
-<h3 id="auto-scalability">Auto-scalability</h3>
-<p>Block reader can auto-scale, that is, depending on the backlog (total number of all the blocks which are waiting in the <code>blocksMetadataInput</code> port queue of all partitions) it can create more partitions or reduce them. Details are discussed in the last section which covers the <a href="#partitioning">partitioner and stats-listener</a>.</p>
-<h3 id="configuration">Configuration</h3>
-<ol>
-<li><a name="maxReaders"></a><strong>maxReaders</strong>: when auto-scaling is enabled, this controls the maximum number of block reader partitions that can be created.</li>
-<li><a name="minReaders"></a><strong>minReaders</strong>: when auto-scaling is enabled, this controls the minimum number of block reader partitions that should always exist.</li>
-<li><a name="collectStats"></a><strong>collectStats</strong>: this enables or disables auto-scaling. When it is set to <code>true</code> the stats (number of blocks in the queue) are collected and this triggers partitioning; otherwise auto-scaling is disabled.</li>
-<li><strong>intervalMillis</strong>: when auto-scaling is enabled, this specifies the interval at which the reader will trigger the logic of computing the backlog and auto-scale.</li>
-</ol>
-<h2 id="abstractfsblockreader"><a name="AbstractFSBlockReader"></a> AbstractFSBlockReader</h2>
-<p>This abstract implementation deals with files. Different types of file systems that are implementations of <code>org.apache.hadoop.fs.FileSystem</code> are supported. The user can override <code>getFSInstance()</code> method to create an instance of a specific <code>FileSystem</code>. By default, filesystem instance is created from the filesytem URI that comes from the default hadoop configuration.</p>
-<pre><code class="java">protected FileSystem getFSInstance() throws IOException
-{
- return FileSystem.newInstance(configuration);
-}
-</code></pre>
-
-<p>It uses this filesystem instance to setup a stream of type <code>org.apache.hadoop.fs.FSDataInputStream</code> to read the block.</p>
-<pre><code class="java">@Override
-protected FSDataInputStream setupStream(BlockMetadata.FileBlockMetadata block) throws IOException
-{
- return fs.open(new Path(block.getFilePath()));
-}
-</code></pre>
-
-<p>All the ports and configurations are derived from the super class. It doesn't provide an implementation of <a href="#convertToRecord"><code>convertToRecord(byte[] bytes)</code></a> method which is delegated to concrete sub-classes.</p>
-<h3 id="example-application">Example Application</h3>
-<p>This simple dag demonstrates how any concrete implementation of <code>AbstractFSBlockReader</code> can be plugged into an application. </p>
-<p><img alt="Application with FSBlockReader" src="../images/blockreader/fsreaderexample.png" /></p>
-<p>In the above application, file splitter creates block metadata for files which are sent to block reader. Partitions of the block reader parses the file blocks for records which are filtered, transformed and then persisted to a file (created per block). Therefore block reader is parallel partitioned with the 2 downstream operators - filter/converter and record output operator. The code which implements this dag is below.</p>
-<pre><code class="java">public class ExampleApplication implements StreamingApplication
-{
- @Override
- public void populateDAG(DAG dag, Configuration configuration)
- {
- FileSplitterInput input = dag.addOperator("File-splitter", new FileSplitterInput());
- //any concrete implementation of AbstractFSBlockReader based on the use-case can be added here.
- LineReader blockReader = dag.addOperator("Block-reader", new LineReader());
- Filter filter = dag.addOperator("Filter", new Filter());
- RecordOutputOperator recordOutputOperator = dag.addOperator("Record-writer", new RecordOutputOperator());
-
- dag.addStream("file-block metadata", input.blocksMetadataOutput, blockReader.blocksMetadataInput);
- dag.addStream("records", blockReader.messages, filter.input);
- dag.addStream("filtered-records", filter.output, recordOutputOperator.input);
- }
-
- /**
- * Concrete implementation of {@link AbstractFSBlockReader} for which a record is a line in the file.
- */
- public static class LineReader extends AbstractFSBlockReader.AbstractFSReadAheadLineReader<String>
- {
-
- @Override
- protected String convertToRecord(byte[] bytes)
- {
- return new String(bytes);
- }
- }
-
- /**
- * Considers any line starting with a '.' as invalid. Emits the valid records.
- */
- public static class Filter extends BaseOperator
- {
- public final transient DefaultOutputPort<AbstractBlockReader.ReaderRecord<String>> output = new DefaultOutputPort<>();
- public final transient DefaultInputPort<AbstractBlockReader.ReaderRecord<String>> input = new DefaultInputPort<AbstractBlockReader.ReaderRecord<String>>()
- {
- @Override
- public void process(AbstractBlockReader.ReaderRecord<String> stringRecord)
- {
- //filter records and transform
- //if the string starts with a '.' ignore the string.
- if (!StringUtils.startsWith(stringRecord.getRecord(), ".")) {
- output.emit(stringRecord);
- }
- }
- };
- }
-
- /**
- * Persists the valid records to corresponding block files.
- */
- public static class RecordOutputOperator extends AbstractFileOutputOperator<AbstractBlockReader.ReaderRecord<String>>
- {
- @Override
- protected String getFileName(AbstractBlockReader.ReaderRecord<String> tuple)
- {
- return Long.toHexString(tuple.getBlockId());
- }
-
- @Override
- protected byte[] getBytesForTuple(AbstractBlockReader.ReaderRecord<String> tuple)
- {
- return tuple.getRecord().getBytes();
- }
- }
-}
-</code></pre>
-
-<p>Configuration to parallel partition block reader with its downstream operators.</p>
-<pre><code class="xml"> <property>
- <name>dt.operator.Filter.port.input.attr.PARTITION_PARALLEL</name>
- <value>true</value>
- </property>
- <property>
- <name>dt.operator.Record-writer.port.input.attr.PARTITION_PARALLEL</name>
- <value>true</value>
- </property>
-</code></pre>
-
-<h2 id="abstractfsreadaheadlinereader">AbstractFSReadAheadLineReader</h2>
-<p>This extension of <a href="#AbstractFSBlockReader"><code>AbstractFSBlockReader</code></a> parses lines from a block and binds the <code>readerContext</code> field to an instance of <code>ReaderContext.ReadAheadLineReaderContext</code>.</p>
-<p>It is abstract because it doesn't provide an implementation of <a href="#convertToRecord"><code>convertToRecord(byte[] bytes)</code></a> since the user may want to convert the bytes that make a line into some other type. </p>
-<h3 id="readaheadlinereadercontext">ReadAheadLineReaderContext</h3>
-<p>In order to handle a line split across adjacent blocks, ReadAheadLineReaderContext always reads beyond the block boundary and ignores the bytes till the first end-of-line character of all the blocks except the first block of the file. This ensures that no line is missed or incomplete.</p>
-<p>This is one of the most common ways of handling a split record. It doesn't require any further information to decide if a line is complete. However, the cost of this consistent way to handle a line split is that it always reads from the next block.</p>
-<h2 id="abstractfslinereader">AbstractFSLineReader</h2>
-<p>Similar to <code>AbstractFSReadAheadLineReader</code>, even this parses lines from a block. However, it binds the <code>readerContext</code> field to an instance of <code>ReaderContext.LineReaderContext</code>.</p>
-<h3 id="linereadercontext">LineReaderContext</h3>
-<p>This handles the line split differently from <code>ReadAheadLineReaderContext</code>. It doesn't always read from the next block. If the end of the last line is aligned with the block boundary then it stops processing the block. It does read from the next block when the boundaries are not aligned, that is, last line extends beyond the block boundary. The result of this is an inconsistency in reading the next block.</p>
-<p>When the boundary of the last line of the previous block was aligned with its block, then the first line of the current block is a valid line. However, in the other case the bytes from the block start offset to the first end-of-line character should be ignored. Therefore, this means that any record formed by this reader context has to be validated. For example, if the lines are of fixed size then size of each record can be validated or if each line begins with a special field then that knowledge can be used to check if a record is complete.</p>
-<p>If the validations of completeness fails for a line then <a href="#convertToRecord"><code>convertToRecord(byte[] bytes)</code></a> should return null.</p>
-<h2 id="fsslicereader">FSSliceReader</h2>
-<p>A concrete extension of <a href="#AbstractFSBlockReader"><code>AbstractFSBlockReader</code></a> that reads fixed-size <code>byte[]</code> from a block and emits the byte array wrapped in <code>com.datatorrent.netlet.util.Slice</code>.</p>
-<p>This operator binds the <code>readerContext</code> to an instance of <code>ReaderContext.FixedBytesReaderContext</code>.</p>
-<h3 id="fixedbytesreadercontext">FixedBytesReaderContext</h3>
-<p>This implementation of <code>ReaderContext</code> never reads beyond a block boundary which can result in the last <code>byte[]</code> of a block to be of a shorter length than the rest of the records.</p>
-<h3 id="configuration_1">Configuration</h3>
-<p><strong>readerContext.length</strong>: length of each record. By default, this is initialized to the default hdfs block size.</p>
-<h2 id="partitioner-and-statslistener">Partitioner and StatsListener</h2>
-<p>The logical instance of the block reader acts as the Partitioner (unless a custom partitioner is set using the operator attribute - <code>PARTITIONER</code>) as well as a StatsListener. This is because the
-<code>AbstractBlockReader</code> implements both the <code>com.datatorrent.api.Partitioner</code> and <code>com.datatorrent.api.StatsListener</code> interfaces and provides an implementation of <code>definePartitions(...)</code> and <code>processStats(...)</code> which make it auto-scalable.</p>
-<h3 id="processstats">processStats <a name="processStats"></a></h3>
-<p>The application master invokes <code>Response processStats(BatchedOperatorStats stats)</code> method on the logical instance with the stats (<code>tuplesProcessedPSMA</code>, <code>tuplesEmittedPSMA</code>, <code>latencyMA</code>, etc.) of each partition. The data which this operator is interested in is the <code>queueSize</code> of the input port <code>blocksMetadataInput</code>.</p>
-<p>Usually the <code>queueSize</code> of an input port gives the count of waiting control tuples plus data tuples. However, if a stats listener is interested only in the count of data tuples then that can be expressed by annotating the class with <code>@DataQueueSize</code>. In this case <code>AbstractBlockReader</code> itself is the <code>StatsListener</code> which is why it is annotated with <code>@DataQueueSize</code>.</p>
-<p>The logical instance caches the queue size per partition and at regular intervals (configured by <code>intervalMillis</code>) sums these values to find the total backlog which is then used to decide whether re-partitioning is needed. The flow-diagram below describes this logic.</p>
-<p><img alt="Processing of total-backlog" src="../images/blockreader/totalBacklogProcessing.png" /></p>
-<p>The goal of this logic is to create as many partitions within bounds (see <a href="#maxReaders"><code>maxReaders</code></a> and <a href="#minReaders"><code>minReaders</code></a> above) to quickly reduce this backlog or if the backlog is small then remove any idle partitions.</p>
-<h3 id="definepartitions">definePartitions</h3>
-<p>Based on the <code>repartitionRequired</code> field of the <code>Response</code> object which is returned by <em><a href="#processStats">processStats</a></em> method, the application master invokes </p>
-<pre><code class="java">Collection<Partition<AbstractBlockReader<...>>> definePartitions(Collection<Partition<AbstractBlockReader<...>>> partitions, PartitioningContext context)
-</code></pre>
-
-<p>on the logical instance which is also the partitioner instance. The implementation calculates the difference between required partitions and the existing count of partitions. If this difference is negative, then equivalent number of partitions are removed otherwise new partitions are created. </p>
-<p>Please note auto-scaling can be disabled by setting <a href="#collectStats"><code>collectStats</code></a> to <code>false</code>. If the use-case requires only static partitioning, then that can be achieved by setting <a href="https://github.com/chandnisingh/incubator-apex-core/blob/master/common/src/main/java/com/datatorrent/common/partitioner/StatelessPartitioner.java"><code>StatelessPartitioner</code></a> as the operator attribute- <code>PARTITIONER</code> on the block reader.</p>
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- <h1 id="abstractfileoutputoperator">AbstractFileOutputOperator</h1>
-<p>The abstract file output operator in Apache Apex Malhar library — <a href="https://github.com/apache/incubator-apex-malhar/blob/devel-3/library/src/main/java/com/datatorrent/lib/io/fs/AbstractFileOutputOperator.java"><code>AbstractFileOutputOperator</code></a> writes streaming data to files. The main features of this operator are:</p>
-<ol>
-<li>Persisting data to files.</li>
-<li>Automatic rotation of files based on:<br />
- a. maximum length of a file.<br />
- b. time-based rotation where time is specified using a count of application windows.</li>
-<li>Fault-tolerance.</li>
-<li>Compression and encryption of data before it is persisted.</li>
-</ol>
-<p>In this tutorial we will cover the details of the basic structure and implementation of all the above features in <code>AbstractFileOutputOperator</code>. Configuration items related to each feature are discussed as they are introduced in the section of that feature.</p>
-<h2 id="persisting-data-to-files">Persisting data to files</h2>
-<p>The principal function of this operator is to persist tuples to files efficiently. These files are created under a specific directory on the file system. The relevant configuration item is:</p>
-<p><strong>filePath</strong>: path specifying the directory where files are written.</p>
-<p>Different types of file system that are implementations of <code>org.apache.hadoop.fs.FileSystem</code> are supported. The file system instance which is used for creating streams is constructed from the <code>filePath</code> URI.</p>
-<pre><code class="java">FileSystem.newInstance(new Path(filePath).toUri(), new Configuration())
-</code></pre>
-
-<p>Tuples may belong to different files therefore expensive IO operations like creating multiple output streams, flushing of data to disk, and closing streams are handled carefully.</p>
-<h3 id="ports">Ports</h3>
-<ul>
-<li><code>input</code>: the input port on which tuples to be persisted are received.</li>
-</ul>
-<h3 id="streamscache"><code>streamsCache</code></h3>
-<p>This transient state caches output streams per file in memory. The file to which the data is appended may change with incoming tuples. It will be highly inefficient to keep re-opening streams for a file just because tuples for that file are interleaved with tuples for another file. Therefore, the operator maintains a cache of limited size with open output streams.</p>
-<p><code>streamsCache</code> is of type <code>com.google.common.cache.LoadingCache</code>. A <code>LoadingCache</code> has an attached <code>CacheLoader</code> which is responsible to load value of a key when the key is not present in the cache. Details are explained here- <a href="https://github.com/google/guava/wiki/CachesExplained">CachesExplained</a>.</p>
-<p>The operator constructs this cache in <code>setup(...)</code>. It is built with the following configuration items:</p>
-<ul>
-<li><strong>maxOpenFiles</strong>: maximum size of the cache. The cache evicts entries that haven't been used recently when the cache size is approaching this limit. <em>Default</em>: 100</li>
-<li><strong>expireStreamAfterAcessMillis</strong>: expires streams after the specified duration has passed since the stream was last accessed. <em>Default</em>: value of attribute- <code>OperatorContext.SPIN_MILLIS</code>.</li>
-</ul>
-<p>An important point to note here is that the guava cache does not perform cleanup and evict values asynchronously, that is, instantly after a value expires. Instead, it performs small amounts of maintenance during write operations, or during occasional read operations if writes are rare.</p>
-<h4 id="cacheloader">CacheLoader</h4>
-<p><code>streamsCache</code> is created with a <code>CacheLoader</code> that opens an <code>FSDataOutputStream</code> for a file which is not in the cache. The output stream is opened in either <code>append</code> or <code>create</code> mode and the basic logic to determine this is explained by the simple diagram below.</p>
-<p><img alt="Opening an output stream" src="../images/fileoutput/diagram1.png" /></p>
-<p>This process gets complicated when fault-tolerance (writing to temporary files) and rotation is added.</p>
-<p>Following are few configuration items used for opening the streams:</p>
-<ul>
-<li><strong>replication</strong>: specifies the replication factor of the output files. <em>Default</em>: <code>fs.getDefaultReplication(new Path(filePath))</code></li>
-<li><strong>filePermission</strong>: specifies the permission of the output files. The permission is an octal number similar to that used by the Unix chmod command. <em>Default</em>: 0777</li>
-</ul>
-<h4 id="removallistener">RemovalListener</h4>
-<p>A <code>Guava</code> cache also allows specification of removal listener which can perform some operation when an entry is removed from the cache. Since <code>streamsCache</code> is of limited size and also has time-based expiry enabled, it is imperative that when a stream is evicted from the cache it is closed properly. Therefore, we attach a removal listener to <code>streamsCache</code> which closes the stream when it is evicted.</p>
-<h3 id="setupoperatorcontext-context"><code>setup(OperatorContext context)</code></h3>
-<p>During setup the following main tasks are performed:</p>
-<ol>
-<li>FileSystem instance is created.</li>
-<li>The cache of streams is created.</li>
-<li>Files are recovered (see Fault-tolerance section).</li>
-<li>Stray part files are cleaned (see Automatic rotation section).</li>
-</ol>
-<h3 id="processtupleinput-tuple"><a name="processTuple"></a><code>processTuple(INPUT tuple)</code></h3>
-<p>The code snippet below highlights the basic steps of processing a tuple.</p>
-<pre><code class="java">protected void processTuple(INPUT tuple)
-{
- //which file to write to is derived from the tuple.
- String fileName = getFileName(tuple);
-
- //streamsCache is queried for the output stream. If the stream is already opened then it is returned immediately otherwise the cache loader creates one.
- FilterOutputStream fsOutput = streamsCache.get(fileName).getFilterStream();
-
- byte[] tupleBytes = getBytesForTuple(tuple);
-
- fsOutput.write(tupleBytes);
-}
-</code></pre>
-
-<h3 id="endwindow"><a name="endWindow"></a>endWindow()</h3>
-<p>It should be noted that while processing a tuple we do not flush the stream after every write. Since flushing is expensive it is done periodically for all the open streams in the operator's <code>endWindow()</code>.</p>
-<pre><code class="java">Map<String, FSFilterStreamContext> openStreams = streamsCache.asMap();
-for (FSFilterStreamContext streamContext: openStreams.values()) {
- ...
- //this flushes the stream
- streamContext.finalizeContext();
- ...
-}
-</code></pre>
-
-<p><code>FSFilterStreamContext</code> will be explained with compression and encryption.</p>
-<h3 id="teardown"><a name="teardown"></a>teardown()</h3>
-<p>When any operator in a DAG fails then the application master invokes <code>teardown()</code> for that operator and its downstream operators. In <code>AbstractFileOutputOperator</code> we have a bunch of open streams in the cache and the operator (acting as HDFS client) holds leases for all the corresponding files. It is important to release these leases for clean re-deployment. Therefore, we try to close all the open streams in <code>teardown()</code>.</p>
-<h2 id="automatic-rotation">Automatic rotation</h2>
-<p>In a streaming application where data is being continuously processed, when this output operator is used, data will be continuously written to an output file. The users may want to be able to take the data from time to time to use it, copy it out of Hadoop or do some other processing. Having all the data in a single file makes it difficult as the user needs to keep track of how much data has been read from the file each time so that the same data is not read again. Also users may already have processes and scripts in place that work with full files and not partial data from a file.</p>
-<p>To help solve these problems the operator supports creating many smaller files instead of writing to just one big file. Data is written to a file and when some condition is met the file is finalized and data is written to a new file. This is called file rotation. The user can determine when the file gets rotated. Each of these files is called a part file as they contain portion of the data.</p>
-<h3 id="part-filename">Part filename</h3>
-<p>The filename for a part file is formed by using the original file name and the part number. The part number starts from 0 and is incremented each time a new part file created. The default filename has the format, assuming origfile represents the original filename and partnum represents the part number,</p>
-<p><code>origfile.partnum</code></p>
-<p>This naming scheme can be changed by the user. It can be done so by overriding the following method</p>
-<pre><code class="java">protected String getPartFileName(String fileName, int part)
-</code></pre>
-
-<p>This method is passed the original filename and part number as arguments and should return the part filename.</p>
-<h3 id="mechanisms">Mechanisms</h3>
-<p>The user has a couple of ways to specify when a file gets rotated. First is based on size and second on time. In the first case the files are limited by size and in the second they are rotated by time.</p>
-<h4 id="size-based">Size Based</h4>
-<p>With size based rotation the user specifies a size limit. Once the size of the currently file reaches this limit the file is rotated. The size limit can be specified by setting the following property</p>
-<p><code>maxLength</code></p>
-<p>Like any other property this can be set in Java application code or in the property file.</p>
-<h4 id="time-based">Time Based</h4>
-<p>In time based rotation user specifies a time interval. This interval is specified as number of application windows. The files are rotated periodically once the specified number of application windows have elapsed. Since the interval is application window based it is not always exactly constant time. The interval can be specified using the following property</p>
-<p><code>rotationWindows</code></p>
-<h3 id="setupoperatorcontext-context_1"><code>setup(OperatorContext context)</code></h3>
-<p>When an operator is being started there may be stray part files and they need to be cleaned up. One common scenario, when these could be present, is in the case of failure, where a node running the operator failed and a previous instance of the operator was killed. This cleanup and other initial processing for the part files happens in the operator setup. The following diagram describes this process</p>
-<p><img alt="Rotation setup" src="../images/fileoutput/FileRotation.png" /></p>
-<h2 id="fault-tolerance">Fault-tolerance</h2>
-<p>There are two issues that should be addressed in order to make the operator fault-tolerant:</p>
-<ol>
-<li>
-<p>The operator flushes data to the filesystem every application window. This implies that after a failure when the operator is re-deployed and tuples of a window are replayed, then duplicate data will be saved to the files. This is handled by recording how much the operator has written to each file every window in a state that is checkpointed and truncating files back to the recovery checkpoint after re-deployment.</p>
-</li>
-<li>
-<p>While writing to HDFS, if the operator gets killed and didn't have the opportunity to close a file, then later when it is redeployed it will attempt to truncate/restore that file. Restoring a file may fail because the lease that the previous process (operator instance before failure) had acquired from namenode to write to a file may still linger and therefore there can be exceptions in acquiring the lease again by the new process (operator instance after failure). This is handled by always writing data to temporary files and renaming these files to actual files when a file is finalized (closed) for writing, that is, we are sure that no more data will be written to it. The relevant configuration item is: </p>
-</li>
-<li><strong>alwaysWriteToTmp</strong>: enables/disables writing to a temporary file. <em>Default</em>: true.</li>
-</ol>
-<p>Most of the complexity in the code comes from making this operator fault-tolerant.</p>
-<h3 id="checkpointed-states-needed-for-fault-tolerance">Checkpointed states needed for fault-tolerance</h3>
-<ul>
-<li>
-<p><code>endOffsets</code>: contains the size of each file as it is being updated by the operator. It helps the operator to restore a file during recovery in operator <code>setup(...)</code> and is also used while loading a stream to find out if the operator has seen a file before.</p>
-</li>
-<li>
-<p><code>fileNameToTmpName</code>: contains the name of the temporary file per actual file. It is needed because the name of a temporary file is random. They are named based on the timestamp when the stream is created. During recovery the operator needs to know the temp file which it was writing to and if it needs restoration then it creates a new temp file and updates this mapping.</p>
-</li>
-<li>
-<p><code>finalizedFiles</code>: contains set of files which were requested to be finalized per window id.</p>
-</li>
-<li>
-<p><code>finalizedPart</code>: contains the latest <code>part</code> of each file which was requested to be finalized.</p>
-</li>
-</ul>
-<p>The use of <code>finalizedFiles</code> and <code>finalizedPart</code> are explained in detail under <a href="#requestFinalize"><code>requestFinalize(...)</code></a> method.</p>
-<h3 id="recovering-files">Recovering files</h3>
-<p>When the operator is re-deployed, it checks in its <code>setup(...)</code> method if the state of a file which it has seen before the failure is consistent with the file's state on the file system, that is, the size of the file on the file system should match the size in the <code>endOffsets</code>. When it doesn't the operator truncates the file.</p>
-<p>For example, let's say the operator wrote 100 bytes to test1.txt by the end of window 10. It wrote another 20 bytes by the end of window 12 but failed in window 13. When the operator gets re-deployed it is restored with window 10 (recovery checkpoint) state. In the previous run, by the end of window 10, the size of file on the filesystem was 100 bytes but now it is 120 bytes. Tuples for windows 11 and 12 are going to be replayed. Therefore, in order to avoid writing duplicates to test1.txt, the operator truncates the file to 100 bytes (size at the end of window 10) discarding the last 20 bytes.</p>
-<h3 id="requestfinalizestring-filename"><a name="requestFinalize"></a><code>requestFinalize(String fileName)</code></h3>
-<p>When the operator is always writing to temporary files (in order to avoid HDFS Lease exceptions), then it is necessary to rename the temporary files to the actual files once it has been determined that the files are closed. This is refered to as <em>finalization</em> of files and the method allows the user code to specify when a file is ready for finalization.</p>
-<p>In this method, the requested file (or in the case of rotation — all the file parts including the latest open part which have not yet been requested for finalization) are registered for finalization. Registration is basically adding the file names to <code>finalizedFiles</code> state and updating <code>finalizedPart</code>.</p>
-<p>The process of <em>finalization</em> of all the files which were requested till the window <em>w</em> is deferred till window <em>w</em> is committed. This is because until a window is committed it can be replayed after a failure which means that a file can be open for writing even after it was requested for finalization.</p>
-<p>When rotation is enabled, part files as and when they get completed are requested for finalization. However, when rotation is not enabled user code needs to invoke this method as the knowledge that when a file is closed is unknown to this abstract operator.</p>
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- <h1 id="file-splitter">File Splitter</h1>
-<p>This is a simple operator whose main function is to split a file virtually and create metadata describing the files and the splits. </p>
-<h2 id="why-is-it-needed">Why is it needed?</h2>
-<p>It is a common operation to read a file and parse it. This operation can be parallelized by having multiple partitions of such operators and each partition operating on different files. However, at times when a file is large then a single partition reading it can become a bottleneck.
-In these cases, throughput can be increased if instances of the partitioned operator can read and parse non-overlapping sets of file blocks. This is where file splitter comes in handy. It creates metadata of blocks of file which serves as tasks handed out to downstream operator partitions.
-The downstream partitions can read/parse the block without the need of interacting with other partitions.</p>
-<h2 id="class-diagram">Class Diagram</h2>
-<p><img alt="FileSplitter class dierarchy" src="../images/filesplitter/classdiagram.png" /></p>
-<h2 id="abstractfilesplitter">AbstractFileSplitter</h2>
-<p>The abstract implementation defines the logic of processing <code>FileInfo</code>. This comprises the following tasks - </p>
-<ul>
-<li>
-<p>building <code>FileMetadata</code> per file and emitting it. This metadata contains the file information such as filepath, no. of blocks in it, length of the file, all the block ids, etc.</p>
-</li>
-<li>
-<p>creating <code>BlockMetadataIterator</code> from <code>FileMetadata</code>. The iterator lazy-loads the block metadata when needed. We use an iterator because the no. of blocks in a file can be huge if the block size is small and loading all of them at once in memory may cause out of memory errors.</p>
-</li>
-<li>
-<p>retrieving <code>BlockMetadata.FileBlockMetadata</code> from the block metadata iterator and emitting it. The FileBlockMetadata contains the block id, start offset of the block, length of file in the block, etc. The number of block metadata emitted per window are controlled by <code>blocksThreshold</code> setting which by default is 1. </p>
-</li>
-</ul>
-<p>The main utility method that performs all the above tasks is the <a href="#process_method"><code>process()</code></a> method. Concrete implementations can invoke this method whenever they have data to process.</p>
-<h3 id="ports">Ports</h3>
-<p>Declares only output ports on which file metadata and block metadata are emitted.</p>
-<ul>
-<li>filesMetadataOutput: metadata for each file is emitted on this port. </li>
-<li>blocksMetadataOutput: metadata for each block is emitted on this port. </li>
-</ul>
-<h3 id="process-method"><a name="process_method"></a><code>process()</code> method</h3>
-<p>When process() is invoked, any pending blocks from the current file are emitted on the 'blocksMetadataOutput' port. If the threshold for blocks per window is still not met then a new input file is processed - corresponding metadata is emitted on 'filesMetadataOutput' and more of its blocks are emitted. This operation is repeated until the <code>blocksThreshold</code> is reached or there are no more new files.</p>
-<pre><code class="java"> protected void process()
- {
- if (blockMetadataIterator != null && blockCount < blocksThreshold) {
- emitBlockMetadata();
- }
-
- FileInfo fileInfo;
- while (blockCount < blocksThreshold && (fileInfo = getFileInfo()) != null) {
- if (!processFileInfo(fileInfo)) {
- break;
- }
- }
- }
-</code></pre>
-
-<h3 id="abstract-methods">Abstract methods</h3>
-<ul>
-<li>
-<p><code>FileInfo getFileInfo()</code>: called from within the <code>process()</code> and provides the next file to process.</p>
-</li>
-<li>
-<p><code>long getDefaultBlockSize()</code>: provides the block size which is used when user hasn't configured the size.</p>
-</li>
-<li>
-<p><code>FileStatus getFileStatus(Path path)</code>: provides the <code>org.apache.hadoop.fs.FileStatus</code> instance for a path. </p>
-</li>
-</ul>
-<h3 id="configuration">Configuration</h3>
-<ol>
-<li><strong>blockSize</strong>: size of a block.</li>
-<li><strong>blocksThreshold</strong><a name="blocksThreshold"></a>: threshold on the number of blocks emitted by file splitter every window. This setting is used for throttling the work for downstream operators.</li>
-</ol>
-<h2 id="filesplitterbase">FileSplitterBase</h2>
-<p>Simple operator that receives tuples of type <code>FileInfo</code> on its <code>input</code> port. <code>FileInfo</code> contains the information (currently just the file path) about the file which this operator uses to create file metadata and block metadata.</p>
-<h3 id="example-application">Example application</h3>
-<p>This is a simple sub-dag that demonstrates how FileSplitterBase can be plugged into an application.
-<img alt="Application with FileSplitterBase" src="../images/filesplitter/baseexample.png" /></p>
-<p>The upstream operator emits tuples of type <code>FileInfo</code> on its output port which is connected to splitter input port. The downstream receives tuples of type <code>BlockMetadata.FileBlockMetadata</code> from the splitter's block metadata output port.</p>
-<pre><code class="java">public class ApplicationWithBaseSplitter implements StreamingApplication
-{
- @Override
- public void populateDAG(DAG dag, Configuration configuration)
- {
- JMSInput input = dag.addOperator("Input", new JMSInput());
- FileSplitterBase splitter = dag.addOperator("Splitter", new FileSplitterBase());
- FSSliceReader blockReader = dag.addOperator("BlockReader", new FSSliceReader());
- ...
- dag.addStream("file-info", input.output, splitter.input);
- dag.addStream("block-metadata", splitter.blocksMetadataOutput, blockReader.blocksMetadataInput);
- ...
- }
-
- public static class JMSInput extends AbstractJMSInputOperator<AbstractFileSplitter.FileInfo>
- {
-
- public final transient DefaultOutputPort<AbstractFileSplitter.FileInfo> output = new DefaultOutputPort<>();
-
- @Override
- protected AbstractFileSplitter.FileInfo convert(Message message) throws JMSException
- {
- //assuming the message is a text message containing the absolute path of the file.
- return new AbstractFileSplitter.FileInfo(null, ((TextMessage)message).getText());
- }
-
- @Override
- protected void emit(AbstractFileSplitter.FileInfo payload)
- {
- output.emit(payload);
- }
- }
-}
-</code></pre>
-
-<h3 id="ports_1">Ports</h3>
-<p>Declares an input port on which it receives tuples from the upstream operator. Output ports are inherited from AbstractFileSplitter.</p>
-<ul>
-<li>input: non optional port on which tuples of type <code>FileInfo</code> are received.</li>
-</ul>
-<h3 id="configuration_1">Configuration</h3>
-<ol>
-<li><strong>file</strong>: path of the file from which the filesystem is inferred. FileSplitter creates an instance of <code>org.apache.hadoop.fs.FileSystem</code> which is why this path is needed. </li>
-</ol>
-<pre><code>FileSystem.newInstance(new Path(file).toUri(), new Configuration());
-</code></pre>
-
-<p>The fs instance is then used to fetch the default block size and <code>org.apache.hadoop.fs.FileStatus</code> for each file path.</p>
-<h2 id="filesplitterinput">FileSplitterInput</h2>
-<p>This is an input operator that discovers files itself. The scanning of the directories for new files is asynchronous which is handled by <code>TimeBasedDirectoryScanner</code>. The function of TimeBasedDirectoryScanner is to periodically scan specified directories and find files which were newly added or modified. The interaction between the operator and the scanner is depicted in the diagram below.</p>
-<p><img alt="Interaction between operator and scanner" src="../images/filesplitter/sequence.png" /></p>
-<h3 id="example-application_1">Example application</h3>
-<p>This is a simple sub-dag that demonstrates how FileSplitterInput can be plugged into an application.</p>
-<p><img alt="Application with FileSplitterInput" src="../images/filesplitter/inputexample.png" /></p>
-<p>Splitter is the input operator here that sends block metadata to the downstream BlockReader.</p>
-<pre><code class="java"> @Override
- public void populateDAG(DAG dag, Configuration configuration)
- {
- FileSplitterInput input = dag.addOperator("Input", new FileSplitterInput());
- FSSliceReader reader = dag.addOperator("Block Reader", new FSSliceReader());
- ...
- dag.addStream("block-metadata", input.blocksMetadataOutput, reader.blocksMetadataInput);
- ...
- }
-
-</code></pre>
-
-<h3 id="ports_2">Ports</h3>
-<p>Since it is an input operator there are no input ports and output ports are inherited from AbstractFileSplitter.</p>
-<h3 id="configuration_2">Configuration</h3>
-<ol>
-<li><strong>scanner</strong>: the component that scans directories asynchronously. It is of type <code>com.datatorrent.lib.io.fs.FileSplitter.TimeBasedDirectoryScanner</code>. The basic implementation of TimeBasedDirectoryScanner can be customized by users. </li>
-</ol>
-<p>a. <strong>files</strong>: comma separated list of directories to scan. </p>
-<p>b. <strong>recursive</strong>: flag that controls whether the directories should be scanned recursively. </p>
-<p>c. <strong>scanIntervalMillis</strong>: interval specified in milliseconds after which another scan iteration is triggered. </p>
-<p>d. <strong>filePatternRegularExp</strong>: regular expression for accepted file names. </p>
-<p>e. <strong>trigger</strong>: a flag that triggers a scan iteration instantly. If the scanner thread is idling then it will initiate a scan immediately otherwise if a scan is in progress, then the new iteration will be triggered immediately after the completion of current one.
-2. <strong>idempotentStorageManager</strong>: by default FileSplitterInput is idempotent.
-Idempotency ensures that the operator will process the same set of files/blocks in a window if it has seen that window previously, i.e., before a failure. For example, let's say the operator completed window 10 and failed somewhere between window 11. If the operator gets restored at window 10 then it will process the same file/block again in window 10 which it did in the previous run before the failure. Idempotency is important but comes with higher cost because at the end of each window the operator needs to persist some state with respect to that window. Therefore, if one doesn't care about idempotency then they can set this property to be an instance of <code>com.datatorrent.lib.io.IdempotentStorageManager.NoopIdempotentStorageManager</code>.</p>
-<h2 id="handling-of-split-records">Handling of split records</h2>
-<p>Splitting of files to create tasks for downstream operator needs to be a simple operation that doesn't consume a lot of resources and is fast. This is why the file splitter doesn't open files to read. The downside of that is if the file contains records then a record may split across adjacent blocks. Handling of this is left to the downstream operator.</p>
-<p>We have created Block readers in Apex-malhar library that handle line splits efficiently. The 2 line readers- <code>AbstractFSLineReader</code> and <code>AbstractFSReadAheadLineReader</code> can be found here <a href="https://github.com/apache/incubator-apex-malhar/blob/master/library/src/main/java/com/datatorrent/lib/io/block/AbstractFSBlockReader.java">AbstractFSBlockReader</a>.</p>
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- <h1 id="kafka-input-operator">KAFKA INPUT OPERATOR</h1>
-<h3 id="introduction-about-kafka-input-operator">Introduction: About Kafka Input Operator</h3>
-<p>This is an input operator that consumes data from Kafka messaging system for further processing in Apex. Kafka Input Operator is an fault-tolerant and scalable Malhar Operator.</p>
-<h3 id="why-is-it-needed">Why is it needed ?</h3>
-<p>Kafka is a pull-based and distributed publish subscribe messaging system, topics are partitioned and replicated across
-nodes. Kafka input operator is needed when you want to read data from multiple
-partitions of a Kafka topic in parallel in an Apex application.</p>
-<h3 id="abstractkafkainputoperator">AbstractKafkaInputOperator</h3>
-<p>This is the abstract implementation that serves as base class for consuming messages from Kafka messaging system. This class doesn’t have any ports.</p>
-<p><img alt="AbstractKafkaInput.png" src="../images/kafkainput/image00.png" /></p>
-<h4 id="configuration-parameters">Configuration Parameters</h4>
-<p><table>
-<col width="25%" />
-<col width="75%" />
-<tbody>
-<tr class="odd">
-<td align="left"><p>Parameter</p></td>
-<td align="left"><p>Description</p></td>
-</tr>
-<tr class="even">
-<td align="left"><p>maxTuplesPerWindow</p></td>
-<td align="left"><p>Controls the maximum number of messages emitted in each streaming window from this operator. Minimum value is 1. Default value = MAX_VALUE </p></td>
-</tr>
-<tr class="odd">
-<td align="left"><p>idempotentStorageManager</p></td>
-<td align="left"><p>This is an instance of IdempotentStorageManager. Idempotency ensures that the operator will process the same set of messages in a window before and after a failure. For example, let's say the operator completed window 10 and failed somewhere between window 11. If the operator gets restored at window 10 then it will process the same messages again in window 10 which it did in the previous run before the failure. Idempotency is important but comes with higher cost because at the end of each window the operator needs to persist some state with respect to that window. Default Value = com.datatorrent.lib.io.IdempotentStorageManager.<br>NoopIdempotentStorageManager</p></td>
-</tr>
-<tr class="even">
-<td align="left"><p>strategy</p></td>
-<td align="left"><p>Operator supports two types of partitioning strategies, ONE_TO_ONE and ONE_TO_MANY.</p>
-<p>ONE_TO_ONE: If this is enabled, the AppMaster creates one input operator instance per Kafka topic partition. So the number of Kafka topic partitions equals the number of operator instances.</p>
-<p>ONE_TO_MANY: The AppMaster creates K = min(initialPartitionCount, N) Kafka input operator instances where N is the number of Kafka topic partitions. If K is less than N, the remaining topic partitions are assigned to the K operator instances in round-robin fashion. If K is less than initialPartitionCount, the AppMaster creates one input operator instance per Kafka topic partition. For example, if initialPartitionCount = 5 and number of Kafka partitions(N) = 2 then AppMaster creates 2 Kafka input operator instances.
-Default Value = ONE_TO_ONE</p></td>
-</tr>
-<tr class="odd">
-<td align="left"><p>msgRateUpperBound</p></td>
-<td align="left"><p>Maximum messages upper bound. Operator repartitions when the <em>msgProcessedPS</em> exceeds this bound. <em>msgProcessedPS</em> is the average number of messages processed per second by this operator.</p></td>
-</tr>
-<tr class="even">
-<td align="left"><p>byteRateUpperBound</p></td>
-<td align="left"><p>Maximum bytes upper bound. Operator repartitions when the <em>bytesPS</em> exceeds this bound. <em>bytesPS</em> is the average number of bytes processed per second by this operator.</p>
-<p></p></td>
-</tr>
-<tr class="odd">
-<td align="left"><p>offsetManager</p></td>
-<td align="left"><p>This is an optional parameter that is useful when the application restarts or start at specific offsets (offsets are explained below)</p></td>
-</tr>
-<tr class="even">
-<td align="left"><p>repartitionInterval</p></td>
-<td align="left"><p>Interval specified in milliseconds. This value specifies the minimum time required between two repartition actions. Default Value = 30 Seconds</p></td>
-</tr>
-<tr class="odd">
-<td align="left"><p>repartitionCheckInterval</p></td>
-<td align="left"><p>Interval specified in milliseconds. This value specifies the minimum interval between two offset updates. Default Value = 5 Seconds</p></td>
-</tr>
-<tr class="even">
-<td align="left"><p>initialPartitionCount</p></td>
-<td align="left"><p>When the ONE_TO_MANY partition strategy is enabled, this value indicates the number of Kafka input operator instances. Default Value = 1</p></td>
-</tr>
-<tr class="odd">
-<td align="left"><p>consumer</p></td>
-<td align="left"><p>This is an instance of com.datatorrent.contrib.kafka.KafkaConsumer. Default Value = Instance of SimpleKafkaConsumer.</p></td>
-</tr>
-</tbody>
-</table></p>
-<h4 id="abstract-methods">Abstract Methods</h4>
-<p>void emitTuple(Message message): Abstract method that emits tuples
-extracted from Kafka message.</p>
-<h3 id="kafkaconsumer">KafkaConsumer</h3>
-<p>This is an abstract implementation of Kafka consumer. It sends the fetch
-requests to the leading brokers of Kafka partitions. For each request,
-it receives the set of messages and stores them into the buffer which is
-ArrayBlockingQueue. SimpleKafkaConsumer which extends
-KafkaConsumer and serves the functionality of Simple Consumer API and
-HighLevelKafkaConsumer which extends KafkaConsumer and serves the
-functionality of High Level Consumer API.</p>
-<h3 id="pre-requisites">Pre-requisites</h3>
-<p>This operator referred the Kafka Consumer API of version
-0.8.1.1. So, this operator will work with any 0.8.x and 0.7.x version of Apache Kafka.</p>
-<h4 id="configuration-parameters_1">Configuration Parameters</h4>
-<table>
-<col width="15%" />
-<col width="15%" />
-<col width="15%" />
-<col width="55%" />
-<tbody>
-<tr class="odd">
-<td align="left"><p>Parameter</p></td>
-<td align="left"><p>Type</p></td>
-<td align="left"><p>Default</p></td>
-<td align="left"><p>Description</p></td>
-</tr>
-<tr class="even">
-<td align="left"><p>zookeeper</p></td>
-<td align="left"><p>String</p></td>
-<td align="left"><p></p></td>
-<td align="left"><p>Specifies the zookeeper quorum of Kafka clusters that you want to consume messages from. zookeeper is a string in the form of hostname1:port1,hostname2:port2,hostname3:port3 where hostname1,hostname2,hostname3 are hosts and port1,port2,port3 are ports of zookeeper server. If the topic name is the same across the Kafka clusters and want to consume data from these clusters, then configure the zookeeper as follows: c1::hs1:p1,hs2:p2,hs3:p3;c2::hs4:p4,hs5:p5,c3::hs6:p6</p>
-<p>where</p>
-<p>c1,c2,c3 indicates the cluster names, hs1,hs2,hs3,hs4,hs5,hs6 are zookeeper hosts and p1,p2,p3,p4,p5,p6 are corresponding ports. Here, cluster name is optional in case of single cluster</p></td>
-</tr>
-<tr class="odd">
-<td align="left"><p>cacheSize</p></td>
-<td align="left"><p>int</p></td>
-<td align="left"><p>1024</p></td>
-<td align="left"><p>Maximum of buffered messages hold in memory.</p></td>
-</tr>
-<tr class="even">
-<td align="left"><p>topic</p></td>
-<td align="left"><p>String</p></td>
-<td align="left"><p>default_topic</p></td>
-<td align="left"><p>Indicates the name of the topic.</p></td>
-</tr>
-<tr class="odd">
-<td align="left"><p>initialOffset</p></td>
-<td align="left"><p>String</p></td>
-<td align="left"><p>latest</p></td>
-<td align="left"><p>Indicates the type of offset i.e, “earliest or latest”. If initialOffset is “latest”, then the operator consumes messages from latest point of Kafka queue. If initialOffset is “earliest”, then the operator consumes messages starting from message queue. This can be overridden by OffsetManager.</p></td>
-</tr>
-</tbody>
-</table>
-
-<h4 id="abstract-methods_1">Abstract Methods</h4>
-<ol>
-<li>void commitOffset(): Commit the offsets at checkpoint.</li>
-<li>Map <KafkaPartition, Long> getCurrentOffsets(): Return the current
- offset status.</li>
-<li>resetPartitionsAndOffset(Set <KafkaPartition> partitionIds,
- Map <KafkaPartition, Long> startOffset): Reset the partitions with
- parittionIds and offsets with startOffset.</li>
-</ol>
-<h4 id="configuration-parameters-for-simplekafkaconsumer">Configuration Parameters for SimpleKafkaConsumer</h4>
-<table>
-<col width="25%" />
-<col width="15%" />
-<col width="15%" />
-<col width="45%" />
-<tbody>
-<tr class="odd">
-<td align="left"><p>Parameter</p></td>
-<td align="left"><p>Type</p></td>
-<td align="left"><p>Default</p></td>
-<td align="left"><p>Description</p></td>
-</tr>
-<tr class="even">
-<td align="left"><p>bufferSize</p></td>
-<td align="left"><p>int</p></td>
-<td align="left"><p>1 MB</p></td>
-<td align="left"><p>Specifies the maximum total size of messages for each fetch request.</p></td>
-</tr>
-<tr class="odd">
-<td align="left"><p>metadataRefreshInterval</p></td>
-<td align="left"><p>int</p></td>
-<td align="left"><p>30 Seconds</p></td>
-<td align="left"><p>Interval in between refresh the metadata change(broker change) in milliseconds. Enabling metadata refresh guarantees an automatic reconnect when a new broker is elected as the host. A value of -1 disables this feature.</p></td>
-</tr>
-<tr class="even">
-<td align="left"><p>metadataRefreshRetryLimit</p></td>
-<td align="left"><p>int</p></td>
-<td align="left"><p>-1</p></td>
-<td align="left"><p>Specifies the maximum brokers' metadata refresh retry limit. -1 means unlimited retry.</p></td>
-</tr>
-</tbody>
-</table>
-
-<h3 id="offsetmanager">OffsetManager</h3>
-<p>This is an interface for offset management and is useful when consuming data
-from specified offsets. Updates the offsets for all the Kafka partitions
-periodically. Below is the code snippet: </p>
-<pre><code class="java">public interface OffsetManager
-{
- public Map<KafkaPartition, Long> loadInitialOffsets();
- public void updateOffsets(Map<KafkaPartition, Long> offsetsOfPartitions);
-}
-</code></pre>
-
-<h4 id="abstract-methods_2">Abstract Methods</h4>
-<p>Map <KafkaPartition, Long> loadInitialOffsets(): Specifies the initial offset for consuming messages; called at the activation stage.</p>
-<p>updateOffsets(Map <KafkaPartition, Long> offsetsOfPartitions): This
-method is called at every repartitionCheckInterval to update offsets.</p>
-<h3 id="partitioning">Partitioning</h3>
-<p>The logical instance of the KafkaInputOperator acts as the Partitioner
-as well as a StatsListener. This is because the
-AbstractKafkaInputOperator implements both the
-com.datatorrent.api.Partitioner and com.datatorrent.api.StatsListener
-interfaces and provides an implementation of definePartitions(...) and
-processStats(...) which makes it auto-scalable.</p>
-<h4 id="response-processstatsbatchedoperatorstats-stats">Response processStats(BatchedOperatorStats stats)</h4>
-<p>The application master invokes this method on the logical instance with
-the stats (tuplesProcessedPS, bytesPS, etc.) of each partition.
-Re-partitioning happens based on whether any new Kafka partitions added for
-the topic or bytesPS and msgPS cross their respective upper bounds.</p>
-<h4 id="definepartitions">DefinePartitions</h4>
-<p>Based on the repartitionRequired field of the Response object which is
-returned by processStats(...) method, the application master invokes
-definePartitions(...) on the logical instance which is also the
-partitioner instance. Dynamic partition can be disabled by setting the
-parameter repartitionInterval value to a negative value.</p>
-<h3 id="abstractsingleportkafkainputoperator">AbstractSinglePortKafkaInputOperator</h3>
-<p>This class extends AbstractKafkaInputOperator and having single output
-port, will emit the messages through this port.</p>
-<h4 id="ports">Ports</h4>
-<p>outputPort <T>: Tuples extracted from Kafka messages are emitted through
-this port.</p>
-<h4 id="abstract-methods_3">Abstract Methods</h4>
-<p>T getTuple(Message msg) : Converts the Kafka message to tuple.</p>
-<h3 id="concrete-classes">Concrete Classes</h3>
-<ol>
-<li>
-<p>KafkaSinglePortStringInputOperator :
-This class extends AbstractSinglePortKafkaInputOperator and getTuple() method extracts string from Kafka message.</p>
-</li>
-<li>
-<p>KafkaSinglePortByteArrayInputOperator:
-This class extends AbstractSinglePortKafkaInputOperator and getTuple() method extracts byte array from Kafka message.</p>
-</li>
-</ol>
-<h3 id="application-example">Application Example</h3>
-<p>This section builds an Apex application using Kafka input operator.
-Below is the code snippet:</p>
-<pre><code class="java">@ApplicationAnnotation(name = "KafkaApp")
-public class ExampleKafkaApplication implements StreamingApplication
-{
-@Override
-public void populateDAG(DAG dag, Configuration entries)
-{
- KafkaSinglePortByteArrayInputOperator input = dag.addOperator("MessageReader", new KafkaSinglePortByteArrayInputOperator());
-
- ConsoleOutputOperator output = dag.addOperator("Output", new ConsoleOutputOperator());
-
- dag.addStream("MessageData", input.outputPort, output.input);
-}
-}
-</code></pre>
-
-<p>Below is the configuration for “test” Kafka topic name and
-“localhost:2181” is the zookeeper forum:</p>
-<pre><code class="xml"><property>
-<name>dt.operator.MessageReader.prop.topic</name>
-<value>test</value>
-</property>
-
-<property>
-<name>dt.operator.KafkaInputOperator.prop.zookeeper</nam>
-<value>localhost:2181</value>
-</property>
-</code></pre>
-
- </div>
- </div>
- <footer>
-
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diff --git a/docs/malhar-3.3/search.html b/docs/malhar-3.3/search.html
deleted file mode 100644
index 96dc007..0000000
--- a/docs/malhar-3.3/search.html
+++ /dev/null
@@ -1,171 +0,0 @@
-<!DOCTYPE html>
-<!--[if IE 8]><html class="no-js lt-ie9" lang="en" > <![endif]-->
-<!--[if gt IE 8]><!--> <html class="no-js" lang="en" > <!--<![endif]-->
-<head>
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- <script type="text/javascript" src="./js/highlight.pack.js"></script>
- <script src="./js/theme.js"></script>
- <script>var base_url = '.';</script>
- <script data-main="./mkdocs/js/search.js" src="./mkdocs/js/require.js"></script>
-
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-</head>
-
-<body class="wy-body-for-nav" role="document">
-
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- <input type="text" name="q" placeholder="Search docs" />
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-
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- <a class="" href=".">Apache Apex Malhar</a>
-
- </li>
-<li>
-
- <li>
- <ul class="subnav">
- <li><span>Operators</span></li>
-
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-
- <li class="toctree-l1 ">
- <a class="" href="operators/kafkaInputOperator/">Kafka Input</a>
-
- </li>
-
-
-
- <li class="toctree-l1 ">
- <a class="" href="operators/file_splitter/">File Splitter</a>
-
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-
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- <a class="" href="operators/block_reader/">Block Reader</a>
-
- </li>
-
-
-
- <li class="toctree-l1 ">
- <a class="" href="operators/file_output/">File Output</a>
-
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-<li>
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- <div role="navigation" aria-label="breadcrumbs navigation">
- <ul class="wy-breadcrumbs">
- <li><a href=".">Docs</a> »</li>
-
-
- <li class="wy-breadcrumbs-aside">
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- </ul>
- <hr/>
-</div>
- <div role="main">
- <div class="section">
-
-
- <h1 id="search">Search Results</h1>
-
- <form id="content_search" action="search.html">
- <span role="status" aria-live="polite" class="ui-helper-hidden-accessible"></span>
- <input name="q" id="mkdocs-search-query" type="text" class="search_input search-query ui-autocomplete-input" placeholder="Search the Docs" autocomplete="off" autofocus>
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diff --git a/docs/malhar-3.3/searchbox.html b/docs/malhar-3.3/searchbox.html
deleted file mode 100644
index 177fcb3..0000000
--- a/docs/malhar-3.3/searchbox.html
+++ /dev/null
@@ -1,5 +0,0 @@
-<div role="search">
- <form id ="rtd-search-form" class="wy-form" action="{{ base_url }}/search.html" method="get">
- <input type="text" name="q" placeholder="Search docs" />
- </form>
-</div>
diff --git a/docs/malhar-3.3/sitemap.xml b/docs/malhar-3.3/sitemap.xml
deleted file mode 100644
index 1455cd3..0000000
--- a/docs/malhar-3.3/sitemap.xml
+++ /dev/null
@@ -1,40 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<urlset xmlns="http://www.sitemaps.org/schemas/sitemap/0.9">
-
-
- <url>
- <loc>/</loc>
- <lastmod>2016-03-11</lastmod>
- <changefreq>daily</changefreq>
- </url>
-
-
-
-
- <url>
- <loc>/operators/kafkaInputOperator/</loc>
- <lastmod>2016-03-11</lastmod>
- <changefreq>daily</changefreq>
- </url>
-
- <url>
- <loc>/operators/file_splitter/</loc>
- <lastmod>2016-03-11</lastmod>
- <changefreq>daily</changefreq>
- </url>
-
- <url>
- <loc>/operators/block_reader/</loc>
- <lastmod>2016-03-11</lastmod>
- <changefreq>daily</changefreq>
- </url>
-
- <url>
- <loc>/operators/file_output/</loc>
- <lastmod>2016-03-11</lastmod>
- <changefreq>daily</changefreq>
- </url>
-
-
-
-</urlset>
\ No newline at end of file
diff --git a/docs/malhar-3.3/toc.html b/docs/malhar-3.3/toc.html
deleted file mode 100644
index 6cd2fc9..0000000
--- a/docs/malhar-3.3/toc.html
+++ /dev/null
@@ -1,23 +0,0 @@
-{% if nav_item.children %}
- <ul class="subnav">
- <li><span>{{ nav_item.title }}</span></li>
-
- {% for nav_item in nav_item.children %}
- {% include 'toc.html' %}
- {% endfor %}
- </ul>
-{% else %}
- <li class="toctree-l1 {% if nav_item.active%}current{%endif%}">
- <a class="{% if nav_item.active%}current{%endif%}" href="{{ nav_item.url }}">{{ nav_item.title }}</a>
- {% if nav_item == current_page %}
- <ul>
- {% for toc_item in toc %}
- <li class="toctree-l3"><a href="{{ toc_item.url }}">{{ toc_item.title }}</a></li>
- {% for toc_item in toc_item.children %}
- <li><a class="toctree-l4" href="{{ toc_item.url }}">{{ toc_item.title }}</a></li>
- {% endfor %}
- {% endfor %}
- </ul>
- {% endif %}
- </li>
-{% endif %}
diff --git a/docs/malhar-3.3/versions.html b/docs/malhar-3.3/versions.html
deleted file mode 100644
index d12d197..0000000
--- a/docs/malhar-3.3/versions.html
+++ /dev/null
@@ -1,15 +0,0 @@
-<div class="rst-versions" role="note" style="cursor: pointer">
- <span class="rst-current-version" data-toggle="rst-current-version">
- {% if repo_name == 'GitHub' %}
- <a href="{{ repo_url }}" class="icon icon-github" style="float: left; color: #fcfcfc"> GitHub</a>
- {% elif repo_name == 'Bitbucket' %}
- <a href="{{ repo_url }}" class="icon icon-bitbucket" style="float: left; color: #fcfcfc"> BitBucket</a>
- {% endif %}
- {% if previous_page %}
- <span><a href="{{ previous_page.url }}" style="color: #fcfcfc;">« Previous</a></span>
- {% endif %}
- {% if next_page %}
- <span style="margin-left: 15px"><a href="{{ next_page.url }}" style="color: #fcfcfc">Next »</a></span>
- {% endif %}
- </span>
-</div>
diff --git a/gulpfile.js b/gulpfile.js
index 767d8a6..ab9e2ac 100644
--- a/gulpfile.js
+++ b/gulpfile.js
@@ -118,17 +118,9 @@
.pipe(gulp.dest(path.join(BUILD_LOCATION, 'fonts')));
});
-// Copies docs to dist
-gulp.task('copy:docs', function() {
- return gulp.src([
- './docs/*/**'
- ])
- .pipe(gulp.dest(path.join(BUILD_LOCATION, 'docs')));
-});
-
// Default task is to build the site
-gulp.task('default', ['less', 'html', 'copy:js', 'copy:images', 'copy:fonts', 'copy:docs']);
+gulp.task('default', ['less', 'html', 'copy:js', 'copy:images', 'copy:fonts']);
// Fetch all JIRAs assodicated with the projects to create a roadmap file
