You can run Flink using Tez as an execution environment. Flink on Tez is currently included in flink-staging in alpha. All classes are located in the org.apache.flink.tez package.
Apache Tez is a scalable data processing platform. Tez provides an API for specifying a directed acyclic graph (DAG), and functionality for placing the DAG vertices in YARN containers, as well as data shuffling. In Flink‘s architecture, Tez is at about the same level as Flink’s network stack. While Flink's network stack focuses heavily on low latency in order to support pipelining, data streaming, and iterative algorithms, Tez focuses on scalability and elastic resource usage.
Thus, by replacing Flink‘s network stack with Tez, users can get scalability and elastic resource usage in shared clusters while retaining Flink’s APIs, optimizer, and runtime algorithms (local sorts, hash tables, etc).
Flink programs can run almost unmodified using Tez as an execution environment. Tez supports local execution (e.g., for debugging), and remote execution on YARN.
The LocalTezEnvironment can be used run programs using the local mode provided by Tez. This example shows how WordCount can be run using the Tez local mode. It is identical to a normal Flink WordCount, except that the LocalTezEnvironment is used. To run in local Tez mode, you can simply run a Flink on Tez program from your IDE (e.g., right click and run).
{% highlight java %} public class WordCountExample { public static void main(String[] args) throws Exception { final LocalTezEnvironment env = LocalTezEnvironment.create();
DataSet<String> text = env.fromElements(
"Who's there?",
"I think I hear them. Stand, ho! Who's there?");
DataSet<Tuple2<String, Integer>> wordCounts = text
.flatMap(new LineSplitter())
.groupBy(0)
.sum(1);
wordCounts.print();
env.execute("Word Count Example");
}
public static class LineSplitter implements FlatMapFunction<String, Tuple2<String, Integer>> {
@Override
public void flatMap(String line, Collector<Tuple2<String, Integer>> out) {
for (String word : line.split(" ")) {
out.collect(new Tuple2<String, Integer>(word, 1));
}
}
}
} {% endhighlight %}
Install Tez on your Hadoop 2 cluster following the instructions from the Apache Tez website. If you are able to run the examples that ship with Tez, then Tez has been successfully installed.
Currently, you need to build Flink yourself to obtain Flink on Tez (the reason is a Hadoop version compatibility: Tez releases artifacts on Maven central with a Hadoop 2.6.0 dependency). Build Flink using mvn -DskipTests clean package -Pinclude-tez -Dhadoop.version=X.X.X -Dtez.version=X.X.X. Make sure that the Hadoop version matches the version that Tez uses. Obtain the jar file contained in the Flink distribution under flink-staging/flink-tez/target/flink-tez-x.y.z-flink-fat-jar.jar and upload it to some directory in HDFS. E.g., to upload the file to the directory /apps, execute {% highlight bash %} $ hadoop fs -put /path/to/flink-tez-x.y.z-flink-fat-jar.jar /apps {% endhighlight %}
Edit the tez-site.xml configuration file, adding an entry that points to the location of the file. E.g., assuming that the file is in the directory /apps/, add the following entry to tez-site.xml: {% highlight xml %} tez.aux.uris ${fs.default.name}/apps/flink-tez-x.y.z-flink-fat-jar.jar {% endhighlight %}
At this point, you should be able to run the pre-packaged examples, e.g., run WordCount: {% highlight bash %} $ hadoop jar /path/to/flink-tez-x.y.z-flink-fat-jar.jar wc hdfs:/path/to/text hdfs:/path/to/output {% endhighlight %}
Application packaging is currently a bit different than in Flink standalone mode. Flink programs that run on Tez need to be packaged in a “fat jar” file that contain the Flink client. This jar can then be executed via the hadoop jar command. An easy way to do that is to use the provided flink-tez-quickstart maven archetype. Create a new project as
{% highlight bash %} $ mvn archetype:generate
-DarchetypeGroupId=org.apache.flink
-DarchetypeArtifactId=flink-tez-quickstart
-DarchetypeVersion={{site.version}} {% endhighlight %}
and specify the group id, artifact id, version, and package of your project. For example, let us assume the following options: org.myorganization, flink-on-tez, 0.1, and org.myorganization. You should see the following output on your terminal:
{% highlight bash %} $ mvn archetype:generate -DarchetypeGroupId=org.apache.flink -DarchetypeArtifactId=flink-tez-quickstart [INFO] Scanning for projects... [INFO] [INFO] ------------------------------------------------------------------------ [INFO] Building Maven Stub Project (No POM) 1 [INFO] ------------------------------------------------------------------------ [INFO] [INFO] >>> maven-archetype-plugin:2.2:generate (default-cli) > generate-sources @ standalone-pom >>> [INFO] [INFO] <<< maven-archetype-plugin:2.2:generate (default-cli) < generate-sources @ standalone-pom <<< [INFO] [INFO] --- maven-archetype-plugin:2.2:generate (default-cli) @ standalone-pom --- [INFO] Generating project in Interactive mode [INFO] Archetype [org.apache.flink:flink-tez-quickstart:0.9-SNAPSHOT] found in catalog local Define value for property ‘groupId’: : org.myorganization Define value for property ‘artifactId’: : flink-on-tez Define value for property ‘version’: 1.0-SNAPSHOT: : 0.1 Define value for property ‘package’: org.myorganization: : Confirm properties configuration: groupId: org.myorganization artifactId: flink-on-tez version: 0.1 package: org.myorganization Y: : Y [INFO] ---------------------------------------------------------------------------- [INFO] Using following parameters for creating project from Archetype: flink-tez-quickstart:0.9-SNAPSHOT [INFO] ---------------------------------------------------------------------------- [INFO] Parameter: groupId, Value: org.myorganization [INFO] Parameter: artifactId, Value: flink-on-tez [INFO] Parameter: version, Value: 0.1 [INFO] Parameter: package, Value: org.myorganization [INFO] Parameter: packageInPathFormat, Value: org/myorganization [INFO] Parameter: package, Value: org.myorganization [INFO] Parameter: version, Value: 0.1 [INFO] Parameter: groupId, Value: org.myorganization [INFO] Parameter: artifactId, Value: flink-on-tez [INFO] project created from Archetype in dir: /Users/kostas/Dropbox/flink-tez-quickstart-test/flink-on-tez [INFO] ------------------------------------------------------------------------ [INFO] BUILD SUCCESS [INFO] ------------------------------------------------------------------------ [INFO] Total time: 44.130 s [INFO] Finished at: 2015-02-26T17:59:45+01:00 [INFO] Final Memory: 15M/309M [INFO] ------------------------------------------------------------------------ {% endhighlight %}
The project contains an example called YarnJob.java that provides the skeleton for a Flink-on-Tez job. Program execution is currently done using Hadoop's ProgramDriver, see the Driver.java class for an example. Create the fat jar using mvn -DskipTests clean package. The resulting jar will be located in the target/ directory. You can now execute a job as follows:
{% highlight bash %} $ mvn -DskipTests clean package $ hadoop jar flink-on-tez/target/flink-on-tez-0.1-flink-fat-jar.jar yarnjob [command-line parameters] {% endhighlight %}
Flink programs that run on YARN using Tez as an execution engine need to use the RemoteTezEnvironment and register the class that contains the main method with that environment: {% highlight java %} public class WordCountExample { public static void main(String[] args) throws Exception { final RemoteTezEnvironment env = RemoteTezEnvironment.create();
DataSet<String> text = env.fromElements(
"Who's there?",
"I think I hear them. Stand, ho! Who's there?");
DataSet<Tuple2<String, Integer>> wordCounts = text
.flatMap(new LineSplitter())
.groupBy(0)
.sum(1);
wordCounts.print();
env.registerMainClass(WordCountExample.class);
env.execute("Word Count Example");
}
public static class LineSplitter implements FlatMapFunction<String, Tuple2<String, Integer>> {
@Override
public void flatMap(String line, Collector<Tuple2<String, Integer>> out) {
for (String word : line.split(" ")) {
out.collect(new Tuple2<String, Integer>(word, 1));
}
}
}
} {% endhighlight %}
Flink on Tez reuses the Flink APIs, the Flink optimizer, and the Flink local runtime, including Flink‘s hash table and sort implementations. Tez replaces Flink’s network stack and control plan, and is responsible for scheduling and network shuffles.
The figure below shows how a Flink program passes through the Flink stack and generates a Tez DAG (instead of a JobGraph that would be created using normal Flink execution).
All local processing, including memory management, sorting, and hashing is performed by Flink as usual. Local processing is encapsulated in Tez vertices, as seen in the figure below. Tez vertices are connected by edges. Tez is currently based on a key-value data model. In the current implementation, the elements that are processed by Flink operators are wrapped inside Tez values, and the Tez key field is used to indicate the index of the target task that the elements are destined to.
Currently, Flink on Tez does not support all features of the Flink API. We are working to enable all of the missing features listed below. In the meantime, if your project depends on these features, we suggest to use Flink on YARN or Flink standalone.
The following features are currently missing.
Dedicated client: jobs need to be submitted via Hadoop's command-line client
Self-joins: currently binary operators that receive the same input are not supported due to TEZ-1190.
Iterative programs are currently not supported.
Broadcast variables are currently not supported.
Accummulators and counters are currently not supported.
Performance: The current implementation has not been heavily tested for performance, and misses several optimizations, including task chaining.
Streaming API: Streaming programs will not currently compile to Tez DAGs.
Scala API: The current implementation has only been tested with the Java API.