website2/docs/topology-development-streamlet-scala.md

id: topology-development-streamlet-scala title: The Heron Streamlet API for Scala sidebar_label: The Heron Streamlet API for Scala

Getting started

In order to use the Heron Streamlet API for Scala, you'll need to install the heron-api library.

Maven setup

In order to use the heron-api library, add this to the dependencies block of your pom.xml configuration file:

<dependency>
    <groupId>org.apache.heron</groupId>
    <artifactId>heron-api</artifactId>
    <version>{{< heronVersion >}}</version>
</dependency>

Compiling a JAR with dependencies

In order to run a Scala topology created using the Heron Streamlet API in a Heron cluster, you'll need to package your topology as a “fat” JAR with dependencies included. You can use the Maven Assembly Plugin to generate JARs with dependencies. To install the plugin and add a Maven goal for a single JAR, add this to the plugins block in your pom.xml:

<plugin>
    <artifactId>maven-assembly-plugin</artifactId>
    <configuration>
        <descriptorRefs>
            <descriptorRef>jar-with-dependencies</descriptorRef>
        </descriptorRefs>
        <archive>
            <manifest>
                <mainClass></mainClass>
            </manifest>
        </archive>
    </configuration>
    <executions>
        <execution>
            <id>make-assembly</id>
            <phase>package</phase>
            <goals>
                <goal>single</goal>
            </goals>
        </execution>
    </executions>
</plugin>

Once your pom.xml is properly set up, you can compile the JAR with dependencies using this command:

$ mvn assembly:assembly

By default, this will add a JAR in your project‘s target folder with the name PROJECT-NAME-VERSION-jar-with-dependencies.jar. Here’s an example topology submission command using a compiled JAR:

$ mvn assembly:assembly
$ heron submit local \
  target/my-project-1.2.3-jar-with-dependencies.jar \
  com.example.Main \
  MyTopology arg1 arg2

Streamlet API topology configuration

Every Streamlet API topology needs to be configured using a Config object. Here's an example default configuration:

import org.apache.heron.streamlet.Config
import org.apache.heron.streamlet.scala.Runner

val topologyConfig = Config.defaultConfig()

// Apply topology configuration using the topologyConfig object
val topologyRunner = new Runner()
topologyRunner.run("name-for-topology", topologyConfig, topologyBuilder)

The table below shows the configurable parameters for Heron topologies:

Parameter	Default
Delivery semantics	At most once
Serializer	Kryo
Number of total container topologies	2
Per-container CPU	1.0
Per-container RAM	100 MB

Here's an example non-default configuration:

val topologyConfig = Config.newBuilder()
        .setNumContainers(5)
        .setPerContainerRamInGigabytes(10)
        .setPerContainerCpu(3.5f)
        .setDeliverySemantics(Config.DeliverySemantics.EFFECTIVELY_ONCE)
        .setSerializer(Config.Serializer.JAVA)
        .setUserConfig("some-key", "some-value")
        .build()

Delivery semantics

You can apply delivery semantics to a Streamlet API topology like this:

topologyConfig
        .setDeliverySemantics(Config.DeliverySemantics.EFFECTIVELY_ONCE)

The other available options in the DeliverySemantics enum are ATMOST_ONCE and ATLEAST_ONCE.

Streamlets

In the Heron Streamlet API for Scala, processing graphs consist of streamlets. One or more supplier streamlets inject data into your graph to be processed by downstream operators.

Operations

Operation	Description	Example
`map`	Create a new streamlet by applying the supplied mapping function to each element in the original streamlet	Add 1 to each element in a streamlet of integers
`flatMap`	Like a map operation but with the important difference that each element of the streamlet is flattened	Flatten a sentence into individual words
`filter`	Create a new streamlet containing only the elements that satisfy the supplied filtering function	Remove all inappropriate words from a streamlet of strings
`union`	Unifies two streamlets into one, without modifying the elements of the two streamlets	Unite two different `Streamlet<String>`s into a single streamlet
`clone`	Creates any number of identical copies of a streamlet	Create three separate streamlets from the same source
`transform`	Transform a streamlet using whichever logic you‘d like (useful for transformations that don’t neatly map onto the available operations)
`join`	Create a new streamlet by combining two separate key-value streamlets into one on the basis of each element's key. Supported Join Types: Inner (as default), Outer-Left, Outer-Right and Outer	Combine key-value pairs listing current scores (e.g. `("h4x0r", 127)`) for each user into a single per-user stream
`keyBy`	Returns a new key-value streamlet by applying the supplied extractors to each element in the original streamlet
`reduceByKey`	Produces a streamlet of key-value on each key, and in accordance with a reduce function that you apply to all the accumulated values	Count the number of times a value has been encountered
`reduceByKeyAndWindow`	Produces a streamlet of key-value on each key, within a time window, and in accordance with a reduce function that you apply to all the accumulated values	Count the number of times a value has been encountered within a specified time window
`countByKey`	A special reduce operation of counting number of tuples on each key	Count the number of times a value has been encountered
`countByKeyAndWindow`	A special reduce operation of counting number of tuples on each key, within a time window	Count the number of times a value has been encountered within a specified time window
`split`	Split a streamlet into multiple streamlets with different id
`withStream`	Select a stream with id from a streamlet that contains multiple streams
`applyOperator`	Returns a new streamlet by applying an user defined operator to the original streamlet	Apply an existing bolt as an operator
`repartition`	Create a new streamlet by applying a new parallelism level to the original streamlet	Increase the parallelism of a streamlet from 5 to 10
`toSink`	Sink operations terminate the processing graph by storing elements in a database, logging elements to stdout, etc.	Store processing graph results in an AWS Redshift table
`log`	Logs the final results of a processing graph to stdout. This must be the last step in the graph.
`consume`	Consume operations are like sink operations except they don't require implementing a full sink interface (consume operations are thus suited for simple operations like logging)	Log processing graph results using a custom formatting function

Map operations

Map operations create a new streamlet by applying the supplied mapping function to each element in the original streamlet. Here's an example:

builder.newSource(() => 1)
    .map[Int]((i: Int) => i + 12) // or .map[Int](_.+(12)) as synthetic function

In this example, a supplier streamlet emits an indefinite series of 1s. The map operation then adds 12 to each incoming element, producing a streamlet of 13s.

FlatMap operations

FlatMap operations are like map operations but with the important difference that each element of the streamlet is “flattened” into a collection type. In this example, a supplier streamlet emits the same sentence over and over again; the flatMap operation transforms each sentence into a Scala List of individual words:

builder.newSource(() => "I have nothing to declare but my genius")
    .flatMap[String](_.split(" "))

The effect of this operation is to transform the Streamlet[String] into a Streamlet[List[String]].

One of the core differences between map and flatMap operations is that flatMap operations typically transform non-collection types into collection types.

Filter operations

Filter operations retain elements in a streamlet, while potentially excluding some or all elements, on the basis of a provided filtering function. Here's an example:

import java.util.concurrent.ThreadLocalRandom

builder.newSource(() => ThreadLocalRandom.current().nextInt(1, 11))
        .filter(_.<(7))

In this example, a source streamlet consisting of random integers between 1 and 10 is modified by a filter operation that removes all streamlet elements that are lower than 7.

Union operations

Union operations combine two streamlets of the same type into a single streamlet without modifying the elements. Here's an example:

val flowers = builder.newSource(() => "flower")
val butterflies = builder.newSource(() => "butterfly")

val combinedSpringStreamlet = flowers.union(butterflies)

Here, one streamlet is an endless series of “flowers” while the other is an endless series of “butterflies”. The union operation combines them into a single streamlet of alternating “flowers” and “butterflies”.

Clone operations

Clone operations enable you to create any number of “copies” of a streamlet. Each of the “copy” streamlets contains all the elements of the original and can be manipulated just like the original streamlet. Here's an example:

import scala.util.Random

val integers = builder.newSource(() => Random.nextInt(100))

val copies = integers.clone(5)
val ints1 = copies.get(0)
val ints2 = copies.get(1)
val ints3 = copies.get(2)
// and so on...

In this example, a streamlet of random integers between 0 and 99 is split into 5 identical streamlets.

Transform operations

Transform operations are highly flexible operations that are most useful for:

operations involving state in stateful topologies
operations that don't neatly fit into the other categories or into a lambda-based logic

Transform operations require you to implement three different methods:

A setup function that enables you to pass a context object to the operation and to specify what happens prior to the transform step
A transform operation that performs the desired transformation
A cleanup function that allows you to specify what happens after the transform step

The context object available to a transform operation provides access to:

the current state of the topology
the topology's configuration
the name of the stream
the stream partition
the current task ID

Here's a Scala example of a transform operation in a topology where a stateful record is kept of the number of items processed:

import org.apache.heron.streamlet.Context
import org.apache.heron.streamlet.scala.SerializableTransformer

class CountNumberOfItems extends SerializableTransformer[String, String] {
    private val numberOfItems = new AtomicLong()

    override def setup(context: Context): Unit = {
      numberOfItems.incrementAndGet()
      context.getState().put("number-of-items", numberOfItems)
    }

    override def transform(i: String, f: String => Unit): Unit = {
      val transformedString = i.toUpperCase
      f(transformedString)
    }

    override def cleanup(): Unit =
      println(s"Successfully processed new state: $numberOfItems")
  }

This operation does a few things:

In the setup method, the Context object is used to access the current state (which has the semantics of a Java Map). The current number of items processed is incremented by one and then saved as the new state.
In the transform method, the incoming string is transformed as UpperCase in some way and then “accepted” as the new value.
In the cleanup step, the current count of items processed is logged.

Here's that operation within the context of a streamlet processing graph:

builder.newSource(() => "Some string over and over");
        .transform(new CountNumberOfItems())
        .log()

Join operations

For a more in-depth conceptual discussion of joins, see the Heron Streamlet API doc.

Join operations unify two streamlets on a key (join operations thus require KV streamlets). Each KeyValue object in a streamlet has, by definition, a key. When a join operation is added to a processing graph,

import org.apache.heron.streamlet.{Config, KeyValue, WindowConfig}
import org.apache.heron.streamlet.scala.Builder

val builder = Builder.newBuilder()

val streamlet1 = builder
  .newSource(() =>
    new KeyValue[String, String]("heron-api", "topology-api"))
  .setName("streamlet1")

val streamlet2 = builder
  .newSource(() =>
    new KeyValue[String, String]("heron-api", "streamlet-api"))
  .setName("streamlet2")

streamlet1.join[KeyValue[String, String], KeyValue[String, String], String](
  streamlet2,
  (kv: KeyValue[String, String]) => kv,
  (kv: KeyValue[String, String]) => kv,
  WindowConfig.TumblingCountWindow(10),
  (kv1: KeyValue[String, String], kv2: KeyValue[String, String]) =>
    kv1.getValue + " - " + kv2.getValue
)

In this case, the resulting streamlet would consist of an indefinite stream with two KeyValue objects with the key heron-api but different values (topology-api and streamlet-api).

The effect of a join operation is to create a new streamlet for each key.

Key by operations

Key by operations convert each item in the original streamlet into a key-value pair and return a new streamlet. Here is an example:

val builder = Builder.newBuilder()

builder
  .newSource(() => "Paco de Lucia is one of the most popular virtuoso")
  // Convert each sentence into individual words
  .flatMap[String](_.split(" "))
  .keyBy[String, Int](
      // Key extractor (in this case, each word acts as the key)
      (word: String) => word,
      // Value extractor (get the length of each word)
      (word: String) => word.length
  )
  // The result is logged
  .log();

Reduce by key operations

You can apply reduce operations to streamlets by specifying:

a key extractor that determines what counts as the key for the streamlet
a value extractor that determines which final value is chosen for each element of the streamlet
a reduce function that produces a single value for each key in the streamlet

Reduce by key operations produce a new streamlet of key-value window objects (which include a key-value pair including the extracted key and calculated value). Here's an example:

val builder = Builder.newBuilder()

builder
  .newSource(() => "Paco de Lucia is one of the most popular virtuoso")
  // Convert each sentence into individual words
  .flatMap[String](_.split(" "))
  .reduceByKey[String, Int](
      // Key extractor (in this case, each word acts as the key)
      (word: String) => word,
      // Value extractor (each word appears only once, hence the value is always 1)
      (word: String) => 1,
      // Reduce operation (a running sum)
      (x: Int, y: Int) => x + y)
  // The result is logged
  .log();

Reduce by key and window operations

You can apply reduce operations to streamlets by specifying:

a key extractor that determines what counts as the key for the streamlet
a value extractor that determines which final value is chosen for each element of the streamlet
a time window across which the operation will take place
a reduce function that produces a single value for each key in the streamlet

Reduce by key and window operations produce a new streamlet of key-value window objects (which include a key-value pair including the extracted key and calculated value, as well as information about the window in which the operation took place). Here's an example:

import org.apache.heron.streamlet.WindowConfig;

val builder = Builder.newBuilder()

builder
  .newSource(() => "Paco de Lucia is one of the most popular virtuoso")
  // Convert each sentence into individual words
  .flatMap[String](_.split(" "))
  .reduceByKeyAndWindow[String, Int](
      // Key extractor (in this case, each word acts as the key)
      (word: String) => word,
      // Value extractor (each word appears only once, hence the value is always 1)
      (word: String) => 1,
      // Window configuration
      WindowConfig.TumblingCountWindow(50),
      // Reduce operation (a running sum)
      (x: Int, y: Int) => x + y)
  // The result is logged
  .log();

Count by key operations

Count by key operations extract keys from data in the original streamlet and count the number of times a key has been encountered. Here's an example:

val builder = Builder.newBuilder()

builder
  .newSource(() => "Paco de Lucia is one of the most popular virtuoso")
  // Convert each sentence into individual words
  .flatMap[String](_.split(" "))
  // Count the number of occurrences of each word
  .countByKey[String]((word: String) => word)
  // The result is logged
  .log();

Count by key and window operations

Count by key and window operations extract keys from data in the original streamlet and count the number of times a key has been encountered within each time window. Here's an example:

val builder = Builder.newBuilder()

builder
  .newSource(() => "Paco de Lucia is one of the most popular virtuoso")
  // Convert each sentence into individual words
  .flatMap[String](_.split(" "))
  // Count the number of occurrences of each word within each time window
  .countByKeyAndWindow[String](
      (word: String) => word,
      WindowConfig.TumblingCountWindow(50))
  // The result is logged
  .log();

Split operations

Split operations split a streamlet into multiple streamlets with different id by getting the corresponding stream ids from each item in the origina streamlet. Here is an example:

val builder = Builder.newBuilder()

builder
  .newSource(() => "Paco de Lucia is one of the most popular virtuoso")
  // Convert each sentence into individual words
  .flatMap[String](_.split(" "))
  // Count the number of occurrences of each word within each time window
  .split(Map(
      "long_word" -> { word: String => word.length >= 4 },
      "short_word" -> { word: String => word.length < 4 }
  ))
  .withStream("short_word)
  // The result is logged
  .log();

With stream operations

With stream operations select a stream with id from a streamlet that contains multiple streams. They are often used with split.

Apply operator operations

Apply operator operations apply a user defined operator (like a bolt) to each element of the original streamlet and return a new streamlet. Here is an example:

val builder = Builder.newBuilder()

private class MyBoltOperator extends MyBolt
    with IStreamletOperator[String, String] {
}

builder
  .newSource(() => "Paco de Lucia is one of the most popular virtuoso")
  // Convert each sentence into individual words
  .flatMap[String](_.split(" "))
  // Apply user defined operation
  .applyOperator(new MyBoltOperator())
  // The result is logged
  .log();

Repartition operations

When you assign a number of partitions to a processing step, each step that comes after it inherits that number of partitions. Thus, if you assign 5 partitions to a map operation, then any mapToKV, flatMap, filter, etc. operations that come after it will also be assigned 5 partitions. But you can also change the number of partitions for a processing step (as well as the number of partitions for downstream operations) using repartition. Here's an example:

import java.util.concurrent.ThreadLocalRandom;

val builder = Builder.newBuilder

val numbers = builder
  .newSource(() => ThreadLocalRandom.current().nextInt(1, 11))

numbers
  .setNumPartitions(5)
  .map(i => i + 1)
  .repartition(2)
  .filter(i => i > 7 && i < 2)
  .log()

In this example, the supplier streamlet emits random integers between 1 and 10. That operation is assigned 5 partitions. After the map operation, the repartition function is used to assign 2 partitions to all downstream operations.

Sink operations

In processing graphs like the ones you build using the Heron Streamlet API, sinks are essentially the terminal points in your graph, where your processing logic comes to an end. A processing graph can end with writing to a database, publishing to a topic in a pub-sub messaging system, and so on. With the Streamlet API, you can implement your own custom sinks. Here's an example:

import org.apache.heron.streamlet.Context
import org.apache.heron.streamlet.scala.Sink

class FormattedLogSink extends Sink[String] {
    private var streamName: Option[String] = None

    override def setup(context: Context): Unit =
      streamName = Some(context.getStreamName)

    override def put(tuple: String): Unit =
      println(s"The current value of tuple is $tuple in stream: $streamName")

    override def cleanup(): Unit = {}
  }

In this example, the sink fetches the name of the enclosing streamlet from the context passed in the setup method. The put method specifies how the sink handles each element that is received (in this case, a formatted message is logged to stdout). The cleanup method enables you to specify what happens after the element has been processed by the sink.

Here is the FormattedLogSink at work in an example processing graph:

val builder = Builder.newBuilder

builder.newSource(() => "Here is a string to be passed to the sink")
        .toSink(new FormattedLogSink)

Log operations rely on a log sink that is provided out of the box. You'll need to implement other sinks yourself.

Log operations

Log operations are special cases of consume operations that log streamlet elements to stdout.

Streamlet elements will be using their toString representations and at the INFO level.

Consume operations

Consume operations are like sink operations except they don‘t require implementing a full sink interface. Consume operations are thus suited for simple operations like formatted logging. Here’s an example:

val builder = Builder.newBuilder
      .newSource(() => Random.nextInt(10))
      .filter(i => i % 2 == 0)
      .consume(i => println(s"Even number found: $i"))