We'll use wordcount as an example to illustrate how to write Gearpump applications.
Repository and library dependencies can be found at Maven Setting.
You can get your preferred IDE ready for Gearpump by following this guide.
Gearpump supports two level APIs:
Low level API, which is more similar to Akka programming, operating on each event. The API document can be found at Low Level API Doc.
High level API (aka DSL), which is operating on streaming instead of individual event. The API document can be found at DSL API Doc.
And both APIs have their Java version and Scala version.
So, before you writing your first Gearpump application, you need to decide which API to use and which language to use.
The easiest way to write your streaming application is to write it with Gearpump DSL. Below will demostrate how to write WordCount application via Gearpump DSL.
:::scala /** WordCount with High level DSL */ object WordCount extends AkkaApp with ArgumentsParser { override val options: Array[(String, CLIOption[Any])] = Array.empty override def main(akkaConf: Config, args: Array[String]): Unit = { val context = ClientContext(akkaConf) val app = StreamApp("dsl", context) val data = "This is a good start, bingo!! bingo!!" //count for each word and output to log app.source(data.lines.toList, 1, "source"). // word => (word, count) flatMap(line => line.split("[\\s]+")).map((_, 1)). // (word, count1), (word, count2) => (word, count1 + count2) groupByKey().sum.log val appId = context.submit(app) context.close() } }
:::java /** Java version of WordCount with high level DSL API */ public class WordCount { public static void main(String[] args) throws InterruptedException { main(ClusterConfig.defaultConfig(), args); } public static void main(Config akkaConf, String[] args) throws InterruptedException { ClientContext context = new ClientContext(akkaConf); JavaStreamApp app = new JavaStreamApp("JavaDSL", context, UserConfig.empty()); List<String> source = Lists.newArrayList("This is a good start, bingo!! bingo!!"); //create a stream from the string list. JavaStream<String> sentence = app.source(source, 1, UserConfig.empty(), "source"); //tokenize the strings and create a new stream JavaStream<String> words = sentence.flatMap(new FlatMapFunction<String, String>() { @Override public Iterator<String> apply(String s) { return Lists.newArrayList(s.split("\\s+")).iterator(); } }, "flatMap"); //map each string as (string, 1) pair JavaStream<Tuple2<String, Integer>> ones = words.map(new MapFunction<String, Tuple2<String, Integer>>() { @Override public Tuple2<String, Integer> apply(String s) { return new Tuple2<String, Integer>(s, 1); } }, "map"); //group by according to string JavaStream<Tuple2<String, Integer>> groupedOnes = ones.groupBy(new GroupByFunction<Tuple2<String, Integer>, String>() { @Override public String apply(Tuple2<String, Integer> tuple) { return tuple._1(); } }, 1, "groupBy"); //for each group, make the sum JavaStream<Tuple2<String, Integer>> wordcount = groupedOnes.reduce(new ReduceFunction<Tuple2<String, Integer>>() { @Override public Tuple2<String, Integer> apply(Tuple2<String, Integer> t1, Tuple2<String, Integer> t2) { return new Tuple2<String, Integer>(t1._1(), t1._2() + t2._2()); } }, "reduce"); //output result using log wordcount.log(); app.run(); context.close(); } }
An application is a Directed Acyclic Graph (DAG) of processors. In the wordcount example, We will firstly define two processors Split
and Sum
, and then weave them together.
In the Split
processor, we simply split a predefined text (the content is simplified for conciseness) and send out each split word to Sum
.
:::scala class Split(taskContext : TaskContext, conf: UserConfig) extends Task(taskContext, conf) { import taskContext.output override def onStart(startTime : StartTime) : Unit = { self ! Message("start") } override def onNext(msg : Message) : Unit = { Split.TEXT_TO_SPLIT.lines.foreach { line => line.split("[\\s]+").filter(_.nonEmpty).foreach { msg => output(new Message(msg, System.currentTimeMillis())) } } self ! Message("continue", System.currentTimeMillis()) } } object Split { val TEXT_TO_SPLIT = "some text" }
:::java public class Split extends Task { public static String TEXT = "This is a good start for java! bingo! bingo! "; public Split(TaskContext taskContext, UserConfig userConf) { super(taskContext, userConf); } private Long now() { return System.currentTimeMillis(); } @Override public void onStart(StartTime startTime) { self().tell(new Message("start", now()), self()); } @Override public void onNext(Message msg) { // Split the TEXT to words String[] words = TEXT.split(" "); for (int i = 0; i < words.length; i++) { context.output(new Message(words[i], now())); } self().tell(new Message("next", now()), self()); } } ```
Essentially, each processor consists of two descriptions:
A Task
to define the operation.
A parallelism level to define the number of tasks of this processor in parallel.
Just like Split
, every processor extends Task
. The onStart
method is called once before any message comes in; onNext
method is called to process every incoming message. Note that Gearpump employs the message-driven model and that's why Split sends itself a message at the end of onStart
and onNext
to trigger next message processing.
The structure of Sum
processor looks much alike. Sum
does not need to send messages to itself since it receives messages from Split
.
:::scala class Sum (taskContext : TaskContext, conf: UserConfig) extends Task(taskContext, conf) { private[wordcount] val map : mutable.HashMap[String, Long] = new mutable.HashMap[String, Long]() private[wordcount] var wordCount : Long = 0 private var snapShotTime : Long = System.currentTimeMillis() private var snapShotWordCount : Long = 0 private var scheduler : Cancellable = null override def onStart(startTime : StartTime) : Unit = { scheduler = taskContext.schedule(new FiniteDuration(5, TimeUnit.SECONDS), new FiniteDuration(5, TimeUnit.SECONDS))(reportWordCount) } override def onNext(msg : Message) : Unit = { if (null == msg) { return } val current = map.getOrElse(msg.msg.asInstanceOf[String], 0L) wordCount += 1 map.put(msg.msg.asInstanceOf[String], current + 1) } override def onStop() : Unit = { if (scheduler != null) { scheduler.cancel() } } def reportWordCount() : Unit = { val current : Long = System.currentTimeMillis() LOG.info(s"Task ${taskContext.taskId} Throughput: ${(wordCount - snapShotWordCount, (current - snapShotTime) / 1000)} (words, second)") snapShotWordCount = wordCount snapShotTime = current } }
:::java public class Sum extends Task { private Logger LOG = super.LOG(); private HashMap<String, Integer> wordCount = new HashMap<String, Integer>(); public Sum(TaskContext taskContext, UserConfig userConf) { super(taskContext, userConf); } @Override public void onStart(StartTime startTime) { //skip } @Override public void onNext(Message messagePayLoad) { String word = (String) (messagePayLoad.msg()); Integer current = wordCount.get(word); if (current == null) { current = 0; } Integer newCount = current + 1; wordCount.put(word, newCount); } }
Besides counting the sum, in Scala version, we also define a scheduler to report throughput every 5 seconds. The scheduler should be cancelled when the computation completes, which could be accomplished overriding the onStop
method. The default implementation of onStop
is a no-op.
A processor could be parallelized to a list of tasks. A Partitioner
defines how the data is shuffled among tasks of Split and Sum. Gearpump has already provided two partitioners
HashPartitioner
: partitions data based on the message's hashcodeShufflePartitioner
: partitions data in a round-robin way.You could define your own partitioner by extending the Partitioner
trait/interface and overriding the getPartition
method.
:::scala trait Partitioner extends Serializable { def getPartition(msg : Message, partitionNum : Int) : Int }
Now, we are able to write our application class, weaving the above components together.
The application class extends App
and `ArgumentsParser which make it easier to parse arguments and run main functions.
:::scala object WordCount extends App with ArgumentsParser { private val LOG: Logger = LogUtil.getLogger(getClass) val RUN_FOR_EVER = -1 override val options: Array[(String, CLIOption[Any])] = Array( "split" -> CLIOption[Int]("<how many split tasks>", required = false, defaultValue = Some(1)), "sum" -> CLIOption[Int]("<how many sum tasks>", required = false, defaultValue = Some(1)) ) def application(config: ParseResult) : StreamApplication = { val splitNum = config.getInt("split") val sumNum = config.getInt("sum") val partitioner = new HashPartitioner() val split = Processor[Split](splitNum) val sum = Processor[Sum](sumNum) val app = StreamApplication("wordCount", Graph[Processor[_ <: Task], Partitioner](split ~ partitioner ~> sum), UserConfig.empty) app } val config = parse(args) val context = ClientContext() val appId = context.submit(application(config)) context.close() }
We override options
value and define an array of command line arguments to parse. We want application users to pass in masters' hosts and ports, the parallelism of split and sum tasks, and how long to run the example. We also specify whether an option is required
and provide defaultValue
for some arguments.
:::java /** Java version of WordCount with Processor Graph API */ public class WordCount { public static void main(String[] args) throws InterruptedException { main(ClusterConfig.defaultConfig(), args); } public static void main(Config akkaConf, String[] args) throws InterruptedException { // For split task, we config to create two tasks int splitTaskNumber = 2; Processor split = new Processor(Split.class).withParallelism(splitTaskNumber); // For sum task, we have two summer. int sumTaskNumber = 2; Processor sum = new Processor(Sum.class).withParallelism(sumTaskNumber); // construct the graph Graph graph = new Graph(); graph.addVertex(split); graph.addVertex(sum); Partitioner partitioner = new HashPartitioner(); graph.addEdge(split, partitioner, sum); UserConfig conf = UserConfig.empty(); StreamApplication app = new StreamApplication("wordcountJava", conf, graph); // create master client // It will read the master settings under gearpump.cluster.masters ClientContext masterClient = new ClientContext(akkaConf); masterClient.submit(app); masterClient.close(); } }
After all these, you need to package everything into a uber jar and submit the jar to Gearpump Cluster. Please check Application submission tool to command line tool syntax.
For a real application, you definitely need to define your own customized message passing between processors. Customized message needs customized serializer to help message passing over wire. Check this guide for how to customize serializer.
Gearpump is also able to as a base platform to develop non-streaming applications. See this guide on how to use Gearpump to develop a distributed shell.