streaming-programming-guide.md

layout: global
title: Spark Streaming Programming Guide

• This will become a table of contents (this text will be scraped). {:toc}

Overview

Spark Streaming is an extension of the core Spark API that allows enables high-throughput, fault-tolerant stream processing of live data streams. Data can be ingested from many sources like Kafka, Flume, Twitter, ZeroMQ or plain old TCP sockets and be processed using complex algorithms expressed with high-level functions like map, reduce, join and window. Finally, processed data can be pushed out to filesystems, databases, and live dashboards. In fact, you can apply Spark's in-built machine learning algorithms, and graph processing algorithms on data streams.

Internally, it works as follows. Spark Streaming receives live input data streams and divides the data into batches, which are then processed by the Spark engine to generate the final stream of results in batches.

Spark Streaming provides a high-level abstraction called discretized stream or DStream, which represents a continuous stream of data. DStreams can be created either from input data stream from sources such as Kafka and Flume, or by applying high-level operations on other DStreams. Internally, a DStream is represented as a sequence of RDDs.

This guide shows you how to start writing Spark Streaming programs with DStreams. You can write Spark Streaming programs in Scala or Java, both of which are presented in this guide. You will find tabs throughout this guide that let you choose between Scala and Java code snippets.

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A Quick Example

Before we go into the details of how to write your own Spark Streaming program, let's take a quick look at what a simple Spark Streaming program looks like. Let's say we want to count the number of words in text data received from a data server listening on a TCP socket. All you need to do is as follows.

First, we import the names of the Spark Streaming classes, and some implicit conversions from StreamingContext into our environment, to add useful methods to other classes we need (like DStream).

StreamingContext is the main entry point for all streaming functionality.

{% highlight scala %} import org.apache.spark.streaming._ import org.apache.spark.streaming.StreamingContext._ {% endhighlight %}

Then we create a StreamingContext object. Besides Spark's configuration, we specify that any DStream will be processed in 1 second batches.

{% highlight scala %} // Create a StreamingContext with a SparkConf configuration val ssc = new StreamingContext(sparkConf, Seconds(1)) {% endhighlight %}

Using this context, we then create a new DStream by specifying the IP address and port of the data server.

{% highlight scala %} // Create a DStream that will connect to serverIP:serverPort val lines = ssc.socketTextStream(serverIP, serverPort) {% endhighlight %}

This lines DStream represents the stream of data that will be received from the data server. Each record in this DStream is a line of text. Next, we want to split the lines by space into words.

{% highlight scala %} // Split each line into words val words = lines.flatMap(_.split(" ")) {% endhighlight %}

flatMap is a one-to-many DStream operation that creates a new DStream by generating multiple new records from each record in the source DStream. In this case, each line will be split into multiple words and the stream of words is represented as the words DStream. Next, we want to count these words.

{% highlight scala %} // Count each word in each batch val pairs = words.map(word => (word, 1)) val wordCounts = pairs.reduceByKey(_ + _)

// Print a few of the counts to the console wordCounts.print() {% endhighlight %}

The words DStream is further mapped (one-to-one transformation) to a DStream of (word, 1) pairs, which is then reduced to get the frequency of words in each batch of data. Finally, wordCounts.print() will print a few of the counts generated every second.

Note that when these lines are executed, Spark Streaming only sets up the computation it will perform when it is started, and no real processing has started yet. To start the processing after all the transformations have been setup, we finally call

{% highlight scala %} ssc.start() // Start the computation ssc.awaitTermination() // Wait for the computation to terminate {% endhighlight %}

The complete code can be found in the Spark Streaming example NetworkWordCount.

First, we create a JavaStreamingContext object, which is the main entry point for all streaming functionality. Besides Spark's configuration, we specify that any DStream would be processed in 1 second batches.

{% highlight java %} // Create a StreamingContext with a SparkConf configuration JavaStreamingContext jssc = StreamingContext(sparkConf, new Duration(1000)) {% endhighlight %}

Using this context, we then create a new DStream by specifying the IP address and port of the data server.

{% highlight java %} // Create a DStream that will connect to serverIP:serverPort JavaDStream lines = jssc.socketTextStream(serverIP, serverPort); {% endhighlight %}

This lines DStream represents the stream of data that will be received from the data server. Each record in this stream is a line of text. Then, we want to split the the lines by space into words.

{% highlight java %} // Split each line into words JavaDStream words = lines.flatMap( new FlatMapFunction<String, String>() { @Override public Iterable call(String x) { return Lists.newArrayList(x.split(" ")); } }); {% endhighlight %}

flatMap is a DStream operation that creates a new DStream by generating multiple new records from each record in the source DStream. In this case, each line will be split into multiple words and the stream of words is represented as the words DStream. Note that we defined the transformation using a FlatMapFunction object. As we will discover along the way, there are a number of such convenience classes in the Java API that help define DStream transformations.

Next, we want to count these words.

{% highlight java %} // Count each word in each batch JavaPairDStream<String, Integer> pairs = words.map( new PairFunction<String, String, Integer>() { @Override public Tuple2<String, Integer> call(String s) throws Exception { return new Tuple2<String, Integer>(s, 1); } }); JavaPairDStream<String, Integer> wordCounts = pairs.reduceByKey( new Function2<Integer, Integer, Integer>() { @Override public Integer call(Integer i1, Integer i2) throws Exception { return i1 + i2; } }); wordCounts.print(); // Print a few of the counts to the console {% endhighlight %}

The words DStream is further mapped (one-to-one transformation) to a DStream of (word, 1) pairs, using a PairFunction object. Then, it is reduced to get the frequency of words in each batch of data, using a Function2 object. Finally, wordCounts.print() will print a few of the counts generated every second.

Note that when these lines are executed, Spark Streaming only sets up the computation it will perform when it is started, and no real processing has started yet. To start the processing after all the transformations have been setup, we finally call

{% highlight java %} jssc.start(); // Start the computation jssc.awaitTermination(); // Wait for the computation to terminate {% endhighlight %}

The complete code can be found in the Spark Streaming example JavaNetworkWordCount.

If you have already downloaded and built Spark, you can run this example as follows. You will first need to run Netcat (a small utility found in most Unix-like systems) as a data server by using

{% highlight bash %} $ nc -lk 9999 {% endhighlight %}

Then, in a different terminal, you can start the example by using

{% highlight bash %} $ ./bin/run-example org.apache.spark.streaming.examples.NetworkWordCount local[2] localhost 9999 {% endhighlight %}

{% highlight bash %} $ ./bin/run-example org.apache.spark.streaming.examples.JavaNetworkWordCount local[2] localhost 9999 {% endhighlight %}

Then, any lines typed in the terminal running the netcat server will be counted and printed on screen every second. It will look something like this.

{% highlight bash %} # TERMINAL 1: # Running Netcat $ nc -lk 9999 hello world ... {% endhighlight %}

{% highlight bash %} TERMINAL 2: RUNNING NetworkWordCount or JavaNetworkWordCount $ ./bin/run-example org.apache.spark.streaming.examples.NetworkWordCount local[2] localhost 9999 ... Time: 1357008430000 ms (hello,1) (world,1) ... {% endhighlight %}

If you plan to run the Scala code for Spark Streaming-based use cases in the Spark shell, you should start the shell with the SparkConfiguration pre-configured to discard old batches periodically: