# Stop Thinking, Just Do!

Sung-Soo Kim's Blog

# 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.

## Scala

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. We create a local StreamingContext with two execution threads, and batch interval of 1 second.

    import org.apache.spark._
import org.apache.spark.streaming._
import org.apache.spark.streaming.StreamingContext._ // not necessary in Spark 1.3+
// Create a local StreamingContext with two working thread and batch interval of 1 second.
// The master requires 2 cores to prevent from a starvation scenario.
val conf = new SparkConf().setMaster("local[2]").setAppName("NetworkWordCount")
val ssc = new StreamingContext(conf, Seconds(1))


Using this context, we can create a DStream that represents streaming data from a TCP source, specified as hostname (e.g. localhost) and port (e.g. 9999).

    // Create a DStream that will connect to hostname:port, like localhost:9999
val lines = ssc.socketTextStream("localhost", 9999)


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.

    // Split each line into words
val words = lines.flatMap(_.split(" "))


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.

    import org.apache.spark.streaming.StreamingContext._ // not necessary in Spark 1.3+
// Count each word in each batch
val pairs = words.map(word => (word, 1))
val wordCounts = pairs.reduceByKey(_ + _)
// Print the first ten elements of each RDD generated in this DStream to the console
wordCounts.print()


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

    ssc.start()             // Start the computation
ssc.awaitTermination()  // Wait for the computation to terminate


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

## Java

First, we create a JavaStreamingContext object, which is the main entry point for all streaming functionality. We create a local StreamingContext with two execution threads, and a batch interval of 1 second.

    import org.apache.spark.*;
import org.apache.spark.api.java.function.*;
import org.apache.spark.streaming.*;
import org.apache.spark.streaming.api.java.*;
import scala.Tuple2;
// Create a local StreamingContext with two working thread and batch interval of 1 second
SparkConf conf = new SparkConf().setMaster("local[2]").setAppName("NetworkWordCount")
JavaStreamingContext jssc = new JavaStreamingContext(conf, Durations.seconds(1))


Using this context, we can create a DStream that represents streaming data from a TCP source, specified as hostname (e.g. localhost) and port (e.g. 9999).

    // Create a DStream that will connect to hostname:port, like localhost:9999


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.

    // Split each line into words
new FlatMapFunction<String, String>() {
@Override public Iterable<String> call(String x) {
return Arrays.asList(x.split(" "));
}
});


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.

    // 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;
}
});
// Print the first ten elements of each RDD generated in this DStream to the console
wordCounts.print();


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 after it is started, and no real processing has started yet. To start the processing after all the transformations have been setup, we finally call start method.

    jssc.start();              // Start the computation
jssc.awaitTermination();   // Wait for the computation to terminate


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

## Python

First, we import StreamingContext, which is the main entry point for all streaming functionality. We create a local StreamingContext with two execution threads, and batch interval of 1 second.

    from pyspark import SparkContext
from pyspark.streaming import StreamingContext
# Create a local StreamingContext with two working thread and batch interval of 1 second
sc = SparkContext("local[2]", "NetworkWordCount")
ssc = StreamingContext(sc, 1)


Using this context, we can create a DStream that represents streaming data from a TCP source, specified as hostname (e.g. localhost) and port (e.g. 9999).

    # Create a DStream that will connect to hostname:port, like localhost:9999
lines = ssc.socketTextStream("localhost", 9999)


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.

    # Split each line into words
words = lines.flatMap(lambda line: line.split(" "))


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.

    # Count each word in each batch
pairs = words.map(lambda word: (word, 1))
wordCounts = pairs.reduceByKey(lambda x, y: x + y)
# Print the first ten elements of each RDD generated in this DStream to the console
wordCounts.pprint()


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.pprint() 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

    ssc.start()             # Start the computation
ssc.awaitTermination()  # Wait for the computation to terminate


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

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

$nc -lk 9999  Then, in a different terminal, you can start the example by using $ ./bin/run-example streaming.NetworkWordCount localhost 9999

$./bin/run-example streaming.JavaNetworkWordCount localhost 9999$ ./bin/spark-submit examples/src/main/python/streaming/network_wordcount.py localhost 9999


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 the following.

# TERMINAL 1:
# Running Netcat

$nc -lk 9999 hello world ... # TERMINAL 2: RUNNING NetworkWordCount$ ./bin/run-example streaming.NetworkWordCount localhost 9999
...
-------------------------------------------
Time: 1357008430000 ms
-------------------------------------------
(hello,1)
(world,1)
...

# TERMINAL 2: RUNNING JavaNetworkWordCount

$./bin/run-example streaming.JavaNetworkWordCount localhost 9999 ... ------------------------------------------- Time: 1357008430000 ms ------------------------------------------- (hello,1) (world,1) ... # TERMINAL 2: RUNNING network_wordcount.py$ ./bin/spark-submit examples/src/main/python/streaming/network_wordcount.py localhost 9999
...
-------------------------------------------
Time: 2014-10-14 15:25:21
-------------------------------------------
(hello,1)
(world,1)
...