# Overview

Spark Streaming is an extension of the core Spark API that enables scalable, high-throughput, fault-tolerant stream processing of live data streams. Data can be ingested from many sources like Kafka, Flume, Twitter, ZeroMQ, Kinesis or TCP sockets can 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 machine learning 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 streams from sources such as Kafka, Flume, and Kinesis, 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, Java or Python (introduced in Spark 1.2), all of which are presented in this guide. You will find tabs throughout this guide that let you choose between code snippets of different languages.

Note: Python API for Spark Streaming has been introduced in Spark 1.2. It has all the DStream transformations and almost all the output operations available in Scala and Java interfaces. However, it has only support for basic sources like text files and text data over sockets. APIs for additional sources, like Kafka and Flume, will be available in the future. Further information about available features in the Python API are mentioned throughout this document; look out for the tag Python API.

# 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. 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._

// 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._
// 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.

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
JavaReceiverInputDStream<String> lines = jssc.socketTextStream("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.

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) ...

# Basic Concepts

Next, we move beyond the simple example and elaborate on the basics of Spark Streaming.

Similar to Spark, Spark Streaming is available through Maven Central. To write your own Spark Streaming program, you will have to add the following dependency to your SBT or Maven project.

<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-streaming_2.10</artifactId>
<version>1.2.0</version>
</dependency>

libraryDependencies += "org.apache.spark" % "spark-streaming_2.10" % "1.2.0"


For ingesting data from sources like Kafka, Flume, and Kinesis that are not present in the Spark Streaming core API, you will have to add the corresponding artifact spark-streaming-xyz_2.10 to the dependencies. For example, some of the common ones are as follows.

SourceArtifact
Kafka spark-streaming-kafka_2.10
Flume spark-streaming-flume_2.10
Kinesis
ZeroMQ spark-streaming-zeromq_2.10
MQTT spark-streaming-mqtt_2.10

For an up-to-date list, please refer to the Apache repository for the full list of supported sources and artifacts.

## Initializing StreamingContext

To initialize a Spark Streaming program, a StreamingContext object has to be created which is the main entry point of all Spark Streaming functionality.

A StreamingContext object can be created from a SparkConf object.

import org.apache.spark._
import org.apache.spark.streaming._

val conf = new SparkConf().setAppName(appName).setMaster(master)
val ssc = new StreamingContext(conf, Seconds(1))

The appName parameter is a name for your application to show on the cluster UI. master is a Spark, Mesos or YARN cluster URL, or a special “local[*]” string to run in local mode. In practice, when running on a cluster, you will not want to hardcode master in the program, but rather launch the application with spark-submit and receive it there. However, for local testing and unit tests, you can pass “local[*]” to run Spark Streaming in-process (detects the number of cores in the local system). Note that this internally creates a SparkContext (starting point of all Spark functionality) which can be accessed as ssc.sparkContext.

The batch interval must be set based on the latency requirements of your application and available cluster resources. See the Performance Tuning section for more details.

A StreamingContext object can also be created from an existing SparkContext object.

import org.apache.spark.streaming._

val sc = ...                // existing SparkContext
val ssc = new StreamingContext(sc, Seconds(1))

A JavaStreamingContext object can be created from a SparkConf object.

import org.apache.spark.*;
import org.apache.spark.streaming.api.java.*;

SparkConf conf = new SparkConf().setAppName(appName).setMaster(master);
JavaStreamingContext ssc = new JavaStreamingContext(conf, Duration(1000));

The appName parameter is a name for your application to show on the cluster UI. master is a Spark, Mesos or YARN cluster URL, or a special “local[*]” string to run in local mode. In practice, when running on a cluster, you will not want to hardcode master in the program, but rather launch the application with spark-submit and receive it there. However, for local testing and unit tests, you can pass “local[*]” to run Spark Streaming in-process. Note that this internally creates a JavaSparkContext (starting point of all Spark functionality) which can be accessed as ssc.sparkContext.

The batch interval must be set based on the latency requirements of your application and available cluster resources. See the Performance Tuning section for more details.

A JavaStreamingContext object can also be created from an existing JavaSparkContext.

import org.apache.spark.streaming.api.java.*;

JavaSparkContext sc = ...   //existing JavaSparkContext
JavaStreamingContext ssc = new JavaStreamingContext(sc, Durations.seconds(1));

A StreamingContext object can be created from a SparkContext object.

from pyspark import SparkContext
from pyspark.streaming import StreamingContext

sc = SparkContext(master, appName)
ssc = StreamingContext(sc, 1)

The appName parameter is a name for your application to show on the cluster UI. master is a Spark, Mesos or YARN cluster URL, or a special “local[*]” string to run in local mode. In practice, when running on a cluster, you will not want to hardcode master in the program, but rather launch the application with spark-submit and receive it there. However, for local testing and unit tests, you can pass “local[*]” to run Spark Streaming in-process (detects the number of cores in the local system).

The batch interval must be set based on the latency requirements of your application and available cluster resources. See the Performance Tuning section for more details.

After a context is defined, you have to do the following.

1. Define the input sources by creating input DStreams.
2. Define the streaming computations by applying transformation and output operations to DStreams.
3. Start receiving data and processing it using streamingContext.start().
4. Wait for the processing to be stopped (manually or due to any error) using streamingContext.awaitTermination().
5. The processing can be manually stopped using streamingContext.stop().
##### Points to remember:
• Once a context has been started, no new streaming computations can be set up or added to it.
• Once a context has been stopped, it cannot be restarted.
• Only one StreamingContext can be active in a JVM at the same time.
• stop() on StreamingContext also stops the SparkContext. To stop only the StreamingContext, set optional parameter of stop() called stopSparkContext to false.
• A SparkContext can be re-used to create multiple StreamingContexts, as long as the previous StreamingContext is stopped (without stopping the SparkContext) before the next StreamingContext is created.

## Discretized Streams (DStreams)

Discretized Stream or DStream is the basic abstraction provided by Spark Streaming. It represents a continuous stream of data, either the input data stream received from source, or the processed data stream generated by transforming the input stream. Internally, a DStream is represented by a continuous series of RDDs, which is Spark’s abstraction of an immutable, distributed dataset (see Spark Programming Guide for more details). Each RDD in a DStream contains data from a certain interval, as shown in the following figure.

Any operation applied on a DStream translates to operations on the underlying RDDs. For example, in the earlier example of converting a stream of lines to words, the flatMap operation is applied on each RDD in the lines DStream to generate the RDDs of the words DStream. This is shown in the following figure.

These underlying RDD transformations are computed by the Spark engine. The DStream operations hide most of these details and provide the developer with higher-level API for convenience. These operations are discussed in detail in later sections.

Input DStreams are DStreams representing the stream of input data received from streaming sources. In the quick example, lines was an input DStream as it represented the stream of data received from the netcat server. Every input DStream (except file stream, discussed later in this section) is associated with a Receiver (Scala doc, Java doc) object which receives the data from a source and stores it in Spark’s memory for processing.

Spark Streaming provides two categories of built-in streaming sources.

• Basic sources: Sources directly available in the StreamingContext API. Example: file systems, socket connections, and Akka actors.
• Advanced sources: Sources like Kafka, Flume, Kinesis, Twitter, etc. are available through extra utility classes. These require linking against extra dependencies as discussed in the linking section.

We are going to discuss some of the sources present in each category later in this section.

Note that, if you want to receive multiple streams of data in parallel in your streaming application, you can create multiple input DStreams (discussed further in the Performance Tuning section). This will create multiple receivers which will simultaneously receive multiple data streams. But note that Spark worker/executor as a long-running task, hence it occupies one of the cores allocated to the Spark Streaming application. Hence, it is important to remember that Spark Streaming application needs to be allocated enough cores (or threads, if running locally) to process the received data, as well as, to run the receiver(s).

##### Points to remember
• When running a Spark Streaming program locally, do not use “local” or “local[1]” as the master URL. Either of these means that only one thread will be used for running tasks locally. If you are using a input DStream based on a receiver (e.g. sockets, Kafka, Flume, etc.), then the single thread will be used to run the receiver, leaving no thread for processing the received data. Hence, when running locally, always use “local[n]” as the master URL where n > number of receivers to run (see Spark Properties for information on how to set the master).

• Extending the logic to running on a cluster, the number of cores allocated to the Spark Streaming application must be more than the number of receivers. Otherwise the system will receive data, but not be able to process them.

### Basic Sources

We have already taken a look at the ssc.socketTextStream(...) in the quick example which creates a DStream from text data received over a TCP socket connection. Besides sockets, the StreamingContext API provides methods for creating DStreams from files and Akka actors as input sources.

• File Streams: For reading data from files on any file system compatible with the HDFS API (that is, HDFS, S3, NFS, etc.), a DStream can be created as

  streamingContext.fileStream[KeyClass, ValueClass, InputFormatClass](dataDirectory)

  streamingContext.fileStream<KeyClass, ValueClass, InputFormatClass>(dataDirectory);

  streamingContext.textFileStream(dataDirectory)


Spark Streaming will monitor the directory dataDirectory and process any files created in that directory (files written in nested directories not supported). Note that

• The files must have the same data format.
• The files must be created in the dataDirectory by atomically moving or renaming them into the data directory.
• Once moved, the files must not be changed. So if the files are being continuously appended, the new data will not be read.

For simple text files, there is an easier method streamingContext.textFileStream(dataDirectory). And file streams do not require running a receiver, hence does not require allocating cores.

Python API As of Spark 1.2, fileStream is not available in the Python API, only textFileStream is available.

• Streams based on Custom Actors: DStreams can be created with data streams received through Akka actors by using streamingContext.actorStream(actorProps, actor-name). See the Custom Receiver Guide for more details.

Python API Since actors are available only in the Java and Scala libraries, actorStream is not available in the Python API.

• Queue of RDDs as a Stream: For testing a Spark Streaming application with test data, one can also create a DStream based on a queue of RDDs, using streamingContext.queueStream(queueOfRDDs). Each RDD pushed into the queue will be treated as a batch of data in the DStream, and processed like a stream.

For more details on streams from sockets, files, and actors, see the API documentations of the relevant functions in StreamingContext for Scala, JavaStreamingContext for Java, and StreamingContext for Python.

Python API As of Spark 1.2, these sources are not available in the Python API.

This category of sources require interfacing with external non-Spark libraries, some of them with complex dependencies (e.g., Kafka and Flume). Hence, to minimize issues related to version conflicts of dependencies, the functionality to create DStreams from these sources have been moved to separate libraries, that can be linked to explicitly when necessary. For example, if you want to create a DStream using data from Twitter’s stream of tweets, you have to do the following.

1. Linking: Add the artifact spark-streaming-twitter_2.10 to the SBT/Maven project dependencies.
2. Programming: Import the TwitterUtils class and create a DStream with TwitterUtils.createStream as shown below.
3. Deploying: Generate an uber JAR with all the dependencies (including the dependency spark-streaming-twitter_2.10 and its transitive dependencies) and then deploy the application. This is further explained in the Deploying section.
import org.apache.spark.streaming.twitter._

TwitterUtils.createStream(ssc)
import org.apache.spark.streaming.twitter.*;

TwitterUtils.createStream(jssc);

Note that these advanced sources are not available in the Spark shell, hence applications based on these advanced sources cannot be tested in the shell. If you really want to use them in the Spark shell you will have to download the corresponding Maven artifact’s JAR along with its dependencies and it in the classpath.

Some of these advanced sources are as follows.

### Custom Sources

Python API As of Spark 1.2, these sources are not available in the Python API.

Input DStreams can also be created out of custom data sources. All you have to do is implement an user-defined receiver (see next section to understand what that is) that can receive data from the custom sources and push it into Spark. See the Custom Receiver Guide for details.

There can be two kinds of data sources based on their reliability. Sources (like Kafka and Flume) allow the transferred data to be acknowledged. If the system receiving data from these reliable sources acknowledge the received data correctly, it can be ensured that no data gets lost due to any kind of failure. This leads to two kinds of receivers.

1. Reliable Receiver - A reliable receiver correctly acknowledges a reliable source that the data has been received and stored in Spark with replication.
2. Unreliable Receiver - These are receivers for sources that do not support acknowledging. Even for reliable sources, one may implement an unreliable receiver that do not go into the complexity of acknowledging correctly.

The details of how to write a reliable receiver are discussed in the Custom Receiver Guide.

## Transformations on DStreams

Similar to that of RDDs, transformations allow the data from the input DStream to be modified. DStreams support many of the transformations available on normal Spark RDD’s. Some of the common ones are as follows.

TransformationMeaning
map(func) Return a new DStream by passing each element of the source DStream through a function func.
flatMap(func) Similar to map, but each input item can be mapped to 0 or more output items.
filter(func) Return a new DStream by selecting only the records of the source DStream on which func returns true.
repartition(numPartitions) Changes the level of parallelism in this DStream by creating more or fewer partitions.
union(otherStream) Return a new DStream that contains the union of the elements in the source DStream and otherDStream.
count() Return a new DStream of single-element RDDs by counting the number of elements in each RDD of the source DStream.
reduce(func) Return a new DStream of single-element RDDs by aggregating the elements in each RDD of the source DStream using a function func (which takes two arguments and returns one). The function should be associative so that it can be computed in parallel.
countByValue() When called on a DStream of elements of type K, return a new DStream of (K, Long) pairs where the value of each key is its frequency in each RDD of the source DStream.
reduceByKey(func, [numTasks]) When called on a DStream of (K, V) pairs, return a new DStream of (K, V) pairs where the values for each key are aggregated using the given reduce function. Note: By default, this uses Spark's default number of parallel tasks (2 for local mode, and in cluster mode the number is determined by the config property spark.default.parallelism) to do the grouping. You can pass an optional numTasks argument to set a different number of tasks.
join(otherStream, [numTasks]) When called on two DStreams of (K, V) and (K, W) pairs, return a new DStream of (K, (V, W)) pairs with all pairs of elements for each key.
cogroup(otherStream, [numTasks]) When called on DStream of (K, V) and (K, W) pairs, return a new DStream of (K, Seq[V], Seq[W]) tuples.
transform(func) Return a new DStream by applying a RDD-to-RDD function to every RDD of the source DStream. This can be used to do arbitrary RDD operations on the DStream.
updateStateByKey(func) Return a new "state" DStream where the state for each key is updated by applying the given function on the previous state of the key and the new values for the key. This can be used to maintain arbitrary state data for each key.

The last two transformations are worth highlighting again.

The updateStateByKey operation allows you to maintain arbitrary state while continuously updating it with new information. To use this, you will have to do two steps.

1. Define the state - The state can be of arbitrary data type.
2. Define the state update function - Specify with a function how to update the state using the previous state and the new values from input stream.

Let’s illustrate this with an example. Say you want to maintain a running count of each word seen in a text data stream. Here, the running count is the state and it is an integer. We define the update function as

def updateFunction(newValues: Seq[Int], runningCount: Option[Int]): Option[Int] = {
val newCount = ...  // add the new values with the previous running count to get the new count
Some(newCount)
}

This is applied on a DStream containing words (say, the pairs DStream containing (word, 1) pairs in the earlier example).

val runningCounts = pairs.updateStateByKey[Int](updateFunction _)
import com.google.common.base.Optional;
Function2<List<Integer>, Optional<Integer>, Optional<Integer>> updateFunction =
new Function2<List<Integer>, Optional<Integer>, Optional<Integer>>() {
@Override public Optional<Integer> call(List<Integer> values, Optional<Integer> state) {
Integer newSum = ...  // add the new values with the previous running count to get the new count
return Optional.of(newSum);
}
};

This is applied on a DStream containing words (say, the pairs DStream containing (word, 1) pairs in the quick example).

JavaPairDStream<String, Integer> runningCounts = pairs.updateStateByKey(updateFunction);
def updateFunction(newValues, runningCount):
if runningCount is None:
runningCount = 0
return sum(newValues, runningCount)  # add the new values with the previous running count to get the new count

This is applied on a DStream containing words (say, the pairs DStream containing (word, 1) pairs in the earlier example).

runningCounts = pairs.updateStateByKey(updateFunction)

The update function will be called for each word, with newValues having a sequence of 1’s (from the (word, 1) pairs) and the runningCount having the previous count. For the complete Scala code, take a look at the example stateful_network_wordcount.py.

Note that using updateStateByKey requires the checkpoint directory to be configured, which is discussed in detail in the checkpointing section.

#### Transform Operation

The transform operation (along with its variations like transformWith) allows arbitrary RDD-to-RDD functions to be applied on a DStream. It can be used to apply any RDD operation that is not exposed in the DStream API. For example, the functionality of joining every batch in a data stream with another dataset is not directly exposed in the DStream API. However, you can easily use transform to do this. This enables very powerful possibilities. For example, if you want to do real-time data cleaning by joining the input data stream with precomputed spam information (maybe generated with Spark as well) and then filtering based on it.

val spamInfoRDD = ssc.sparkContext.newAPIHadoopRDD(...) // RDD containing spam information

val cleanedDStream = wordCounts.transform(rdd => {
rdd.join(spamInfoRDD).filter(...) // join data stream with spam information to do data cleaning
...
})
import org.apache.spark.streaming.api.java.*;
// RDD containing spam information
final JavaPairRDD<String, Double> spamInfoRDD = jssc.sparkContext().newAPIHadoopRDD(...);

JavaPairDStream<String, Integer> cleanedDStream = wordCounts.transform(
new Function<JavaPairRDD<String, Integer>, JavaPairRDD<String, Integer>>() {
@Override public JavaPairRDD<String, Integer> call(JavaPairRDD<String, Integer> rdd) throws Exception {
rdd.join(spamInfoRDD).filter(...); // join data stream with spam information to do data cleaning
...
}
});
spamInfoRDD = sc.pickleFile(...) # RDD containing spam information

# join data stream with spam information to do data cleaning
cleanedDStream = wordCounts.transform(lambda rdd: rdd.join(spamInfoRDD).filter(...))

In fact, you can also use machine learning and graph computation algorithms in the transform method.

#### Window Operations

Finally, Spark Streaming also provides windowed computations, which allow you to apply transformations over a sliding window of data. This following figure illustrates this sliding window.

As shown in the figure, every time the window slides over a source DStream, the source RDDs that fall within the window are combined and operated upon to produce the RDDs of the windowed DStream. In this specific case, the operation is applied over last 3 time units of data, and slides by 2 time units. This shows that any window operation needs to specify two parameters.

• window length - The duration of the window (3 in the figure)
• sliding interval - The interval at which the window operation is performed (2 in the figure).

These two parameters must be multiples of the batch interval of the source DStream (1 in the figure).

Let’s illustrate the window operations with an example. Say, you want to extend the earlier example by generating word counts over last 30 seconds of data, every 10 seconds. To do this, we have to apply the reduceByKey operation on the pairs DStream of (word, 1) pairs over the last 30 seconds of data. This is done using the operation reduceByKeyAndWindow.

// Reduce last 30 seconds of data, every 10 seconds
val windowedWordCounts = pairs.reduceByKeyAndWindow((a:Int,b:Int) => (a + b), Seconds(30), Seconds(10))
// Reduce function adding two integers, defined separately for clarity
Function2<Integer, Integer, Integer> reduceFunc = new Function2<Integer, Integer, Integer>() {
@Override public Integer call(Integer i1, Integer i2) throws Exception {
return i1 + i2;
}
};

// Reduce last 30 seconds of data, every 10 seconds
JavaPairDStream<String, Integer> windowedWordCounts = pairs.reduceByKeyAndWindow(reduceFunc, Durations.seconds(30), Durations.seconds(10));
# Reduce last 30 seconds of data, every 10 seconds
windowedWordCounts = pairs.reduceByKeyAndWindow(lambda x, y: x + y, lambda x, y: x - y, 30, 10)

Some of the common window operations are as follows. All of these operations take the said two parameters - windowLength and slideInterval.

TransformationMeaning
window(windowLength, slideInterval) Return a new DStream which is computed based on windowed batches of the source DStream.
countByWindow(windowLength, slideInterval) Return a sliding window count of elements in the stream.
reduceByWindow(func, windowLength, slideInterval) Return a new single-element stream, created by aggregating elements in the stream over a sliding interval using func. The function should be associative so that it can be computed correctly in parallel.
reduceByKeyAndWindow(func, windowLength, slideInterval, [numTasks]) When called on a DStream of (K, V) pairs, returns a new DStream of (K, V) pairs where the values for each key are aggregated using the given reduce function func over batches in a sliding window. Note: By default, this uses Spark's default number of parallel tasks (2 for local mode, and in cluster mode the number is determined by the config property spark.default.parallelism) to do the grouping. You can pass an optional numTasks argument to set a different number of tasks.
reduceByKeyAndWindow(func, invFunc, windowLength, slideInterval, [numTasks]) A more efficient version of the above reduceByKeyAndWindow() where the reduce value of each window is calculated incrementally using the reduce values of the previous window. This is done by reducing the new data that enter the sliding window, and "inverse reducing" the old data that leave the window. An example would be that of "adding" and "subtracting" counts of keys as the window slides. However, it is applicable to only "invertible reduce functions", that is, those reduce functions which have a corresponding "inverse reduce" function (taken as parameter invFunc. Like in reduceByKeyAndWindow, the number of reduce tasks is configurable through an optional argument. Note that [checkpointing](#checkpointing) must be enabled for using this operation.
countByValueAndWindow(windowLength, slideInterval, [numTasks]) When called on a DStream of (K, V) pairs, returns a new DStream of (K, Long) pairs where the value of each key is its frequency within a sliding window. Like in reduceByKeyAndWindow, the number of reduce tasks is configurable through an optional argument.

The complete list of DStream transformations is available in the API documentation. For the Scala API, see DStream and PairDStreamFunctions. For the Java API, see JavaDStream and JavaPairDStream. For the Python API, see DStream.

## Output Operations on DStreams

Output operations allow DStream’s data to be pushed out external systems like a database or a file systems. Since the output operations actually allow the transformed data to be consumed by external systems, they trigger the actual execution of all the DStream transformations (similar to actions for RDDs). Currently, the following output operations are defined:

Output OperationMeaning
print() Prints first ten elements of every batch of data in a DStream on the driver node running the streaming application. This is useful for development and debugging.
Python API This is called pprint() in the Python API.
saveAsTextFiles(prefix, [suffix]) Save this DStream's contents as a text files. The file name at each batch interval is generated based on prefix and suffix: "prefix-TIME_IN_MS[.suffix]".
saveAsObjectFiles(prefix, [suffix]) Save this DStream's contents as a SequenceFile of serialized Java objects. The file name at each batch interval is generated based on prefix and suffix: "prefix-TIME_IN_MS[.suffix]".
Python API This is not available in the Python API.
saveAsHadoopFiles(prefix, [suffix]) Save this DStream's contents as a Hadoop file. The file name at each batch interval is generated based on prefix and suffix: "prefix-TIME_IN_MS[.suffix]".
Python API This is not available in the Python API.
foreachRDD(func) The most generic output operator that applies a function, func, to each RDD generated from the stream. This function should push the data in each RDD to a external system, like saving the RDD to files, or writing it over the network to a database. Note that the function func is executed in the driver process running the streaming application, and will usually have RDD actions in it that will force the computation of the streaming RDDs.

### Design Patterns for using foreachRDD

dstream.foreachRDD is a powerful primitive that allows data to sent out to external systems. However, it is important to understand how to use this primitive correctly and efficiently. Some of the common mistakes to avoid are as follows.

Often writing data to external system requires creating a connection object (e.g. TCP connection to a remote server) and using it to send data to a remote system. For this purpose, a developer may inadvertently try creating a connection object at the Spark driver, but try to use it in a Spark worker to save records in the RDDs. For example (in Scala),

dstream.foreachRDD { rdd =>
val connection = createNewConnection()  // executed at the driver
rdd.foreach { record =>
connection.send(record) // executed at the worker
}
}
def sendRecord(rdd):
connection = createNewConnection()  # executed at the driver
rdd.foreach(lambda record: connection.send(record))
connection.close()

dstream.foreachRDD(sendRecord)

This is incorrect as this requires the connection object to be serialized and sent from the driver to the worker. Such connection objects are rarely transferrable across machines. This error may manifest as serialization errors (connection object not serializable), initialization errors (connection object needs to be initialized at the workers), etc. The correct solution is to create the connection object at the worker.

However, this can lead to another common mistake - creating a new connection for every record. For example,

dstream.foreachRDD { rdd =>
rdd.foreach { record =>
val connection = createNewConnection()
connection.send(record)
connection.close()
}
}
def sendRecord(record):
connection = createNewConnection()
connection.send(record)
connection.close()

dstream.foreachRDD(lambda rdd: rdd.foreach(sendRecord))

Typically, creating a connection object has time and resource overheads. Therefore, creating and destroying a connection object for each record can incur unnecessarily high overheads and can significantly reduce the overall throughput of the system. A better solution is to use rdd.foreachPartition - create a single connection object and send all the records in a RDD partition using that connection.

dstream.foreachRDD { rdd =>
rdd.foreachPartition { partitionOfRecords =>
val connection = createNewConnection()
partitionOfRecords.foreach(record => connection.send(record))
connection.close()
}
}
def sendPartition(iter):
connection = createNewConnection()
for record in iter:
connection.send(record)
connection.close()

dstream.foreachRDD(lambda rdd: rdd.foreachPartition(sendPartition))

This amortizes the connection creation overheads over many records.

Finally, this can be further optimized by reusing connection objects across multiple RDDs/batches. One can maintain a static pool of connection objects than can be reused as RDDs of multiple batches are pushed to the external system, thus further reducing the overheads.

dstream.foreachRDD { rdd =>
rdd.foreachPartition { partitionOfRecords =>
// ConnectionPool is a static, lazily initialized pool of connections
val connection = ConnectionPool.getConnection()
partitionOfRecords.foreach(record => connection.send(record))
}
}
def sendPartition(iter):
# ConnectionPool is a static, lazily initialized pool of connections
connection = ConnectionPool.getConnection()
for record in iter:
connection.send(record)
ConnectionPool.returnConnection(connection)

dstream.foreachRDD(lambda rdd: rdd.foreachPartition(sendPartition))

Note that the connections in the pool should be lazily created on demand and timed out if not used for a while. This achieves the most efficient sending of data to external systems.

##### Other points to remember:
• DStreams are executed lazily by the output operations, just like RDDs are lazily executed by RDD actions. Specifically, RDD actions inside the DStream output operations force the processing of the received data. Hence, if your application does not have any output operation, or has output operations like dstream.foreachRDD() without any RDD action inside them, then nothing will get executed. The system will simply receive the data and discard it.

• By default, output operations are executed one-at-a-time. And they are executed in the order they are defined in the application.

## Caching / Persistence

Similar to RDDs, DStreams also allow developers to persist the stream’s data in memory. That is, using persist() method on a DStream will automatically persist every RDD of that DStream in memory. This is useful if the data in the DStream will be computed multiple times (e.g., multiple operations on the same data). For window-based operations like reduceByWindow and reduceByKeyAndWindow and state-based operations like updateStateByKey, this is implicitly true. Hence, DStreams generated by window-based operations are automatically persisted in memory, without the developer calling persist().

For input streams that receive data over the network (such as, Kafka, Flume, sockets, etc.), the default persistence level is set to replicate the data to two nodes for fault-tolerance.

Note that, unlike RDDs, the default persistence level of DStreams keeps the data serialized in memory. This is further discussed in the Performance Tuning section. More information on different persistence levels can be found in Spark Programming Guide.

## Checkpointing

A streaming application must operate 24/7 and hence must be resilient to failures unrelated to the application logic (e.g., system failures, JVM crashes, etc.). For this to be possible, Spark Streaming needs to checkpoints enough information to a fault- tolerant storage system such that it can recover from failures. There are two types of data that are checkpointed.

• Metadata checkpointing - Saving of the information defining the streaming computation to fault-tolerant storage like HDFS. This is used to recover from failure of the node running the driver of the streaming application (discussed in detail later). Metadata includes:
• Configuration - The configuration that were used to create the streaming application.
• DStream operations - The set of DStream operations that define the streaming application.
• Incomplete batches - Batches whose jobs are queued but have not completed yet.
• Data checkpointing - Saving of the generated RDDs to reliable storage. This is necessary in some stateful transformations that combine data across multiple batches. In such transformations, the generated RDDs depends on RDDs of previous batches, which causes the length of the dependency chain to keep increasing with time. To avoid such unbounded increase in recovery time (proportional to dependency chain), intermediate RDDs of stateful transformations are periodically checkpointed to reliable storage (e.g. HDFS) to cut off the dependency chains.

To summarize, metadata checkpointing is primarily needed for recovery from driver failures, whereas data or RDD checkpointing is necessary even for basic functioning if stateful transformations are used.

#### When to enable Checkpointing

Checkpointing must be enabled for applications with any of the following requirements:

• Usage of stateful transformations - If either updateStateByKey or reduceByKeyAndWindow (with inverse function) is used in the application, then the checkpoint directory must be provided for allowing periodic RDD checkpointing.
• Recovering from failures of the driver running the application - Metadata checkpoints are used for to recover with progress information.

Note that simple streaming applications without the aforementioned stateful transformations can be run without enabling checkpointing. The recovery from driver failures will also be partial in that case (some received but unprocessed data may be lost). This is often acceptable and many run Spark Streaming applications in this way. Support for non-Hadoop environments is expected to improve in the future.

#### How to configure Checkpointing

Checkpointing can be enabled by setting a directory in a fault-tolerant, reliable file system (e.g., HDFS, S3, etc.) to which the checkpoint information will be saved. This is done by using streamingContext.checkpoint(checkpointDirectory). This will allow you to use the aforementioned stateful transformations. Additionally, if you want make the application recover from driver failures, you should rewrite your streaming application to have the following behavior.

• When the program is being started for the first time, it will create a new StreamingContext, set up all the streams and then call start().
• When the program is being restarted after failure, it will re-create a StreamingContext from the checkpoint data in the checkpoint directory.

This behavior is made simple by using StreamingContext.getOrCreate. This is used as follows.

// Function to create and setup a new StreamingContext
def functionToCreateContext(): StreamingContext = {
val ssc = new StreamingContext(...)   // new context
val lines = ssc.socketTextStream(...) // create DStreams
...
ssc.checkpoint(checkpointDirectory)   // set checkpoint directory
ssc
}

// Get StreamingContext from checkpoint data or create a new one
val context = StreamingContext.getOrCreate(checkpointDirectory, functionToCreateContext _)

// Do additional setup on context that needs to be done,
// irrespective of whether it is being started or restarted
context. ...

// Start the context
context.start()
context.awaitTermination()

If the checkpointDirectory exists, then the context will be recreated from the checkpoint data. If the directory does not exist (i.e., running for the first time), then the function functionToCreateContext will be called to create a new context and set up the DStreams. See the Scala example RecoverableNetworkWordCount. This example appends the word counts of network data into a file.

This behavior is made simple by using JavaStreamingContext.getOrCreate. This is used as follows.

// Create a factory object that can create a and setup a new JavaStreamingContext
JavaStreamingContextFactory contextFactory = new JavaStreamingContextFactory() {
@Override public JavaStreamingContext create() {
JavaStreamingContext jssc = new JavaStreamingContext(...);  // new context
JavaDStream<String> lines = jssc.socketTextStream(...);     // create DStreams
...
jssc.checkpoint(checkpointDirectory);                       // set checkpoint directory
return jssc;
}
};

// Get JavaStreamingContext from checkpoint data or create a new one
JavaStreamingContext context = JavaStreamingContext.getOrCreate(checkpointDirectory, contextFactory);

// Do additional setup on context that needs to be done,
// irrespective of whether it is being started or restarted
context. ...

// Start the context
context.start();
context.awaitTermination();

If the checkpointDirectory exists, then the context will be recreated from the checkpoint data. If the directory does not exist (i.e., running for the first time), then the function contextFactory will be called to create a new context and set up the DStreams. See the Scala example JavaRecoverableNetworkWordCount. This example appends the word counts of network data into a file.

This behavior is made simple by using StreamingContext.getOrCreate. This is used as follows.

# Function to create and setup a new StreamingContext
def functionToCreateContext():
sc = SparkContext(...)   # new context
ssc = new StreamingContext(...)
lines = ssc.socketTextStream(...) # create DStreams
...
ssc.checkpoint(checkpointDirectory)   # set checkpoint directory
return ssc

# Get StreamingContext from checkpoint data or create a new one
context = StreamingContext.getOrCreate(checkpointDirectory, functionToCreateContext)

# Do additional setup on context that needs to be done,
# irrespective of whether it is being started or restarted
context. ...

# Start the context
context.start()
context.awaitTermination()

If the checkpointDirectory exists, then the context will be recreated from the checkpoint data. If the directory does not exist (i.e., running for the first time), then the function functionToCreateContext will be called to create a new context and set up the DStreams. See the Python example recoverable_network_wordcount.py. This example appends the word counts of network data into a file.

You can also explicitly create a StreamingContext from the checkpoint data and start the computation by using StreamingContext.getOrCreate(checkpointDirectory, None).

In addition to using getOrCreate one also needs to ensure that the driver process gets restarted automatically on failure. This can only be done by the deployment infrastructure that is used to run the application. This is further discussed in the Deployment section.

Note that checkpointing of RDDs incurs the cost of saving to reliable storage. This may cause an increase in the processing time of those batches where RDDs get checkpointed. Hence, the interval of checkpointing needs to be set carefully. At small batch sizes (say 1 second), checkpointing every batch may significantly reduce operation throughput. Conversely, checkpointing too infrequently causes the lineage and task sizes to grow which may have detrimental effects. For stateful transformations that require RDD checkpointing, the default interval is a multiple of the batch interval that is at least 10 seconds. It can be set by using dstream.checkpoint(checkpointInterval). Typically, a checkpoint interval of 5 - 10 times of sliding interval of a DStream is good setting to try.

## Deploying Applications

This section discusses the steps to deploy a Spark Streaming application.

### Requirements

To run a Spark Streaming applications, you need to have the following.

• Cluster with a cluster manager - This is the general requirement of any Spark application, and discussed in detail in the deployment guide.

• Package the application JAR - You have to compile your streaming application into a JAR. If you are using spark-submit to start the application, then you will not need to provide Spark and Spark Streaming in the JAR. However, if your application uses advanced sources (e.g. Kafka, Flume, Twitter), then you will have to package the extra artifact they link to, along with their dependencies, in the JAR that is used to deploy the application. For example, an application using TwitterUtils will have to include spark-streaming-twitter_2.10 and all its transitive dependencies in the application JAR.

• Configuring sufficient memory for the executors - Since the received data must be stored in memory, the executors must be configured with sufficient memory to hold the received data. Note that if you are doing 10 minute window operations, the system has to keep at least last 10 minutes of data in memory. So the memory requirements for the application depends on the operations used in it.

• Configuring checkpointing - If the stream application requires it, then a directory in the Hadoop API compatible fault-tolerant storage (e.g. HDFS, S3, etc.) must be configured as the checkpoint directory and the streaming application written in a way that checkpoint information can be used for failure recovery. See the checkpointing section for more details.

• Configuring automatic restart of the application driver - To automatically recover from a driver failure, the deployment infrastructure that is used to run the streaming application must monitor the driver process and relaunch the driver if it fails. Different cluster managers have different tools to achieve this.
• Spark Standalone - A Spark application driver can be submitted to run within the Spark Standalone cluster (see cluster deploy mode), that is, the application driver itself runs on one of the worker nodes. Furthermore, the Standalone cluster manager can be instructed to supervise the driver, and relaunch it if the driver fails either due to non-zero exit code, or due to failure of the node running the driver. See cluster mode and supervise in the Spark Standalone guide for more details.
• YARN - Yarn supports a similar mechanism for automatically restarting an application. Please refer to YARN documentation for more details.
• Mesos - Marathon has been used to achieve this with Mesos.
• [Experimental in Spark 1.2] Configuring write ahead logs - In Spark 1.2, we have introduced a new experimental feature of write ahead logs for achieving strong fault-tolerance guarantees. If enabled, all the data received from a receiver gets written into a write ahead log in the configuration checkpoint directory. This prevents data loss on driver recovery, thus ensuring zero data loss (discussed in detail in the Fault-tolerance Semantics section). This can be enabled by setting the configuration parameter spark.streaming.receiver.writeAheadLogs.enable to true. However, these stronger semantics may come at the cost of the receiving throughput of individual receivers. This can be corrected by running more receivers in parallel to increase aggregate throughput. Additionally, it is recommended that the replication of the received data within Spark be disabled when the write ahead log is enabled as the log is already stored in a replicated storage system. This can be done by setting the storage level for the input stream to StorageLevel.MEMORY_AND_DISK_SER.

If a running Spark Streaming application needs to be upgraded with new application code, then there are two possible mechanism.

• The upgraded Spark Streaming application is started and run in parallel to the existing application. Once the new one (receiving the same data as the old one) has been warmed up and ready for prime time, the old one be can be brought down. Note that this can be done for data sources that support sending the data to two destinations (i.e., the earlier and upgraded applications).

• The existing application is shutdown gracefully (see StreamingContext.stop(...) or JavaStreamingContext.stop(...) for graceful shutdown options) which ensure data that have been received is completely processed before shutdown. Then the upgraded application can be started, which will start processing from the same point where the earlier application left off. Note that this can be done only with input sources that support source-side buffering (like Kafka, and Flume) as data needs to be buffered while the previous application was down and the upgraded application is not yet up. And restarting from earlier checkpoint information of pre-upgrade code cannot be done. The checkpoint information essentially contains serialized Scala/Java/Python objects and trying to deserialize objects with new, modified classes may lead to errors. In this case, either start the upgraded app with a different checkpoint directory, or delete the previous checkpoint directory.

### Other Considerations

If the data is being received by the receivers faster than what can be processed, you can limit the rate by setting the configuration parameter spark.streaming.receiver.maxRate.

## Monitoring Applications

Beyond Spark’s monitoring capabilities, there are additional capabilities specific to Spark Streaming. When a StreamingContext is used, the Spark web UI shows an additional Streaming tab which shows statistics about running receivers (whether receivers are active, number of records received, receiver error, etc.) and completed batches (batch processing times, queueing delays, etc.). This can be used to monitor the progress of the streaming application.

The following two metrics in web UI are particularly important:

• Processing Time - The time to process each batch of data.
• Scheduling Delay - the time a batch waits in a queue for the processing of previous batches to finish.

If the batch processing time is consistently more than the batch interval and/or the queueing delay keeps increasing, then it indicates the system is not able to process the batches as fast they are being generated and falling behind. In that case, consider reducing the batch processing time.

The progress of a Spark Streaming program can also be monitored using the StreamingListener interface, which allows you to get receiver status and processing times. Note that this is a developer API and it is likely to be improved upon (i.e., more information reported) in the future.

# Performance Tuning

Getting the best performance of a Spark Streaming application on a cluster requires a bit of tuning. This section explains a number of the parameters and configurations that can tuned to improve the performance of you application. At a high level, you need to consider two things:

1. Reducing the processing time of each batch of data by efficiently using cluster resources.

2. Setting the right batch size such that the batches of data can be processed as fast as they are received (that is, data processing keeps up with the data ingestion).

## Reducing the Processing Time of each Batch

There are a number of optimizations that can be done in Spark to minimize the processing time of each batch. These have been discussed in detail in Tuning Guide. This section highlights some of the most important ones.

### Level of Parallelism in Data Receiving

Receiving data over the network (like Kafka, Flume, socket, etc.) requires the data to deserialized and stored in Spark. If the data receiving becomes a bottleneck in the system, then consider parallelizing the data receiving. Note that each input DStream creates a single receiver (running on a worker machine) that receives a single stream of data. Receiving multiple data streams can therefore be achieved by creating multiple input DStreams and configuring them to receive different partitions of the data stream from the source(s). For example, a single Kafka input DStream receiving two topics of data can be split into two Kafka input streams, each receiving only one topic. This would run two receivers on two workers, thus allowing data to be received in parallel, and increasing overall throughput. These multiple DStream can be unioned together to create a single DStream. Then the transformations that was being applied on the single input DStream can applied on the unified stream. This is done as follows.

val numStreams = 5
val kafkaStreams = (1 to numStreams).map { i => KafkaUtils.createStream(...) }
val unifiedStream = streamingContext.union(kafkaStreams)
unifiedStream.print()
int numStreams = 5;
List<JavaPairDStream<String, String>> kafkaStreams = new ArrayList<JavaPairDStream<String, String>>(numStreams);
for (int i = 0; i < numStreams; i++) {
}
JavaPairDStream<String, String> unifiedStream = streamingContext.union(kafkaStreams.get(0), kafkaStreams.subList(1, kafkaStreams.size()));
unifiedStream.print();

Another parameter that should be considered is the receiver’s blocking interval, which is determined by the configuration parameter spark.streaming.blockInterval. For most receivers, the received data is coalesced together into blocks of data before storing inside Spark’s memory. The number of blocks in each batch determines the number of tasks that will be used to process those the received data in a map-like transformation. The number of tasks per receiver per batch will be approximately (batch interval / block interval). For example, block interval of 200 ms will create 10 tasks per 2 second batches. Too low the number of tasks (that is, less than the number of cores per machine), then it will be inefficient as all available cores will not be used to process the data. To increase the number of tasks for a given batch interval, reduce the block interval. However, the recommended minimum value of block interval is about 50 ms, below which the task launching overheads may be a problem.

An alternative to receiving data with multiple input streams / receivers is to explicitly repartition the input data stream (using inputStream.repartition(<number of partitions>)). This distributes the received batches of data across specified number of machines in the cluster before further processing.

### Level of Parallelism in Data Processing

Cluster resources can be under-utilized if the number of parallel tasks used in any stage of the computation is not high enough. For example, for distributed reduce operations like reduceByKey and reduceByKeyAndWindow, the default number of parallel tasks is controlled by thespark.default.parallelism configuration property. You can pass the level of parallelism as an argument (see PairDStreamFunctions documentation), or set the spark.default.parallelism configuration property to change the default.

### Data Serialization

The overhead of data serialization can be significant, especially when sub-second batch sizes are to be achieved. There are two aspects to it.

• Serialization of RDD data in Spark: Please refer to the detailed discussion on data serialization in the Tuning Guide. However, note that unlike Spark, by default RDDs are persisted as serialized byte arrays to minimize pauses related to GC.

• Serialization of input data: To ingest external data into Spark, data received as bytes (say, from the network) needs to deserialized from bytes and re-serialized into Spark’s serialization format. Hence, the deserialization overhead of input data may be a bottleneck.

If the number of tasks launched per second is high (say, 50 or more per second), then the overhead of sending out tasks to the slaves maybe significant and will make it hard to achieve sub-second latencies. The overhead can be reduced by the following changes:

• Task Serialization: Using Kryo serialization for serializing tasks can reduce the task sizes, and therefore reduce the time taken to send them to the slaves.

• Execution mode: Running Spark in Standalone mode or coarse-grained Mesos mode leads to better task launch times than the fine-grained Mesos mode. Please refer to the Running on Mesos guide for more details.

These changes may reduce batch processing time by 100s of milliseconds, thus allowing sub-second batch size to be viable.

## Setting the Right Batch Size

For a Spark Streaming application running on a cluster to be stable, the system should be able to process data as fast as it is being received. In other words, batches of data should be processed as fast as they are being generated. Whether this is true for an application can be found by monitoring the processing times in the streaming web UI, where the batch processing time should be less than the batch interval.

Depending on the nature of the streaming computation, the batch interval used may have significant impact on the data rates that can be sustained by the application on a fixed set of cluster resources. For example, let us consider the earlier WordCountNetwork example. For a particular data rate, the system may be able to keep up with reporting word counts every 2 seconds (i.e., batch interval of 2 seconds), but not every 500 milliseconds. So the batch interval needs to be set such that the expected data rate in production can be sustained.

A good approach to figure out the right batch size for your application is to test it with a conservative batch interval (say, 5-10 seconds) and a low data rate. To verify whether the system is able to keep up with data rate, you can check the value of the end-to-end delay experienced by each processed batch (either look for “Total delay” in Spark driver log4j logs, or use the StreamingListener interface). If the delay is maintained to be comparable to the batch size, then system is stable. Otherwise, if the delay is continuously increasing, it means that the system is unable to keep up and it therefore unstable. Once you have an idea of a stable configuration, you can try increasing the data rate and/or reducing the batch size. Note that momentary increase in the delay due to temporary data rate increases maybe fine as long as the delay reduces back to a low value (i.e., less than batch size).

## Memory Tuning

Tuning the memory usage and GC behavior of Spark applications have been discussed in great detail in the Tuning Guide. It is recommended that you read that. In this section, we highlight a few customizations that are strongly recommended to minimize GC related pauses in Spark Streaming applications and achieving more consistent batch processing times.

• Default persistence level of DStreams: Unlike RDDs, the default persistence level of DStreams serializes the data in memory (that is, StorageLevel.MEMORY_ONLY_SER for DStream compared to StorageLevel.MEMORY_ONLY for RDDs). Even though keeping the data serialized incurs higher serialization/deserialization overheads, it significantly reduces GC pauses.

• Clearing persistent RDDs: By default, all persistent RDDs generated by Spark Streaming will be cleared from memory based on Spark’s built-in policy (LRU). If spark.cleaner.ttl is set, then persistent RDDs that are older than that value are periodically cleared. As mentioned earlier, this needs to be careful set based on operations used in the Spark Streaming program. However, a smarter unpersisting of RDDs can be enabled by setting the configuration property spark.streaming.unpersist to true. This makes the system to figure out which RDDs are not necessary to be kept around and unpersists them. This is likely to reduce the RDD memory usage of Spark, potentially improving GC behavior as well.

• Concurrent garbage collector: Using the concurrent mark-and-sweep GC further minimizes the variability of GC pauses. Even though concurrent GC is known to reduce the overall processing throughput of the system, its use is still recommended to achieve more consistent batch processing times.

# Fault-tolerance Semantics

In this section, we will discuss the behavior of Spark Streaming applications in the event of node failures. To understand this, let us remember the basic fault-tolerance semantics of Spark’s RDDs.

1. An RDD is an immutable, deterministically re-computable, distributed dataset. Each RDD remembers the lineage of deterministic operations that were used on a fault-tolerant input dataset to create it.
2. If any partition of an RDD is lost due to a worker node failure, then that partition can be re-computed from the original fault-tolerant dataset using the lineage of operations.
3. Assuming that all of the RDD transformations are deterministic, the data in the final transformed RDD will always be the same irrespective of failures in the Spark cluster.

Spark operates on data on fault-tolerant file systems like HDFS or S3. Hence, all of the RDDs generated from the fault-tolerant data are also fault-tolerant. However, this is not the case for Spark Streaming as the data in most cases is received over the network (except when fileStream is used). To achieve the same fault-tolerance properties for all of the generated RDDs, the received data is replicated among multiple Spark executors in worker nodes in the cluster (default replication factor is 2). This leads to two kinds of data in the system that needs to recovered in the event of failures:

1. Data received and replicated - This data survives failure of a single worker node as a copy of it exists on one of the nodes.
2. Data received but buffered for replication - Since this is not replicated, the only way to recover that data is to get it again from the source.

Furthermore, there are two kinds of failures that we should be concerned about:

1. Failure of a Worker Node - Any of the worker nodes running executors can fail, and all in-memory data on those nodes will be lost. If any receivers were running on failed nodes, then their buffered data will be lost.
2. Failure of the Driver Node - If the driver node running the Spark Streaming application fails, then obviously the SparkContext is lost, and all executors with their in-memory data are lost.

With this basic knowledge, let us understand the fault-tolerance semantics of Spark Streaming.

## Semantics with files as input source

If all of the input data is already present in a fault-tolerant files system like HDFS, Spark Streaming can always recover from any failure and process all the data. This gives exactly-once semantics, that all the data will be processed exactly once no matter what fails.

## Semantics with input sources based on receivers

For input sources based on receivers, the fault-tolerance semantics depend on both the failure scenario and the type of receiver. As we discussed earlier, there are two types of receivers:

1. Reliable Receiver - These receivers acknowledge reliable sources only after ensuring that the received data has been replicated. If such a receiver fails, the buffered (unreplicated) data does not get acknowledged to the source. If the receiver is restarted, the source will resend the data, and therefore no data will be lost due to the failure.
2. Unreliable Receiver - Such receivers can lose data when they fail due to worker or driver failures.

Depending on what type of receivers are used we achieve the following semantics. If a worker node fails, then there is no data loss with reliable receivers. With unreliable receivers, data received but not replicated can get lost. If the driver node fails, then besides these losses, all the past data that was received and replicated in memory will be lost. This will affect the results of the stateful transformations.

To avoid this loss of past received data, Spark 1.2 introduces an experimental feature of write ahead logs which saves the received data to fault-tolerant storage. With the write ahead logs enabled and reliable receivers, there is zero data loss and exactly-once semantics.

The following table summarizes the semantics under failures:

Deployment Scenario Worker Failure Driver Failure
Spark 1.1 or earlier, or
Spark 1.2 without write ahead log
Buffered data lost with unreliable receivers
Zero data loss with reliable receivers and files
Buffered data lost with unreliable receivers
Past data lost with all receivers
Zero data loss with files
Spark 1.2 with write ahead log Zero data loss with reliable receivers and files Zero data loss with reliable receivers and files

## Semantics of output operations

Since all data is modeled as RDDs with their lineage of deterministic operations, any recomputation always leads to the same result. As a result, all DStream transformations are guaranteed to have exactly-once semantics. That is, the final transformed result will be same even if there were was a worker node failure. However, output operations (like foreachRDD) have at-least once semantics, that is, the transformed data may get written to an external entity more than once in the event of a worker failure. While this is acceptable for saving to HDFS using the saveAs***Files operations (as the file will simply get over-written by the same data), additional transactions-like mechanisms may be necessary to achieve exactly-once semantics for output operations.

# Migration Guide from 0.9.1 or below to 1.x

Between Spark 0.9.1 and Spark 1.0, there were a few API changes made to ensure future API stability. This section elaborates the steps required to migrate your existing code to 1.0.

Input DStreams: All operations that create an input stream (e.g., StreamingContext.socketStream, FlumeUtils.createStream, etc.) now returns InputDStream / ReceiverInputDStream (instead of DStream) for Scala, and JavaInputDStream / JavaPairInputDStream / JavaReceiverInputDStream / JavaPairReceiverInputDStream (instead of JavaDStream) for Java. This ensures that functionality specific to input streams can be added to these classes in the future without breaking binary compatibility. Note that your existing Spark Streaming applications should not require any change (as these new classes are subclasses of DStream/JavaDStream) but may require recompilation with Spark 1.0.

Custom Network Receivers: Since the release to Spark Streaming, custom network receivers could be defined in Scala using the class NetworkReceiver. However, the API was limited in terms of error handling and reporting, and could not be used from Java. Starting Spark 1.0, this class has been replaced by Receiver which has the following advantages.

• Methods like stop and restart have been added to for better control of the lifecycle of a receiver. See the custom receiver guide for more details.
• Custom receivers can be implemented using both Scala and Java.

• Make your custom receiver class extend org.apache.spark.streaming.receiver.Receiver instead of org.apache.spark.streaming.dstream.NetworkReceiver.
• Earlier, a BlockGenerator object had to be created by the custom receiver, to which received data was added for being stored in Spark. It had to be explicitly started and stopped from onStart() and onStop() methods. The new Receiver class makes this unnecessary as it adds a set of methods named store(<data>) that can be called to store the data in Spark. So, to migrate your custom network receiver, remove any BlockGenerator object (does not exist any more in Spark 1.0 anyway), and use store(...) methods on received data.

Actor-based Receivers: Data could have been received using any Akka Actors by extending the actor class with org.apache.spark.streaming.receivers.Receiver trait. This has been renamed to org.apache.spark.streaming.receiver.ActorHelper and the pushBlock(...) methods to store received data has been renamed to store(...). Other helper classes in the org.apache.spark.streaming.receivers package were also moved to org.apache.spark.streaming.receiver package and renamed for better clarity.

# Where to Go from Here

• API documentation
• Scala docs
• Java docs
• Python docs
• More examples in Scala and Java and Python
• Paper and video describing Spark Streaming.