spark-instrumented-optimizer/docs/streaming-programming-guide.md

---
layout: global
displayTitle: Spark Streaming Programming Guide
title: Spark Streaming
description: Spark Streaming programming guide and tutorial for Spark SPARK_VERSION_SHORT
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  contributor license agreements.  See the NOTICE file distributed with
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* 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 enables scalable, high-throughput,
fault-tolerant stream processing of live data streams. Data can be ingested from many sources
like Kafka, Kinesis, or TCP sockets, and 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](ml-guide.html) and
[graph processing](graphx-programming-guide.html) algorithms on data streams.

<p style="text-align: center;">
  <img
    src="img/streaming-arch.png"
    title="Spark Streaming architecture"
    alt="Spark Streaming"
    width="70%"
  />
</p>

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.

<p style="text-align: center;">
  <img src="img/streaming-flow.png"
       title="Spark Streaming data flow"
       alt="Spark Streaming"
       width="70%" />
</p>

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, and Kinesis, or by applying high-level
operations on other DStreams. Internally, a DStream is represented as a sequence of
[RDDs](api/scala/index.html#org.apache.spark.rdd.RDD).

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:** There are a few APIs that are either different or not available in Python. Throughout this guide, you will find the tag <span class="badge" style="background-color: grey">Python API</span> highlighting these differences.

***************************************************************************************************

# 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.

<div class="codetabs">
<div data-lang="scala"  markdown="1" >
First, we import the names of the Spark Streaming classes and some implicit
conversions from StreamingContext into our environment in order to add useful methods to
other classes we need (like DStream). [StreamingContext](api/scala/index.html#org.apache.spark.streaming.StreamingContext) 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.

{% highlight scala %}
import org.apache.spark._
import org.apache.spark.streaming._
import org.apache.spark.streaming.StreamingContext._ // not necessary since Spark 1.3

// Create a local StreamingContext with two working thread and batch interval of 1 second.
// The master requires 2 cores to prevent a starvation scenario.

val conf = new SparkConf().setMaster("local[2]").setAppName("NetworkWordCount")
val ssc = new StreamingContext(conf, Seconds(1))
{% endhighlight %}

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`).

{% highlight scala %}
// Create a DStream that will connect to hostname:port, like localhost:9999
val lines = ssc.socketTextStream("localhost", 9999)
{% 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 characters 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 %}
import org.apache.spark.streaming.StreamingContext._ // not necessary since 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()
{% 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]({{site.SPARK_GITHUB_URL}}/blob/v{{site.SPARK_VERSION_SHORT}}/examples/src/main/scala/org/apache/spark/examples/streaming/NetworkWordCount.scala).
<br>

</div>
<div data-lang="java" markdown="1">

First, we create a
[JavaStreamingContext](api/java/index.html?org/apache/spark/streaming/api/java/JavaStreamingContext.html) 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.

{% highlight java %}
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));
{% endhighlight %}

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`).

{% highlight java %}
// Create a DStream that will connect to hostname:port, like localhost:9999
JavaReceiverInputDStream<String> lines = jssc.socketTextStream("localhost", 9999);
{% 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 lines by
space into words.

{% highlight java %}
// Split each line into words
JavaDStream<String> words = lines.flatMap(x -> Arrays.asList(x.split(" ")).iterator());
{% 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](api/scala/index.html#org.apache.spark.api.java.function.FlatMapFunction) object.
As we will discover along the way, there are a number of such convenience classes in the Java API
that help defines DStream transformations.

Next, we want to count these words.

{% highlight java %}
// Count each word in each batch
JavaPairDStream<String, Integer> pairs = words.mapToPair(s -> new Tuple2<>(s, 1));
JavaPairDStream<String, Integer> wordCounts = pairs.reduceByKey((i1, i2) -> i1 + i2);

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

The `words` DStream is further mapped (one-to-one transformation) to a DStream of `(word,
1)` pairs, using a [PairFunction](api/scala/index.html#org.apache.spark.api.java.function.PairFunction)
object. Then, it is reduced to get the frequency of words in each batch of data,
using a [Function2](api/scala/index.html#org.apache.spark.api.java.function.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.

{% 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]({{site.SPARK_GITHUB_URL}}/blob/v{{site.SPARK_VERSION_SHORT}}/examples/src/main/java/org/apache/spark/examples/streaming/JavaNetworkWordCount.java).
<br>

</div>
<div data-lang="python"  markdown="1" >
First, we import [StreamingContext](api/python/pyspark.streaming.html#pyspark.streaming.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.

{% highlight python %}
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)
{% endhighlight %}

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`).

{% highlight python %}
# Create a DStream that will connect to hostname:port, like localhost:9999
lines = ssc.socketTextStream("localhost", 9999)
{% 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 python %}
# Split each line into words
words = lines.flatMap(lambda line: line.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 python %}
# 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()
{% 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.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

{% highlight python %}
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]({{site.SPARK_GITHUB_URL}}/blob/v{{site.SPARK_VERSION_SHORT}}/examples/src/main/python/streaming/network_wordcount.py).
<br>

</div>
</div>

If you have already [downloaded](index.html#downloading) and [built](index.html#building) 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

<div class="codetabs">
<div data-lang="scala" markdown="1">
{% highlight bash %}
$ ./bin/run-example streaming.NetworkWordCount localhost 9999
{% endhighlight %}
</div>
<div data-lang="java" markdown="1">
{% highlight bash %}
$ ./bin/run-example streaming.JavaNetworkWordCount localhost 9999
{% endhighlight %}
</div>
<div data-lang="python" markdown="1">
{% highlight bash %}
$ ./bin/spark-submit examples/src/main/python/streaming/network_wordcount.py localhost 9999
{% endhighlight %}
</div>
</div>


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.

<table width="100%">
    <td>
{% highlight bash %}
# TERMINAL 1:
# Running Netcat

$ nc -lk 9999

hello world



...
{% endhighlight %}
    </td>
    <td width="2%"></td>
    <td>
<div class="codetabs">

<div data-lang="scala" markdown="1">
{% highlight bash %}
# TERMINAL 2: RUNNING NetworkWordCount

$ ./bin/run-example streaming.NetworkWordCount localhost 9999
...
-------------------------------------------
Time: 1357008430000 ms
-------------------------------------------
(hello,1)
(world,1)
...
{% endhighlight %}
</div>

<div data-lang="java" markdown="1">
{% highlight bash %}
# TERMINAL 2: RUNNING JavaNetworkWordCount

$ ./bin/run-example streaming.JavaNetworkWordCount localhost 9999
...
-------------------------------------------
Time: 1357008430000 ms
-------------------------------------------
(hello,1)
(world,1)
...
{% endhighlight %}
</div>
<div data-lang="python" markdown="1">
{% highlight bash %}
# 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)
...
{% endhighlight %}
</div>
</div>
    </td>
</table>


***************************************************************************************************
***************************************************************************************************

# Basic Concepts

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

## Linking

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.

<div class="codetabs">
<div data-lang="Maven" markdown="1">

	<dependency>
        <groupId>org.apache.spark</groupId>
        <artifactId>spark-streaming_{{site.SCALA_BINARY_VERSION}}</artifactId>
        <version>{{site.SPARK_VERSION}}</version>
        <scope>provided</scope>
    </dependency>
</div>
<div data-lang="SBT" markdown="1">

	libraryDependencies += "org.apache.spark" % "spark-streaming_{{site.SCALA_BINARY_VERSION}}" % "{{site.SPARK_VERSION}}" % "provided"
</div>
</div>

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

<table class="table">
<tr><th>Source</th><th>Artifact</th></tr>
<tr><td> Kafka </td><td> spark-streaming-kafka-0-10_{{site.SCALA_BINARY_VERSION}} </td></tr>
<tr><td> Kinesis<br/></td><td>spark-streaming-kinesis-asl_{{site.SCALA_BINARY_VERSION}} [Amazon Software License] </td></tr>
<tr><td></td><td></td></tr>
</table>

For an up-to-date list, please refer to the
[Maven repository](https://search.maven.org/#search%7Cga%7C1%7Cg%3A%22org.apache.spark%22%20AND%20v%3A%22{{site.SPARK_VERSION_SHORT}}%22)
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.

<div class="codetabs">
<div data-lang="scala" markdown="1">

A [StreamingContext](api/scala/index.html#org.apache.spark.streaming.StreamingContext) object can be created from a [SparkConf](api/scala/index.html#org.apache.spark.SparkConf) object.

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

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

The `appName` parameter is a name for your application to show on the cluster UI.
`master` is a [Spark, Mesos, Kubernetes or YARN cluster URL](submitting-applications.html#master-urls),
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`](submitting-applications.html) 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](api/scala/index.html#org.apache.spark.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](#setting-the-right-batch-interval)
section for more details.

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

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

val sc = ...                // existing SparkContext
val ssc = new StreamingContext(sc, Seconds(1))
{% endhighlight %}


</div>
<div data-lang="java" markdown="1">

A [JavaStreamingContext](api/java/index.html?org/apache/spark/streaming/api/java/JavaStreamingContext.html) object can be created from a [SparkConf](api/java/index.html?org/apache/spark/SparkConf.html) object.

{% highlight java %}
import org.apache.spark.*;
import org.apache.spark.streaming.api.java.*;

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

The `appName` parameter is a name for your application to show on the cluster UI.
`master` is a [Spark, Mesos or YARN cluster URL](submitting-applications.html#master-urls),
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`](submitting-applications.html) 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](api/java/index.html?org/apache/spark/api/java/JavaSparkContext.html) (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](#setting-the-right-batch-interval)
section for more details.

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

{% highlight java %}
import org.apache.spark.streaming.api.java.*;

JavaSparkContext sc = ...   //existing JavaSparkContext
JavaStreamingContext ssc = new JavaStreamingContext(sc, Durations.seconds(1));
{% endhighlight %}
</div>
<div data-lang="python" markdown="1">

A [StreamingContext](api/python/pyspark.streaming.html#pyspark.streaming.StreamingContext) object can be created from a [SparkContext](api/python/pyspark.html#pyspark.SparkContext) object.

{% highlight python %}
from pyspark import SparkContext
from pyspark.streaming import StreamingContext

sc = SparkContext(master, appName)
ssc = StreamingContext(sc, 1)
{% endhighlight %}

The `appName` parameter is a name for your application to show on the cluster UI.
`master` is a [Spark, Mesos or YARN cluster URL](submitting-applications.html#master-urls),
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`](submitting-applications.html) 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](#setting-the-right-batch-interval)
section for more details.
</div>
</div>

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

1. Define the input sources by creating input DStreams.
1. Define the streaming computations by applying transformation and output operations to DStreams.
1. Start receiving data and processing it using `streamingContext.start()`.
1. Wait for the processing to be stopped (manually or due to any error) using `streamingContext.awaitTermination()`.
1. The processing can be manually stopped using `streamingContext.stop()`.

##### Points to remember:
{:.no_toc}
- 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 the 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](rdd-programming-guide.html#resilient-distributed-datasets-rdds) for more details). Each RDD in a DStream contains data from a certain interval,
as shown in the following figure.

<p style="text-align: center;">
  <img src="img/streaming-dstream.png"
       title="Spark Streaming data flow"
       alt="Spark Streaming"
       width="70%" />
</p>

Any operation applied on a DStream translates to operations on the underlying RDDs. For example,
in the [earlier example](#a-quick-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.

<p style="text-align: center;">
  <img src="img/streaming-dstream-ops.png"
       title="Spark Streaming data flow"
       alt="Spark Streaming"
       width="70%" />
</p>


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

***

## Input DStreams and Receivers
Input DStreams are DStreams representing the stream of input data received from streaming
sources. In the [quick example](#a-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](api/scala/index.html#org.apache.spark.streaming.receiver.Receiver),
[Java doc](api/java/org/apache/spark/streaming/receiver/Receiver.html)) 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.
  Examples: file systems, and socket connections.
- *Advanced sources*: Sources like Kafka, Kinesis, etc. are available through
  extra utility classes. These require linking against extra dependencies as discussed in the
  [linking](#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](#level-of-parallelism-in-data-receiving) section). This will
create multiple receivers which will simultaneously receive multiple data streams. But note that a
Spark worker/executor is a long-running task, hence it occupies one of the cores allocated to the
Spark Streaming application. Therefore, it is important to remember that a 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
{:.no_toc}

- 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
  an input DStream based on a receiver (e.g. sockets, Kafka, 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](configuration.html#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 it.

### Basic Sources
{:.no_toc}

We have already taken a look at the `ssc.socketTextStream(...)` in the [quick example](#a-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 as input sources.

#### File Streams
{:.no_toc}

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
via `StreamingContext.fileStream[KeyClass, ValueClass, InputFormatClass]`.

File streams do not require running a receiver so there is no need to allocate any cores for receiving file data.

For simple text files, the easiest method is `StreamingContext.textFileStream(dataDirectory)`.

<div class="codetabs">
<div data-lang="scala" markdown="1">

{% highlight scala %}
streamingContext.fileStream[KeyClass, ValueClass, InputFormatClass](dataDirectory)
{% endhighlight %}
For text files

{% highlight scala %}
streamingContext.textFileStream(dataDirectory)
{% endhighlight %}
</div>

<div data-lang="java" markdown="1">
{% highlight java %}
streamingContext.fileStream<KeyClass, ValueClass, InputFormatClass>(dataDirectory);
{% endhighlight %}
For text files

{% highlight java %}
streamingContext.textFileStream(dataDirectory);
{% endhighlight %}
</div>

<div data-lang="python" markdown="1">
`fileStream` is not available in the Python API; only `textFileStream` is available.
{% highlight python %}
streamingContext.textFileStream(dataDirectory)
{% endhighlight %}
</div>

</div>

##### How Directories are Monitored
{:.no_toc}

Spark Streaming will monitor the directory `dataDirectory` and process any files created in that directory.

   * A simple directory can be monitored, such as `"hdfs://namenode:8040/logs/"`.
     All files directly under such a path will be processed as they are discovered.
   + A [POSIX glob pattern](http://pubs.opengroup.org/onlinepubs/009695399/utilities/xcu_chap02.html#tag_02_13_02) can be supplied, such as
     `"hdfs://namenode:8040/logs/2017/*"`.
     Here, the DStream will consist of all files in the directories
     matching the pattern.
     That is: it is a pattern of directories, not of files in directories.
   + All files must be in the same data format.
   * A file is considered part of a time period based on its modification time,
     not its creation time.
   + Once processed, changes to a file within the current window will not cause the file to be reread.
     That is: *updates are ignored*.
   + The more files under a directory, the longer it will take to
     scan for changes — even if no files have been modified.
   * If a wildcard is used to identify directories, such as `"hdfs://namenode:8040/logs/2016-*"`,
     renaming an entire directory to match the path will add the directory to the list of
     monitored directories. Only the files in the directory whose modification time is
     within the current window will be included in the stream.
   + Calling [`FileSystem.setTimes()`](https://hadoop.apache.org/docs/current/api/org/apache/hadoop/fs/FileSystem.html#setTimes-org.apache.hadoop.fs.Path-long-long-)
     to fix the timestamp is a way to have the file picked up in a later window, even if its contents have not changed.


##### Using Object Stores as a source of data
{:.no_toc}

"Full" Filesystems such as HDFS tend to set the modification time on their files as soon
as the output stream is created.
When a file is opened, even before data has been completely written,
it may be included in the `DStream` - after which updates to the file within the same window
will be ignored. That is: changes may be missed, and data omitted from the stream.

To guarantee that changes are picked up in a window, write the file
to an unmonitored directory, then, immediately after the output stream is closed,
rename it into the destination directory.
Provided the renamed file appears in the scanned destination directory during the window
of its creation, the new data will be picked up.

In contrast, Object Stores such as Amazon S3 and Azure Storage usually have slow rename operations, as the
data is actually copied.
Furthermore, renamed object may have the time of the `rename()` operation as its modification time, so
may not be considered part of the window which the original create time implied they were.

Careful testing is needed against the target object store to verify that the timestamp behavior
of the store is consistent with that expected by Spark Streaming. It may be
that writing directly into a destination directory is the appropriate strategy for
streaming data via the chosen object store.

For more details on this topic, consult the [Hadoop Filesystem Specification](https://hadoop.apache.org/docs/stable2/hadoop-project-dist/hadoop-common/filesystem/introduction.html).

#### Streams based on Custom Receivers
{:.no_toc}

DStreams can be created with data streams received through custom receivers. See the [Custom Receiver
  Guide](streaming-custom-receivers.html) for more details.

#### Queue of RDDs as a Stream
{:.no_toc}

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 and files, see the API documentations of the relevant functions in
[StreamingContext](api/scala/index.html#org.apache.spark.streaming.StreamingContext) for
Scala, [JavaStreamingContext](api/java/index.html?org/apache/spark/streaming/api/java/JavaStreamingContext.html)
for Java, and [StreamingContext](api/python/pyspark.streaming.html#pyspark.streaming.StreamingContext) for Python.

### Advanced Sources
{:.no_toc}

<span class="badge" style="background-color: grey">Python API</span> As of Spark {{site.SPARK_VERSION_SHORT}},
out of these sources, Kafka and Kinesis are available in the Python API.

This category of sources requires interfacing with external non-Spark libraries, some of them with
complex dependencies (e.g., Kafka). Hence, to minimize issues related to version conflicts
of dependencies, the functionality to create DStreams from these sources has been moved to separate
libraries that can be [linked](#linking) to explicitly when necessary.

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 add it to the classpath.

Some of these advanced sources are as follows.

- **Kafka:** Spark Streaming {{site.SPARK_VERSION_SHORT}} is compatible with Kafka broker versions 0.10 or higher. See the [Kafka Integration Guide](streaming-kafka-0-10-integration.html) for more details.

- **Kinesis:** Spark Streaming {{site.SPARK_VERSION_SHORT}} is compatible with Kinesis Client Library 1.2.1. See the [Kinesis Integration Guide](streaming-kinesis-integration.html) for more details.

### Custom Sources
{:.no_toc}

<span class="badge" style="background-color: grey">Python API</span> This is not yet supported in Python.

Input DStreams can also be created out of custom data sources. All you have to do is implement a
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](streaming-custom-receivers.html) for details.

### Receiver Reliability
{:.no_toc}

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

1. *Reliable Receiver* - A *reliable receiver* correctly sends acknowledgment to a reliable
  source when the data has been received and stored in Spark with replication.
1. *Unreliable Receiver* - An *unreliable receiver* does *not* send acknowledgment to a source. This can be used for sources that do not support acknowledgment, or even for reliable sources when one does not want or need to go into the complexity of acknowledgment.

The details of how to write a reliable receiver are discussed in the
[Custom Receiver Guide](streaming-custom-receivers.html).

***

## 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.

<table class="table">
<tr><th style="width:25%">Transformation</th><th>Meaning</th></tr>
<tr>
  <td> <b>map</b>(<i>func</i>) </td>
  <td> Return a new DStream by passing each element of the source DStream through a
  function <i>func</i>. </td>
</tr>
<tr>
  <td> <b>flatMap</b>(<i>func</i>) </td>
  <td> Similar to map, but each input item can be mapped to 0 or more output items. </td>
</tr>
<tr>
  <td> <b>filter</b>(<i>func</i>) </td>
  <td> Return a new DStream by selecting only the records of the source DStream on which
  <i>func</i> returns true. </td>
</tr>
<tr>
  <td> <b>repartition</b>(<i>numPartitions</i>) </td>
  <td> Changes the level of parallelism in this DStream by creating more or fewer partitions. </td>
</tr>
<tr>
  <td> <b>union</b>(<i>otherStream</i>) </td>
  <td> Return a new DStream that contains the union of the elements in the source DStream and
  <i>otherDStream</i>. </td>
</tr>
<tr>
  <td> <b>count</b>() </td>
  <td> Return a new DStream of single-element RDDs by counting the number of elements in each RDD
   of the source DStream. </td>
</tr>
<tr>
  <td> <b>reduce</b>(<i>func</i>) </td>
  <td> Return a new DStream of single-element RDDs by aggregating the elements in each RDD of the
  source DStream using a function <i>func</i> (which takes two arguments and returns one).
  The function should be associative and commutative so that it can be computed in parallel. </td>
</tr>
<tr>
  <td> <b>countByValue</b>() </td>
  <td> 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.  </td>
</tr>
<tr>
  <td> <b>reduceByKey</b>(<i>func</i>, [<i>numTasks</i>]) </td>
  <td> 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. <b>Note:</b> 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 <code>spark.default.parallelism</code>) to do the grouping.
  You can pass an optional <code>numTasks</code> argument to set a different number of tasks.</td>
</tr>
<tr>
  <td> <b>join</b>(<i>otherStream</i>, [<i>numTasks</i>]) </td>
  <td> 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. </td>
</tr>
<tr>
  <td> <b>cogroup</b>(<i>otherStream</i>, [<i>numTasks</i>]) </td>
  <td> When called on a DStream of (K, V) and (K, W) pairs, return a new DStream of
  (K, Seq[V], Seq[W]) tuples.</td>
</tr>
<tr>
  <td> <b>transform</b>(<i>func</i>) </td>
  <td> 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. </td>
</tr>
<tr>
  <td> <b>updateStateByKey</b>(<i>func</i>) </td>
  <td> 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.</td>
</tr>
<tr><td></td><td></td></tr>
</table>

A few of these transformations are worth discussing in more detail.

#### UpdateStateByKey Operation
{:.no_toc}
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 an arbitrary data type.
1. Define the state update function - Specify with a function how to update the state using the
previous state and the new values from an input stream.

In every batch, Spark will apply the state  update function for all existing keys, regardless of whether they have new data in a batch or not. If the update function returns `None` then the key-value pair will be eliminated.

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:

<div class="codetabs">
<div data-lang="scala" markdown="1">

{% highlight scala %}
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)
}
{% endhighlight %}

This is applied on a DStream containing words (say, the `pairs` DStream containing `(word,
1)` pairs in the [earlier example](#a-quick-example)).

{% highlight scala %}
val runningCounts = pairs.updateStateByKey[Int](updateFunction _)
{% endhighlight %}

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.

</div>
<div data-lang="java" markdown="1">

{% highlight java %}
Function2<List<Integer>, Optional<Integer>, Optional<Integer>> updateFunction =
  (values, state) -> {
    Integer newSum = ...  // add the new values with the previous running count to get the new count
    return Optional.of(newSum);
  };
{% endhighlight %}

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

{% highlight java %}
JavaPairDStream<String, Integer> runningCounts = pairs.updateStateByKey(updateFunction);
{% endhighlight %}

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
Java code, take a look at the example
[JavaStatefulNetworkWordCount.java]({{site.SPARK_GITHUB_URL}}/blob/v{{site.SPARK_VERSION_SHORT}}/examples/src/main/java/org/apache/spark/examples/streaming/JavaStatefulNetworkWordCount.java).

</div>
<div data-lang="python" markdown="1">

{% highlight python %}
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
{% endhighlight %}

This is applied on a DStream containing words (say, the `pairs` DStream containing `(word,
1)` pairs in the [earlier example](#a-quick-example)).

{% highlight python %}
runningCounts = pairs.updateStateByKey(updateFunction)
{% endhighlight %}

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
Python code, take a look at the example
[stateful_network_wordcount.py]({{site.SPARK_GITHUB_URL}}/blob/v{{site.SPARK_VERSION_SHORT}}/examples/src/main/python/streaming/stateful_network_wordcount.py).

</div>
</div>

Note that using `updateStateByKey` requires the checkpoint directory to be configured, which is
discussed in detail in the [checkpointing](#checkpointing) section.


#### Transform Operation
{:.no_toc}
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,
one can 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.

<div class="codetabs">
<div data-lang="scala" markdown="1">

{% highlight scala %}
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
  ...
}
{% endhighlight %}

</div>
<div data-lang="java" markdown="1">

{% highlight java %}
import org.apache.spark.streaming.api.java.*;
// RDD containing spam information
JavaPairRDD<String, Double> spamInfoRDD = jssc.sparkContext().newAPIHadoopRDD(...);

JavaPairDStream<String, Integer> cleanedDStream = wordCounts.transform(rdd -> {
  rdd.join(spamInfoRDD).filter(...); // join data stream with spam information to do data cleaning
  ...
});
{% endhighlight %}

</div>
<div data-lang="python" markdown="1">

{% highlight python %}
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(...))
{% endhighlight %}
</div>
</div>

Note that the supplied function gets called in every batch interval. This allows you to do
time-varying RDD operations, that is, RDD operations, number of partitions, broadcast variables,
etc. can be changed between batches.

#### Window Operations
{:.no_toc}
Spark Streaming also provides *windowed computations*, which allow you to apply
transformations over a sliding window of data. The following figure illustrates this sliding
window.

<p style="text-align: center;">
  <img src="img/streaming-dstream-window.png"
       title="Spark Streaming data flow"
       alt="Spark Streaming"
       width="60%" />
</p>

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 the last 3 time
units of data, and slides by 2 time units. This shows that any window operation needs to
specify two parameters.

 * <i>window length</i> - The duration of the window (3 in the figure).
 * <i>sliding interval</i> - 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](#a-quick-example) by generating word counts over the 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`.

<div class="codetabs">
<div data-lang="scala" markdown="1">

{% highlight scala %}
// Reduce last 30 seconds of data, every 10 seconds
val windowedWordCounts = pairs.reduceByKeyAndWindow((a:Int,b:Int) => (a + b), Seconds(30), Seconds(10))
{% endhighlight %}

</div>
<div data-lang="java" markdown="1">

{% highlight java %}
// Reduce last 30 seconds of data, every 10 seconds
JavaPairDStream<String, Integer> windowedWordCounts = pairs.reduceByKeyAndWindow((i1, i2) -> i1 + i2, Durations.seconds(30), Durations.seconds(10));
{% endhighlight %}

</div>
<div data-lang="python" markdown="1">

{% highlight python %}
# Reduce last 30 seconds of data, every 10 seconds
windowedWordCounts = pairs.reduceByKeyAndWindow(lambda x, y: x + y, lambda x, y: x - y, 30, 10)
{% endhighlight %}

</div>
</div>

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

<table class="table">
<tr><th style="width:25%">Transformation</th><th>Meaning</th></tr>
<tr>
  <td> <b>window</b>(<i>windowLength</i>, <i>slideInterval</i>) </td>
  <td> Return a new DStream which is computed based on windowed batches of the source DStream.
  </td>
</tr>
<tr>
  <td> <b>countByWindow</b>(<i>windowLength</i>, <i>slideInterval</i>) </td>
  <td> Return a sliding window count of elements in the stream.
  </td>
</tr>
<tr>
  <td> <b>reduceByWindow</b>(<i>func</i>, <i>windowLength</i>, <i>slideInterval</i>) </td>
  <td> Return a new single-element stream, created by aggregating elements in the stream over a
  sliding interval using <i>func</i>. The function should be associative and commutative so that it can be computed
  correctly in parallel.
  </td>
</tr>
<tr>
  <td> <b>reduceByKeyAndWindow</b>(<i>func</i>, <i>windowLength</i>, <i>slideInterval</i>,
  [<i>numTasks</i>]) </td>
  <td> 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 <i>func</i>
  over batches in a sliding window. <b>Note:</b> 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 <code>spark.default.parallelism</code>) to do the grouping. You can pass an optional
  <code>numTasks</code> argument to set a different number of tasks.
  </td>
</tr>
<tr>
  <td> <b>reduceByKeyAndWindow</b>(<i>func</i>, <i>invFunc</i>, <i>windowLength</i>,
  <i>slideInterval</i>, [<i>numTasks</i>]) </td>
  <td markdown="1"> A more efficient version of the above <code>reduceByKeyAndWindow()</code> 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 enters the sliding window, and "inverse reducing" the
  old data that leaves the window. An example would be that of "adding" and "subtracting" counts
  of keys as the window slides. However, it is applicable only to "invertible reduce functions",
  that is, those reduce functions which have a corresponding "inverse reduce" function (taken as
  parameter <i>invFunc</i>). Like in <code>reduceByKeyAndWindow</code>, the number of reduce tasks
  is configurable through an optional argument. Note that [checkpointing](#checkpointing) must be
  enabled for using this operation.
</td>
</tr>
<tr>
  <td> <b>countByValueAndWindow</b>(<i>windowLength</i>,
  <i>slideInterval</i>, [<i>numTasks</i>]) </td>
  <td> 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
  <code>reduceByKeyAndWindow</code>, the number of reduce tasks is configurable through an
  optional argument.
</td>
</tr>
<tr><td></td><td></td></tr>
</table>

#### Join Operations
{:.no_toc}
Finally, its worth highlighting how easily you can perform different kinds of joins in Spark Streaming.


##### Stream-stream joins
{:.no_toc}
Streams can be very easily joined with other streams.

<div class="codetabs">
<div data-lang="scala" markdown="1">
{% highlight scala %}
val stream1: DStream[String, String] = ...
val stream2: DStream[String, String] = ...
val joinedStream = stream1.join(stream2)
{% endhighlight %}
</div>
<div data-lang="java" markdown="1">
{% highlight java %}
JavaPairDStream<String, String> stream1 = ...
JavaPairDStream<String, String> stream2 = ...
JavaPairDStream<String, Tuple2<String, String>> joinedStream = stream1.join(stream2);
{% endhighlight %}
</div>
<div data-lang="python" markdown="1">
{% highlight python %}
stream1 = ...
stream2 = ...
joinedStream = stream1.join(stream2)
{% endhighlight %}
</div>
</div>
Here, in each batch interval, the RDD generated by `stream1` will be joined with the RDD generated by `stream2`. You can also do `leftOuterJoin`, `rightOuterJoin`, `fullOuterJoin`. Furthermore, it is often very useful to do joins over windows of the streams. That is pretty easy as well. 

<div class="codetabs">
<div data-lang="scala" markdown="1">
{% highlight scala %}
val windowedStream1 = stream1.window(Seconds(20))
val windowedStream2 = stream2.window(Minutes(1))
val joinedStream = windowedStream1.join(windowedStream2)
{% endhighlight %}
</div>
<div data-lang="java" markdown="1">
{% highlight java %}
JavaPairDStream<String, String> windowedStream1 = stream1.window(Durations.seconds(20));
JavaPairDStream<String, String> windowedStream2 = stream2.window(Durations.minutes(1));
JavaPairDStream<String, Tuple2<String, String>> joinedStream = windowedStream1.join(windowedStream2);
{% endhighlight %}
</div>
<div data-lang="python" markdown="1">
{% highlight python %}
windowedStream1 = stream1.window(20)
windowedStream2 = stream2.window(60)
joinedStream = windowedStream1.join(windowedStream2)
{% endhighlight %}
</div>
</div>

##### Stream-dataset joins
{:.no_toc}
This has already been shown earlier while explain `DStream.transform` operation. Here is yet another example of joining a windowed stream with a dataset.

<div class="codetabs">
<div data-lang="scala" markdown="1">
{% highlight scala %}
val dataset: RDD[String, String] = ...
val windowedStream = stream.window(Seconds(20))...
val joinedStream = windowedStream.transform { rdd => rdd.join(dataset) }
{% endhighlight %}
</div>
<div data-lang="java" markdown="1">
{% highlight java %}
JavaPairRDD<String, String> dataset = ...
JavaPairDStream<String, String> windowedStream = stream.window(Durations.seconds(20));
JavaPairDStream<String, String> joinedStream = windowedStream.transform(rdd -> rdd.join(dataset));
{% endhighlight %}
</div>
<div data-lang="python" markdown="1">
{% highlight python %}
dataset = ... # some RDD
windowedStream = stream.window(20)
joinedStream = windowedStream.transform(lambda rdd: rdd.join(dataset))
{% endhighlight %}
</div>
</div>

In fact, you can also dynamically change the dataset you want to join against. The function provided to `transform` is evaluated every batch interval and therefore will use the current dataset that `dataset` reference points to.

The complete list of DStream transformations is available in the API documentation. For the Scala API,
see [DStream](api/scala/index.html#org.apache.spark.streaming.dstream.DStream)
and [PairDStreamFunctions](api/scala/index.html#org.apache.spark.streaming.dstream.PairDStreamFunctions).
For the Java API, see [JavaDStream](api/java/index.html?org/apache/spark/streaming/api/java/JavaDStream.html)
and [JavaPairDStream](api/java/index.html?org/apache/spark/streaming/api/java/JavaPairDStream.html).
For the Python API, see [DStream](api/python/pyspark.streaming.html#pyspark.streaming.DStream).

***

## Output Operations on DStreams
Output operations allow DStream's data to be pushed out to 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:

<table class="table">
<tr><th style="width:30%">Output Operation</th><th>Meaning</th></tr>
<tr>
  <td> <b>print</b>()</td>
  <td> Prints the 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.
  <br/>
  <span class="badge" style="background-color: grey">Python API</span> This is called
  <b>pprint()</b> in the Python API.
  </td>
</tr>
<tr>
  <td> <b>saveAsTextFiles</b>(<i>prefix</i>, [<i>suffix</i>]) </td>
  <td> Save this DStream's contents as text files. The file name at each batch interval is
  generated based on <i>prefix</i> and <i>suffix</i>: <i>"prefix-TIME_IN_MS[.suffix]"</i>. </td>
</tr>
<tr>
  <td> <b>saveAsObjectFiles</b>(<i>prefix</i>, [<i>suffix</i>]) </td>
  <td> Save this DStream's contents as <code>SequenceFiles</code> of serialized Java objects. The file
  name at each batch interval is generated based on <i>prefix</i> and
  <i>suffix</i>: <i>"prefix-TIME_IN_MS[.suffix]"</i>.
  <br/>
  <span class="badge" style="background-color: grey">Python API</span> This is not available in
  the Python API.
  </td>
</tr>
<tr>
  <td> <b>saveAsHadoopFiles</b>(<i>prefix</i>, [<i>suffix</i>]) </td>
  <td> Save this DStream's contents as Hadoop files. The file name at each batch interval is
  generated based on <i>prefix</i> and <i>suffix</i>: <i>"prefix-TIME_IN_MS[.suffix]"</i>.
  <br>
  <span class="badge" style="background-color: grey">Python API</span> This is not available in
  the Python API.
  </td>
</tr>
<tr>
  <td> <b>foreachRDD</b>(<i>func</i>) </td>
  <td> The most generic output operator that applies a function, <i>func</i>, to each RDD generated from
  the stream. This function should push the data in each RDD to an external system, such as saving the RDD to
  files, or writing it over the network to a database. Note that the function <i>func</i> 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.</td>
</tr>
<tr><td></td><td></td></tr>
</table>

### Design Patterns for using foreachRDD
{:.no_toc}
`dstream.foreachRDD` is a powerful primitive that allows data to be 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, and then try to use it in a Spark worker to save records in the RDDs.
For example (in Scala),

<div class="codetabs">
<div data-lang="scala" markdown="1">
{% highlight scala %}
dstream.foreachRDD { rdd =>
  val connection = createNewConnection()  // executed at the driver
  rdd.foreach { record =>
    connection.send(record) // executed at the worker
  }
}
{% endhighlight %}
</div>
<div data-lang="java" markdown="1">
{% highlight java %}
dstream.foreachRDD(rdd -> {
  Connection connection = createNewConnection(); // executed at the driver
  rdd.foreach(record -> {
    connection.send(record); // executed at the worker
  });
});
{% endhighlight %}
</div>
<div data-lang="python" markdown="1">
{% highlight python %}
def sendRecord(rdd):
    connection = createNewConnection()  # executed at the driver
    rdd.foreach(lambda record: connection.send(record))
    connection.close()

dstream.foreachRDD(sendRecord)
{% endhighlight %}
</div>
</div>

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 transferable 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,

<div class="codetabs">
<div data-lang="scala" markdown="1">
{% highlight scala %}
dstream.foreachRDD { rdd =>
  rdd.foreach { record =>
    val connection = createNewConnection()
    connection.send(record)
    connection.close()
  }
}
{% endhighlight %}
</div>
<div data-lang="java" markdown="1">
{% highlight java %}
dstream.foreachRDD(rdd -> {
  rdd.foreach(record -> {
    Connection connection = createNewConnection();
    connection.send(record);
    connection.close();
  });
});
{% endhighlight %}
</div>
<div data-lang="python" markdown="1">
{% highlight python %}
def sendRecord(record):
    connection = createNewConnection()
    connection.send(record)
    connection.close()

dstream.foreachRDD(lambda rdd: rdd.foreach(sendRecord))
{% endhighlight %}
</div>
</div>

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.

<div class="codetabs">
<div data-lang="scala" markdown="1">
{% highlight scala %}
dstream.foreachRDD { rdd =>
  rdd.foreachPartition { partitionOfRecords =>
    val connection = createNewConnection()
    partitionOfRecords.foreach(record => connection.send(record))
    connection.close()
  }
}
{% endhighlight %}
</div>
<div data-lang="java" markdown="1">
{% highlight java %}
dstream.foreachRDD(rdd -> {
  rdd.foreachPartition(partitionOfRecords -> {
    Connection connection = createNewConnection();
    while (partitionOfRecords.hasNext()) {
      connection.send(partitionOfRecords.next());
    }
    connection.close();
  });
});
{% endhighlight %}
</div>
<div data-lang="python" markdown="1">
{% highlight python %}
def sendPartition(iter):
    connection = createNewConnection()
    for record in iter:
        connection.send(record)
    connection.close()

dstream.foreachRDD(lambda rdd: rdd.foreachPartition(sendPartition))
{% endhighlight %}
</div>
</div>

  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.

<div class="codetabs">
<div data-lang="scala" markdown="1">
{% highlight scala %}
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))
    ConnectionPool.returnConnection(connection)  // return to the pool for future reuse
  }
}
{% endhighlight %}
</div>

<div data-lang="java" markdown="1">
{% highlight java %}
dstream.foreachRDD(rdd -> {
  rdd.foreachPartition(partitionOfRecords -> {
    // ConnectionPool is a static, lazily initialized pool of connections
    Connection connection = ConnectionPool.getConnection();
    while (partitionOfRecords.hasNext()) {
      connection.send(partitionOfRecords.next());
    }
    ConnectionPool.returnConnection(connection); // return to the pool for future reuse
  });
});
{% endhighlight %}
</div>
<div data-lang="python" markdown="1">
{% highlight python %}
def sendPartition(iter):
    # ConnectionPool is a static, lazily initialized pool of connections
    connection = ConnectionPool.getConnection()
    for record in iter:
        connection.send(record)
    # return to the pool for future reuse
    ConnectionPool.returnConnection(connection)

dstream.foreachRDD(lambda rdd: rdd.foreachPartition(sendPartition))
{% endhighlight %}
</div>
</div>

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:
{:.no_toc}
- 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.

***

## DataFrame and SQL Operations
You can easily use [DataFrames and SQL](sql-programming-guide.html) operations on streaming data. You have to create a SparkSession using the SparkContext that the StreamingContext is using. Furthermore, this has to done such that it can be restarted on driver failures. This is done by creating a lazily instantiated singleton instance of SparkSession. This is shown in the following example. It modifies the earlier [word count example](#a-quick-example) to generate word counts using DataFrames and SQL. Each RDD is converted to a DataFrame, registered as a temporary table and then queried using SQL.

<div class="codetabs">
<div data-lang="scala" markdown="1">
{% highlight scala %}

/** DataFrame operations inside your streaming program */

val words: DStream[String] = ...

words.foreachRDD { rdd =>

  // Get the singleton instance of SparkSession
  val spark = SparkSession.builder.config(rdd.sparkContext.getConf).getOrCreate()
  import spark.implicits._

  // Convert RDD[String] to DataFrame
  val wordsDataFrame = rdd.toDF("word")

  // Create a temporary view
  wordsDataFrame.createOrReplaceTempView("words")

  // Do word count on DataFrame using SQL and print it
  val wordCountsDataFrame = 
    spark.sql("select word, count(*) as total from words group by word")
  wordCountsDataFrame.show()
}

{% endhighlight %}

See the full [source code]({{site.SPARK_GITHUB_URL}}/blob/v{{site.SPARK_VERSION_SHORT}}/examples/src/main/scala/org/apache/spark/examples/streaming/SqlNetworkWordCount.scala).
</div>
<div data-lang="java" markdown="1">
{% highlight java %}

/** Java Bean class for converting RDD to DataFrame */
public class JavaRow implements java.io.Serializable {
  private String word;

  public String getWord() {
    return word;
  }

  public void setWord(String word) {
    this.word = word;
  }
}

...

/** DataFrame operations inside your streaming program */

JavaDStream<String> words = ... 

words.foreachRDD((rdd, time) -> {
  // Get the singleton instance of SparkSession
  SparkSession spark = SparkSession.builder().config(rdd.sparkContext().getConf()).getOrCreate();

  // Convert RDD[String] to RDD[case class] to DataFrame
  JavaRDD<JavaRow> rowRDD = rdd.map(word -> {
    JavaRow record = new JavaRow();
    record.setWord(word);
    return record;
  });
  DataFrame wordsDataFrame = spark.createDataFrame(rowRDD, JavaRow.class);

  // Creates a temporary view using the DataFrame
  wordsDataFrame.createOrReplaceTempView("words");

  // Do word count on table using SQL and print it
  DataFrame wordCountsDataFrame =
    spark.sql("select word, count(*) as total from words group by word");
  wordCountsDataFrame.show();
});
{% endhighlight %}

See the full [source code]({{site.SPARK_GITHUB_URL}}/blob/v{{site.SPARK_VERSION_SHORT}}/examples/src/main/java/org/apache/spark/examples/streaming/JavaSqlNetworkWordCount.java).
</div>
<div data-lang="python" markdown="1">
{% highlight python %}

# Lazily instantiated global instance of SparkSession
def getSparkSessionInstance(sparkConf):
    if ("sparkSessionSingletonInstance" not in globals()):
        globals()["sparkSessionSingletonInstance"] = SparkSession \
            .builder \
            .config(conf=sparkConf) \
            .getOrCreate()
    return globals()["sparkSessionSingletonInstance"]

...

# DataFrame operations inside your streaming program

words = ... # DStream of strings

def process(time, rdd):
    print("========= %s =========" % str(time))
    try:
        # Get the singleton instance of SparkSession
        spark = getSparkSessionInstance(rdd.context.getConf())

        # Convert RDD[String] to RDD[Row] to DataFrame
        rowRdd = rdd.map(lambda w: Row(word=w))
        wordsDataFrame = spark.createDataFrame(rowRdd)

        # Creates a temporary view using the DataFrame
        wordsDataFrame.createOrReplaceTempView("words")

        # Do word count on table using SQL and print it
        wordCountsDataFrame = spark.sql("select word, count(*) as total from words group by word")
        wordCountsDataFrame.show()
    except:
        pass

words.foreachRDD(process)
{% endhighlight %}

See the full [source code]({{site.SPARK_GITHUB_URL}}/blob/v{{site.SPARK_VERSION_SHORT}}/examples/src/main/python/streaming/sql_network_wordcount.py).

</div>
</div>

You can also run SQL queries on tables defined on streaming data from a different thread (that is, asynchronous to the running StreamingContext). Just make sure that you set the StreamingContext to remember a sufficient amount of streaming data such that the query can run. Otherwise the StreamingContext, which is unaware of the any asynchronous SQL queries, will delete off old streaming data before the query can complete. For example, if you want to query the last batch, but your query can take 5 minutes to run, then call `streamingContext.remember(Minutes(5))` (in Scala, or equivalent in other languages).

See the [DataFrames and SQL](sql-programming-guide.html) guide to learn more about DataFrames.

***

## MLlib Operations
You can also easily use machine learning algorithms provided by [MLlib](ml-guide.html). First of all, there are streaming machine learning algorithms (e.g. [Streaming Linear Regression](mllib-linear-methods.html#streaming-linear-regression), [Streaming KMeans](mllib-clustering.html#streaming-k-means), etc.) which can simultaneously learn from the streaming data as well as apply the model on the streaming data. Beyond these, for a much larger class of machine learning algorithms, you can learn a learning model offline (i.e. using historical data) and then apply the model online on streaming data. See the [MLlib](ml-guide.html) guide for more details.

***

## Caching / Persistence
Similar to RDDs, DStreams also allow developers to persist the stream's data in memory. That is,
using the `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, 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](#memory-tuning) section. More
information on different persistence levels can be found in the [Spark Programming Guide](rdd-programming-guide.html#rdd-persistence).

***

## 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 *checkpoint* 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 was 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 depend on RDDs of previous batches, which causes the length
  of the dependency chain to keep increasing with time. To avoid such unbounded increases 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
{:.no_toc}

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 to
  allow for periodic RDD checkpointing.
- *Recovering from failures of the driver running the application* - Metadata checkpoints are used
   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
{:.no_toc}

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 to 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.

<div class="codetabs">
<div data-lang="scala" markdown="1">

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

{% highlight scala %}
// 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()
{% endhighlight %}

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]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/scala/org/apache/spark/examples/streaming/RecoverableNetworkWordCount.scala).
This example appends the word counts of network data into a file.

</div>
<div data-lang="java" markdown="1">

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

{% highlight java %}
// Create a factory object that can create 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();
{% endhighlight %}

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 Java example
[JavaRecoverableNetworkWordCount]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/java/org/apache/spark/examples/streaming/JavaRecoverableNetworkWordCount.java).
This example appends the word counts of network data into a file.

</div>
<div data-lang="python" markdown="1">

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

{% highlight python %}
# Function to create and setup a new StreamingContext
def functionToCreateContext():
    sc = SparkContext(...)  # new context
    ssc = 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()
{% endhighlight %}

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]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/python/streaming/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)`.

</div>
</div>

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](#deploying-applications) 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 sliding intervals of a DStream is a good setting to try.

***

## Accumulators, Broadcast Variables, and Checkpoints

[Accumulators](rdd-programming-guide.html#accumulators) and [Broadcast variables](rdd-programming-guide.html#broadcast-variables) 
cannot be recovered from checkpoint in Spark Streaming. If you enable checkpointing and use 
[Accumulators](rdd-programming-guide.html#accumulators) or [Broadcast variables](rdd-programming-guide.html#broadcast-variables) 
as well, you'll have to create lazily instantiated singleton instances for 
[Accumulators](rdd-programming-guide.html#accumulators) and [Broadcast variables](rdd-programming-guide.html#broadcast-variables) 
so that they can be re-instantiated after the driver restarts on failure. 
This is shown in the following example.

<div class="codetabs">
<div data-lang="scala" markdown="1">
{% highlight scala %}

object WordBlacklist {

  @volatile private var instance: Broadcast[Seq[String]] = null

  def getInstance(sc: SparkContext): Broadcast[Seq[String]] = {
    if (instance == null) {
      synchronized {
        if (instance == null) {
          val wordBlacklist = Seq("a", "b", "c")
          instance = sc.broadcast(wordBlacklist)
        }
      }
    }
    instance
  }
}

object DroppedWordsCounter {

  @volatile private var instance: LongAccumulator = null

  def getInstance(sc: SparkContext): LongAccumulator = {
    if (instance == null) {
      synchronized {
        if (instance == null) {
          instance = sc.longAccumulator("WordsInBlacklistCounter")
        }
      }
    }
    instance
  }
}

wordCounts.foreachRDD { (rdd: RDD[(String, Int)], time: Time) =>
  // Get or register the blacklist Broadcast
  val blacklist = WordBlacklist.getInstance(rdd.sparkContext)
  // Get or register the droppedWordsCounter Accumulator
  val droppedWordsCounter = DroppedWordsCounter.getInstance(rdd.sparkContext)
  // Use blacklist to drop words and use droppedWordsCounter to count them
  val counts = rdd.filter { case (word, count) =>
    if (blacklist.value.contains(word)) {
      droppedWordsCounter.add(count)
      false
    } else {
      true
    }
  }.collect().mkString("[", ", ", "]")
  val output = "Counts at time " + time + " " + counts
})

{% endhighlight %}

See the full [source code]({{site.SPARK_GITHUB_URL}}/blob/v{{site.SPARK_VERSION_SHORT}}/examples/src/main/scala/org/apache/spark/examples/streaming/RecoverableNetworkWordCount.scala).
</div>
<div data-lang="java" markdown="1">
{% highlight java %}

class JavaWordBlacklist {

  private static volatile Broadcast<List<String>> instance = null;

  public static Broadcast<List<String>> getInstance(JavaSparkContext jsc) {
    if (instance == null) {
      synchronized (JavaWordBlacklist.class) {
        if (instance == null) {
          List<String> wordBlacklist = Arrays.asList("a", "b", "c");
          instance = jsc.broadcast(wordBlacklist);
        }
      }
    }
    return instance;
  }
}

class JavaDroppedWordsCounter {

  private static volatile LongAccumulator instance = null;

  public static LongAccumulator getInstance(JavaSparkContext jsc) {
    if (instance == null) {
      synchronized (JavaDroppedWordsCounter.class) {
        if (instance == null) {
          instance = jsc.sc().longAccumulator("WordsInBlacklistCounter");
        }
      }
    }
    return instance;
  }
}

wordCounts.foreachRDD((rdd, time) -> {
  // Get or register the blacklist Broadcast
  Broadcast<List<String>> blacklist = JavaWordBlacklist.getInstance(new JavaSparkContext(rdd.context()));
  // Get or register the droppedWordsCounter Accumulator
  LongAccumulator droppedWordsCounter = JavaDroppedWordsCounter.getInstance(new JavaSparkContext(rdd.context()));
  // Use blacklist to drop words and use droppedWordsCounter to count them
  String counts = rdd.filter(wordCount -> {
    if (blacklist.value().contains(wordCount._1())) {
      droppedWordsCounter.add(wordCount._2());
      return false;
    } else {
      return true;
    }
  }).collect().toString();
  String output = "Counts at time " + time + " " + counts;
}

{% endhighlight %}

See the full [source code]({{site.SPARK_GITHUB_URL}}/blob/v{{site.SPARK_VERSION_SHORT}}/examples/src/main/java/org/apache/spark/examples/streaming/JavaRecoverableNetworkWordCount.java).
</div>
<div data-lang="python" markdown="1">
{% highlight python %}
def getWordBlacklist(sparkContext):
    if ("wordBlacklist" not in globals()):
        globals()["wordBlacklist"] = sparkContext.broadcast(["a", "b", "c"])
    return globals()["wordBlacklist"]

def getDroppedWordsCounter(sparkContext):
    if ("droppedWordsCounter" not in globals()):
        globals()["droppedWordsCounter"] = sparkContext.accumulator(0)
    return globals()["droppedWordsCounter"]

def echo(time, rdd):
    # Get or register the blacklist Broadcast
    blacklist = getWordBlacklist(rdd.context)
    # Get or register the droppedWordsCounter Accumulator
    droppedWordsCounter = getDroppedWordsCounter(rdd.context)

    # Use blacklist to drop words and use droppedWordsCounter to count them
    def filterFunc(wordCount):
        if wordCount[0] in blacklist.value:
            droppedWordsCounter.add(wordCount[1])
            False
        else:
            True

    counts = "Counts at time %s %s" % (time, rdd.filter(filterFunc).collect())

wordCounts.foreachRDD(echo)

{% endhighlight %}

See the full [source code]({{site.SPARK_GITHUB_URL}}/blob/v{{site.SPARK_VERSION_SHORT}}/examples/src/main/python/streaming/recoverable_network_wordcount.py).

</div>
</div>

***

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

### Requirements
{:.no_toc}

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](cluster-overview.html).

- *Package the application JAR* - You have to compile your streaming application into a JAR.
  If you are using [`spark-submit`](submitting-applications.html) 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](#advanced-sources) (e.g. Kafka),
  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 `KafkaUtils`
  will have to include `spark-streaming-kafka-0-10_{{site.SCALA_BINARY_VERSION}}` 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](#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](cluster-overview.html#cluster-manager-types)
  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](spark-standalone.html#launching-spark-applications)), 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](spark-standalone.html) for more details.
    + *YARN* - Yarn supports a similar mechanism for automatically restarting an application.
      Please refer to YARN documentation for more details.
    + *Mesos* - [Marathon](https://github.com/mesosphere/marathon) has been used to achieve this
      with Mesos.

- *Configuring write-ahead logs* - Since Spark 1.2,
  we have introduced _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](#fault-tolerance-semantics) section). This can be enabled by setting
  the [configuration parameter](configuration.html#spark-streaming)
  `spark.streaming.receiver.writeAheadLog.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](#level-of-parallelism-in-data-receiving)
  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`. While using S3 (or any file system that
  does not support flushing) for _write-ahead logs_, please remember to enable
  `spark.streaming.driver.writeAheadLog.closeFileAfterWrite` and
  `spark.streaming.receiver.writeAheadLog.closeFileAfterWrite`. See
  [Spark Streaming Configuration](configuration.html#spark-streaming) for more details.
  Note that Spark will not encrypt data written to the write-ahead log when I/O encryption is
  enabled. If encryption of the write-ahead log data is desired, it should be stored in a file
  system that supports encryption natively.

- *Setting the max receiving rate* - If the cluster resources is not large enough for the streaming
  application to process data as fast as it is being received, the receivers can be rate limited
  by setting a maximum rate limit in terms of records / sec.
  See the [configuration parameters](configuration.html#spark-streaming)
  `spark.streaming.receiver.maxRate` for receivers and `spark.streaming.kafka.maxRatePerPartition`
  for Direct Kafka approach. In Spark 1.5, we have introduced a feature called *backpressure* that
  eliminate the need to set this rate limit, as Spark Streaming automatically figures out the
  rate limits and dynamically adjusts them if the processing conditions change. This backpressure
  can be enabled by setting the [configuration parameter](configuration.html#spark-streaming)
  `spark.streaming.backpressure.enabled` to `true`.

### Upgrading Application Code
{:.no_toc}

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

- 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 is 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(...)`](api/scala/index.html#org.apache.spark.streaming.StreamingContext)
or [`JavaStreamingContext.stop(...)`](api/java/index.html?org/apache/spark/streaming/api/java/JavaStreamingContext.html)
for graceful shutdown options) which ensure data that has 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) 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.

***

## Monitoring Applications
Beyond Spark's [monitoring capabilities](monitoring.html), there are additional capabilities
specific to Spark Streaming. When a StreamingContext is used, the
[Spark web UI](monitoring.html#web-interfaces) 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 that the system is
not able to process the batches as fast they are being generated and is falling behind.
In that case, consider
[reducing](#reducing-the-batch-processing-times) the batch processing time.

The progress of a Spark Streaming program can also be monitored using the
[StreamingListener](api/scala/index.html#org.apache.spark.streaming.scheduler.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 out 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 be 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 Batch Processing Times
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 the [Tuning Guide](tuning.html). This section
highlights some of the most important ones.

### Level of Parallelism in Data Receiving
{:.no_toc}
Receiving data over the network (like Kafka, socket, etc.) requires the data to be 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,
allowing data to be received in parallel, thus increasing overall throughput. These multiple
DStreams can be unioned together to create a single DStream. Then the transformations that were
being applied on a single input DStream can be applied on the unified stream. This is done as follows.

<div class="codetabs">
<div data-lang="scala" markdown="1">
{% highlight scala %}
val numStreams = 5
val kafkaStreams = (1 to numStreams).map { i => KafkaUtils.createStream(...) }
val unifiedStream = streamingContext.union(kafkaStreams)
unifiedStream.print()
{% endhighlight %}
</div>
<div data-lang="java" markdown="1">
{% highlight java %}
int numStreams = 5;
List<JavaPairDStream<String, String>> kafkaStreams = new ArrayList<>(numStreams);
for (int i = 0; i < numStreams; i++) {
  kafkaStreams.add(KafkaUtils.createStream(...));
}
JavaPairDStream<String, String> unifiedStream = streamingContext.union(kafkaStreams.get(0), kafkaStreams.subList(1, kafkaStreams.size()));
unifiedStream.print();
{% endhighlight %}
</div>
<div data-lang="python" markdown="1">
{% highlight python %}
numStreams = 5
kafkaStreams = [KafkaUtils.createStream(...) for _ in range (numStreams)]
unifiedStream = streamingContext.union(*kafkaStreams)
unifiedStream.pprint()
{% endhighlight %}
</div>
</div>

Another parameter that should be considered is the receiver's block interval,
which is determined by the [configuration parameter](configuration.html#spark-streaming)
`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 
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. If the number of tasks is too low (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 the specified number of machines in the cluster
before further processing.

For direct stream, please refer to [Spark Streaming + Kafka Integration Guide](streaming-kafka-0-10-integration.html)

### Level of Parallelism in Data Processing
{:.no_toc}
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
the `spark.default.parallelism` [configuration property](configuration.html#spark-properties). You
can pass the level of parallelism as an argument (see
[`PairDStreamFunctions`](api/scala/index.html#org.apache.spark.streaming.dstream.PairDStreamFunctions)
documentation), or set the `spark.default.parallelism`
[configuration property](configuration.html#spark-properties) to change the default.

### Data Serialization
{:.no_toc}
The overheads of data serialization can be reduced by tuning the serialization formats. In the case of streaming, there are two types of data that are being serialized.

* **Input data**: By default, the input data received through Receivers is stored in the executors' memory with [StorageLevel.MEMORY_AND_DISK_SER_2](api/scala/index.html#org.apache.spark.storage.StorageLevel$). That is, the data is serialized into bytes to reduce GC overheads, and replicated for tolerating executor failures. Also, the data is kept first in memory, and spilled over to disk only if the memory is insufficient to hold all of the input data necessary for the streaming computation. This serialization obviously has overheads -- the receiver must deserialize the received data and re-serialize it using Spark's serialization format. 

* **Persisted RDDs generated by Streaming Operations**: RDDs generated by streaming computations may be persisted in memory. For example, window operations persist data in memory as they would be processed multiple times. However, unlike the Spark Core default of [StorageLevel.MEMORY_ONLY](api/scala/index.html#org.apache.spark.storage.StorageLevel$), persisted RDDs generated by streaming computations are persisted with [StorageLevel.MEMORY_ONLY_SER](api/scala/index.html#org.apache.spark.storage.StorageLevel$) (i.e. serialized) by default to minimize GC overheads.

In both cases, using Kryo serialization can reduce both CPU and memory overheads. See the [Spark Tuning Guide](tuning.html#data-serialization) for more details. For Kryo, consider registering custom classes, and disabling object reference tracking (see Kryo-related configurations in the [Configuration Guide](configuration.html#compression-and-serialization)).

In specific cases where the amount of data that needs to be retained for the streaming application is not large, it may be feasible to persist data (both types) as deserialized objects without incurring excessive GC overheads. For example, if you are using batch intervals of a few seconds and no window operations, then you can try disabling serialization in persisted data by explicitly setting the storage level accordingly. This would reduce the CPU overheads due to serialization, potentially improving performance without too much GC overheads.

### Task Launching Overheads
{:.no_toc}
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 may be significant and will make it hard to achieve sub-second
latencies. The overhead can be reduced by the following changes:

* **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](running-on-mesos.html) 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 Interval
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](#monitoring-applications) 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 the 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](api/scala/index.html#org.apache.spark.streaming.scheduler.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 a momentary increase in the delay due to
temporary data rate increases may be 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 has been discussed in great detail
in the [Tuning Guide](tuning.html#memory-tuning). It is strongly recommended that you read that. In this section, we discuss a few tuning parameters specifically in the context of Spark Streaming applications.

The amount of cluster memory required by a Spark Streaming application depends heavily on the type of transformations used. For example, if you want to use a window operation on the last 10 minutes of data, then your cluster should have sufficient memory to hold 10 minutes worth of data in memory. Or if you want to use `updateStateByKey` with a large number of keys, then the necessary memory  will be high. On the contrary, if you want to do a simple map-filter-store operation, then the necessary memory will be low.

In general, since the data received through receivers is stored with StorageLevel.MEMORY_AND_DISK_SER_2, the data that does not fit in memory will spill over to the disk. This may reduce the performance of the streaming application, and hence it is advised to provide sufficient memory as required by your streaming application. Its best to try and see the memory usage on a small scale and estimate accordingly. 

Another aspect of memory tuning is garbage collection. For a streaming application that requires low latency, it is undesirable to have large pauses caused by JVM Garbage Collection. 

There are a few parameters that can help you tune the memory usage and GC overheads:

* **Persistence Level of DStreams**: As mentioned earlier in the [Data Serialization](#data-serialization) section, the input data and RDDs are by default persisted as serialized bytes. This reduces both the memory usage and GC overheads, compared to deserialized persistence. Enabling Kryo serialization further reduces serialized sizes and memory usage. Further reduction in memory usage can be achieved with compression (see the Spark configuration `spark.rdd.compress`), at the cost of CPU time.

* **Clearing old data**: By default, all input data and persisted RDDs generated by DStream transformations are automatically cleared. Spark Streaming decides when to clear the data based on the transformations that are used. For example, if you are using a window operation of 10 minutes, then Spark Streaming will keep around the last 10 minutes of data, and actively throw away older data. 
Data can be retained for a longer duration (e.g. interactively querying older data) by setting `streamingContext.remember`.

* **CMS Garbage Collector**: Use of the concurrent mark-and-sweep GC is strongly recommended for keeping GC-related pauses consistently low. 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. Make sure you set the CMS GC on both the driver (using `--driver-java-options` in `spark-submit`) and the executors (using [Spark configuration](configuration.html#runtime-environment) `spark.executor.extraJavaOptions`).

* **Other tips**: To further reduce GC overheads, here are some more tips to try.
    - Persist RDDs using the `OFF_HEAP` storage level. See more detail in the [Spark Programming Guide](rdd-programming-guide.html#rdd-persistence).
    - Use more executors with smaller heap sizes. This will reduce the GC pressure within each JVM heap.

***

##### Important points to remember:
{:.no_toc}
- A DStream is associated with a single receiver. For attaining read parallelism multiple receivers i.e. multiple DStreams need to be created. A receiver is run within an executor. It occupies one core. Ensure that there are enough cores for processing after receiver slots are booked i.e. `spark.cores.max` should take the receiver slots into account. The receivers are allocated to executors in a round robin fashion.

- When data is received from a stream source, receiver creates blocks of data.  A new block of data is generated every blockInterval milliseconds. N blocks of data are created during the batchInterval where N = batchInterval/blockInterval. These blocks are distributed by the BlockManager of the current executor to the block managers of other executors. After that, the Network Input Tracker running on the driver is informed about the block locations for further processing.

- An RDD is created on the driver for the blocks created during the batchInterval. The blocks generated during the batchInterval are partitions of the RDD. Each partition is a task in spark. blockInterval== batchinterval would mean that a single partition is created and probably it is processed locally.

- The map tasks on the blocks are processed in the executors (one that received the block, and another where the block was replicated) that has the blocks irrespective of block interval, unless non-local scheduling kicks in.
Having bigger blockinterval means bigger blocks. A high value of `spark.locality.wait` increases the chance of processing a block on the local node. A balance needs to be found out between these two parameters to ensure that the bigger blocks are processed locally.

- Instead of relying on batchInterval and blockInterval, you can define the number of partitions by calling `inputDstream.repartition(n)`. This reshuffles the data in RDD randomly to create n number of partitions. Yes, for greater parallelism. Though comes at the cost of a shuffle. An RDD's processing is scheduled by driver's jobscheduler as a job. At a given point of time only one job is active. So, if one job is executing the other jobs are queued.

- If you have two dstreams there will be two RDDs formed and there will be two jobs created which will be scheduled one after the another. To avoid this, you can union two dstreams. This will ensure that a single unionRDD is formed for the two RDDs of the dstreams. This unionRDD is then considered as a single job. However, the partitioning of the RDDs is not impacted.

- If the batch processing time is more than batchinterval then obviously the receiver's memory will start filling up and will end up in throwing exceptions (most probably BlockNotFoundException). Currently, there is  no way to pause the receiver. Using SparkConf configuration `spark.streaming.receiver.maxRate`, rate of receiver can be limited.


***************************************************************************************************
***************************************************************************************************

# Fault-tolerance Semantics
In this section, we will discuss the behavior of Spark Streaming applications in the event
of failures. 

## Background
{:.no_toc}
To understand the semantics provided by Spark Streaming, 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.
1. 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.
1. 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 in 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 need 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 other nodes.
1. *Data received but buffered for replication* - Since this is not replicated,
   the only way to recover this 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.
1. *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.

## Definitions
{:.no_toc}
The semantics of streaming systems are often captured in terms of how many times each record can be processed by the system. There are three types of guarantees that a system can provide under all possible operating conditions (despite failures, etc.)

1. *At most once*: Each record will be either processed once or not processed at all.
2. *At least once*: Each record will be processed one or more times. This is stronger than *at-most once* as it ensure that no data will be lost. But there may be duplicates.
3. *Exactly once*: Each record will be processed exactly once - no data will be lost and no data will be processed multiple times. This is obviously the strongest guarantee of the three.

## Basic Semantics
{:.no_toc}
In any stream processing system, broadly speaking, there are three steps in processing the data.

1. *Receiving the data*: The data is received from sources using Receivers or otherwise.

1. *Transforming the data*: The received data is transformed using DStream and RDD transformations.

1. *Pushing out the data*: The final transformed data is pushed out to external systems like file systems, databases, dashboards, etc.

If a streaming application has to achieve end-to-end exactly-once guarantees, then each step has to provide an exactly-once guarantee. That is, each record must be received exactly once, transformed exactly once, and pushed to downstream systems exactly once. Let's understand the semantics of these steps in the context of Spark Streaming.

1. *Receiving the data*: Different input sources provide different guarantees. This is discussed in detail in the next subsection.

1. *Transforming the data*: All data that has been received will be processed _exactly once_, thanks to the guarantees that RDDs provide. Even if there are failures, as long as the received input data is accessible, the final transformed RDDs will always have the same contents.

1. *Pushing out the data*: Output operations by default ensure _at-least once_ semantics because it depends on the type of output operation (idempotent, or not) and the semantics of the downstream system (supports transactions or not). But users can implement their own transaction mechanisms to achieve _exactly-once_ semantics. This is discussed in more details later in the section.

## Semantics of Received Data
{:.no_toc}
Different input sources provide different guarantees, ranging from _at-least once_ to _exactly once_. Read for more details.

### With Files
{:.no_toc}
If all of the input data is already present in a fault-tolerant file system like
HDFS, Spark Streaming can always recover from any failure and process all of the data. This gives
*exactly-once* semantics, meaning all of the data will be processed exactly once no matter what fails.

### With Receiver-based Sources
{:.no_toc}
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](#receiver-reliability), 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 source will not receive
  acknowledgment for the buffered (unreplicated) data. Therefore, if the receiver is
  restarted, the source will resend the data, and no data will be lost due to the failure.
1. *Unreliable Receiver* - Such receivers do *not* send acknowledgment and therefore *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 of 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 introduced _write
ahead logs_ which save the received data to fault-tolerant storage. With the [write-ahead logs
enabled](#deploying-applications) and reliable receivers, there is zero data loss. In terms of semantics, it provides an at-least once guarantee. 

The following table summarizes the semantics under failures:

<table class="table">
  <tr>
    <th style="width:30%">Deployment Scenario</th>
    <th>Worker Failure</th>
    <th>Driver Failure</th>
  </tr>
  <tr>
    <td>
      <i>Spark 1.1 or earlier,</i> OR<br/>
      <i>Spark 1.2 or later without write-ahead logs</i>
    </td>
    <td>
      Buffered data lost with unreliable receivers<br/>
      Zero data loss with reliable receivers<br/>
      At-least once semantics
    </td>
    <td>
      Buffered data lost with unreliable receivers<br/>
      Past data lost with all receivers<br/>
      Undefined semantics
    </td>
  </tr>
  <tr>
    <td><i>Spark 1.2 or later with write-ahead logs</i></td>
    <td>
        Zero data loss with reliable receivers<br/>
        At-least once semantics
    </td>
    <td>
        Zero data loss with reliable receivers and files<br/>
        At-least once semantics
    </td>
  </tr>
  <tr>
    <td></td>
    <td></td>
    <td></td>
  </tr>
</table>

### With Kafka Direct API
{:.no_toc}
In Spark 1.3, we have introduced a new Kafka Direct API, which can ensure that all the Kafka data is received by Spark Streaming exactly once. Along with this, if you implement exactly-once output operation, you can achieve end-to-end exactly-once guarantees. This approach is further discussed in the [Kafka Integration Guide](streaming-kafka-0-10-integration.html).

## Semantics of output operations
{:.no_toc}
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 file systems using the
`saveAs***Files` operations (as the file will simply get overwritten with the same data),
additional effort may be necessary to achieve exactly-once semantics. There are two approaches.

- *Idempotent updates*: Multiple attempts always write the same data. For example, `saveAs***Files` always writes the same data to the generated files.

- *Transactional updates*: All updates are made transactionally so that updates are made exactly once atomically. One way to do this would be the following.

    - Use the batch time (available in `foreachRDD`) and the partition index of the RDD to create an identifier. This identifier uniquely identifies a blob data in the streaming application.
    - Update external system with this blob transactionally (that is, exactly once, atomically) using the identifier. That is, if the identifier is not already committed, commit the partition data and the identifier atomically. Else, if this was already committed, skip the update.

          dstream.foreachRDD { (rdd, time) =>
            rdd.foreachPartition { partitionIterator =>
              val partitionId = TaskContext.get.partitionId()
              val uniqueId = generateUniqueId(time.milliseconds, partitionId)
              // use this uniqueId to transactionally commit the data in partitionIterator
            }
          }

***************************************************************************************************
***************************************************************************************************

# Where to Go from Here
* Additional guides
    - [Kafka Integration Guide](streaming-kafka-0-10-integration.html)
    - [Kinesis Integration Guide](streaming-kinesis-integration.html)
    - [Custom Receiver Guide](streaming-custom-receivers.html)
* Third-party DStream data sources can be found in [Third Party Projects](https://spark.apache.org/third-party-projects.html)
* API documentation
  - Scala docs
    * [StreamingContext](api/scala/index.html#org.apache.spark.streaming.StreamingContext) and
  [DStream](api/scala/index.html#org.apache.spark.streaming.dstream.DStream)
    * [KafkaUtils](api/scala/index.html#org.apache.spark.streaming.kafka.KafkaUtils$),
    [KinesisUtils](api/scala/index.html#org.apache.spark.streaming.kinesis.KinesisUtils$),
  - Java docs
    * [JavaStreamingContext](api/java/index.html?org/apache/spark/streaming/api/java/JavaStreamingContext.html),
    [JavaDStream](api/java/index.html?org/apache/spark/streaming/api/java/JavaDStream.html) and
    [JavaPairDStream](api/java/index.html?org/apache/spark/streaming/api/java/JavaPairDStream.html)
    * [KafkaUtils](api/java/index.html?org/apache/spark/streaming/kafka/KafkaUtils.html),
    [KinesisUtils](api/java/index.html?org/apache/spark/streaming/kinesis/KinesisUtils.html)
  - Python docs
    * [StreamingContext](api/python/pyspark.streaming.html#pyspark.streaming.StreamingContext) and [DStream](api/python/pyspark.streaming.html#pyspark.streaming.DStream)
    * [KafkaUtils](api/python/pyspark.streaming.html#pyspark.streaming.kafka.KafkaUtils)

* More examples in [Scala]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/scala/org/apache/spark/examples/streaming)
  and [Java]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/java/org/apache/spark/examples/streaming)
  and [Python]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/python/streaming)
* [Paper](http://www.eecs.berkeley.edu/Pubs/TechRpts/2012/EECS-2012-259.pdf) and [video](http://youtu.be/g171ndOHgJ0) describing Spark Streaming.