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layout: global
title: Streaming (Alpha) Programming Guide
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# Overview
A Spark Streaming application is very similar to a Spark application; it consists of a *driver program* that runs the user's `main` function and continuous executes various *parallel operations* on input streams of data. The main abstraction Spark Streaming provides is a *discretized stream* (DStream), which is a continuous sequence of RDDs (distributed collection of elements) representing a continuous stream of data. DStreams can created from live incoming data (such as data from a socket, Kafka, etc.) or it can be generated by transformation of existing DStreams using parallel operators like map, reduce, and window. The basic processing model is as follows:
(i) While a Spark Streaming driver program is running, the system receives data from various sources and and divides the data into batches. Each batch of data is treated as a RDD, that is a immutable and parallel collection of data. These input data RDDs are automatically persisted in memory (serialized by default) and replicated to two nodes for fault-tolerance. This sequence of RDDs is collectively referred to as an InputDStream.
(ii) Data received by InputDStreams are processed processed using DStream operations. Since all data is represented as RDDs and all DStream operations as RDD operations, data is automatically recovered in the event of node failures.
This guide shows some how to start programming with DStreams.
# Initializing Spark Streaming
The first thing a Spark Streaming program must do is create a `StreamingContext` object, which tells Spark how to access a cluster. A `StreamingContext` can be created from an existing `SparkContext`, or directly:
{% highlight scala %}
import spark.SparkContext
import SparkContext._
new StreamingContext(master, frameworkName, batchDuration)
new StreamingContext(sparkContext, batchDuration)
{% endhighlight %}
The `master` parameter is either the [Mesos master URL](running-on-mesos.html) (for running on a cluster)or the special "local" string (for local mode) that is used to create a Spark Context. For more information about this please refer to the [Spark programming guide](scala-programming-guide.html).
# Creating Input Sources - InputDStreams
The StreamingContext is used to creating InputDStreams from input sources:
{% highlight scala %}
context.neworkStream(host, port) // Creates a stream that uses a TCP socket to read data from <host>:<port>
context.flumeStream(host, port) // Creates a stream populated by a Flume flow
{% endhighlight %}
A complete list of input sources is available in the [DStream API doc](api/streaming/index.html#spark.streaming.StreamingContext).
## DStream Operations
Once an input stream has been created, you can transform it using _stream operators_. Most of these operators return new DStreams which you can further transform. Eventually, you'll need to call an _output operator_, which forces evaluation of the stream by writing data out to an external source.
### Transformations
DStreams support many of the transformations available on normal Spark RDD's:
<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 stream formed by passing each element of the source through a function <i>func</i>. </td>
</tr>
<tr>
<td> <b>filter</b>(<i>func</i>) </td>
<td> Return a new stream formed by selecting those elements of the source on which <i>func</i> returns true. </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 (so <i>func</i> should return a Seq rather than a single item). </td>
</tr>
<tr>
<td> <b>mapPartitions</b>(<i>func</i>) </td>
<td> Similar to map, but runs separately on each partition (block) of the DStream, so <i>func</i> must be of type
Iterator[T] => Iterator[U] when running on an DStream of type T. </td>
</tr>
<tr>
<td> <b>union</b>(<i>otherStream</i>) </td>
<td> Return a new stream that contains the union of the elements in the source stream and the argument. </td>
</tr>
<tr>
<td> <b>groupByKey</b>([<i>numTasks</i>]) </td>
<td> When called on a stream of (K, V) pairs, returns a stream of (K, Seq[V]) pairs. <br />
<b>Note:</b> By default, this uses only 8 parallel tasks 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>reduceByKey</b>(<i>func</i>, [<i>numTasks</i>]) </td>
<td> When called on a stream of (K, V) pairs, returns a stream of (K, V) pairs where the values for each key are aggregated using the given reduce function. Like in <code>groupByKey</code>, the number of reduce tasks is configurable through an optional second argument. </td>
</tr>
<tr>
<td> <b>join</b>(<i>otherStream</i>, [<i>numTasks</i>]) </td>
<td> When called on streams of type (K, V) and (K, W), returns a stream 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 streams of type (K, V) and (K, W), returns a stream of (K, Seq[V], Seq[W]) tuples. This operation is also called <code>groupWith</code>. </td>
</tr>
<tr>
<td> <b>reduce</b>(<i>func</i>) </td>
<td> Create a new single-element stream by aggregating the elements of the stream using a function func (which takes two arguments and returns one). The function should be associative so that it can be computed correctly in parallel. </td>
</tr>
</table>
Spark Streaming features windowed computations, which allow you to report statistics over a sliding window of data. All window functions take a <i>windowTime</i>, which represents the width of the window and a <i>slideTime</i>, which represents the frequency during which the window is calculated.
<table class="table">
<tr><th style="width:25%">Transformation</th><th>Meaning</th></tr>
<tr>
<td> <b>window</b>(<i>windowTime</i>, </i>slideTime</i>) </td>
<td> Return a new stream which is computed based on windowed batches of the source stream. <i>windowTime</i> is the width of the window and <i>slideTime</i> is the frequency during which the window is calculated. Both times must be multiples of the batch interval.
</td>
</tr>
<tr>
<td> <b>countByWindow</b>(<i>windowTime</i>, </i>slideTime</i>) </td>
<td> Return a sliding count of elements in the stream. <i>windowTime</i> and <i>slideTime</i> are exactly as defined in <code>window()</code>.
</td>
</tr>
<tr>
<td> <b>reduceByWindow</b>(<i>func</i>, <i>windowTime</i>, </i>slideTime</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 so that it can be computed correctly in parallel. <i>windowTime</i> and <i>slideTime</i> are exactly as defined in <code>window()</code>.
</td>
</tr>
<tr>
<td> <b>groupByKeyAndWindow</b>(windowTime, slideTime, [<i>numTasks</i>])
</td>
<td> When called on a stream of (K, V) pairs, returns a stream of (K, Seq[V]) pairs over a sliding window. <br />
<b>Note:</b> By default, this uses only 8 parallel tasks to do the grouping. You can pass an optional <code>numTasks</code> argument to set a different number of tasks. <i>windowTime</i> and <i>slideTime</i> are exactly as defined in <code>window()</code>.
</td>
</tr>
<tr>
<td> <b>reduceByKeyAndWindow</b>(<i>func</i>, [<i>numTasks</i>]) </td>
<td> When called on a stream of (K, V) pairs, returns a stream of (K, V) pairs where the values for each key are aggregated using the given reduce function over batches within a sliding window. Like in <code>groupByKeyAndWindow</code>, the number of reduce tasks is configurable through an optional second argument.
<i>windowTime</i> and <i>slideTime</i> are exactly as defined in <code>window()</code>.
</td>
</tr>
<tr>
<td> <b>countByKeyAndWindow</b>([<i>numTasks</i>]) </td>
<td> When called on a stream of (K, V) pairs, returns a stream of (K, Int) pairs where the values for each key are the count within a sliding window. Like in <code>countByKeyAndWindow</code>, the number of reduce tasks is configurable through an optional second argument.
<i>windowTime</i> and <i>slideTime</i> are exactly as defined in <code>window()</code>.
</td>
</tr>
</table>
### Output Operators
When an output operator is called, it triggers the computation of a stream. Currently the following output operators are defined:
<table class="table">
<tr><th style="width:25%">Operator</th><th>Meaning</th></tr>
<tr>
<td> <b>foreachRDD</b>(<i>func</i>) </td>
<td> The fundamental output operator. Applies a function, <i>func</i>, to each RDD generated from the stream. This function should have side effects, such as printing output, saving the RDD to external files, or writing it over the network to an external system. </td>
</tr>
<tr>
<td> <b>print</b>() </td>
<td> Prints first ten elements of every batch of data in a DStream on the driver. </td>
</tr>
<tr>
<td> <b>saveAsObjectFiles</b>(<i>prefix</i>, [<i>suffix</i>]) </td>
<td> Save this DStream's contents as a <code>SequenceFile</code> of serialized objects. The file name at each batch interval is calculated based on <i>prefix</i> and <i>suffix</i>: <i>"prefix-TIME_IN_MS[.suffix]"</i>.
</td>
</tr>
<tr>
<td> <b>saveAsTextFiles</b>(<i>prefix</i>, [<i>suffix</i>]) </td>
<td> Save this DStream's contents as a text files. The file name at each batch interval is calculated based on <i>prefix</i> and <i>suffix</i>: <i>"prefix-TIME_IN_MS[.suffix]"</i>. </td>
</tr>
<tr>
<td> <b>saveAsHadoopFiles</b>(<i>prefix</i>, [<i>suffix</i>]) </td>
<td> Save this DStream's contents as a Hadoop file. The file name at each batch interval is calculated based on <i>prefix</i> and <i>suffix</i>: <i>"prefix-TIME_IN_MS[.suffix]"</i>. </td>
</tr>
</table>
