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<section id="pyspark-streaming-dstream">
<h1>pyspark.streaming.DStream<a class="headerlink" href="#pyspark-streaming-dstream" title="Permalink to this headline">#</a></h1>
<dl class="py class">
<dt class="sig sig-object py" id="pyspark.streaming.DStream">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">pyspark.streaming.</span></span><span class="sig-name descname"><span class="pre">DStream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">jdstream</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ssc</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">jrdd_deserializer</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/pyspark/streaming/dstream.html#DStream"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyspark.streaming.DStream" title="Permalink to this definition">#</a></dt>
<dd><p>A Discretized Stream (DStream), the basic abstraction in Spark Streaming,
is a continuous sequence of RDDs (of the same type) representing a
continuous stream of data (see <code class="xref py py-class docutils literal notranslate"><span class="pre">RDD</span></code> in the Spark core documentation
for more details on RDDs).</p>
<p>DStreams can either be created from live data (such as, data from TCP
sockets, etc.) using a <a class="reference internal" href="pyspark.streaming.StreamingContext.html#pyspark.streaming.StreamingContext" title="pyspark.streaming.StreamingContext"><code class="xref py py-class docutils literal notranslate"><span class="pre">StreamingContext</span></code></a> or it can be
generated by transforming existing DStreams using operations such as
<cite>map</cite>, <cite>window</cite> and <cite>reduceByKeyAndWindow</cite>. While a Spark Streaming
program is running, each DStream periodically generates a RDD, either
from live data or by transforming the RDD generated by a parent DStream.</p>
<dl class="simple">
<dt>DStreams internally is characterized by a few basic properties:</dt><dd><ul class="simple">
<li><p>A list of other DStreams that the DStream depends on</p></li>
<li><p>A time interval at which the DStream generates an RDD</p></li>
<li><p>A function that is used to generate an RDD after each time interval</p></li>
</ul>
</dd>
</dl>
<p class="rubric">Methods</p>
<table class="autosummary longtable table autosummary">
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</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="pyspark.streaming.DStream.cache.html#pyspark.streaming.DStream.cache" title="pyspark.streaming.DStream.cache"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cache</span></code></a>()</p></td>
<td><p>Persist the RDDs of this DStream with the default storage level (<cite>MEMORY_ONLY</cite>).</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="pyspark.streaming.DStream.checkpoint.html#pyspark.streaming.DStream.checkpoint" title="pyspark.streaming.DStream.checkpoint"><code class="xref py py-obj docutils literal notranslate"><span class="pre">checkpoint</span></code></a>(interval)</p></td>
<td><p>Enable periodic checkpointing of RDDs of this DStream</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="pyspark.streaming.DStream.cogroup.html#pyspark.streaming.DStream.cogroup" title="pyspark.streaming.DStream.cogroup"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cogroup</span></code></a>(other[, numPartitions])</p></td>
<td><p>Return a new DStream by applying 'cogroup' between RDDs of this DStream and <cite>other</cite> DStream.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="pyspark.streaming.DStream.combineByKey.html#pyspark.streaming.DStream.combineByKey" title="pyspark.streaming.DStream.combineByKey"><code class="xref py py-obj docutils literal notranslate"><span class="pre">combineByKey</span></code></a>(createCombiner, mergeValue, ...)</p></td>
<td><p>Return a new DStream by applying combineByKey to each RDD.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="pyspark.streaming.DStream.context.html#pyspark.streaming.DStream.context" title="pyspark.streaming.DStream.context"><code class="xref py py-obj docutils literal notranslate"><span class="pre">context</span></code></a>()</p></td>
<td><p>Return the StreamingContext associated with this DStream</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="pyspark.streaming.DStream.count.html#pyspark.streaming.DStream.count" title="pyspark.streaming.DStream.count"><code class="xref py py-obj docutils literal notranslate"><span class="pre">count</span></code></a>()</p></td>
<td><p>Return a new DStream in which each RDD has a single element generated by counting each RDD of this DStream.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="pyspark.streaming.DStream.countByValue.html#pyspark.streaming.DStream.countByValue" title="pyspark.streaming.DStream.countByValue"><code class="xref py py-obj docutils literal notranslate"><span class="pre">countByValue</span></code></a>()</p></td>
<td><p>Return a new DStream in which each RDD contains the counts of each distinct value in each RDD of this DStream.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="pyspark.streaming.DStream.countByValueAndWindow.html#pyspark.streaming.DStream.countByValueAndWindow" title="pyspark.streaming.DStream.countByValueAndWindow"><code class="xref py py-obj docutils literal notranslate"><span class="pre">countByValueAndWindow</span></code></a>(windowDuration, ...[, ...])</p></td>
<td><p>Return a new DStream in which each RDD contains the count of distinct elements in RDDs in a sliding window over this DStream.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="pyspark.streaming.DStream.countByWindow.html#pyspark.streaming.DStream.countByWindow" title="pyspark.streaming.DStream.countByWindow"><code class="xref py py-obj docutils literal notranslate"><span class="pre">countByWindow</span></code></a>(windowDuration, slideDuration)</p></td>
<td><p>Return a new DStream in which each RDD has a single element generated by counting the number of elements in a window over this DStream.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="pyspark.streaming.DStream.filter.html#pyspark.streaming.DStream.filter" title="pyspark.streaming.DStream.filter"><code class="xref py py-obj docutils literal notranslate"><span class="pre">filter</span></code></a>(f)</p></td>
<td><p>Return a new DStream containing only the elements that satisfy predicate.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="pyspark.streaming.DStream.flatMap.html#pyspark.streaming.DStream.flatMap" title="pyspark.streaming.DStream.flatMap"><code class="xref py py-obj docutils literal notranslate"><span class="pre">flatMap</span></code></a>(f[, preservesPartitioning])</p></td>
<td><p>Return a new DStream by applying a function to all elements of this DStream, and then flattening the results</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="pyspark.streaming.DStream.flatMapValues.html#pyspark.streaming.DStream.flatMapValues" title="pyspark.streaming.DStream.flatMapValues"><code class="xref py py-obj docutils literal notranslate"><span class="pre">flatMapValues</span></code></a>(f)</p></td>
<td><p>Return a new DStream by applying a flatmap function to the value of each key-value pairs in this DStream without changing the key.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="pyspark.streaming.DStream.foreachRDD.html#pyspark.streaming.DStream.foreachRDD" title="pyspark.streaming.DStream.foreachRDD"><code class="xref py py-obj docutils literal notranslate"><span class="pre">foreachRDD</span></code></a>(func)</p></td>
<td><p>Apply a function to each RDD in this DStream.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="pyspark.streaming.DStream.fullOuterJoin.html#pyspark.streaming.DStream.fullOuterJoin" title="pyspark.streaming.DStream.fullOuterJoin"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fullOuterJoin</span></code></a>(other[, numPartitions])</p></td>
<td><p>Return a new DStream by applying 'full outer join' between RDDs of this DStream and <cite>other</cite> DStream.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="pyspark.streaming.DStream.glom.html#pyspark.streaming.DStream.glom" title="pyspark.streaming.DStream.glom"><code class="xref py py-obj docutils literal notranslate"><span class="pre">glom</span></code></a>()</p></td>
<td><p>Return a new DStream in which RDD is generated by applying glom() to RDD of this DStream.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="pyspark.streaming.DStream.groupByKey.html#pyspark.streaming.DStream.groupByKey" title="pyspark.streaming.DStream.groupByKey"><code class="xref py py-obj docutils literal notranslate"><span class="pre">groupByKey</span></code></a>([numPartitions])</p></td>
<td><p>Return a new DStream by applying groupByKey on each RDD.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="pyspark.streaming.DStream.groupByKeyAndWindow.html#pyspark.streaming.DStream.groupByKeyAndWindow" title="pyspark.streaming.DStream.groupByKeyAndWindow"><code class="xref py py-obj docutils literal notranslate"><span class="pre">groupByKeyAndWindow</span></code></a>(windowDuration, ...[, ...])</p></td>
<td><p>Return a new DStream by applying <cite>groupByKey</cite> over a sliding window.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="pyspark.streaming.DStream.join.html#pyspark.streaming.DStream.join" title="pyspark.streaming.DStream.join"><code class="xref py py-obj docutils literal notranslate"><span class="pre">join</span></code></a>(other[, numPartitions])</p></td>
<td><p>Return a new DStream by applying 'join' between RDDs of this DStream and <cite>other</cite> DStream.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="pyspark.streaming.DStream.leftOuterJoin.html#pyspark.streaming.DStream.leftOuterJoin" title="pyspark.streaming.DStream.leftOuterJoin"><code class="xref py py-obj docutils literal notranslate"><span class="pre">leftOuterJoin</span></code></a>(other[, numPartitions])</p></td>
<td><p>Return a new DStream by applying 'left outer join' between RDDs of this DStream and <cite>other</cite> DStream.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="pyspark.streaming.DStream.map.html#pyspark.streaming.DStream.map" title="pyspark.streaming.DStream.map"><code class="xref py py-obj docutils literal notranslate"><span class="pre">map</span></code></a>(f[, preservesPartitioning])</p></td>
<td><p>Return a new DStream by applying a function to each element of DStream.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="pyspark.streaming.DStream.mapPartitions.html#pyspark.streaming.DStream.mapPartitions" title="pyspark.streaming.DStream.mapPartitions"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mapPartitions</span></code></a>(f[, preservesPartitioning])</p></td>
<td><p>Return a new DStream in which each RDD is generated by applying mapPartitions() to each RDDs of this DStream.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="pyspark.streaming.DStream.mapPartitionsWithIndex.html#pyspark.streaming.DStream.mapPartitionsWithIndex" title="pyspark.streaming.DStream.mapPartitionsWithIndex"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mapPartitionsWithIndex</span></code></a>(f[, ...])</p></td>
<td><p>Return a new DStream in which each RDD is generated by applying mapPartitionsWithIndex() to each RDDs of this DStream.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="pyspark.streaming.DStream.mapValues.html#pyspark.streaming.DStream.mapValues" title="pyspark.streaming.DStream.mapValues"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mapValues</span></code></a>(f)</p></td>
<td><p>Return a new DStream by applying a map function to the value of each key-value pairs in this DStream without changing the key.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="pyspark.streaming.DStream.partitionBy.html#pyspark.streaming.DStream.partitionBy" title="pyspark.streaming.DStream.partitionBy"><code class="xref py py-obj docutils literal notranslate"><span class="pre">partitionBy</span></code></a>(numPartitions[, partitionFunc])</p></td>
<td><p>Return a copy of the DStream in which each RDD are partitioned using the specified partitioner.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="pyspark.streaming.DStream.persist.html#pyspark.streaming.DStream.persist" title="pyspark.streaming.DStream.persist"><code class="xref py py-obj docutils literal notranslate"><span class="pre">persist</span></code></a>(storageLevel)</p></td>
<td><p>Persist the RDDs of this DStream with the given storage level</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="pyspark.streaming.DStream.pprint.html#pyspark.streaming.DStream.pprint" title="pyspark.streaming.DStream.pprint"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pprint</span></code></a>([num])</p></td>
<td><p>Print the first num elements of each RDD generated in this DStream.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="pyspark.streaming.DStream.reduce.html#pyspark.streaming.DStream.reduce" title="pyspark.streaming.DStream.reduce"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reduce</span></code></a>(func)</p></td>
<td><p>Return a new DStream in which each RDD has a single element generated by reducing each RDD of this DStream.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="pyspark.streaming.DStream.reduceByKey.html#pyspark.streaming.DStream.reduceByKey" title="pyspark.streaming.DStream.reduceByKey"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reduceByKey</span></code></a>(func[, numPartitions])</p></td>
<td><p>Return a new DStream by applying reduceByKey to each RDD.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="pyspark.streaming.DStream.reduceByKeyAndWindow.html#pyspark.streaming.DStream.reduceByKeyAndWindow" title="pyspark.streaming.DStream.reduceByKeyAndWindow"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reduceByKeyAndWindow</span></code></a>(func, invFunc, ...[, ...])</p></td>
<td><p>Return a new DStream by applying incremental <cite>reduceByKey</cite> over a sliding window.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="pyspark.streaming.DStream.reduceByWindow.html#pyspark.streaming.DStream.reduceByWindow" title="pyspark.streaming.DStream.reduceByWindow"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reduceByWindow</span></code></a>(reduceFunc, invReduceFunc, ...)</p></td>
<td><p>Return a new DStream in which each RDD has a single element generated by reducing all elements in a sliding window over this DStream.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="pyspark.streaming.DStream.repartition.html#pyspark.streaming.DStream.repartition" title="pyspark.streaming.DStream.repartition"><code class="xref py py-obj docutils literal notranslate"><span class="pre">repartition</span></code></a>(numPartitions)</p></td>
<td><p>Return a new DStream with an increased or decreased level of parallelism.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="pyspark.streaming.DStream.rightOuterJoin.html#pyspark.streaming.DStream.rightOuterJoin" title="pyspark.streaming.DStream.rightOuterJoin"><code class="xref py py-obj docutils literal notranslate"><span class="pre">rightOuterJoin</span></code></a>(other[, numPartitions])</p></td>
<td><p>Return a new DStream by applying 'right outer join' between RDDs of this DStream and <cite>other</cite> DStream.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="pyspark.streaming.DStream.saveAsTextFiles.html#pyspark.streaming.DStream.saveAsTextFiles" title="pyspark.streaming.DStream.saveAsTextFiles"><code class="xref py py-obj docutils literal notranslate"><span class="pre">saveAsTextFiles</span></code></a>(prefix[, suffix])</p></td>
<td><p>Save each RDD in this DStream as at text file, using string representation of elements.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="pyspark.streaming.DStream.slice.html#pyspark.streaming.DStream.slice" title="pyspark.streaming.DStream.slice"><code class="xref py py-obj docutils literal notranslate"><span class="pre">slice</span></code></a>(begin, end)</p></td>
<td><p>Return all the RDDs between 'begin' to 'end' (both included)</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="pyspark.streaming.DStream.transform.html#pyspark.streaming.DStream.transform" title="pyspark.streaming.DStream.transform"><code class="xref py py-obj docutils literal notranslate"><span class="pre">transform</span></code></a>(func)</p></td>
<td><p>Return a new DStream in which each RDD is generated by applying a function on each RDD of this DStream.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="pyspark.streaming.DStream.transformWith.html#pyspark.streaming.DStream.transformWith" title="pyspark.streaming.DStream.transformWith"><code class="xref py py-obj docutils literal notranslate"><span class="pre">transformWith</span></code></a>(func, other[, keepSerializer])</p></td>
<td><p>Return a new DStream in which each RDD is generated by applying a function on each RDD of this DStream and 'other' DStream.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="pyspark.streaming.DStream.union.html#pyspark.streaming.DStream.union" title="pyspark.streaming.DStream.union"><code class="xref py py-obj docutils literal notranslate"><span class="pre">union</span></code></a>(other)</p></td>
<td><p>Return a new DStream by unifying data of another DStream with this DStream.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="pyspark.streaming.DStream.updateStateByKey.html#pyspark.streaming.DStream.updateStateByKey" title="pyspark.streaming.DStream.updateStateByKey"><code class="xref py py-obj docutils literal notranslate"><span class="pre">updateStateByKey</span></code></a>(updateFunc[, ...])</p></td>
<td><p>Return a new &quot;state&quot; 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 of the key.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="pyspark.streaming.DStream.window.html#pyspark.streaming.DStream.window" title="pyspark.streaming.DStream.window"><code class="xref py py-obj docutils literal notranslate"><span class="pre">window</span></code></a>(windowDuration[, slideDuration])</p></td>
<td><p>Return a new DStream in which each RDD contains all the elements in seen in a sliding window of time over this DStream.</p></td>
</tr>
</tbody>
</table>
</dd></dl>
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