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  <h1>Source code for pyspark.streaming.dstream</h1><div class="highlight"><pre>
<span class="c">#</span>
<span class="c"># Licensed to the Apache Software Foundation (ASF) under one or more</span>
<span class="c"># contributor license agreements.  See the NOTICE file distributed with</span>
<span class="c"># this work for additional information regarding copyright ownership.</span>
<span class="c"># The ASF licenses this file to You under the Apache License, Version 2.0</span>
<span class="c"># (the &quot;License&quot;); you may not use this file except in compliance with</span>
<span class="c"># the License.  You may obtain a copy of the License at</span>
<span class="c">#</span>
<span class="c">#    http://www.apache.org/licenses/LICENSE-2.0</span>
<span class="c">#</span>
<span class="c"># Unless required by applicable law or agreed to in writing, software</span>
<span class="c"># distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
<span class="c"># WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
<span class="c"># See the License for the specific language governing permissions and</span>
<span class="c"># limitations under the License.</span>
<span class="c">#</span>

<span class="kn">import</span> <span class="nn">sys</span>
<span class="kn">import</span> <span class="nn">operator</span>
<span class="kn">import</span> <span class="nn">time</span>
<span class="kn">from</span> <span class="nn">itertools</span> <span class="kn">import</span> <span class="n">chain</span>
<span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">datetime</span>

<span class="k">if</span> <span class="n">sys</span><span class="o">.</span><span class="n">version</span> <span class="o">&lt;</span> <span class="s">&quot;3&quot;</span><span class="p">:</span>
    <span class="kn">from</span> <span class="nn">itertools</span> <span class="kn">import</span> <span class="n">imap</span> <span class="k">as</span> <span class="nb">map</span><span class="p">,</span> <span class="n">ifilter</span> <span class="k">as</span> <span class="nb">filter</span>

<span class="kn">from</span> <span class="nn">py4j.protocol</span> <span class="kn">import</span> <span class="n">Py4JJavaError</span>

<span class="kn">from</span> <span class="nn">pyspark</span> <span class="kn">import</span> <span class="n">RDD</span>
<span class="kn">from</span> <span class="nn">pyspark.storagelevel</span> <span class="kn">import</span> <span class="n">StorageLevel</span>
<span class="kn">from</span> <span class="nn">pyspark.streaming.util</span> <span class="kn">import</span> <span class="n">rddToFileName</span><span class="p">,</span> <span class="n">TransformFunction</span>
<span class="kn">from</span> <span class="nn">pyspark.rdd</span> <span class="kn">import</span> <span class="n">portable_hash</span>
<span class="kn">from</span> <span class="nn">pyspark.resultiterable</span> <span class="kn">import</span> <span class="n">ResultIterable</span>

<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s">&quot;DStream&quot;</span><span class="p">]</span>


<div class="viewcode-block" id="DStream"><a class="viewcode-back" href="../../../pyspark.streaming.html#pyspark.streaming.DStream">[docs]</a><span class="k">class</span> <span class="nc">DStream</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    A Discretized Stream (DStream), the basic abstraction in Spark Streaming,</span>
<span class="sd">    is a continuous sequence of RDDs (of the same type) representing a</span>
<span class="sd">    continuous stream of data (see L{RDD} in the Spark core documentation</span>
<span class="sd">    for more details on RDDs).</span>

<span class="sd">    DStreams can either be created from live data (such as, data from TCP</span>
<span class="sd">    sockets, Kafka, Flume, etc.) using a L{StreamingContext} or it can be</span>
<span class="sd">    generated by transforming existing DStreams using operations such as</span>
<span class="sd">    `map`, `window` and `reduceByKeyAndWindow`. While a Spark Streaming</span>
<span class="sd">    program is running, each DStream periodically generates a RDD, either</span>
<span class="sd">    from live data or by transforming the RDD generated by a parent DStream.</span>

<span class="sd">    DStreams internally is characterized by a few basic properties:</span>
<span class="sd">     - A list of other DStreams that the DStream depends on</span>
<span class="sd">     - A time interval at which the DStream generates an RDD</span>
<span class="sd">     - A function that is used to generate an RDD after each time interval</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">jdstream</span><span class="p">,</span> <span class="n">ssc</span><span class="p">,</span> <span class="n">jrdd_deserializer</span><span class="p">):</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">_jdstream</span> <span class="o">=</span> <span class="n">jdstream</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">_ssc</span> <span class="o">=</span> <span class="n">ssc</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">_sc</span> <span class="o">=</span> <span class="n">ssc</span><span class="o">.</span><span class="n">_sc</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">_jrdd_deserializer</span> <span class="o">=</span> <span class="n">jrdd_deserializer</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">is_cached</span> <span class="o">=</span> <span class="bp">False</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">is_checkpointed</span> <span class="o">=</span> <span class="bp">False</span>

<div class="viewcode-block" id="DStream.context"><a class="viewcode-back" href="../../../pyspark.streaming.html#pyspark.streaming.DStream.context">[docs]</a>    <span class="k">def</span> <span class="nf">context</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Return the StreamingContext associated with this DStream</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_ssc</span>
</div>
<div class="viewcode-block" id="DStream.count"><a class="viewcode-back" href="../../../pyspark.streaming.html#pyspark.streaming.DStream.count">[docs]</a>    <span class="k">def</span> <span class="nf">count</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Return a new DStream in which each RDD has a single element</span>
<span class="sd">        generated by counting each RDD of this DStream.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">mapPartitions</span><span class="p">(</span><span class="k">lambda</span> <span class="n">i</span><span class="p">:</span> <span class="p">[</span><span class="nb">sum</span><span class="p">(</span><span class="mi">1</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="n">i</span><span class="p">)])</span><span class="o">.</span><span class="n">reduce</span><span class="p">(</span><span class="n">operator</span><span class="o">.</span><span class="n">add</span><span class="p">)</span>
</div>
<div class="viewcode-block" id="DStream.filter"><a class="viewcode-back" href="../../../pyspark.streaming.html#pyspark.streaming.DStream.filter">[docs]</a>    <span class="k">def</span> <span class="nf">filter</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">f</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Return a new DStream containing only the elements that satisfy predicate.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">def</span> <span class="nf">func</span><span class="p">(</span><span class="n">iterator</span><span class="p">):</span>
            <span class="k">return</span> <span class="nb">filter</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="n">iterator</span><span class="p">)</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">mapPartitions</span><span class="p">(</span><span class="n">func</span><span class="p">,</span> <span class="bp">True</span><span class="p">)</span>
</div>
<div class="viewcode-block" id="DStream.flatMap"><a class="viewcode-back" href="../../../pyspark.streaming.html#pyspark.streaming.DStream.flatMap">[docs]</a>    <span class="k">def</span> <span class="nf">flatMap</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">f</span><span class="p">,</span> <span class="n">preservesPartitioning</span><span class="o">=</span><span class="bp">False</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Return a new DStream by applying a function to all elements of</span>
<span class="sd">        this DStream, and then flattening the results</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">def</span> <span class="nf">func</span><span class="p">(</span><span class="n">s</span><span class="p">,</span> <span class="n">iterator</span><span class="p">):</span>
            <span class="k">return</span> <span class="n">chain</span><span class="o">.</span><span class="n">from_iterable</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="n">iterator</span><span class="p">))</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">mapPartitionsWithIndex</span><span class="p">(</span><span class="n">func</span><span class="p">,</span> <span class="n">preservesPartitioning</span><span class="p">)</span>
</div>
<div class="viewcode-block" id="DStream.map"><a class="viewcode-back" href="../../../pyspark.streaming.html#pyspark.streaming.DStream.map">[docs]</a>    <span class="k">def</span> <span class="nf">map</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">f</span><span class="p">,</span> <span class="n">preservesPartitioning</span><span class="o">=</span><span class="bp">False</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Return a new DStream by applying a function to each element of DStream.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">def</span> <span class="nf">func</span><span class="p">(</span><span class="n">iterator</span><span class="p">):</span>
            <span class="k">return</span> <span class="nb">map</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="n">iterator</span><span class="p">)</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">mapPartitions</span><span class="p">(</span><span class="n">func</span><span class="p">,</span> <span class="n">preservesPartitioning</span><span class="p">)</span>
</div>
<div class="viewcode-block" id="DStream.mapPartitions"><a class="viewcode-back" href="../../../pyspark.streaming.html#pyspark.streaming.DStream.mapPartitions">[docs]</a>    <span class="k">def</span> <span class="nf">mapPartitions</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">f</span><span class="p">,</span> <span class="n">preservesPartitioning</span><span class="o">=</span><span class="bp">False</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Return a new DStream in which each RDD is generated by applying</span>
<span class="sd">        mapPartitions() to each RDDs of this DStream.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">def</span> <span class="nf">func</span><span class="p">(</span><span class="n">s</span><span class="p">,</span> <span class="n">iterator</span><span class="p">):</span>
            <span class="k">return</span> <span class="n">f</span><span class="p">(</span><span class="n">iterator</span><span class="p">)</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">mapPartitionsWithIndex</span><span class="p">(</span><span class="n">func</span><span class="p">,</span> <span class="n">preservesPartitioning</span><span class="p">)</span>
</div>
<div class="viewcode-block" id="DStream.mapPartitionsWithIndex"><a class="viewcode-back" href="../../../pyspark.streaming.html#pyspark.streaming.DStream.mapPartitionsWithIndex">[docs]</a>    <span class="k">def</span> <span class="nf">mapPartitionsWithIndex</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">f</span><span class="p">,</span> <span class="n">preservesPartitioning</span><span class="o">=</span><span class="bp">False</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Return a new DStream in which each RDD is generated by applying</span>
<span class="sd">        mapPartitionsWithIndex() to each RDDs of this DStream.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="k">lambda</span> <span class="n">rdd</span><span class="p">:</span> <span class="n">rdd</span><span class="o">.</span><span class="n">mapPartitionsWithIndex</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="n">preservesPartitioning</span><span class="p">))</span>
</div>
<div class="viewcode-block" id="DStream.reduce"><a class="viewcode-back" href="../../../pyspark.streaming.html#pyspark.streaming.DStream.reduce">[docs]</a>    <span class="k">def</span> <span class="nf">reduce</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">func</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Return a new DStream in which each RDD has a single element</span>
<span class="sd">        generated by reducing each RDD of this DStream.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="p">(</span><span class="bp">None</span><span class="p">,</span> <span class="n">x</span><span class="p">))</span><span class="o">.</span><span class="n">reduceByKey</span><span class="p">(</span><span class="n">func</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
</div>
<div class="viewcode-block" id="DStream.reduceByKey"><a class="viewcode-back" href="../../../pyspark.streaming.html#pyspark.streaming.DStream.reduceByKey">[docs]</a>    <span class="k">def</span> <span class="nf">reduceByKey</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">func</span><span class="p">,</span> <span class="n">numPartitions</span><span class="o">=</span><span class="bp">None</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Return a new DStream by applying reduceByKey to each RDD.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">if</span> <span class="n">numPartitions</span> <span class="ow">is</span> <span class="bp">None</span><span class="p">:</span>
            <span class="n">numPartitions</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sc</span><span class="o">.</span><span class="n">defaultParallelism</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">combineByKey</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="p">,</span> <span class="n">func</span><span class="p">,</span> <span class="n">func</span><span class="p">,</span> <span class="n">numPartitions</span><span class="p">)</span>
</div>
<div class="viewcode-block" id="DStream.combineByKey"><a class="viewcode-back" href="../../../pyspark.streaming.html#pyspark.streaming.DStream.combineByKey">[docs]</a>    <span class="k">def</span> <span class="nf">combineByKey</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">createCombiner</span><span class="p">,</span> <span class="n">mergeValue</span><span class="p">,</span> <span class="n">mergeCombiners</span><span class="p">,</span>
                     <span class="n">numPartitions</span><span class="o">=</span><span class="bp">None</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Return a new DStream by applying combineByKey to each RDD.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">if</span> <span class="n">numPartitions</span> <span class="ow">is</span> <span class="bp">None</span><span class="p">:</span>
            <span class="n">numPartitions</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sc</span><span class="o">.</span><span class="n">defaultParallelism</span>

        <span class="k">def</span> <span class="nf">func</span><span class="p">(</span><span class="n">rdd</span><span class="p">):</span>
            <span class="k">return</span> <span class="n">rdd</span><span class="o">.</span><span class="n">combineByKey</span><span class="p">(</span><span class="n">createCombiner</span><span class="p">,</span> <span class="n">mergeValue</span><span class="p">,</span> <span class="n">mergeCombiners</span><span class="p">,</span> <span class="n">numPartitions</span><span class="p">)</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">func</span><span class="p">)</span>
</div>
<div class="viewcode-block" id="DStream.partitionBy"><a class="viewcode-back" href="../../../pyspark.streaming.html#pyspark.streaming.DStream.partitionBy">[docs]</a>    <span class="k">def</span> <span class="nf">partitionBy</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">numPartitions</span><span class="p">,</span> <span class="n">partitionFunc</span><span class="o">=</span><span class="n">portable_hash</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Return a copy of the DStream in which each RDD are partitioned</span>
<span class="sd">        using the specified partitioner.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="k">lambda</span> <span class="n">rdd</span><span class="p">:</span> <span class="n">rdd</span><span class="o">.</span><span class="n">partitionBy</span><span class="p">(</span><span class="n">numPartitions</span><span class="p">,</span> <span class="n">partitionFunc</span><span class="p">))</span>
</div>
<div class="viewcode-block" id="DStream.foreachRDD"><a class="viewcode-back" href="../../../pyspark.streaming.html#pyspark.streaming.DStream.foreachRDD">[docs]</a>    <span class="k">def</span> <span class="nf">foreachRDD</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">func</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Apply a function to each RDD in this DStream.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">if</span> <span class="n">func</span><span class="o">.</span><span class="n">__code__</span><span class="o">.</span><span class="n">co_argcount</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
            <span class="n">old_func</span> <span class="o">=</span> <span class="n">func</span>
            <span class="n">func</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">t</span><span class="p">,</span> <span class="n">rdd</span><span class="p">:</span> <span class="n">old_func</span><span class="p">(</span><span class="n">rdd</span><span class="p">)</span>
        <span class="n">jfunc</span> <span class="o">=</span> <span class="n">TransformFunction</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_sc</span><span class="p">,</span> <span class="n">func</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_jrdd_deserializer</span><span class="p">)</span>
        <span class="n">api</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_ssc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">PythonDStream</span>
        <span class="n">api</span><span class="o">.</span><span class="n">callForeachRDD</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_jdstream</span><span class="p">,</span> <span class="n">jfunc</span><span class="p">)</span>
</div>
<div class="viewcode-block" id="DStream.pprint"><a class="viewcode-back" href="../../../pyspark.streaming.html#pyspark.streaming.DStream.pprint">[docs]</a>    <span class="k">def</span> <span class="nf">pprint</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">num</span><span class="o">=</span><span class="mi">10</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Print the first num elements of each RDD generated in this DStream.</span>

<span class="sd">        @param num: the number of elements from the first will be printed.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">def</span> <span class="nf">takeAndPrint</span><span class="p">(</span><span class="n">time</span><span class="p">,</span> <span class="n">rdd</span><span class="p">):</span>
            <span class="n">taken</span> <span class="o">=</span> <span class="n">rdd</span><span class="o">.</span><span class="n">take</span><span class="p">(</span><span class="n">num</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
            <span class="k">print</span><span class="p">(</span><span class="s">&quot;-------------------------------------------&quot;</span><span class="p">)</span>
            <span class="k">print</span><span class="p">(</span><span class="s">&quot;Time: </span><span class="si">%s</span><span class="s">&quot;</span> <span class="o">%</span> <span class="n">time</span><span class="p">)</span>
            <span class="k">print</span><span class="p">(</span><span class="s">&quot;-------------------------------------------&quot;</span><span class="p">)</span>
            <span class="k">for</span> <span class="n">record</span> <span class="ow">in</span> <span class="n">taken</span><span class="p">[:</span><span class="n">num</span><span class="p">]:</span>
                <span class="k">print</span><span class="p">(</span><span class="n">record</span><span class="p">)</span>
            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">taken</span><span class="p">)</span> <span class="o">&gt;</span> <span class="n">num</span><span class="p">:</span>
                <span class="k">print</span><span class="p">(</span><span class="s">&quot;...&quot;</span><span class="p">)</span>
            <span class="k">print</span><span class="p">(</span><span class="s">&quot;&quot;</span><span class="p">)</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">foreachRDD</span><span class="p">(</span><span class="n">takeAndPrint</span><span class="p">)</span>
</div>
<div class="viewcode-block" id="DStream.mapValues"><a class="viewcode-back" href="../../../pyspark.streaming.html#pyspark.streaming.DStream.mapValues">[docs]</a>    <span class="k">def</span> <span class="nf">mapValues</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">f</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Return a new DStream by applying a map function to the value of</span>
<span class="sd">        each key-value pairs in this DStream without changing the key.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="n">map_values_fn</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">kv</span><span class="p">:</span> <span class="p">(</span><span class="n">kv</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">f</span><span class="p">(</span><span class="n">kv</span><span class="p">[</span><span class="mi">1</span><span class="p">]))</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">map_values_fn</span><span class="p">,</span> <span class="n">preservesPartitioning</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
</div>
<div class="viewcode-block" id="DStream.flatMapValues"><a class="viewcode-back" href="../../../pyspark.streaming.html#pyspark.streaming.DStream.flatMapValues">[docs]</a>    <span class="k">def</span> <span class="nf">flatMapValues</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">f</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Return a new DStream by applying a flatmap function to the value</span>
<span class="sd">        of each key-value pairs in this DStream without changing the key.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="n">flat_map_fn</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">kv</span><span class="p">:</span> <span class="p">((</span><span class="n">kv</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">f</span><span class="p">(</span><span class="n">kv</span><span class="p">[</span><span class="mi">1</span><span class="p">]))</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">flatMap</span><span class="p">(</span><span class="n">flat_map_fn</span><span class="p">,</span> <span class="n">preservesPartitioning</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
</div>
<div class="viewcode-block" id="DStream.glom"><a class="viewcode-back" href="../../../pyspark.streaming.html#pyspark.streaming.DStream.glom">[docs]</a>    <span class="k">def</span> <span class="nf">glom</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Return a new DStream in which RDD is generated by applying glom()</span>
<span class="sd">        to RDD of this DStream.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">def</span> <span class="nf">func</span><span class="p">(</span><span class="n">iterator</span><span class="p">):</span>
            <span class="k">yield</span> <span class="nb">list</span><span class="p">(</span><span class="n">iterator</span><span class="p">)</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">mapPartitions</span><span class="p">(</span><span class="n">func</span><span class="p">)</span>
</div>
<div class="viewcode-block" id="DStream.cache"><a class="viewcode-back" href="../../../pyspark.streaming.html#pyspark.streaming.DStream.cache">[docs]</a>    <span class="k">def</span> <span class="nf">cache</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Persist the RDDs of this DStream with the default storage level</span>
<span class="sd">        (C{MEMORY_ONLY_SER}).</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">is_cached</span> <span class="o">=</span> <span class="bp">True</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">persist</span><span class="p">(</span><span class="n">StorageLevel</span><span class="o">.</span><span class="n">MEMORY_ONLY_SER</span><span class="p">)</span>
        <span class="k">return</span> <span class="bp">self</span>
</div>
<div class="viewcode-block" id="DStream.persist"><a class="viewcode-back" href="../../../pyspark.streaming.html#pyspark.streaming.DStream.persist">[docs]</a>    <span class="k">def</span> <span class="nf">persist</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">storageLevel</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Persist the RDDs of this DStream with the given storage level</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">is_cached</span> <span class="o">=</span> <span class="bp">True</span>
        <span class="n">javaStorageLevel</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sc</span><span class="o">.</span><span class="n">_getJavaStorageLevel</span><span class="p">(</span><span class="n">storageLevel</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">_jdstream</span><span class="o">.</span><span class="n">persist</span><span class="p">(</span><span class="n">javaStorageLevel</span><span class="p">)</span>
        <span class="k">return</span> <span class="bp">self</span>
</div>
<div class="viewcode-block" id="DStream.checkpoint"><a class="viewcode-back" href="../../../pyspark.streaming.html#pyspark.streaming.DStream.checkpoint">[docs]</a>    <span class="k">def</span> <span class="nf">checkpoint</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">interval</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Enable periodic checkpointing of RDDs of this DStream</span>

<span class="sd">        @param interval: time in seconds, after each period of that, generated</span>
<span class="sd">                         RDD will be checkpointed</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">is_checkpointed</span> <span class="o">=</span> <span class="bp">True</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">_jdstream</span><span class="o">.</span><span class="n">checkpoint</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_ssc</span><span class="o">.</span><span class="n">_jduration</span><span class="p">(</span><span class="n">interval</span><span class="p">))</span>
        <span class="k">return</span> <span class="bp">self</span>
</div>
<div class="viewcode-block" id="DStream.groupByKey"><a class="viewcode-back" href="../../../pyspark.streaming.html#pyspark.streaming.DStream.groupByKey">[docs]</a>    <span class="k">def</span> <span class="nf">groupByKey</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">numPartitions</span><span class="o">=</span><span class="bp">None</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Return a new DStream by applying groupByKey on each RDD.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">if</span> <span class="n">numPartitions</span> <span class="ow">is</span> <span class="bp">None</span><span class="p">:</span>
            <span class="n">numPartitions</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sc</span><span class="o">.</span><span class="n">defaultParallelism</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="k">lambda</span> <span class="n">rdd</span><span class="p">:</span> <span class="n">rdd</span><span class="o">.</span><span class="n">groupByKey</span><span class="p">(</span><span class="n">numPartitions</span><span class="p">))</span>
</div>
<div class="viewcode-block" id="DStream.countByValue"><a class="viewcode-back" href="../../../pyspark.streaming.html#pyspark.streaming.DStream.countByValue">[docs]</a>    <span class="k">def</span> <span class="nf">countByValue</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Return a new DStream in which each RDD contains the counts of each</span>
<span class="sd">        distinct value in each RDD of this DStream.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="bp">None</span><span class="p">))</span><span class="o">.</span><span class="n">reduceByKey</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">:</span> <span class="bp">None</span><span class="p">)</span><span class="o">.</span><span class="n">count</span><span class="p">()</span>
</div>
<div class="viewcode-block" id="DStream.saveAsTextFiles"><a class="viewcode-back" href="../../../pyspark.streaming.html#pyspark.streaming.DStream.saveAsTextFiles">[docs]</a>    <span class="k">def</span> <span class="nf">saveAsTextFiles</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">prefix</span><span class="p">,</span> <span class="n">suffix</span><span class="o">=</span><span class="bp">None</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Save each RDD in this DStream as at text file, using string</span>
<span class="sd">        representation of elements.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">def</span> <span class="nf">saveAsTextFile</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="n">rdd</span><span class="p">):</span>
            <span class="n">path</span> <span class="o">=</span> <span class="n">rddToFileName</span><span class="p">(</span><span class="n">prefix</span><span class="p">,</span> <span class="n">suffix</span><span class="p">,</span> <span class="n">t</span><span class="p">)</span>
            <span class="k">try</span><span class="p">:</span>
                <span class="n">rdd</span><span class="o">.</span><span class="n">saveAsTextFile</span><span class="p">(</span><span class="n">path</span><span class="p">)</span>
            <span class="k">except</span> <span class="n">Py4JJavaError</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
                <span class="c"># after recovered from checkpointing, the foreachRDD may</span>
                <span class="c"># be called twice</span>
                <span class="k">if</span> <span class="s">&#39;FileAlreadyExistsException&#39;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="nb">str</span><span class="p">(</span><span class="n">e</span><span class="p">):</span>
                    <span class="k">raise</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">foreachRDD</span><span class="p">(</span><span class="n">saveAsTextFile</span><span class="p">)</span>

    <span class="c"># TODO: uncomment this until we have ssc.pickleFileStream()</span>
    <span class="c"># def saveAsPickleFiles(self, prefix, suffix=None):</span>
    <span class="c">#     &quot;&quot;&quot;</span>
    <span class="c">#     Save each RDD in this DStream as at binary file, the elements are</span>
    <span class="c">#     serialized by pickle.</span>
    <span class="c">#     &quot;&quot;&quot;</span>
    <span class="c">#     def saveAsPickleFile(t, rdd):</span>
    <span class="c">#         path = rddToFileName(prefix, suffix, t)</span>
    <span class="c">#         try:</span>
    <span class="c">#             rdd.saveAsPickleFile(path)</span>
    <span class="c">#         except Py4JJavaError as e:</span>
    <span class="c">#             # after recovered from checkpointing, the foreachRDD may</span>
    <span class="c">#             # be called twice</span>
    <span class="c">#             if &#39;FileAlreadyExistsException&#39; not in str(e):</span>
    <span class="c">#                 raise</span>
    <span class="c">#     return self.foreachRDD(saveAsPickleFile)</span>
</div>
<div class="viewcode-block" id="DStream.transform"><a class="viewcode-back" href="../../../pyspark.streaming.html#pyspark.streaming.DStream.transform">[docs]</a>    <span class="k">def</span> <span class="nf">transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">func</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Return a new DStream in which each RDD is generated by applying a function</span>
<span class="sd">        on each RDD of this DStream.</span>

<span class="sd">        `func` can have one argument of `rdd`, or have two arguments of</span>
<span class="sd">        (`time`, `rdd`)</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">if</span> <span class="n">func</span><span class="o">.</span><span class="n">__code__</span><span class="o">.</span><span class="n">co_argcount</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
            <span class="n">oldfunc</span> <span class="o">=</span> <span class="n">func</span>
            <span class="n">func</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">t</span><span class="p">,</span> <span class="n">rdd</span><span class="p">:</span> <span class="n">oldfunc</span><span class="p">(</span><span class="n">rdd</span><span class="p">)</span>
        <span class="k">assert</span> <span class="n">func</span><span class="o">.</span><span class="n">__code__</span><span class="o">.</span><span class="n">co_argcount</span> <span class="o">==</span> <span class="mi">2</span><span class="p">,</span> <span class="s">&quot;func should take one or two arguments&quot;</span>
        <span class="k">return</span> <span class="n">TransformedDStream</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">func</span><span class="p">)</span>
</div>
<div class="viewcode-block" id="DStream.transformWith"><a class="viewcode-back" href="../../../pyspark.streaming.html#pyspark.streaming.DStream.transformWith">[docs]</a>    <span class="k">def</span> <span class="nf">transformWith</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">func</span><span class="p">,</span> <span class="n">other</span><span class="p">,</span> <span class="n">keepSerializer</span><span class="o">=</span><span class="bp">False</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Return a new DStream in which each RDD is generated by applying a function</span>
<span class="sd">        on each RDD of this DStream and &#39;other&#39; DStream.</span>

<span class="sd">        `func` can have two arguments of (`rdd_a`, `rdd_b`) or have three</span>
<span class="sd">        arguments of (`time`, `rdd_a`, `rdd_b`)</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">if</span> <span class="n">func</span><span class="o">.</span><span class="n">__code__</span><span class="o">.</span><span class="n">co_argcount</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
            <span class="n">oldfunc</span> <span class="o">=</span> <span class="n">func</span>
            <span class="n">func</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">t</span><span class="p">,</span> <span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">:</span> <span class="n">oldfunc</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">)</span>
        <span class="k">assert</span> <span class="n">func</span><span class="o">.</span><span class="n">__code__</span><span class="o">.</span><span class="n">co_argcount</span> <span class="o">==</span> <span class="mi">3</span><span class="p">,</span> <span class="s">&quot;func should take two or three arguments&quot;</span>
        <span class="n">jfunc</span> <span class="o">=</span> <span class="n">TransformFunction</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_sc</span><span class="p">,</span> <span class="n">func</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_jrdd_deserializer</span><span class="p">,</span> <span class="n">other</span><span class="o">.</span><span class="n">_jrdd_deserializer</span><span class="p">)</span>
        <span class="n">dstream</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">PythonTransformed2DStream</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_jdstream</span><span class="o">.</span><span class="n">dstream</span><span class="p">(),</span>
                                                          <span class="n">other</span><span class="o">.</span><span class="n">_jdstream</span><span class="o">.</span><span class="n">dstream</span><span class="p">(),</span> <span class="n">jfunc</span><span class="p">)</span>
        <span class="n">jrdd_serializer</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_jrdd_deserializer</span> <span class="k">if</span> <span class="n">keepSerializer</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sc</span><span class="o">.</span><span class="n">serializer</span>
        <span class="k">return</span> <span class="n">DStream</span><span class="p">(</span><span class="n">dstream</span><span class="o">.</span><span class="n">asJavaDStream</span><span class="p">(),</span> <span class="bp">self</span><span class="o">.</span><span class="n">_ssc</span><span class="p">,</span> <span class="n">jrdd_serializer</span><span class="p">)</span>
</div>
<div class="viewcode-block" id="DStream.repartition"><a class="viewcode-back" href="../../../pyspark.streaming.html#pyspark.streaming.DStream.repartition">[docs]</a>    <span class="k">def</span> <span class="nf">repartition</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">numPartitions</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Return a new DStream with an increased or decreased level of parallelism.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="k">lambda</span> <span class="n">rdd</span><span class="p">:</span> <span class="n">rdd</span><span class="o">.</span><span class="n">repartition</span><span class="p">(</span><span class="n">numPartitions</span><span class="p">))</span>
</div>
    <span class="nd">@property</span>
    <span class="k">def</span> <span class="nf">_slideDuration</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Return the slideDuration in seconds of this DStream</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_jdstream</span><span class="o">.</span><span class="n">dstream</span><span class="p">()</span><span class="o">.</span><span class="n">slideDuration</span><span class="p">()</span><span class="o">.</span><span class="n">milliseconds</span><span class="p">()</span> <span class="o">/</span> <span class="mf">1000.0</span>

<div class="viewcode-block" id="DStream.union"><a class="viewcode-back" href="../../../pyspark.streaming.html#pyspark.streaming.DStream.union">[docs]</a>    <span class="k">def</span> <span class="nf">union</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Return a new DStream by unifying data of another DStream with this DStream.</span>

<span class="sd">        @param other: Another DStream having the same interval (i.e., slideDuration)</span>
<span class="sd">                     as this DStream.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_slideDuration</span> <span class="o">!=</span> <span class="n">other</span><span class="o">.</span><span class="n">_slideDuration</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s">&quot;the two DStream should have same slide duration&quot;</span><span class="p">)</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">transformWith</span><span class="p">(</span><span class="k">lambda</span> <span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">:</span> <span class="n">a</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="n">b</span><span class="p">),</span> <span class="n">other</span><span class="p">,</span> <span class="bp">True</span><span class="p">)</span>
</div>
<div class="viewcode-block" id="DStream.cogroup"><a class="viewcode-back" href="../../../pyspark.streaming.html#pyspark.streaming.DStream.cogroup">[docs]</a>    <span class="k">def</span> <span class="nf">cogroup</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">,</span> <span class="n">numPartitions</span><span class="o">=</span><span class="bp">None</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Return a new DStream by applying &#39;cogroup&#39; between RDDs of this</span>
<span class="sd">        DStream and `other` DStream.</span>

<span class="sd">        Hash partitioning is used to generate the RDDs with `numPartitions` partitions.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">if</span> <span class="n">numPartitions</span> <span class="ow">is</span> <span class="bp">None</span><span class="p">:</span>
            <span class="n">numPartitions</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sc</span><span class="o">.</span><span class="n">defaultParallelism</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">transformWith</span><span class="p">(</span><span class="k">lambda</span> <span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">:</span> <span class="n">a</span><span class="o">.</span><span class="n">cogroup</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">numPartitions</span><span class="p">),</span> <span class="n">other</span><span class="p">)</span>
</div>
<div class="viewcode-block" id="DStream.join"><a class="viewcode-back" href="../../../pyspark.streaming.html#pyspark.streaming.DStream.join">[docs]</a>    <span class="k">def</span> <span class="nf">join</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">,</span> <span class="n">numPartitions</span><span class="o">=</span><span class="bp">None</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Return a new DStream by applying &#39;join&#39; between RDDs of this DStream and</span>
<span class="sd">        `other` DStream.</span>

<span class="sd">        Hash partitioning is used to generate the RDDs with `numPartitions`</span>
<span class="sd">        partitions.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">if</span> <span class="n">numPartitions</span> <span class="ow">is</span> <span class="bp">None</span><span class="p">:</span>
            <span class="n">numPartitions</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sc</span><span class="o">.</span><span class="n">defaultParallelism</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">transformWith</span><span class="p">(</span><span class="k">lambda</span> <span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">:</span> <span class="n">a</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">numPartitions</span><span class="p">),</span> <span class="n">other</span><span class="p">)</span>
</div>
<div class="viewcode-block" id="DStream.leftOuterJoin"><a class="viewcode-back" href="../../../pyspark.streaming.html#pyspark.streaming.DStream.leftOuterJoin">[docs]</a>    <span class="k">def</span> <span class="nf">leftOuterJoin</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">,</span> <span class="n">numPartitions</span><span class="o">=</span><span class="bp">None</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Return a new DStream by applying &#39;left outer join&#39; between RDDs of this DStream and</span>
<span class="sd">        `other` DStream.</span>

<span class="sd">        Hash partitioning is used to generate the RDDs with `numPartitions`</span>
<span class="sd">        partitions.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">if</span> <span class="n">numPartitions</span> <span class="ow">is</span> <span class="bp">None</span><span class="p">:</span>
            <span class="n">numPartitions</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sc</span><span class="o">.</span><span class="n">defaultParallelism</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">transformWith</span><span class="p">(</span><span class="k">lambda</span> <span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">:</span> <span class="n">a</span><span class="o">.</span><span class="n">leftOuterJoin</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">numPartitions</span><span class="p">),</span> <span class="n">other</span><span class="p">)</span>
</div>
<div class="viewcode-block" id="DStream.rightOuterJoin"><a class="viewcode-back" href="../../../pyspark.streaming.html#pyspark.streaming.DStream.rightOuterJoin">[docs]</a>    <span class="k">def</span> <span class="nf">rightOuterJoin</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">,</span> <span class="n">numPartitions</span><span class="o">=</span><span class="bp">None</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Return a new DStream by applying &#39;right outer join&#39; between RDDs of this DStream and</span>
<span class="sd">        `other` DStream.</span>

<span class="sd">        Hash partitioning is used to generate the RDDs with `numPartitions`</span>
<span class="sd">        partitions.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">if</span> <span class="n">numPartitions</span> <span class="ow">is</span> <span class="bp">None</span><span class="p">:</span>
            <span class="n">numPartitions</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sc</span><span class="o">.</span><span class="n">defaultParallelism</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">transformWith</span><span class="p">(</span><span class="k">lambda</span> <span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">:</span> <span class="n">a</span><span class="o">.</span><span class="n">rightOuterJoin</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">numPartitions</span><span class="p">),</span> <span class="n">other</span><span class="p">)</span>
</div>
<div class="viewcode-block" id="DStream.fullOuterJoin"><a class="viewcode-back" href="../../../pyspark.streaming.html#pyspark.streaming.DStream.fullOuterJoin">[docs]</a>    <span class="k">def</span> <span class="nf">fullOuterJoin</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">,</span> <span class="n">numPartitions</span><span class="o">=</span><span class="bp">None</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Return a new DStream by applying &#39;full outer join&#39; between RDDs of this DStream and</span>
<span class="sd">        `other` DStream.</span>

<span class="sd">        Hash partitioning is used to generate the RDDs with `numPartitions`</span>
<span class="sd">        partitions.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">if</span> <span class="n">numPartitions</span> <span class="ow">is</span> <span class="bp">None</span><span class="p">:</span>
            <span class="n">numPartitions</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sc</span><span class="o">.</span><span class="n">defaultParallelism</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">transformWith</span><span class="p">(</span><span class="k">lambda</span> <span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">:</span> <span class="n">a</span><span class="o">.</span><span class="n">fullOuterJoin</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">numPartitions</span><span class="p">),</span> <span class="n">other</span><span class="p">)</span>
</div>
    <span class="k">def</span> <span class="nf">_jtime</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">timestamp</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot; Convert datetime or unix_timestamp into Time</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">timestamp</span><span class="p">,</span> <span class="n">datetime</span><span class="p">):</span>
            <span class="n">timestamp</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">mktime</span><span class="p">(</span><span class="n">timestamp</span><span class="o">.</span><span class="n">timetuple</span><span class="p">())</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">Time</span><span class="p">(</span><span class="nb">long</span><span class="p">(</span><span class="n">timestamp</span> <span class="o">*</span> <span class="mi">1000</span><span class="p">))</span>

<div class="viewcode-block" id="DStream.slice"><a class="viewcode-back" href="../../../pyspark.streaming.html#pyspark.streaming.DStream.slice">[docs]</a>    <span class="k">def</span> <span class="nf">slice</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">begin</span><span class="p">,</span> <span class="n">end</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Return all the RDDs between &#39;begin&#39; to &#39;end&#39; (both included)</span>

<span class="sd">        `begin`, `end` could be datetime.datetime() or unix_timestamp</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="n">jrdds</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_jdstream</span><span class="o">.</span><span class="n">slice</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_jtime</span><span class="p">(</span><span class="n">begin</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">_jtime</span><span class="p">(</span><span class="n">end</span><span class="p">))</span>
        <span class="k">return</span> <span class="p">[</span><span class="n">RDD</span><span class="p">(</span><span class="n">jrdd</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sc</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_jrdd_deserializer</span><span class="p">)</span> <span class="k">for</span> <span class="n">jrdd</span> <span class="ow">in</span> <span class="n">jrdds</span><span class="p">]</span>
</div>
    <span class="k">def</span> <span class="nf">_validate_window_param</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">window</span><span class="p">,</span> <span class="n">slide</span><span class="p">):</span>
        <span class="n">duration</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_jdstream</span><span class="o">.</span><span class="n">dstream</span><span class="p">()</span><span class="o">.</span><span class="n">slideDuration</span><span class="p">()</span><span class="o">.</span><span class="n">milliseconds</span><span class="p">()</span>
        <span class="k">if</span> <span class="nb">int</span><span class="p">(</span><span class="n">window</span> <span class="o">*</span> <span class="mi">1000</span><span class="p">)</span> <span class="o">%</span> <span class="n">duration</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s">&quot;windowDuration must be multiple of the slide duration (</span><span class="si">%d</span><span class="s"> ms)&quot;</span>
                             <span class="o">%</span> <span class="n">duration</span><span class="p">)</span>
        <span class="k">if</span> <span class="n">slide</span> <span class="ow">and</span> <span class="nb">int</span><span class="p">(</span><span class="n">slide</span> <span class="o">*</span> <span class="mi">1000</span><span class="p">)</span> <span class="o">%</span> <span class="n">duration</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s">&quot;slideDuration must be multiple of the slide duration (</span><span class="si">%d</span><span class="s"> ms)&quot;</span>
                             <span class="o">%</span> <span class="n">duration</span><span class="p">)</span>

<div class="viewcode-block" id="DStream.window"><a class="viewcode-back" href="../../../pyspark.streaming.html#pyspark.streaming.DStream.window">[docs]</a>    <span class="k">def</span> <span class="nf">window</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">windowDuration</span><span class="p">,</span> <span class="n">slideDuration</span><span class="o">=</span><span class="bp">None</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Return a new DStream in which each RDD contains all the elements in seen in a</span>
<span class="sd">        sliding window of time over this DStream.</span>

<span class="sd">        @param windowDuration: width of the window; must be a multiple of this DStream&#39;s</span>
<span class="sd">                              batching interval</span>
<span class="sd">        @param slideDuration:  sliding interval of the window (i.e., the interval after which</span>
<span class="sd">                              the new DStream will generate RDDs); must be a multiple of this</span>
<span class="sd">                              DStream&#39;s batching interval</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">_validate_window_param</span><span class="p">(</span><span class="n">windowDuration</span><span class="p">,</span> <span class="n">slideDuration</span><span class="p">)</span>
        <span class="n">d</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_ssc</span><span class="o">.</span><span class="n">_jduration</span><span class="p">(</span><span class="n">windowDuration</span><span class="p">)</span>
        <span class="k">if</span> <span class="n">slideDuration</span> <span class="ow">is</span> <span class="bp">None</span><span class="p">:</span>
            <span class="k">return</span> <span class="n">DStream</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_jdstream</span><span class="o">.</span><span class="n">window</span><span class="p">(</span><span class="n">d</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">_ssc</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_jrdd_deserializer</span><span class="p">)</span>
        <span class="n">s</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_ssc</span><span class="o">.</span><span class="n">_jduration</span><span class="p">(</span><span class="n">slideDuration</span><span class="p">)</span>
        <span class="k">return</span> <span class="n">DStream</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_jdstream</span><span class="o">.</span><span class="n">window</span><span class="p">(</span><span class="n">d</span><span class="p">,</span> <span class="n">s</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">_ssc</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_jrdd_deserializer</span><span class="p">)</span>
</div>
<div class="viewcode-block" id="DStream.reduceByWindow"><a class="viewcode-back" href="../../../pyspark.streaming.html#pyspark.streaming.DStream.reduceByWindow">[docs]</a>    <span class="k">def</span> <span class="nf">reduceByWindow</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">reduceFunc</span><span class="p">,</span> <span class="n">invReduceFunc</span><span class="p">,</span> <span class="n">windowDuration</span><span class="p">,</span> <span class="n">slideDuration</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Return a new DStream in which each RDD has a single element generated by reducing all</span>
<span class="sd">        elements in a sliding window over this DStream.</span>

<span class="sd">        if `invReduceFunc` is not None, the reduction is done incrementally</span>
<span class="sd">        using the old window&#39;s reduced value :</span>

<span class="sd">        1. reduce the new values that entered the window (e.g., adding new counts)</span>

<span class="sd">        2. &quot;inverse reduce&quot; the old values that left the window (e.g., subtracting old counts)</span>
<span class="sd">        This is more efficient than `invReduceFunc` is None.</span>

<span class="sd">        @param reduceFunc:     associative reduce function</span>
<span class="sd">        @param invReduceFunc:  inverse reduce function of `reduceFunc`</span>
<span class="sd">        @param windowDuration: width of the window; must be a multiple of this DStream&#39;s</span>
<span class="sd">                               batching interval</span>
<span class="sd">        @param slideDuration:  sliding interval of the window (i.e., the interval after which</span>
<span class="sd">                               the new DStream will generate RDDs); must be a multiple of this</span>
<span class="sd">                               DStream&#39;s batching interval</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="n">keyed</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">x</span><span class="p">))</span>
        <span class="n">reduced</span> <span class="o">=</span> <span class="n">keyed</span><span class="o">.</span><span class="n">reduceByKeyAndWindow</span><span class="p">(</span><span class="n">reduceFunc</span><span class="p">,</span> <span class="n">invReduceFunc</span><span class="p">,</span>
                                             <span class="n">windowDuration</span><span class="p">,</span> <span class="n">slideDuration</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
        <span class="k">return</span> <span class="n">reduced</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">kv</span><span class="p">:</span> <span class="n">kv</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
</div>
<div class="viewcode-block" id="DStream.countByWindow"><a class="viewcode-back" href="../../../pyspark.streaming.html#pyspark.streaming.DStream.countByWindow">[docs]</a>    <span class="k">def</span> <span class="nf">countByWindow</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">windowDuration</span><span class="p">,</span> <span class="n">slideDuration</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Return a new DStream in which each RDD has a single element generated</span>
<span class="sd">        by counting the number of elements in a window over this DStream.</span>
<span class="sd">        windowDuration and slideDuration are as defined in the window() operation.</span>

<span class="sd">        This is equivalent to window(windowDuration, slideDuration).count(),</span>
<span class="sd">        but will be more efficient if window is large.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">reduceByWindow</span><span class="p">(</span><span class="n">operator</span><span class="o">.</span><span class="n">add</span><span class="p">,</span> <span class="n">operator</span><span class="o">.</span><span class="n">sub</span><span class="p">,</span>
                                                    <span class="n">windowDuration</span><span class="p">,</span> <span class="n">slideDuration</span><span class="p">)</span>
</div>
<div class="viewcode-block" id="DStream.countByValueAndWindow"><a class="viewcode-back" href="../../../pyspark.streaming.html#pyspark.streaming.DStream.countByValueAndWindow">[docs]</a>    <span class="k">def</span> <span class="nf">countByValueAndWindow</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">windowDuration</span><span class="p">,</span> <span class="n">slideDuration</span><span class="p">,</span> <span class="n">numPartitions</span><span class="o">=</span><span class="bp">None</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Return a new DStream in which each RDD contains the count of distinct elements in</span>
<span class="sd">        RDDs in a sliding window over this DStream.</span>

<span class="sd">        @param windowDuration: width of the window; must be a multiple of this DStream&#39;s</span>
<span class="sd">                              batching interval</span>
<span class="sd">        @param slideDuration:  sliding interval of the window (i.e., the interval after which</span>
<span class="sd">                              the new DStream will generate RDDs); must be a multiple of this</span>
<span class="sd">                              DStream&#39;s batching interval</span>
<span class="sd">        @param numPartitions:  number of partitions of each RDD in the new DStream.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="n">keyed</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
        <span class="n">counted</span> <span class="o">=</span> <span class="n">keyed</span><span class="o">.</span><span class="n">reduceByKeyAndWindow</span><span class="p">(</span><span class="n">operator</span><span class="o">.</span><span class="n">add</span><span class="p">,</span> <span class="n">operator</span><span class="o">.</span><span class="n">sub</span><span class="p">,</span>
                                             <span class="n">windowDuration</span><span class="p">,</span> <span class="n">slideDuration</span><span class="p">,</span> <span class="n">numPartitions</span><span class="p">)</span>
        <span class="k">return</span> <span class="n">counted</span><span class="o">.</span><span class="n">filter</span><span class="p">(</span><span class="k">lambda</span> <span class="n">kv</span><span class="p">:</span> <span class="n">kv</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">count</span><span class="p">()</span>
</div>
<div class="viewcode-block" id="DStream.groupByKeyAndWindow"><a class="viewcode-back" href="../../../pyspark.streaming.html#pyspark.streaming.DStream.groupByKeyAndWindow">[docs]</a>    <span class="k">def</span> <span class="nf">groupByKeyAndWindow</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">windowDuration</span><span class="p">,</span> <span class="n">slideDuration</span><span class="p">,</span> <span class="n">numPartitions</span><span class="o">=</span><span class="bp">None</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Return a new DStream by applying `groupByKey` over a sliding window.</span>
<span class="sd">        Similar to `DStream.groupByKey()`, but applies it over a sliding window.</span>

<span class="sd">        @param windowDuration: width of the window; must be a multiple of this DStream&#39;s</span>
<span class="sd">                              batching interval</span>
<span class="sd">        @param slideDuration:  sliding interval of the window (i.e., the interval after which</span>
<span class="sd">                              the new DStream will generate RDDs); must be a multiple of this</span>
<span class="sd">                              DStream&#39;s batching interval</span>
<span class="sd">        @param numPartitions:  Number of partitions of each RDD in the new DStream.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="n">ls</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">mapValues</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="p">[</span><span class="n">x</span><span class="p">])</span>
        <span class="n">grouped</span> <span class="o">=</span> <span class="n">ls</span><span class="o">.</span><span class="n">reduceByKeyAndWindow</span><span class="p">(</span><span class="k">lambda</span> <span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">:</span> <span class="n">a</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">b</span><span class="p">)</span> <span class="ow">or</span> <span class="n">a</span><span class="p">,</span> <span class="k">lambda</span> <span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">:</span> <span class="n">a</span><span class="p">[</span><span class="nb">len</span><span class="p">(</span><span class="n">b</span><span class="p">):],</span>
                                          <span class="n">windowDuration</span><span class="p">,</span> <span class="n">slideDuration</span><span class="p">,</span> <span class="n">numPartitions</span><span class="p">)</span>
        <span class="k">return</span> <span class="n">grouped</span><span class="o">.</span><span class="n">mapValues</span><span class="p">(</span><span class="n">ResultIterable</span><span class="p">)</span>
</div>
<div class="viewcode-block" id="DStream.reduceByKeyAndWindow"><a class="viewcode-back" href="../../../pyspark.streaming.html#pyspark.streaming.DStream.reduceByKeyAndWindow">[docs]</a>    <span class="k">def</span> <span class="nf">reduceByKeyAndWindow</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">func</span><span class="p">,</span> <span class="n">invFunc</span><span class="p">,</span> <span class="n">windowDuration</span><span class="p">,</span> <span class="n">slideDuration</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span>
                             <span class="n">numPartitions</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span> <span class="n">filterFunc</span><span class="o">=</span><span class="bp">None</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Return a new DStream by applying incremental `reduceByKey` over a sliding window.</span>

<span class="sd">        The reduced value of over a new window is calculated using the old window&#39;s reduce value :</span>
<span class="sd">         1. reduce the new values that entered the window (e.g., adding new counts)</span>
<span class="sd">         2. &quot;inverse reduce&quot; the old values that left the window (e.g., subtracting old counts)</span>

<span class="sd">        `invFunc` can be None, then it will reduce all the RDDs in window, could be slower</span>
<span class="sd">        than having `invFunc`.</span>

<span class="sd">        @param reduceFunc:     associative reduce function</span>
<span class="sd">        @param invReduceFunc:  inverse function of `reduceFunc`</span>
<span class="sd">        @param windowDuration: width of the window; must be a multiple of this DStream&#39;s</span>
<span class="sd">                              batching interval</span>
<span class="sd">        @param slideDuration:  sliding interval of the window (i.e., the interval after which</span>
<span class="sd">                              the new DStream will generate RDDs); must be a multiple of this</span>
<span class="sd">                              DStream&#39;s batching interval</span>
<span class="sd">        @param numPartitions:  number of partitions of each RDD in the new DStream.</span>
<span class="sd">        @param filterFunc:     function to filter expired key-value pairs;</span>
<span class="sd">                              only pairs that satisfy the function are retained</span>
<span class="sd">                              set this to null if you do not want to filter</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">_validate_window_param</span><span class="p">(</span><span class="n">windowDuration</span><span class="p">,</span> <span class="n">slideDuration</span><span class="p">)</span>
        <span class="k">if</span> <span class="n">numPartitions</span> <span class="ow">is</span> <span class="bp">None</span><span class="p">:</span>
            <span class="n">numPartitions</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sc</span><span class="o">.</span><span class="n">defaultParallelism</span>

        <span class="n">reduced</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">reduceByKey</span><span class="p">(</span><span class="n">func</span><span class="p">,</span> <span class="n">numPartitions</span><span class="p">)</span>

        <span class="k">def</span> <span class="nf">reduceFunc</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">):</span>
            <span class="n">b</span> <span class="o">=</span> <span class="n">b</span><span class="o">.</span><span class="n">reduceByKey</span><span class="p">(</span><span class="n">func</span><span class="p">,</span> <span class="n">numPartitions</span><span class="p">)</span>
            <span class="n">r</span> <span class="o">=</span> <span class="n">a</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="n">b</span><span class="p">)</span><span class="o">.</span><span class="n">reduceByKey</span><span class="p">(</span><span class="n">func</span><span class="p">,</span> <span class="n">numPartitions</span><span class="p">)</span> <span class="k">if</span> <span class="n">a</span> <span class="k">else</span> <span class="n">b</span>
            <span class="k">if</span> <span class="n">filterFunc</span><span class="p">:</span>
                <span class="n">r</span> <span class="o">=</span> <span class="n">r</span><span class="o">.</span><span class="n">filter</span><span class="p">(</span><span class="n">filterFunc</span><span class="p">)</span>
            <span class="k">return</span> <span class="n">r</span>

        <span class="k">def</span> <span class="nf">invReduceFunc</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">):</span>
            <span class="n">b</span> <span class="o">=</span> <span class="n">b</span><span class="o">.</span><span class="n">reduceByKey</span><span class="p">(</span><span class="n">func</span><span class="p">,</span> <span class="n">numPartitions</span><span class="p">)</span>
            <span class="n">joined</span> <span class="o">=</span> <span class="n">a</span><span class="o">.</span><span class="n">leftOuterJoin</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">numPartitions</span><span class="p">)</span>
            <span class="k">return</span> <span class="n">joined</span><span class="o">.</span><span class="n">mapValues</span><span class="p">(</span><span class="k">lambda</span> <span class="n">kv</span><span class="p">:</span> <span class="n">invFunc</span><span class="p">(</span><span class="n">kv</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">kv</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
                                    <span class="k">if</span> <span class="n">kv</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="bp">None</span> <span class="k">else</span> <span class="n">kv</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>

        <span class="n">jreduceFunc</span> <span class="o">=</span> <span class="n">TransformFunction</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_sc</span><span class="p">,</span> <span class="n">reduceFunc</span><span class="p">,</span> <span class="n">reduced</span><span class="o">.</span><span class="n">_jrdd_deserializer</span><span class="p">)</span>
        <span class="k">if</span> <span class="n">invReduceFunc</span><span class="p">:</span>
            <span class="n">jinvReduceFunc</span> <span class="o">=</span> <span class="n">TransformFunction</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_sc</span><span class="p">,</span> <span class="n">invReduceFunc</span><span class="p">,</span> <span class="n">reduced</span><span class="o">.</span><span class="n">_jrdd_deserializer</span><span class="p">)</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="n">jinvReduceFunc</span> <span class="o">=</span> <span class="bp">None</span>
        <span class="k">if</span> <span class="n">slideDuration</span> <span class="ow">is</span> <span class="bp">None</span><span class="p">:</span>
            <span class="n">slideDuration</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_slideDuration</span>
        <span class="n">dstream</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">PythonReducedWindowedDStream</span><span class="p">(</span><span class="n">reduced</span><span class="o">.</span><span class="n">_jdstream</span><span class="o">.</span><span class="n">dstream</span><span class="p">(),</span>
                                                             <span class="n">jreduceFunc</span><span class="p">,</span> <span class="n">jinvReduceFunc</span><span class="p">,</span>
                                                             <span class="bp">self</span><span class="o">.</span><span class="n">_ssc</span><span class="o">.</span><span class="n">_jduration</span><span class="p">(</span><span class="n">windowDuration</span><span class="p">),</span>
                                                             <span class="bp">self</span><span class="o">.</span><span class="n">_ssc</span><span class="o">.</span><span class="n">_jduration</span><span class="p">(</span><span class="n">slideDuration</span><span class="p">))</span>
        <span class="k">return</span> <span class="n">DStream</span><span class="p">(</span><span class="n">dstream</span><span class="o">.</span><span class="n">asJavaDStream</span><span class="p">(),</span> <span class="bp">self</span><span class="o">.</span><span class="n">_ssc</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sc</span><span class="o">.</span><span class="n">serializer</span><span class="p">)</span>
</div>
<div class="viewcode-block" id="DStream.updateStateByKey"><a class="viewcode-back" href="../../../pyspark.streaming.html#pyspark.streaming.DStream.updateStateByKey">[docs]</a>    <span class="k">def</span> <span class="nf">updateStateByKey</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">updateFunc</span><span class="p">,</span> <span class="n">numPartitions</span><span class="o">=</span><span class="bp">None</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Return a new &quot;state&quot; DStream where the state for each key is updated by applying</span>
<span class="sd">        the given function on the previous state of the key and the new values of the key.</span>

<span class="sd">        @param updateFunc: State update function. If this function returns None, then</span>
<span class="sd">                           corresponding state key-value pair will be eliminated.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">if</span> <span class="n">numPartitions</span> <span class="ow">is</span> <span class="bp">None</span><span class="p">:</span>
            <span class="n">numPartitions</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sc</span><span class="o">.</span><span class="n">defaultParallelism</span>

        <span class="k">def</span> <span class="nf">reduceFunc</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">):</span>
            <span class="k">if</span> <span class="n">a</span> <span class="ow">is</span> <span class="bp">None</span><span class="p">:</span>
                <span class="n">g</span> <span class="o">=</span> <span class="n">b</span><span class="o">.</span><span class="n">groupByKey</span><span class="p">(</span><span class="n">numPartitions</span><span class="p">)</span><span class="o">.</span><span class="n">mapValues</span><span class="p">(</span><span class="k">lambda</span> <span class="n">vs</span><span class="p">:</span> <span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">vs</span><span class="p">),</span> <span class="bp">None</span><span class="p">))</span>
            <span class="k">else</span><span class="p">:</span>
                <span class="n">g</span> <span class="o">=</span> <span class="n">a</span><span class="o">.</span><span class="n">cogroup</span><span class="p">(</span><span class="n">b</span><span class="o">.</span><span class="n">partitionBy</span><span class="p">(</span><span class="n">numPartitions</span><span class="p">),</span> <span class="n">numPartitions</span><span class="p">)</span>
                <span class="n">g</span> <span class="o">=</span> <span class="n">g</span><span class="o">.</span><span class="n">mapValues</span><span class="p">(</span><span class="k">lambda</span> <span class="n">ab</span><span class="p">:</span> <span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">ab</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="nb">list</span><span class="p">(</span><span class="n">ab</span><span class="p">[</span><span class="mi">0</span><span class="p">])[</span><span class="mi">0</span><span class="p">]</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">ab</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="k">else</span> <span class="bp">None</span><span class="p">))</span>
            <span class="n">state</span> <span class="o">=</span> <span class="n">g</span><span class="o">.</span><span class="n">mapValues</span><span class="p">(</span><span class="k">lambda</span> <span class="n">vs_s</span><span class="p">:</span> <span class="n">updateFunc</span><span class="p">(</span><span class="n">vs_s</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">vs_s</span><span class="p">[</span><span class="mi">1</span><span class="p">]))</span>
            <span class="k">return</span> <span class="n">state</span><span class="o">.</span><span class="n">filter</span><span class="p">(</span><span class="k">lambda</span> <span class="n">k_v</span><span class="p">:</span> <span class="n">k_v</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="bp">None</span><span class="p">)</span>

        <span class="n">jreduceFunc</span> <span class="o">=</span> <span class="n">TransformFunction</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_sc</span><span class="p">,</span> <span class="n">reduceFunc</span><span class="p">,</span>
                                        <span class="bp">self</span><span class="o">.</span><span class="n">_sc</span><span class="o">.</span><span class="n">serializer</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_jrdd_deserializer</span><span class="p">)</span>
        <span class="n">dstream</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">PythonStateDStream</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_jdstream</span><span class="o">.</span><span class="n">dstream</span><span class="p">(),</span> <span class="n">jreduceFunc</span><span class="p">)</span>
        <span class="k">return</span> <span class="n">DStream</span><span class="p">(</span><span class="n">dstream</span><span class="o">.</span><span class="n">asJavaDStream</span><span class="p">(),</span> <span class="bp">self</span><span class="o">.</span><span class="n">_ssc</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sc</span><span class="o">.</span><span class="n">serializer</span><span class="p">)</span>

</div></div>
<span class="k">class</span> <span class="nc">TransformedDStream</span><span class="p">(</span><span class="n">DStream</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    TransformedDStream is an DStream generated by an Python function</span>
<span class="sd">    transforming each RDD of an DStream to another RDDs.</span>

<span class="sd">    Multiple continuous transformations of DStream can be combined into</span>
<span class="sd">    one transformation.</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">prev</span><span class="p">,</span> <span class="n">func</span><span class="p">):</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">_ssc</span> <span class="o">=</span> <span class="n">prev</span><span class="o">.</span><span class="n">_ssc</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">_sc</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_ssc</span><span class="o">.</span><span class="n">_sc</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">_jrdd_deserializer</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sc</span><span class="o">.</span><span class="n">serializer</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">is_cached</span> <span class="o">=</span> <span class="bp">False</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">is_checkpointed</span> <span class="o">=</span> <span class="bp">False</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">_jdstream_val</span> <span class="o">=</span> <span class="bp">None</span>

        <span class="c"># Using type() to avoid folding the functions and compacting the DStreams which is not</span>
        <span class="c"># not strictly a object of TransformedDStream.</span>
        <span class="c"># Changed here is to avoid bug in KafkaTransformedDStream when calling offsetRanges().</span>
        <span class="k">if</span> <span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">prev</span><span class="p">)</span> <span class="ow">is</span> <span class="n">TransformedDStream</span> <span class="ow">and</span>
                <span class="ow">not</span> <span class="n">prev</span><span class="o">.</span><span class="n">is_cached</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">prev</span><span class="o">.</span><span class="n">is_checkpointed</span><span class="p">):</span>
            <span class="n">prev_func</span> <span class="o">=</span> <span class="n">prev</span><span class="o">.</span><span class="n">func</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">func</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">t</span><span class="p">,</span> <span class="n">rdd</span><span class="p">:</span> <span class="n">func</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="n">prev_func</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="n">rdd</span><span class="p">))</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">prev</span> <span class="o">=</span> <span class="n">prev</span><span class="o">.</span><span class="n">prev</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">prev</span> <span class="o">=</span> <span class="n">prev</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">func</span> <span class="o">=</span> <span class="n">func</span>

    <span class="nd">@property</span>
    <span class="k">def</span> <span class="nf">_jdstream</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_jdstream_val</span> <span class="ow">is</span> <span class="ow">not</span> <span class="bp">None</span><span class="p">:</span>
            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_jdstream_val</span>

        <span class="n">jfunc</span> <span class="o">=</span> <span class="n">TransformFunction</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_sc</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">func</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">prev</span><span class="o">.</span><span class="n">_jrdd_deserializer</span><span class="p">)</span>
        <span class="n">dstream</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">PythonTransformedDStream</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">prev</span><span class="o">.</span><span class="n">_jdstream</span><span class="o">.</span><span class="n">dstream</span><span class="p">(),</span> <span class="n">jfunc</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">_jdstream_val</span> <span class="o">=</span> <span class="n">dstream</span><span class="o">.</span><span class="n">asJavaDStream</span><span class="p">()</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_jdstream_val</span>
</pre></div>

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