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<a href="pyspark-module.html">Package pyspark</a> ::
Module serializers
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<h1 class="epydoc">Module serializers</h1><p class="nomargin-top"><span class="codelink"><a href="pyspark.serializers-pysrc.html">source code</a></span></p>
<p>PySpark supports custom serializers for transferring data; this can
improve performance.</p>
<p>By default, PySpark uses <a
href="pyspark.serializers.PickleSerializer-class.html"
class="link">PickleSerializer</a> to serialize objects using Python's
<code>cPickle</code> serializer, which can serialize nearly any Python
object. Other serializers, like <a
href="pyspark.serializers.MarshalSerializer-class.html"
class="link">MarshalSerializer</a>, support fewer datatypes but can be
faster.</p>
<p>The serializer is chosen when creating <a
href="pyspark-module.html#SparkContext"
class="link">SparkContext</a>:</p>
<pre class="py-doctest">
<span class="py-prompt">>>> </span><span class="py-keyword">from</span> pyspark.context <span class="py-keyword">import</span> SparkContext
<span class="py-prompt">>>> </span><span class="py-keyword">from</span> pyspark.serializers <span class="py-keyword">import</span> MarshalSerializer
<span class="py-prompt">>>> </span>sc = SparkContext(<span class="py-string">'local'</span>, <span class="py-string">'test'</span>, serializer=MarshalSerializer())
<span class="py-prompt">>>> </span>sc.parallelize(list(range(1000))).map(<span class="py-keyword">lambda</span> x: 2 * x).take(10)
<span class="py-output">[0, 2, 4, 6, 8, 10, 12, 14, 16, 18]</span>
<span class="py-output"></span><span class="py-prompt">>>> </span>sc.stop()</pre>
<p>By default, PySpark serialize objects in batches; the batch size can
be controlled through SparkContext's <code>batchSize</code> parameter
(the default size is 1024 objects):</p>
<pre class="py-doctest">
<span class="py-prompt">>>> </span>sc = SparkContext(<span class="py-string">'local'</span>, <span class="py-string">'test'</span>, batchSize=2)
<span class="py-prompt">>>> </span>rdd = sc.parallelize(range(16), 4).map(<span class="py-keyword">lambda</span> x: x)</pre>
<p>Behind the scenes, this creates a JavaRDD with four partitions, each
of which contains two batches of two objects:</p>
<pre class="py-doctest">
<span class="py-prompt">>>> </span>rdd.glom().collect()
<span class="py-output">[[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11], [12, 13, 14, 15]]</span>
<span class="py-output"></span><span class="py-prompt">>>> </span>rdd._jrdd.count()
<span class="py-output">8L</span>
<span class="py-output"></span><span class="py-prompt">>>> </span>sc.stop()</pre>
<p>A batch size of -1 uses an unlimited batch size, and a size of 1
disables batching:</p>
<pre class="py-doctest">
<span class="py-prompt">>>> </span>sc = SparkContext(<span class="py-string">'local'</span>, <span class="py-string">'test'</span>, batchSize=1)
<span class="py-prompt">>>> </span>rdd = sc.parallelize(range(16), 4).map(<span class="py-keyword">lambda</span> x: x)
<span class="py-prompt">>>> </span>rdd.glom().collect()
<span class="py-output">[[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11], [12, 13, 14, 15]]</span>
<span class="py-output"></span><span class="py-prompt">>>> </span>rdd._jrdd.count()
<span class="py-output">16L</span></pre>
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<span class="summary-type"> </span>
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<a href="pyspark.serializers.PickleSerializer-class.html" class="summary-name">PickleSerializer</a><br />
Serializes objects using Python's cPickle serializer:
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<span class="summary-type"> </span>
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<a href="pyspark.serializers.MarshalSerializer-class.html" class="summary-name">MarshalSerializer</a><br />
Serializes objects using Python's Marshal serializer:
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