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<p class="shortcomment cmt">Core Spark functionality.</p><div class="fullcomment"><div class="comment cmt"><p>Core Spark functionality. <a href="SparkContext.html" class="extype" name="org.apache.spark.SparkContext">org.apache.spark.SparkContext</a> serves as the main entry point to
Spark, while <a href="rdd/RDD.html" class="extype" name="org.apache.spark.rdd.RDD">org.apache.spark.rdd.RDD</a> is the data type representing a distributed collection,
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<p class="shortcomment cmt">Spark's broadcast variables, used to broadcast immutable datasets to all nodes.</p><div class="fullcomment"><div class="comment cmt"><p>Spark's broadcast variables, used to broadcast immutable datasets to all nodes.
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GraphX is a graph processing framework built on top of Spark.</p><div class="fullcomment"><div class="comment cmt"><p><span class="badge" style="float: right;">ALPHA COMPONENT</span>
GraphX is a graph processing framework built on top of Spark.
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<p class="shortcomment cmt">DataFrame-based machine learning APIs to let users quickly assemble and configure practical
machine learning pipelines.</p><div class="fullcomment"><div class="comment cmt"><p>DataFrame-based machine learning APIs to let users quickly assemble and configure practical
machine learning pipelines.
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<p class="shortcomment cmt">RDD-based machine learning APIs (in maintenance mode).</p><div class="fullcomment"><div class="comment cmt"><p>RDD-based machine learning APIs (in maintenance mode).</p><p>The <code>spark.mllib</code> package is in maintenance mode as of the Spark 2.0.0 release to encourage
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And once we reach feature parity, this package will be deprecated.
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<p class="shortcomment cmt">Support for approximate results.</p><div class="fullcomment"><div class="comment cmt"><p>Support for approximate results. This provides convenient api and also implementation for
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<p class="shortcomment cmt">Provides several RDD implementations.</p><div class="fullcomment"><div class="comment cmt"><p>Provides several RDD implementations. See <a href="rdd/RDD.html" class="extype" name="org.apache.spark.rdd.RDD">org.apache.spark.rdd.RDD</a>.
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<p class="shortcomment cmt">Spark's scheduling components.</p><div class="fullcomment"><div class="comment cmt"><p>Spark's scheduling components. This includes the <code>org.apache.spark.scheduler.DAGScheduler</code> and
lower level <code>org.apache.spark.scheduler.TaskScheduler</code>.
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<p class="shortcomment cmt">Pluggable serializers for RDD and shuffle data.</p><div class="fullcomment"><div class="comment cmt"><p>Pluggable serializers for RDD and shuffle data.
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<h1>ShuffleDependency<span class="permalink">
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<span class="name">ShuffleDependency</span><span class="tparams">[<span name="K">K</span>, <span name="V">V</span>, <span name="C">C</span>]</span><span class="result"> extends <a href="Dependency.html" class="extype" name="org.apache.spark.Dependency">Dependency</a>[<span class="extype" name="scala.Product2">Product2</span>[<span class="extype" name="org.apache.spark.ShuffleDependency.K">K</span>, <span class="extype" name="org.apache.spark.ShuffleDependency.V">V</span>]]</span>
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<div id="comment" class="fullcommenttop"><div class="comment cmt"><p>:: DeveloperApi ::
Represents a dependency on the output of a shuffle stage. Note that in the case of shuffle,
the RDD is transient since we don't need it on the executor side.
</p></div><dl class="attributes block"> <dt>Annotations</dt><dd>
<span class="name">@DeveloperApi</span><span class="args">()</span>
</dd><dt>Source</dt><dd><a href="https://github.com/apache/spark/tree/v3.1.1/core/src/main/scala/org/apache/spark/Dependency.scala" target="_blank">Dependency.scala</a></dd></dl><div class="toggleContainer block">
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<p class="shortcomment cmt"></p><div class="fullcomment"><div class="comment cmt"></div><dl class="paramcmts block"><dt class="param">_rdd</dt><dd class="cmt"><p>the parent RDD</p></dd><dt class="param">partitioner</dt><dd class="cmt"><p>partitioner used to partition the shuffle output</p></dd><dt class="param">serializer</dt><dd class="cmt"><p><a href="serializer/Serializer.html" class="extype" name="org.apache.spark.serializer.Serializer">Serializer</a> to use. If not set
explicitly then the default serializer, as specified by <code>spark.serializer</code>
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