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<h2 title="Class PairDStreamFunctions" class="title">Class PairDStreamFunctions<K,V></h2>
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<ul class="inheritance">
<li>java.lang.Object</li>
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<li>org.apache.spark.streaming.dstream.PairDStreamFunctions<K,V></li>
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<pre>public class <span class="strong">PairDStreamFunctions<K,V></span>
extends java.lang.Object
implements scala.Serializable</pre>
<div class="block">Extra functions available on DStream of (key, value) pairs through an implicit conversion.</div>
<dl><dt><span class="strong">See Also:</span></dt><dd><a href="../../../../../serialized-form.html#org.apache.spark.streaming.dstream.PairDStreamFunctions">Serialized Form</a></dd></dl>
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<tr>
<th class="colOne" scope="col">Constructor and Description</th>
</tr>
<tr class="altColor">
<td class="colOne"><code><strong><a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html#PairDStreamFunctions(org.apache.spark.streaming.dstream.DStream, scala.reflect.ClassTag, scala.reflect.ClassTag, scala.math.Ordering)">PairDStreamFunctions</a></strong>(<a href="../../../../../org/apache/spark/streaming/dstream/DStream.html" title="class in org.apache.spark.streaming.dstream">DStream</a><scala.Tuple2<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">K</a>,<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">V</a>>> self,
scala.reflect.ClassTag<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">K</a>> kt,
scala.reflect.ClassTag<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">V</a>> vt,
scala.math.Ordering<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">K</a>> ord)</code> </td>
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<caption><span>Methods</span><span class="tabEnd"> </span></caption>
<tr>
<th class="colFirst" scope="col">Modifier and Type</th>
<th class="colLast" scope="col">Method and Description</th>
</tr>
<tr class="altColor">
<td class="colFirst"><code><W> <a href="../../../../../org/apache/spark/streaming/dstream/DStream.html" title="class in org.apache.spark.streaming.dstream">DStream</a><scala.Tuple2<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">K</a>,scala.Tuple2<scala.collection.Iterable<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">V</a>>,scala.collection.Iterable<W>>>></code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html#cogroup(org.apache.spark.streaming.dstream.DStream, scala.reflect.ClassTag)">cogroup</a></strong>(<a href="../../../../../org/apache/spark/streaming/dstream/DStream.html" title="class in org.apache.spark.streaming.dstream">DStream</a><scala.Tuple2<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">K</a>,W>> other,
scala.reflect.ClassTag<W> evidence$10)</code>
<div class="block">Return a new DStream by applying 'cogroup' between RDDs of <code>this</code> DStream and <code>other</code> DStream.</div>
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<td class="colFirst"><code><W> <a href="../../../../../org/apache/spark/streaming/dstream/DStream.html" title="class in org.apache.spark.streaming.dstream">DStream</a><scala.Tuple2<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">K</a>,scala.Tuple2<scala.collection.Iterable<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">V</a>>,scala.collection.Iterable<W>>>></code></td>
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int numPartitions,
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<div class="block">Return a new DStream by applying 'cogroup' between RDDs of <code>this</code> DStream and <code>other</code> DStream.</div>
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<td class="colFirst"><code><W> <a href="../../../../../org/apache/spark/streaming/dstream/DStream.html" title="class in org.apache.spark.streaming.dstream">DStream</a><scala.Tuple2<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">K</a>,scala.Tuple2<scala.collection.Iterable<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">V</a>>,scala.collection.Iterable<W>>>></code></td>
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<a href="../../../../../org/apache/spark/Partitioner.html" title="class in org.apache.spark">Partitioner</a> partitioner,
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<div class="block">Return a new DStream by applying 'cogroup' between RDDs of <code>this</code> DStream and <code>other</code> DStream.</div>
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<td class="colFirst"><code><C> <a href="../../../../../org/apache/spark/streaming/dstream/DStream.html" title="class in org.apache.spark.streaming.dstream">DStream</a><scala.Tuple2<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">K</a>,C>></code></td>
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scala.Function2<C,<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">V</a>,C> mergeValue,
scala.Function2<C,C,C> mergeCombiner,
<a href="../../../../../org/apache/spark/Partitioner.html" title="class in org.apache.spark">Partitioner</a> partitioner,
boolean mapSideCombine,
scala.reflect.ClassTag<C> evidence$1)</code>
<div class="block">Combine elements of each key in DStream's RDDs using custom functions.</div>
</td>
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<tr class="altColor">
<td class="colFirst"><code><U> <a href="../../../../../org/apache/spark/streaming/dstream/DStream.html" title="class in org.apache.spark.streaming.dstream">DStream</a><scala.Tuple2<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">K</a>,U>></code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html#flatMapValues(scala.Function1, scala.reflect.ClassTag)">flatMapValues</a></strong>(scala.Function1<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">V</a>,scala.collection.TraversableOnce<U>> flatMapValuesFunc,
scala.reflect.ClassTag<U> evidence$9)</code>
<div class="block">Return a new DStream by applying a flatmap function to the value of each key-value pairs in
'this' DStream without changing the key.</div>
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</tr>
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<td class="colFirst"><code><W> <a href="../../../../../org/apache/spark/streaming/dstream/DStream.html" title="class in org.apache.spark.streaming.dstream">DStream</a><scala.Tuple2<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">K</a>,scala.Tuple2<scala.Option<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">V</a>>,scala.Option<W>>>></code></td>
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<div class="block">Return a new DStream by applying 'full outer join' between RDDs of <code>this</code> DStream and
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int numPartitions,
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<div class="block">Return a new DStream by applying 'full outer join' between RDDs of <code>this</code> DStream and
<code>other</code> DStream.</div>
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<td class="colFirst"><code><W> <a href="../../../../../org/apache/spark/streaming/dstream/DStream.html" title="class in org.apache.spark.streaming.dstream">DStream</a><scala.Tuple2<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">K</a>,scala.Tuple2<scala.Option<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">V</a>>,scala.Option<W>>>></code></td>
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<code>other</code> DStream.</div>
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<td class="colFirst"><code><a href="../../../../../org/apache/spark/streaming/dstream/DStream.html" title="class in org.apache.spark.streaming.dstream">DStream</a><scala.Tuple2<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">K</a>,scala.collection.Iterable<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">V</a>>>></code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html#groupByKey()">groupByKey</a></strong>()</code>
<div class="block">Return a new DStream by applying <code>groupByKey</code> to each RDD.</div>
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<td class="colFirst"><code><a href="../../../../../org/apache/spark/streaming/dstream/DStream.html" title="class in org.apache.spark.streaming.dstream">DStream</a><scala.Tuple2<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">K</a>,scala.collection.Iterable<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">V</a>>>></code></td>
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<td class="colFirst"><code><a href="../../../../../org/apache/spark/streaming/dstream/DStream.html" title="class in org.apache.spark.streaming.dstream">DStream</a><scala.Tuple2<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">K</a>,scala.collection.Iterable<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">V</a>>>></code></td>
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<div class="block">Return a new DStream by applying <code>groupByKey</code> on each RDD.</div>
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<td class="colFirst"><code><a href="../../../../../org/apache/spark/streaming/dstream/DStream.html" title="class in org.apache.spark.streaming.dstream">DStream</a><scala.Tuple2<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">K</a>,scala.collection.Iterable<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">V</a>>>></code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html#groupByKeyAndWindow(org.apache.spark.streaming.Duration)">groupByKeyAndWindow</a></strong>(<a href="../../../../../org/apache/spark/streaming/Duration.html" title="class in org.apache.spark.streaming">Duration</a> windowDuration)</code>
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<td class="colFirst"><code><U> <a href="../../../../../org/apache/spark/streaming/dstream/DStream.html" title="class in org.apache.spark.streaming.dstream">DStream</a><scala.Tuple2<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">K</a>,U>></code></td>
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<td class="colFirst"><code><a href="../../../../../org/apache/spark/streaming/dstream/DStream.html" title="class in org.apache.spark.streaming.dstream">DStream</a><scala.Tuple2<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">K</a>,<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">V</a>>></code></td>
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<td class="colFirst"><code><a href="../../../../../org/apache/spark/streaming/dstream/DStream.html" title="class in org.apache.spark.streaming.dstream">DStream</a><scala.Tuple2<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">K</a>,<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">V</a>>></code></td>
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<div class="block">Return a new DStream by applying <code>reduceByKey</code> over a sliding window on <code>this</code> DStream.</div>
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<div class="block">Return a new DStream by applying 'right outer join' between RDDs of <code>this</code> DStream and
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<td class="colFirst"><code><W> <a href="../../../../../org/apache/spark/streaming/dstream/DStream.html" title="class in org.apache.spark.streaming.dstream">DStream</a><scala.Tuple2<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">K</a>,scala.Tuple2<scala.Option<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">V</a>>,W>>></code></td>
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<td class="colFirst"><code><W> <a href="../../../../../org/apache/spark/streaming/dstream/DStream.html" title="class in org.apache.spark.streaming.dstream">DStream</a><scala.Tuple2<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">K</a>,scala.Tuple2<scala.Option<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">V</a>>,W>>></code></td>
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<td class="colFirst"><code>void</code></td>
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java.lang.Class<?> valueClass,
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<div class="block">Save each RDD in <code>this</code> DStream as a Hadoop file.</div>
</td>
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<td class="colFirst"><code><F extends org.apache.hadoop.mapred.OutputFormat<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">K</a>,<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">V</a>>> <br>void</code></td>
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<div class="block">Return a new "state" DStream where the state for each key is updated by applying
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<div class="block">Return a new "state" DStream where the state for each key is updated by applying
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<div class="block">Return a new "state" DStream where the state for each key is updated by applying
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<div class="block">Return a new "state" DStream where the state for each key is updated by applying
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<div class="block">Return a new "state" DStream where the state for each key is updated by applying
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<pre>public PairDStreamFunctions(<a href="../../../../../org/apache/spark/streaming/dstream/DStream.html" title="class in org.apache.spark.streaming.dstream">DStream</a><scala.Tuple2<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">K</a>,<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">V</a>>> self,
scala.reflect.ClassTag<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">K</a>> kt,
scala.reflect.ClassTag<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">V</a>> vt,
scala.math.Ordering<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">K</a>> ord)</pre>
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<pre>public <a href="../../../../../org/apache/spark/streaming/dstream/DStream.html" title="class in org.apache.spark.streaming.dstream">DStream</a><scala.Tuple2<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">K</a>,scala.collection.Iterable<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">V</a>>>> groupByKey()</pre>
<div class="block">Return a new DStream by applying <code>groupByKey</code> to each RDD. Hash partitioning is used to
generate the RDDs with Spark's default number of partitions.</div>
<dl><dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
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<pre>public <a href="../../../../../org/apache/spark/streaming/dstream/DStream.html" title="class in org.apache.spark.streaming.dstream">DStream</a><scala.Tuple2<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">K</a>,scala.collection.Iterable<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">V</a>>>> groupByKey(int numPartitions)</pre>
<div class="block">Return a new DStream by applying <code>groupByKey</code> to each RDD. Hash partitioning is used to
generate the RDDs with <code>numPartitions</code> partitions.</div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>numPartitions</code> - (undocumented)</dd>
<dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
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<pre>public <a href="../../../../../org/apache/spark/streaming/dstream/DStream.html" title="class in org.apache.spark.streaming.dstream">DStream</a><scala.Tuple2<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">K</a>,scala.collection.Iterable<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">V</a>>>> groupByKey(<a href="../../../../../org/apache/spark/Partitioner.html" title="class in org.apache.spark">Partitioner</a> partitioner)</pre>
<div class="block">Return a new DStream by applying <code>groupByKey</code> on each RDD. The supplied
org.apache.spark.Partitioner is used to control the partitioning of each RDD.</div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>partitioner</code> - (undocumented)</dd>
<dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
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<pre>public <a href="../../../../../org/apache/spark/streaming/dstream/DStream.html" title="class in org.apache.spark.streaming.dstream">DStream</a><scala.Tuple2<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">K</a>,<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">V</a>>> reduceByKey(scala.Function2<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">V</a>,<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">V</a>,<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">V</a>> reduceFunc)</pre>
<div class="block">Return a new DStream by applying <code>reduceByKey</code> to each RDD. The values for each key are
merged using the associative reduce function. Hash partitioning is used to generate the RDDs
with Spark's default number of partitions.</div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>reduceFunc</code> - (undocumented)</dd>
<dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
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<pre>public <a href="../../../../../org/apache/spark/streaming/dstream/DStream.html" title="class in org.apache.spark.streaming.dstream">DStream</a><scala.Tuple2<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">K</a>,<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">V</a>>> reduceByKey(scala.Function2<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">V</a>,<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">V</a>,<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">V</a>> reduceFunc,
int numPartitions)</pre>
<div class="block">Return a new DStream by applying <code>reduceByKey</code> to each RDD. The values for each key are
merged using the supplied reduce function. Hash partitioning is used to generate the RDDs
with <code>numPartitions</code> partitions.</div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>reduceFunc</code> - (undocumented)</dd><dd><code>numPartitions</code> - (undocumented)</dd>
<dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
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<pre>public <a href="../../../../../org/apache/spark/streaming/dstream/DStream.html" title="class in org.apache.spark.streaming.dstream">DStream</a><scala.Tuple2<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">K</a>,<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">V</a>>> reduceByKey(scala.Function2<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">V</a>,<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">V</a>,<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">V</a>> reduceFunc,
<a href="../../../../../org/apache/spark/Partitioner.html" title="class in org.apache.spark">Partitioner</a> partitioner)</pre>
<div class="block">Return a new DStream by applying <code>reduceByKey</code> to each RDD. The values for each key are
merged using the supplied reduce function. org.apache.spark.Partitioner is used to control
the partitioning of each RDD.</div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>reduceFunc</code> - (undocumented)</dd><dd><code>partitioner</code> - (undocumented)</dd>
<dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
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<pre>public <C> <a href="../../../../../org/apache/spark/streaming/dstream/DStream.html" title="class in org.apache.spark.streaming.dstream">DStream</a><scala.Tuple2<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">K</a>,C>> combineByKey(scala.Function1<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">V</a>,C> createCombiner,
scala.Function2<C,<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">V</a>,C> mergeValue,
scala.Function2<C,C,C> mergeCombiner,
<a href="../../../../../org/apache/spark/Partitioner.html" title="class in org.apache.spark">Partitioner</a> partitioner,
boolean mapSideCombine,
scala.reflect.ClassTag<C> evidence$1)</pre>
<div class="block">Combine elements of each key in DStream's RDDs using custom functions. This is similar to the
combineByKey for RDDs. Please refer to combineByKey in
org.apache.spark.rdd.PairRDDFunctions in the Spark core documentation for more information.</div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>createCombiner</code> - (undocumented)</dd><dd><code>mergeValue</code> - (undocumented)</dd><dd><code>mergeCombiner</code> - (undocumented)</dd><dd><code>partitioner</code> - (undocumented)</dd><dd><code>mapSideCombine</code> - (undocumented)</dd><dd><code>evidence$1</code> - (undocumented)</dd>
<dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
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<pre>public <a href="../../../../../org/apache/spark/streaming/dstream/DStream.html" title="class in org.apache.spark.streaming.dstream">DStream</a><scala.Tuple2<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">K</a>,scala.collection.Iterable<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">V</a>>>> groupByKeyAndWindow(<a href="../../../../../org/apache/spark/streaming/Duration.html" title="class in org.apache.spark.streaming">Duration</a> windowDuration)</pre>
<div class="block">Return a new DStream by applying <code>groupByKey</code> over a sliding window. This is similar to
<code>DStream.groupByKey()</code> but applies it over a sliding window. The new DStream generates RDDs
with the same interval as this DStream. Hash partitioning is used to generate the RDDs with
Spark's default number of partitions.</div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>windowDuration</code> - width of the window; must be a multiple of this DStream's
batching interval</dd>
<dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
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<pre>public <a href="../../../../../org/apache/spark/streaming/dstream/DStream.html" title="class in org.apache.spark.streaming.dstream">DStream</a><scala.Tuple2<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">K</a>,scala.collection.Iterable<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">V</a>>>> groupByKeyAndWindow(<a href="../../../../../org/apache/spark/streaming/Duration.html" title="class in org.apache.spark.streaming">Duration</a> windowDuration,
<a href="../../../../../org/apache/spark/streaming/Duration.html" title="class in org.apache.spark.streaming">Duration</a> slideDuration)</pre>
<div class="block">Return a new DStream by applying <code>groupByKey</code> over a sliding window. Similar to
<code>DStream.groupByKey()</code>, but applies it over a sliding window. Hash partitioning is used to
generate the RDDs with Spark's default number of partitions.</div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>windowDuration</code> - width of the window; must be a multiple of this DStream's
batching interval</dd><dd><code>slideDuration</code> - sliding interval of the window (i.e., the interval after which
the new DStream will generate RDDs); must be a multiple of this
DStream's batching interval</dd>
<dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
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<pre>public <a href="../../../../../org/apache/spark/streaming/dstream/DStream.html" title="class in org.apache.spark.streaming.dstream">DStream</a><scala.Tuple2<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">K</a>,scala.collection.Iterable<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">V</a>>>> groupByKeyAndWindow(<a href="../../../../../org/apache/spark/streaming/Duration.html" title="class in org.apache.spark.streaming">Duration</a> windowDuration,
<a href="../../../../../org/apache/spark/streaming/Duration.html" title="class in org.apache.spark.streaming">Duration</a> slideDuration,
int numPartitions)</pre>
<div class="block">Return a new DStream by applying <code>groupByKey</code> over a sliding window on <code>this</code> DStream.
Similar to <code>DStream.groupByKey()</code>, but applies it over a sliding window.
Hash partitioning is used to generate the RDDs with <code>numPartitions</code> partitions.</div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>windowDuration</code> - width of the window; must be a multiple of this DStream's
batching interval</dd><dd><code>slideDuration</code> - sliding interval of the window (i.e., the interval after which
the new DStream will generate RDDs); must be a multiple of this
DStream's batching interval</dd><dd><code>numPartitions</code> - number of partitions of each RDD in the new DStream; if not specified
then Spark's default number of partitions will be used</dd>
<dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
</li>
</ul>
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<pre>public <a href="../../../../../org/apache/spark/streaming/dstream/DStream.html" title="class in org.apache.spark.streaming.dstream">DStream</a><scala.Tuple2<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">K</a>,scala.collection.Iterable<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">V</a>>>> groupByKeyAndWindow(<a href="../../../../../org/apache/spark/streaming/Duration.html" title="class in org.apache.spark.streaming">Duration</a> windowDuration,
<a href="../../../../../org/apache/spark/streaming/Duration.html" title="class in org.apache.spark.streaming">Duration</a> slideDuration,
<a href="../../../../../org/apache/spark/Partitioner.html" title="class in org.apache.spark">Partitioner</a> partitioner)</pre>
<div class="block">Create a new DStream by applying <code>groupByKey</code> over a sliding window on <code>this</code> DStream.
Similar to <code>DStream.groupByKey()</code>, but applies it over a sliding window.</div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>windowDuration</code> - width of the window; must be a multiple of this DStream's
batching interval</dd><dd><code>slideDuration</code> - sliding interval of the window (i.e., the interval after which
the new DStream will generate RDDs); must be a multiple of this
DStream's batching interval</dd><dd><code>partitioner</code> - partitioner for controlling the partitioning of each RDD in the new
DStream.</dd>
<dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
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<pre>public <a href="../../../../../org/apache/spark/streaming/dstream/DStream.html" title="class in org.apache.spark.streaming.dstream">DStream</a><scala.Tuple2<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">K</a>,<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">V</a>>> reduceByKeyAndWindow(scala.Function2<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">V</a>,<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">V</a>,<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">V</a>> reduceFunc,
<a href="../../../../../org/apache/spark/streaming/Duration.html" title="class in org.apache.spark.streaming">Duration</a> windowDuration)</pre>
<div class="block">Return a new DStream by applying <code>reduceByKey</code> over a sliding window on <code>this</code> DStream.
Similar to <code>DStream.reduceByKey()</code>, but applies it over a sliding window. The new DStream
generates RDDs with the same interval as this DStream. Hash partitioning is used to generate
the RDDs with Spark's default number of partitions.</div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>reduceFunc</code> - associative reduce function</dd><dd><code>windowDuration</code> - width of the window; must be a multiple of this DStream's
batching interval</dd>
<dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
</li>
</ul>
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<pre>public <a href="../../../../../org/apache/spark/streaming/dstream/DStream.html" title="class in org.apache.spark.streaming.dstream">DStream</a><scala.Tuple2<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">K</a>,<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">V</a>>> reduceByKeyAndWindow(scala.Function2<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">V</a>,<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">V</a>,<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">V</a>> reduceFunc,
<a href="../../../../../org/apache/spark/streaming/Duration.html" title="class in org.apache.spark.streaming">Duration</a> windowDuration,
<a href="../../../../../org/apache/spark/streaming/Duration.html" title="class in org.apache.spark.streaming">Duration</a> slideDuration)</pre>
<div class="block">Return a new DStream by applying <code>reduceByKey</code> over a sliding window. This is similar to
<code>DStream.reduceByKey()</code> but applies it over a sliding window. Hash partitioning is used to
generate the RDDs with Spark's default number of partitions.</div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>reduceFunc</code> - associative reduce function</dd><dd><code>windowDuration</code> - width of the window; must be a multiple of this DStream's
batching interval</dd><dd><code>slideDuration</code> - sliding interval of the window (i.e., the interval after which
the new DStream will generate RDDs); must be a multiple of this
DStream's batching interval</dd>
<dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
</li>
</ul>
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<pre>public <a href="../../../../../org/apache/spark/streaming/dstream/DStream.html" title="class in org.apache.spark.streaming.dstream">DStream</a><scala.Tuple2<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">K</a>,<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">V</a>>> reduceByKeyAndWindow(scala.Function2<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">V</a>,<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">V</a>,<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">V</a>> reduceFunc,
<a href="../../../../../org/apache/spark/streaming/Duration.html" title="class in org.apache.spark.streaming">Duration</a> windowDuration,
<a href="../../../../../org/apache/spark/streaming/Duration.html" title="class in org.apache.spark.streaming">Duration</a> slideDuration,
int numPartitions)</pre>
<div class="block">Return a new DStream by applying <code>reduceByKey</code> over a sliding window. This is similar to
<code>DStream.reduceByKey()</code> but applies it over a sliding window. Hash partitioning is used to
generate the RDDs with <code>numPartitions</code> partitions.</div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>reduceFunc</code> - associative reduce function</dd><dd><code>windowDuration</code> - width of the window; must be a multiple of this DStream's
batching interval</dd><dd><code>slideDuration</code> - sliding interval of the window (i.e., the interval after which
the new DStream will generate RDDs); must be a multiple of this
DStream's batching interval</dd><dd><code>numPartitions</code> - number of partitions of each RDD in the new DStream.</dd>
<dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
</li>
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<pre>public <a href="../../../../../org/apache/spark/streaming/dstream/DStream.html" title="class in org.apache.spark.streaming.dstream">DStream</a><scala.Tuple2<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">K</a>,<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">V</a>>> reduceByKeyAndWindow(scala.Function2<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">V</a>,<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">V</a>,<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">V</a>> reduceFunc,
<a href="../../../../../org/apache/spark/streaming/Duration.html" title="class in org.apache.spark.streaming">Duration</a> windowDuration,
<a href="../../../../../org/apache/spark/streaming/Duration.html" title="class in org.apache.spark.streaming">Duration</a> slideDuration,
<a href="../../../../../org/apache/spark/Partitioner.html" title="class in org.apache.spark">Partitioner</a> partitioner)</pre>
<div class="block">Return a new DStream by applying <code>reduceByKey</code> over a sliding window. Similar to
<code>DStream.reduceByKey()</code>, but applies it over a sliding window.</div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>reduceFunc</code> - associative reduce function</dd><dd><code>windowDuration</code> - width of the window; must be a multiple of this DStream's
batching interval</dd><dd><code>slideDuration</code> - sliding interval of the window (i.e., the interval after which
the new DStream will generate RDDs); must be a multiple of this
DStream's batching interval</dd><dd><code>partitioner</code> - partitioner for controlling the partitioning of each RDD
in the new DStream.</dd>
<dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
</li>
</ul>
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<pre>public <a href="../../../../../org/apache/spark/streaming/dstream/DStream.html" title="class in org.apache.spark.streaming.dstream">DStream</a><scala.Tuple2<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">K</a>,<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">V</a>>> reduceByKeyAndWindow(scala.Function2<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">V</a>,<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">V</a>,<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">V</a>> reduceFunc,
scala.Function2<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">V</a>,<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">V</a>,<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">V</a>> invReduceFunc,
<a href="../../../../../org/apache/spark/streaming/Duration.html" title="class in org.apache.spark.streaming">Duration</a> windowDuration,
<a href="../../../../../org/apache/spark/streaming/Duration.html" title="class in org.apache.spark.streaming">Duration</a> slideDuration,
int numPartitions,
scala.Function1<scala.Tuple2<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">K</a>,<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">V</a>>,java.lang.Object> filterFunc)</pre>
<div class="block">Return a new DStream by applying incremental <code>reduceByKey</code> over a sliding window.
The reduced value of over a new window is calculated using the old window's reduced value :
1. reduce the new values that entered the window (e.g., adding new counts)
<p>
2. "inverse reduce" the old values that left the window (e.g., subtracting old counts)
<p>
This is more efficient than reduceByKeyAndWindow without "inverse reduce" function.
However, it is applicable to only "invertible reduce functions".
Hash partitioning is used to generate the RDDs with Spark's default number of partitions.</div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>reduceFunc</code> - associative reduce function</dd><dd><code>invReduceFunc</code> - inverse reduce function</dd><dd><code>windowDuration</code> - width of the window; must be a multiple of this DStream's
batching interval</dd><dd><code>slideDuration</code> - sliding interval of the window (i.e., the interval after which
the new DStream will generate RDDs); must be a multiple of this
DStream's batching interval</dd><dd><code>filterFunc</code> - Optional function to filter expired key-value pairs;
only pairs that satisfy the function are retained</dd><dd><code>numPartitions</code> - (undocumented)</dd>
<dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
</li>
</ul>
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<pre>public <a href="../../../../../org/apache/spark/streaming/dstream/DStream.html" title="class in org.apache.spark.streaming.dstream">DStream</a><scala.Tuple2<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">K</a>,<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">V</a>>> reduceByKeyAndWindow(scala.Function2<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">V</a>,<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">V</a>,<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">V</a>> reduceFunc,
scala.Function2<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">V</a>,<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">V</a>,<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">V</a>> invReduceFunc,
<a href="../../../../../org/apache/spark/streaming/Duration.html" title="class in org.apache.spark.streaming">Duration</a> windowDuration,
<a href="../../../../../org/apache/spark/streaming/Duration.html" title="class in org.apache.spark.streaming">Duration</a> slideDuration,
<a href="../../../../../org/apache/spark/Partitioner.html" title="class in org.apache.spark">Partitioner</a> partitioner,
scala.Function1<scala.Tuple2<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">K</a>,<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">V</a>>,java.lang.Object> filterFunc)</pre>
<div class="block">Return a new DStream by applying incremental <code>reduceByKey</code> over a sliding window.
The reduced value of over a new window is calculated using the old window's reduced value :
1. reduce the new values that entered the window (e.g., adding new counts)
2. "inverse reduce" the old values that left the window (e.g., subtracting old counts)
This is more efficient than reduceByKeyAndWindow without "inverse reduce" function.
However, it is applicable to only "invertible reduce functions".</div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>reduceFunc</code> - associative reduce function</dd><dd><code>invReduceFunc</code> - inverse reduce function</dd><dd><code>windowDuration</code> - width of the window; must be a multiple of this DStream's
batching interval</dd><dd><code>slideDuration</code> - sliding interval of the window (i.e., the interval after which
the new DStream will generate RDDs); must be a multiple of this
DStream's batching interval</dd><dd><code>partitioner</code> - partitioner for controlling the partitioning of each RDD in the new
DStream.</dd><dd><code>filterFunc</code> - Optional function to filter expired key-value pairs;
only pairs that satisfy the function are retained</dd>
<dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
</li>
</ul>
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<pre>public <S> <a href="../../../../../org/apache/spark/streaming/dstream/DStream.html" title="class in org.apache.spark.streaming.dstream">DStream</a><scala.Tuple2<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">K</a>,S>> updateStateByKey(scala.Function2<scala.collection.Seq<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">V</a>>,scala.Option<S>,scala.Option<S>> updateFunc,
scala.reflect.ClassTag<S> evidence$2)</pre>
<div class="block">Return a new "state" DStream where the state for each key is updated by applying
the given function on the previous state of the key and the new values of each key.
Hash partitioning is used to generate the RDDs with Spark's default number of partitions.</div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>updateFunc</code> - State update function. If <code>this</code> function returns None, then
corresponding state key-value pair will be eliminated.</dd><dd><code>evidence$2</code> - (undocumented)</dd>
<dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
</li>
</ul>
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<pre>public <S> <a href="../../../../../org/apache/spark/streaming/dstream/DStream.html" title="class in org.apache.spark.streaming.dstream">DStream</a><scala.Tuple2<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">K</a>,S>> updateStateByKey(scala.Function2<scala.collection.Seq<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">V</a>>,scala.Option<S>,scala.Option<S>> updateFunc,
int numPartitions,
scala.reflect.ClassTag<S> evidence$3)</pre>
<div class="block">Return a new "state" DStream where the state for each key is updated by applying
the given function on the previous state of the key and the new values of each key.
Hash partitioning is used to generate the RDDs with <code>numPartitions</code> partitions.</div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>updateFunc</code> - State update function. If <code>this</code> function returns None, then
corresponding state key-value pair will be eliminated.</dd><dd><code>numPartitions</code> - Number of partitions of each RDD in the new DStream.</dd><dd><code>evidence$3</code> - (undocumented)</dd>
<dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
</li>
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<pre>public <S> <a href="../../../../../org/apache/spark/streaming/dstream/DStream.html" title="class in org.apache.spark.streaming.dstream">DStream</a><scala.Tuple2<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">K</a>,S>> updateStateByKey(scala.Function2<scala.collection.Seq<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">V</a>>,scala.Option<S>,scala.Option<S>> updateFunc,
<a href="../../../../../org/apache/spark/Partitioner.html" title="class in org.apache.spark">Partitioner</a> partitioner,
scala.reflect.ClassTag<S> evidence$4)</pre>
<div class="block">Return a new "state" DStream where the state for each key is updated by applying
the given function on the previous state of the key and the new values of the key.
org.apache.spark.Partitioner is used to control the partitioning of each RDD.</div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>updateFunc</code> - State update function. If <code>this</code> function returns None, then
corresponding state key-value pair will be eliminated.</dd><dd><code>partitioner</code> - Partitioner for controlling the partitioning of each RDD in the new
DStream.</dd><dd><code>evidence$4</code> - (undocumented)</dd>
<dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
</li>
</ul>
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<pre>public <S> <a href="../../../../../org/apache/spark/streaming/dstream/DStream.html" title="class in org.apache.spark.streaming.dstream">DStream</a><scala.Tuple2<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">K</a>,S>> updateStateByKey(scala.Function1<scala.collection.Iterator<scala.Tuple3<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">K</a>,scala.collection.Seq<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">V</a>>,scala.Option<S>>>,scala.collection.Iterator<scala.Tuple2<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">K</a>,S>>> updateFunc,
<a href="../../../../../org/apache/spark/Partitioner.html" title="class in org.apache.spark">Partitioner</a> partitioner,
boolean rememberPartitioner,
scala.reflect.ClassTag<S> evidence$5)</pre>
<div class="block">Return a new "state" DStream where the state for each key is updated by applying
the given function on the previous state of the key and the new values of each key.
org.apache.spark.Partitioner is used to control the partitioning of each RDD.</div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>updateFunc</code> - State update function. Note, that this function may generate a different
tuple with a different key than the input key. Therefore keys may be removed
or added in this way. It is up to the developer to decide whether to
remember the partitioner despite the key being changed.</dd><dd><code>partitioner</code> - Partitioner for controlling the partitioning of each RDD in the new
DStream</dd><dd><code>rememberPartitioner</code> - Whether to remember the paritioner object in the generated RDDs.</dd><dd><code>evidence$5</code> - (undocumented)</dd>
<dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
</li>
</ul>
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<pre>public <S> <a href="../../../../../org/apache/spark/streaming/dstream/DStream.html" title="class in org.apache.spark.streaming.dstream">DStream</a><scala.Tuple2<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">K</a>,S>> updateStateByKey(scala.Function2<scala.collection.Seq<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">V</a>>,scala.Option<S>,scala.Option<S>> updateFunc,
<a href="../../../../../org/apache/spark/Partitioner.html" title="class in org.apache.spark">Partitioner</a> partitioner,
<a href="../../../../../org/apache/spark/rdd/RDD.html" title="class in org.apache.spark.rdd">RDD</a><scala.Tuple2<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">K</a>,S>> initialRDD,
scala.reflect.ClassTag<S> evidence$6)</pre>
<div class="block">Return a new "state" DStream where the state for each key is updated by applying
the given function on the previous state of the key and the new values of the key.
org.apache.spark.Partitioner is used to control the partitioning of each RDD.</div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>updateFunc</code> - State update function. If <code>this</code> function returns None, then
corresponding state key-value pair will be eliminated.</dd><dd><code>partitioner</code> - Partitioner for controlling the partitioning of each RDD in the new
DStream.</dd><dd><code>initialRDD</code> - initial state value of each key.</dd><dd><code>evidence$6</code> - (undocumented)</dd>
<dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
</li>
</ul>
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<a href="../../../../../org/apache/spark/Partitioner.html" title="class in org.apache.spark">Partitioner</a> partitioner,
boolean rememberPartitioner,
<a href="../../../../../org/apache/spark/rdd/RDD.html" title="class in org.apache.spark.rdd">RDD</a><scala.Tuple2<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">K</a>,S>> initialRDD,
scala.reflect.ClassTag<S> evidence$7)</pre>
<div class="block">Return a new "state" DStream where the state for each key is updated by applying
the given function on the previous state of the key and the new values of each key.
org.apache.spark.Partitioner is used to control the partitioning of each RDD.</div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>updateFunc</code> - State update function. Note, that this function may generate a different
tuple with a different key than the input key. Therefore keys may be removed
or added in this way. It is up to the developer to decide whether to
remember the partitioner despite the key being changed.</dd><dd><code>partitioner</code> - Partitioner for controlling the partitioning of each RDD in the new
DStream</dd><dd><code>rememberPartitioner</code> - Whether to remember the paritioner object in the generated RDDs.</dd><dd><code>initialRDD</code> - initial state value of each key.</dd><dd><code>evidence$7</code> - (undocumented)</dd>
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<pre>public <U> <a href="../../../../../org/apache/spark/streaming/dstream/DStream.html" title="class in org.apache.spark.streaming.dstream">DStream</a><scala.Tuple2<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">K</a>,U>> mapValues(scala.Function1<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">V</a>,U> mapValuesFunc,
scala.reflect.ClassTag<U> evidence$8)</pre>
<div class="block">Return a new DStream by applying a map function to the value of each key-value pairs in
'this' DStream without changing the key.</div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>mapValuesFunc</code> - (undocumented)</dd><dd><code>evidence$8</code> - (undocumented)</dd>
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scala.reflect.ClassTag<U> evidence$9)</pre>
<div class="block">Return a new DStream by applying a flatmap function to the value of each key-value pairs in
'this' DStream without changing the key.</div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>flatMapValuesFunc</code> - (undocumented)</dd><dd><code>evidence$9</code> - (undocumented)</dd>
<dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
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<div class="block">Return a new DStream by applying 'cogroup' between RDDs of <code>this</code> DStream and <code>other</code> DStream.
Hash partitioning is used to generate the RDDs with Spark's default number
of partitions.</div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>other</code> - (undocumented)</dd><dd><code>evidence$10</code> - (undocumented)</dd>
<dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
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<div class="block">Return a new DStream by applying 'cogroup' between RDDs of <code>this</code> DStream and <code>other</code> DStream.
Hash partitioning is used to generate the RDDs with <code>numPartitions</code> partitions.</div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>other</code> - (undocumented)</dd><dd><code>numPartitions</code> - (undocumented)</dd><dd><code>evidence$11</code> - (undocumented)</dd>
<dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
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<a href="../../../../../org/apache/spark/Partitioner.html" title="class in org.apache.spark">Partitioner</a> partitioner,
scala.reflect.ClassTag<W> evidence$12)</pre>
<div class="block">Return a new DStream by applying 'cogroup' between RDDs of <code>this</code> DStream and <code>other</code> DStream.
The supplied org.apache.spark.Partitioner is used to partition the generated RDDs.</div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>other</code> - (undocumented)</dd><dd><code>partitioner</code> - (undocumented)</dd><dd><code>evidence$12</code> - (undocumented)</dd>
<dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
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scala.reflect.ClassTag<W> evidence$13)</pre>
<div class="block">Return a new DStream by applying 'join' between RDDs of <code>this</code> DStream and <code>other</code> DStream.
Hash partitioning is used to generate the RDDs with Spark's default number of partitions.</div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>other</code> - (undocumented)</dd><dd><code>evidence$13</code> - (undocumented)</dd>
<dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
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int numPartitions,
scala.reflect.ClassTag<W> evidence$14)</pre>
<div class="block">Return a new DStream by applying 'join' between RDDs of <code>this</code> DStream and <code>other</code> DStream.
Hash partitioning is used to generate the RDDs with <code>numPartitions</code> partitions.</div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>other</code> - (undocumented)</dd><dd><code>numPartitions</code> - (undocumented)</dd><dd><code>evidence$14</code> - (undocumented)</dd>
<dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
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<a href="../../../../../org/apache/spark/Partitioner.html" title="class in org.apache.spark">Partitioner</a> partitioner,
scala.reflect.ClassTag<W> evidence$15)</pre>
<div class="block">Return a new DStream by applying 'join' between RDDs of <code>this</code> DStream and <code>other</code> DStream.
The supplied org.apache.spark.Partitioner is used to control the partitioning of each RDD.</div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>other</code> - (undocumented)</dd><dd><code>partitioner</code> - (undocumented)</dd><dd><code>evidence$15</code> - (undocumented)</dd>
<dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
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scala.reflect.ClassTag<W> evidence$16)</pre>
<div class="block">Return a new DStream by applying 'left outer join' between RDDs of <code>this</code> DStream and
<code>other</code> DStream. Hash partitioning is used to generate the RDDs with Spark's default
number of partitions.</div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>other</code> - (undocumented)</dd><dd><code>evidence$16</code> - (undocumented)</dd>
<dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
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int numPartitions,
scala.reflect.ClassTag<W> evidence$17)</pre>
<div class="block">Return a new DStream by applying 'left outer join' between RDDs of <code>this</code> DStream and
<code>other</code> DStream. Hash partitioning is used to generate the RDDs with <code>numPartitions</code>
partitions.</div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>other</code> - (undocumented)</dd><dd><code>numPartitions</code> - (undocumented)</dd><dd><code>evidence$17</code> - (undocumented)</dd>
<dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
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<a href="../../../../../org/apache/spark/Partitioner.html" title="class in org.apache.spark">Partitioner</a> partitioner,
scala.reflect.ClassTag<W> evidence$18)</pre>
<div class="block">Return a new DStream by applying 'left outer join' between RDDs of <code>this</code> DStream and
<code>other</code> DStream. The supplied org.apache.spark.Partitioner is used to control
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<dl><dt><span class="strong">Parameters:</span></dt><dd><code>other</code> - (undocumented)</dd><dd><code>partitioner</code> - (undocumented)</dd><dd><code>evidence$18</code> - (undocumented)</dd>
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scala.reflect.ClassTag<W> evidence$19)</pre>
<div class="block">Return a new DStream by applying 'right outer join' between RDDs of <code>this</code> DStream and
<code>other</code> DStream. Hash partitioning is used to generate the RDDs with Spark's default
number of partitions.</div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>other</code> - (undocumented)</dd><dd><code>evidence$19</code> - (undocumented)</dd>
<dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
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int numPartitions,
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<div class="block">Return a new DStream by applying 'right outer join' between RDDs of <code>this</code> DStream and
<code>other</code> DStream. Hash partitioning is used to generate the RDDs with <code>numPartitions</code>
partitions.</div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>other</code> - (undocumented)</dd><dd><code>numPartitions</code> - (undocumented)</dd><dd><code>evidence$20</code> - (undocumented)</dd>
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<div class="block">Return a new DStream by applying 'right outer join' between RDDs of <code>this</code> DStream and
<code>other</code> DStream. The supplied org.apache.spark.Partitioner is used to control
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<dl><dt><span class="strong">Parameters:</span></dt><dd><code>other</code> - (undocumented)</dd><dd><code>partitioner</code> - (undocumented)</dd><dd><code>evidence$21</code> - (undocumented)</dd>
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<div class="block">Return a new DStream by applying 'full outer join' between RDDs of <code>this</code> DStream and
<code>other</code> DStream. Hash partitioning is used to generate the RDDs with Spark's default
number of partitions.</div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>other</code> - (undocumented)</dd><dd><code>evidence$22</code> - (undocumented)</dd>
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<pre>public <W> <a href="../../../../../org/apache/spark/streaming/dstream/DStream.html" title="class in org.apache.spark.streaming.dstream">DStream</a><scala.Tuple2<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">K</a>,scala.Tuple2<scala.Option<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">V</a>>,scala.Option<W>>>> fullOuterJoin(<a href="../../../../../org/apache/spark/streaming/dstream/DStream.html" title="class in org.apache.spark.streaming.dstream">DStream</a><scala.Tuple2<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">K</a>,W>> other,
int numPartitions,
scala.reflect.ClassTag<W> evidence$23)</pre>
<div class="block">Return a new DStream by applying 'full outer join' between RDDs of <code>this</code> DStream and
<code>other</code> DStream. Hash partitioning is used to generate the RDDs with <code>numPartitions</code>
partitions.</div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>other</code> - (undocumented)</dd><dd><code>numPartitions</code> - (undocumented)</dd><dd><code>evidence$23</code> - (undocumented)</dd>
<dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
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<h4>fullOuterJoin</h4>
<pre>public <W> <a href="../../../../../org/apache/spark/streaming/dstream/DStream.html" title="class in org.apache.spark.streaming.dstream">DStream</a><scala.Tuple2<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">K</a>,scala.Tuple2<scala.Option<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">V</a>>,scala.Option<W>>>> fullOuterJoin(<a href="../../../../../org/apache/spark/streaming/dstream/DStream.html" title="class in org.apache.spark.streaming.dstream">DStream</a><scala.Tuple2<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">K</a>,W>> other,
<a href="../../../../../org/apache/spark/Partitioner.html" title="class in org.apache.spark">Partitioner</a> partitioner,
scala.reflect.ClassTag<W> evidence$24)</pre>
<div class="block">Return a new DStream by applying 'full outer join' between RDDs of <code>this</code> DStream and
<code>other</code> DStream. The supplied org.apache.spark.Partitioner is used to control
the partitioning of each RDD.</div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>other</code> - (undocumented)</dd><dd><code>partitioner</code> - (undocumented)</dd><dd><code>evidence$24</code> - (undocumented)</dd>
<dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
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<h4>saveAsHadoopFiles</h4>
<pre>public <F extends org.apache.hadoop.mapred.OutputFormat<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">K</a>,<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">V</a>>> void saveAsHadoopFiles(java.lang.String prefix,
java.lang.String suffix,
scala.reflect.ClassTag<F> fm)</pre>
<div class="block">Save each RDD in <code>this</code> DStream as a Hadoop file. The file name at each batch interval
is generated based on <code>prefix</code> and <code>suffix</code>: "prefix-TIME_IN_MS.suffix"</div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>prefix</code> - (undocumented)</dd><dd><code>suffix</code> - (undocumented)</dd><dd><code>fm</code> - (undocumented)</dd></dl>
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<pre>public void saveAsHadoopFiles(java.lang.String prefix,
java.lang.String suffix,
java.lang.Class<?> keyClass,
java.lang.Class<?> valueClass,
java.lang.Class<? extends org.apache.hadoop.mapred.OutputFormat<?,?>> outputFormatClass,
org.apache.hadoop.mapred.JobConf conf)</pre>
<div class="block">Save each RDD in <code>this</code> DStream as a Hadoop file. The file name at each batch interval
is generated based on <code>prefix</code> and <code>suffix</code>: "prefix-TIME_IN_MS.suffix"</div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>prefix</code> - (undocumented)</dd><dd><code>suffix</code> - (undocumented)</dd><dd><code>keyClass</code> - (undocumented)</dd><dd><code>valueClass</code> - (undocumented)</dd><dd><code>outputFormatClass</code> - (undocumented)</dd><dd><code>conf</code> - (undocumented)</dd></dl>
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<h4>saveAsNewAPIHadoopFiles</h4>
<pre>public <F extends org.apache.hadoop.mapreduce.OutputFormat<<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">K</a>,<a href="../../../../../org/apache/spark/streaming/dstream/PairDStreamFunctions.html" title="type parameter in PairDStreamFunctions">V</a>>> void saveAsNewAPIHadoopFiles(java.lang.String prefix,
java.lang.String suffix,
scala.reflect.ClassTag<F> fm)</pre>
<div class="block">Save each RDD in <code>this</code> DStream as a Hadoop file. The file name at each batch interval is
generated based on <code>prefix</code> and <code>suffix</code>: "prefix-TIME_IN_MS.suffix".</div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>prefix</code> - (undocumented)</dd><dd><code>suffix</code> - (undocumented)</dd><dd><code>fm</code> - (undocumented)</dd></dl>
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<a name="saveAsNewAPIHadoopFiles(java.lang.String, java.lang.String, java.lang.Class, java.lang.Class, java.lang.Class, org.apache.hadoop.conf.Configuration)">
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<h4>saveAsNewAPIHadoopFiles</h4>
<pre>public void saveAsNewAPIHadoopFiles(java.lang.String prefix,
java.lang.String suffix,
java.lang.Class<?> keyClass,
java.lang.Class<?> valueClass,
java.lang.Class<? extends org.apache.hadoop.mapreduce.OutputFormat<?,?>> outputFormatClass,
org.apache.hadoop.conf.Configuration conf)</pre>
<div class="block">Save each RDD in <code>this</code> DStream as a Hadoop file. The file name at each batch interval is
generated based on <code>prefix</code> and <code>suffix</code>: "prefix-TIME_IN_MS.suffix".</div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>prefix</code> - (undocumented)</dd><dd><code>suffix</code> - (undocumented)</dd><dd><code>keyClass</code> - (undocumented)</dd><dd><code>valueClass</code> - (undocumented)</dd><dd><code>outputFormatClass</code> - (undocumented)</dd><dd><code>conf</code> - (undocumented)</dd></dl>
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