JavaPairRDD (Spark 3.5.2 JavaDoc)

Object
- org.apache.spark.api.java.JavaPairRDD<K,V>

All Implemented Interfaces:

java.io.Serializable, JavaRDDLike<scala.Tuple2<K,V>,JavaPairRDD<K,V>>

Direct Known Subclasses:

JavaHadoopRDD, JavaNewHadoopRDD
```
public class JavaPairRDD<K,V>
extends Object
```
See Also:

Serialized Form

Constructor Summary

Constructors
Constructor and Description

JavaPairRDD(RDD<scala.Tuple2<K,V>> rdd, scala.reflect.ClassTag<K> kClassTag, scala.reflect.ClassTag<V> vClassTag)

Constructors
Constructor and Description
`JavaPairRDD(RDD<scala.Tuple2<K,V>> rdd, scala.reflect.ClassTag<K> kClassTag, scala.reflect.ClassTag<V> vClassTag)`

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`<U> JavaPairRDD<K,U>`	`aggregateByKey(U zeroValue, Function2<U,V,U> seqFunc, Function2<U,U,U> combFunc)` Aggregate the values of each key, using given combine functions and a neutral "zero value".
`<U> JavaPairRDD<K,U>`	`aggregateByKey(U zeroValue, int numPartitions, Function2<U,V,U> seqFunc, Function2<U,U,U> combFunc)` Aggregate the values of each key, using given combine functions and a neutral "zero value".
`<U> JavaPairRDD<K,U>`	`aggregateByKey(U zeroValue, Partitioner partitioner, Function2<U,V,U> seqFunc, Function2<U,U,U> combFunc)` Aggregate the values of each key, using given combine functions and a neutral "zero value".
`JavaPairRDD<K,V>`	`cache()` Persist this RDD with the default storage level (`MEMORY_ONLY`).
`scala.reflect.ClassTag<scala.Tuple2<K,V>>`	`classTag()`
`JavaPairRDD<K,V>`	`coalesce(int numPartitions)` Return a new RDD that is reduced into `numPartitions` partitions.
`JavaPairRDD<K,V>`	`coalesce(int numPartitions, boolean shuffle)` Return a new RDD that is reduced into `numPartitions` partitions.
`<W> JavaPairRDD<K,scala.Tuple2<Iterable<V>,Iterable<W>>>`	`cogroup(JavaPairRDD<K,W> other)` For each key k in `this` or `other`, return a resulting RDD that contains a tuple with the list of values for that key in `this` as well as `other`.
`<W> JavaPairRDD<K,scala.Tuple2<Iterable<V>,Iterable<W>>>`	`cogroup(JavaPairRDD<K,W> other, int numPartitions)` For each key k in `this` or `other`, return a resulting RDD that contains a tuple with the list of values for that key in `this` as well as `other`.
`<W> JavaPairRDD<K,scala.Tuple2<Iterable<V>,Iterable<W>>>`	`cogroup(JavaPairRDD<K,W> other, Partitioner partitioner)` For each key k in `this` or `other`, return a resulting RDD that contains a tuple with the list of values for that key in `this` as well as `other`.
`<W1,W2> JavaPairRDD<K,scala.Tuple3<Iterable<V>,Iterable<W1>,Iterable<W2>>>`	`cogroup(JavaPairRDD<K,W1> other1, JavaPairRDD<K,W2> other2)` For each key k in `this` or `other1` or `other2`, return a resulting RDD that contains a tuple with the list of values for that key in `this`, `other1` and `other2`.
`<W1,W2> JavaPairRDD<K,scala.Tuple3<Iterable<V>,Iterable<W1>,Iterable<W2>>>`	`cogroup(JavaPairRDD<K,W1> other1, JavaPairRDD<K,W2> other2, int numPartitions)` For each key k in `this` or `other1` or `other2`, return a resulting RDD that contains a tuple with the list of values for that key in `this`, `other1` and `other2`.
`<W1,W2,W3> JavaPairRDD<K,scala.Tuple4<Iterable<V>,Iterable<W1>,Iterable<W2>,Iterable<W3>>>`	`cogroup(JavaPairRDD<K,W1> other1, JavaPairRDD<K,W2> other2, JavaPairRDD<K,W3> other3)` For each key k in `this` or `other1` or `other2` or `other3`, return a resulting RDD that contains a tuple with the list of values for that key in `this`, `other1`, `other2` and `other3`.
`<W1,W2,W3> JavaPairRDD<K,scala.Tuple4<Iterable<V>,Iterable<W1>,Iterable<W2>,Iterable<W3>>>`	`cogroup(JavaPairRDD<K,W1> other1, JavaPairRDD<K,W2> other2, JavaPairRDD<K,W3> other3, int numPartitions)` For each key k in `this` or `other1` or `other2` or `other3`, return a resulting RDD that contains a tuple with the list of values for that key in `this`, `other1`, `other2` and `other3`.
`<W1,W2,W3> JavaPairRDD<K,scala.Tuple4<Iterable<V>,Iterable<W1>,Iterable<W2>,Iterable<W3>>>`	`cogroup(JavaPairRDD<K,W1> other1, JavaPairRDD<K,W2> other2, JavaPairRDD<K,W3> other3, Partitioner partitioner)` For each key k in `this` or `other1` or `other2` or `other3`, return a resulting RDD that contains a tuple with the list of values for that key in `this`, `other1`, `other2` and `other3`.
`<W1,W2> JavaPairRDD<K,scala.Tuple3<Iterable<V>,Iterable<W1>,Iterable<W2>>>`	`cogroup(JavaPairRDD<K,W1> other1, JavaPairRDD<K,W2> other2, Partitioner partitioner)` For each key k in `this` or `other1` or `other2`, return a resulting RDD that contains a tuple with the list of values for that key in `this`, `other1` and `other2`.
`java.util.Map<K,V>`	`collectAsMap()` Return the key-value pairs in this RDD to the master as a Map.
`<C> JavaPairRDD<K,C>`	`combineByKey(Function<V,C> createCombiner, Function2<C,V,C> mergeValue, Function2<C,C,C> mergeCombiners)` Simplified version of combineByKey that hash-partitions the resulting RDD using the existing partitioner/parallelism level and using map-side aggregation.
`<C> JavaPairRDD<K,C>`	`combineByKey(Function<V,C> createCombiner, Function2<C,V,C> mergeValue, Function2<C,C,C> mergeCombiners, int numPartitions)` Simplified version of combineByKey that hash-partitions the output RDD and uses map-side aggregation.
`<C> JavaPairRDD<K,C>`	`combineByKey(Function<V,C> createCombiner, Function2<C,V,C> mergeValue, Function2<C,C,C> mergeCombiners, Partitioner partitioner)` Generic function to combine the elements for each key using a custom set of aggregation functions.
`<C> JavaPairRDD<K,C>`	`combineByKey(Function<V,C> createCombiner, Function2<C,V,C> mergeValue, Function2<C,C,C> mergeCombiners, Partitioner partitioner, boolean mapSideCombine, Serializer serializer)` Generic function to combine the elements for each key using a custom set of aggregation functions.
`JavaPairRDD<K,Long>`	`countApproxDistinctByKey(double relativeSD)` Return approximate number of distinct values for each key in this RDD.
`JavaPairRDD<K,Long>`	`countApproxDistinctByKey(double relativeSD, int numPartitions)` Return approximate number of distinct values for each key in this RDD.
`JavaPairRDD<K,Long>`	`countApproxDistinctByKey(double relativeSD, Partitioner partitioner)` Return approximate number of distinct values for each key in this RDD.
`java.util.Map<K,Long>`	`countByKey()` Count the number of elements for each key, and return the result to the master as a Map.
`PartialResult<java.util.Map<K,BoundedDouble>>`	`countByKeyApprox(long timeout)` Approximate version of countByKey that can return a partial result if it does not finish within a timeout.
`PartialResult<java.util.Map<K,BoundedDouble>>`	`countByKeyApprox(long timeout, double confidence)` Approximate version of countByKey that can return a partial result if it does not finish within a timeout.
`JavaPairRDD<K,V>`	`distinct()` Return a new RDD containing the distinct elements in this RDD.
`JavaPairRDD<K,V>`	`distinct(int numPartitions)` Return a new RDD containing the distinct elements in this RDD.
`JavaPairRDD<K,V>`	`filter(Function<scala.Tuple2<K,V>,Boolean> f)` Return a new RDD containing only the elements that satisfy a predicate.
`JavaPairRDD<K,V>`	`filterByRange(java.util.Comparator<K> comp, K lower, K upper)` Return a RDD containing only the elements in the inclusive range `lower` to `upper`.
`JavaPairRDD<K,V>`	`filterByRange(K lower, K upper)` Return a RDD containing only the elements in the inclusive range `lower` to `upper`.
`scala.Tuple2<K,V>`	`first()` Return the first element in this RDD.
`<U> JavaPairRDD<K,U>`	`flatMapValues(FlatMapFunction<V,U> f)` Pass each value in the key-value pair RDD through a flatMap function without changing the keys; this also retains the original RDD's partitioning.
`JavaPairRDD<K,V>`	`foldByKey(V zeroValue, Function2<V,V,V> func)` Merge the values for each key using an associative function and a neutral "zero value" which may be added to the result an arbitrary number of times, and must not change the result (e.g., Nil for list concatenation, 0 for addition, or 1 for multiplication.).
`JavaPairRDD<K,V>`	`foldByKey(V zeroValue, int numPartitions, Function2<V,V,V> func)` Merge the values for each key using an associative function and a neutral "zero value" which may be added to the result an arbitrary number of times, and must not change the result (e.g ., Nil for list concatenation, 0 for addition, or 1 for multiplication.).
`JavaPairRDD<K,V>`	`foldByKey(V zeroValue, Partitioner partitioner, Function2<V,V,V> func)` Merge the values for each key using an associative function and a neutral "zero value" which may be added to the result an arbitrary number of times, and must not change the result (e.g ., Nil for list concatenation, 0 for addition, or 1 for multiplication.).
`static <K,V> JavaPairRDD<K,V>`	`fromJavaRDD(JavaRDD<scala.Tuple2<K,V>> rdd)` Convert a JavaRDD of key-value pairs to JavaPairRDD.
`static <K,V> JavaPairRDD<K,V>`	`fromRDD(RDD<scala.Tuple2<K,V>> rdd, scala.reflect.ClassTag<K> evidence$5, scala.reflect.ClassTag<V> evidence$6)`
`<W> JavaPairRDD<K,scala.Tuple2<Optional<V>,Optional<W>>>`	`fullOuterJoin(JavaPairRDD<K,W> other)` Perform a full outer join of `this` and `other`.
`<W> JavaPairRDD<K,scala.Tuple2<Optional<V>,Optional<W>>>`	`fullOuterJoin(JavaPairRDD<K,W> other, int numPartitions)` Perform a full outer join of `this` and `other`.
`<W> JavaPairRDD<K,scala.Tuple2<Optional<V>,Optional<W>>>`	`fullOuterJoin(JavaPairRDD<K,W> other, Partitioner partitioner)` Perform a full outer join of `this` and `other`.
`JavaPairRDD<K,Iterable<V>>`	`groupByKey()` Group the values for each key in the RDD into a single sequence.
`JavaPairRDD<K,Iterable<V>>`	`groupByKey(int numPartitions)` Group the values for each key in the RDD into a single sequence.
`JavaPairRDD<K,Iterable<V>>`	`groupByKey(Partitioner partitioner)` Group the values for each key in the RDD into a single sequence.
`<W> JavaPairRDD<K,scala.Tuple2<Iterable<V>,Iterable<W>>>`	`groupWith(JavaPairRDD<K,W> other)` Alias for cogroup.
`<W1,W2> JavaPairRDD<K,scala.Tuple3<Iterable<V>,Iterable<W1>,Iterable<W2>>>`	`groupWith(JavaPairRDD<K,W1> other1, JavaPairRDD<K,W2> other2)` Alias for cogroup.
`<W1,W2,W3> JavaPairRDD<K,scala.Tuple4<Iterable<V>,Iterable<W1>,Iterable<W2>,Iterable<W3>>>`	`groupWith(JavaPairRDD<K,W1> other1, JavaPairRDD<K,W2> other2, JavaPairRDD<K,W3> other3)` Alias for cogroup.
`JavaPairRDD<K,V>`	`intersection(JavaPairRDD<K,V> other)` Return the intersection of this RDD and another one.
`<W> JavaPairRDD<K,scala.Tuple2<V,W>>`	`join(JavaPairRDD<K,W> other)` Return an RDD containing all pairs of elements with matching keys in `this` and `other`.
`<W> JavaPairRDD<K,scala.Tuple2<V,W>>`	`join(JavaPairRDD<K,W> other, int numPartitions)` Return an RDD containing all pairs of elements with matching keys in `this` and `other`.
`<W> JavaPairRDD<K,scala.Tuple2<V,W>>`	`join(JavaPairRDD<K,W> other, Partitioner partitioner)` Return an RDD containing all pairs of elements with matching keys in `this` and `other`.
`scala.reflect.ClassTag<K>`	`kClassTag()`
`JavaRDD<K>`	`keys()` Return an RDD with the keys of each tuple.
`<W> JavaPairRDD<K,scala.Tuple2<V,Optional<W>>>`	`leftOuterJoin(JavaPairRDD<K,W> other)` Perform a left outer join of `this` and `other`.
`<W> JavaPairRDD<K,scala.Tuple2<V,Optional<W>>>`	`leftOuterJoin(JavaPairRDD<K,W> other, int numPartitions)` Perform a left outer join of `this` and `other`.
`<W> JavaPairRDD<K,scala.Tuple2<V,Optional<W>>>`	`leftOuterJoin(JavaPairRDD<K,W> other, Partitioner partitioner)` Perform a left outer join of `this` and `other`.
`java.util.List<V>`	`lookup(K key)` Return the list of values in the RDD for key `key`.
`<U> JavaPairRDD<K,U>`	`mapValues(Function<V,U> f)` Pass each value in the key-value pair RDD through a map function without changing the keys; this also retains the original RDD's partitioning.
`JavaPairRDD<K,V>`	`partitionBy(Partitioner partitioner)` Return a copy of the RDD partitioned using the specified partitioner.
`JavaPairRDD<K,V>`	`persist(StorageLevel newLevel)` Set this RDD's storage level to persist its values across operations after the first time it is computed.
`RDD<scala.Tuple2<K,V>>`	`rdd()`
`JavaPairRDD<K,V>`	`reduceByKey(Function2<V,V,V> func)` Merge the values for each key using an associative and commutative reduce function.
`JavaPairRDD<K,V>`	`reduceByKey(Function2<V,V,V> func, int numPartitions)` Merge the values for each key using an associative and commutative reduce function.
`JavaPairRDD<K,V>`	`reduceByKey(Partitioner partitioner, Function2<V,V,V> func)` Merge the values for each key using an associative and commutative reduce function.
`java.util.Map<K,V>`	`reduceByKeyLocally(Function2<V,V,V> func)` Merge the values for each key using an associative and commutative reduce function, but return the result immediately to the master as a Map.
`JavaPairRDD<K,V>`	`repartition(int numPartitions)` Return a new RDD that has exactly numPartitions partitions.
`JavaPairRDD<K,V>`	`repartitionAndSortWithinPartitions(Partitioner partitioner)` Repartition the RDD according to the given partitioner and, within each resulting partition, sort records by their keys.
`JavaPairRDD<K,V>`	`repartitionAndSortWithinPartitions(Partitioner partitioner, java.util.Comparator<K> comp)` Repartition the RDD according to the given partitioner and, within each resulting partition, sort records by their keys.
`<W> JavaPairRDD<K,scala.Tuple2<Optional<V>,W>>`	`rightOuterJoin(JavaPairRDD<K,W> other)` Perform a right outer join of `this` and `other`.
`<W> JavaPairRDD<K,scala.Tuple2<Optional<V>,W>>`	`rightOuterJoin(JavaPairRDD<K,W> other, int numPartitions)` Perform a right outer join of `this` and `other`.
`<W> JavaPairRDD<K,scala.Tuple2<Optional<V>,W>>`	`rightOuterJoin(JavaPairRDD<K,W> other, Partitioner partitioner)` Perform a right outer join of `this` and `other`.
`JavaPairRDD<K,V>`	`sample(boolean withReplacement, double fraction)` Return a sampled subset of this RDD.
`JavaPairRDD<K,V>`	`sample(boolean withReplacement, double fraction, long seed)` Return a sampled subset of this RDD.
`JavaPairRDD<K,V>`	`sampleByKey(boolean withReplacement, java.util.Map<K,Double> fractions)` Return a subset of this RDD sampled by key (via stratified sampling).
`JavaPairRDD<K,V>`	`sampleByKey(boolean withReplacement, java.util.Map<K,Double> fractions, long seed)` Return a subset of this RDD sampled by key (via stratified sampling).
`JavaPairRDD<K,V>`	`sampleByKeyExact(boolean withReplacement, java.util.Map<K,Double> fractions)` Return a subset of this RDD sampled by key (via stratified sampling) containing exactly math.ceil(numItems * samplingRate) for each stratum (group of pairs with the same key).
`JavaPairRDD<K,V>`	`sampleByKeyExact(boolean withReplacement, java.util.Map<K,Double> fractions, long seed)` Return a subset of this RDD sampled by key (via stratified sampling) containing exactly math.ceil(numItems * samplingRate) for each stratum (group of pairs with the same key).
`void`	`saveAsHadoopDataset(org.apache.hadoop.mapred.JobConf conf)` Output the RDD to any Hadoop-supported storage system, using a Hadoop JobConf object for that storage system.
`<F extends org.apache.hadoop.mapred.OutputFormat<?,?>> void`	`saveAsHadoopFile(String path, Class<?> keyClass, Class<?> valueClass, Class<F> outputFormatClass)` Output the RDD to any Hadoop-supported file system.
`<F extends org.apache.hadoop.mapred.OutputFormat<?,?>> void`	`saveAsHadoopFile(String path, Class<?> keyClass, Class<?> valueClass, Class<F> outputFormatClass, Class<? extends org.apache.hadoop.io.compress.CompressionCodec> codec)` Output the RDD to any Hadoop-supported file system, compressing with the supplied codec.
`<F extends org.apache.hadoop.mapred.OutputFormat<?,?>> void`	`saveAsHadoopFile(String path, Class<?> keyClass, Class<?> valueClass, Class<F> outputFormatClass, org.apache.hadoop.mapred.JobConf conf)` Output the RDD to any Hadoop-supported file system.
`void`	`saveAsNewAPIHadoopDataset(org.apache.hadoop.conf.Configuration conf)` Output the RDD to any Hadoop-supported storage system, using a Configuration object for that storage system.
`<F extends org.apache.hadoop.mapreduce.OutputFormat<?,?>> void`	`saveAsNewAPIHadoopFile(String path, Class<?> keyClass, Class<?> valueClass, Class<F> outputFormatClass)` Output the RDD to any Hadoop-supported file system.
`<F extends org.apache.hadoop.mapreduce.OutputFormat<?,?>> void`	`saveAsNewAPIHadoopFile(String path, Class<?> keyClass, Class<?> valueClass, Class<F> outputFormatClass, org.apache.hadoop.conf.Configuration conf)` Output the RDD to any Hadoop-supported file system.
`JavaPairRDD<K,V>`	`setName(String name)` Assign a name to this RDD
`JavaPairRDD<K,V>`	`sortByKey()` Sort the RDD by key, so that each partition contains a sorted range of the elements in ascending order.
`JavaPairRDD<K,V>`	`sortByKey(boolean ascending)` Sort the RDD by key, so that each partition contains a sorted range of the elements.
`JavaPairRDD<K,V>`	`sortByKey(boolean ascending, int numPartitions)` Sort the RDD by key, so that each partition contains a sorted range of the elements.
`JavaPairRDD<K,V>`	`sortByKey(java.util.Comparator<K> comp)` Sort the RDD by key, so that each partition contains a sorted range of the elements.
`JavaPairRDD<K,V>`	`sortByKey(java.util.Comparator<K> comp, boolean ascending)` Sort the RDD by key, so that each partition contains a sorted range of the elements.
`JavaPairRDD<K,V>`	`sortByKey(java.util.Comparator<K> comp, boolean ascending, int numPartitions)` Sort the RDD by key, so that each partition contains a sorted range of the elements.
`JavaPairRDD<K,V>`	`subtract(JavaPairRDD<K,V> other)` Return an RDD with the elements from `this` that are not in `other`.
`JavaPairRDD<K,V>`	`subtract(JavaPairRDD<K,V> other, int numPartitions)` Return an RDD with the elements from `this` that are not in `other`.
`JavaPairRDD<K,V>`	`subtract(JavaPairRDD<K,V> other, Partitioner p)` Return an RDD with the elements from `this` that are not in `other`.
`<W> JavaPairRDD<K,V>`	`subtractByKey(JavaPairRDD<K,W> other)` Return an RDD with the pairs from `this` whose keys are not in `other`.
`<W> JavaPairRDD<K,V>`	`subtractByKey(JavaPairRDD<K,W> other, int numPartitions)` Return an RDD with the pairs from `this` whose keys are not in `other`.
`<W> JavaPairRDD<K,V>`	`subtractByKey(JavaPairRDD<K,W> other, Partitioner p)` Return an RDD with the pairs from `this` whose keys are not in `other`.
`static <K,V> RDD<scala.Tuple2<K,V>>`	`toRDD(JavaPairRDD<K,V> rdd)`
`JavaPairRDD<K,V>`	`union(JavaPairRDD<K,V> other)` Return the union of this RDD and another one.
`JavaPairRDD<K,V>`	`unpersist()` Mark the RDD as non-persistent, and remove all blocks for it from memory and disk.
`JavaPairRDD<K,V>`	`unpersist(boolean blocking)` Mark the RDD as non-persistent, and remove all blocks for it from memory and disk.
`JavaRDD<V>`	`values()` Return an RDD with the values of each tuple.
`scala.reflect.ClassTag<V>`	`vClassTag()`
`JavaPairRDD<K,V>`	`wrapRDD(RDD<scala.Tuple2<K,V>> rdd)`

Methods inherited from class Object
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface org.apache.spark.api.java.JavaRDDLike
aggregate, cartesian, checkpoint, collect, collectAsync, collectPartitions, context, count, countApprox, countApprox, countApproxDistinct, countAsync, countByValue, countByValueApprox, countByValueApprox, flatMap, flatMapToDouble, flatMapToPair, fold, foreach, foreachAsync, foreachPartition, foreachPartitionAsync, getCheckpointFile, getNumPartitions, getStorageLevel, glom, groupBy, groupBy, id, isCheckpointed, isEmpty, iterator, keyBy, map, mapPartitions, mapPartitions, mapPartitionsToDouble, mapPartitionsToDouble, mapPartitionsToPair, mapPartitionsToPair, mapPartitionsWithIndex, mapToDouble, mapToPair, max, min, name, partitioner, partitions, pipe, pipe, pipe, pipe, pipe, reduce, saveAsObjectFile, saveAsTextFile, saveAsTextFile, take, takeAsync, takeOrdered, takeOrdered, takeSample, takeSample, toDebugString, toLocalIterator, top, top, treeAggregate, treeAggregate, treeAggregate, treeReduce, treeReduce, zip, zipPartitions, zipWithIndex, zipWithUniqueId

- Constructor Detail
 - JavaPairRDD
```
public JavaPairRDD(RDD<scala.Tuple2<K,V>> rdd,
 scala.reflect.ClassTag<K> kClassTag,
 scala.reflect.ClassTag<V> vClassTag)
```
- Method Detail
 - fromRDD
```
public static <K,V> JavaPairRDD<K,V> fromRDD(RDD<scala.Tuple2<K,V>> rdd,
 scala.reflect.ClassTag<K> evidence$5,
 scala.reflect.ClassTag<V> evidence$6)
```
 - toRDD
```
public static <K,V> RDD<scala.Tuple2<K,V>> toRDD(JavaPairRDD<K,V> rdd)
```
 - fromJavaRDD
```
public static <K,V> JavaPairRDD<K,V> fromJavaRDD(JavaRDD<scala.Tuple2<K,V>> rdd)
```
 Convert a JavaRDD of key-value pairs to JavaPairRDD.
 - rdd
```
public RDD<scala.Tuple2<K,V>> rdd()
```
 - kClassTag
```
public scala.reflect.ClassTag<K> kClassTag()
```
 - vClassTag
```
public scala.reflect.ClassTag<V> vClassTag()
```
 - wrapRDD
```
public JavaPairRDD<K,V> wrapRDD(RDD<scala.Tuple2<K,V>> rdd)
```
 - classTag
```
public scala.reflect.ClassTag<scala.Tuple2<K,V>> classTag()
```
 - cache
```
public JavaPairRDD<K,V> cache()
```
 Persist this RDD with the default storage level (MEMORY_ONLY).
 
 Returns:
 
 (undocumented)
 - persist
```
public JavaPairRDD<K,V> persist(StorageLevel newLevel)
```
 Set this RDD's storage level to persist its values across operations after the first time it is computed. Can only be called once on each RDD.
 
 Parameters:
 
 newLevel - (undocumented)
 
 Returns:
 
 (undocumented)
 - unpersist
```
public JavaPairRDD<K,V> unpersist()
```
 Mark the RDD as non-persistent, and remove all blocks for it from memory and disk. This method blocks until all blocks are deleted.
 
 Returns:
 
 (undocumented)
 - unpersist
```
public JavaPairRDD<K,V> unpersist(boolean blocking)
```
 Mark the RDD as non-persistent, and remove all blocks for it from memory and disk.
 
 Parameters:
 
 blocking - Whether to block until all blocks are deleted.
 
 Returns:
 
 (undocumented)
 - distinct
```
public JavaPairRDD<K,V> distinct()
```
 Return a new RDD containing the distinct elements in this RDD.
 
 Returns:
 
 (undocumented)
 - distinct
```
public JavaPairRDD<K,V> distinct(int numPartitions)
```
 Return a new RDD containing the distinct elements in this RDD.
 
 Parameters:
 
 numPartitions - (undocumented)
 
 Returns:
 
 (undocumented)
 - filter
```
public JavaPairRDD<K,V> filter(Function<scala.Tuple2<K,V>,Boolean> f)
```
 Return a new RDD containing only the elements that satisfy a predicate.
 
 Parameters:
 
 f - (undocumented)
 
 Returns:
 
 (undocumented)
 - coalesce
```
public JavaPairRDD<K,V> coalesce(int numPartitions)
```
 Return a new RDD that is reduced into numPartitions partitions.
 
 Parameters:
 
 numPartitions - (undocumented)
 
 Returns:
 
 (undocumented)
 - coalesce
```
public JavaPairRDD<K,V> coalesce(int numPartitions,
 boolean shuffle)
```
 Return a new RDD that is reduced into numPartitions partitions.
 
 Parameters:
 
 numPartitions - (undocumented)
 
 shuffle - (undocumented)
 
 Returns:
 
 (undocumented)
 - repartition
```
public JavaPairRDD<K,V> repartition(int numPartitions)
```
 Return a new RDD that has exactly numPartitions partitions.
 Can increase or decrease the level of parallelism in this RDD. Internally, this uses a shuffle to redistribute data.
 If you are decreasing the number of partitions in this RDD, consider using coalesce, which can avoid performing a shuffle.
 
 Parameters:
 
 numPartitions - (undocumented)
 
 Returns:
 
 (undocumented)
 - sample
```
public JavaPairRDD<K,V> sample(boolean withReplacement,
 double fraction)
```
 Return a sampled subset of this RDD.
 
 Parameters:
 
 withReplacement - (undocumented)
 
 fraction - (undocumented)
 
 Returns:
 
 (undocumented)
 - sample
```
public JavaPairRDD<K,V> sample(boolean withReplacement,
 double fraction,
 long seed)
```
 Return a sampled subset of this RDD.
 
 Parameters:
 
 withReplacement - (undocumented)
 
 fraction - (undocumented)
 
 seed - (undocumented)
 
 Returns:
 
 (undocumented)
 - sampleByKey
```
public JavaPairRDD<K,V> sampleByKey(boolean withReplacement,
 java.util.Map<K,Double> fractions,
 long seed)
```
 Return a subset of this RDD sampled by key (via stratified sampling).
 Create a sample of this RDD using variable sampling rates for different keys as specified by fractions, a key to sampling rate map, via simple random sampling with one pass over the RDD, to produce a sample of size that's approximately equal to the sum of math.ceil(numItems * samplingRate) over all key values.
 
 Parameters:
 
 withReplacement - (undocumented)
 
 fractions - (undocumented)
 
 seed - (undocumented)
 
 Returns:
 
 (undocumented)
 - sampleByKey
```
public JavaPairRDD<K,V> sampleByKey(boolean withReplacement,
 java.util.Map<K,Double> fractions)
```
 Return a subset of this RDD sampled by key (via stratified sampling).
 Create a sample of this RDD using variable sampling rates for different keys as specified by fractions, a key to sampling rate map, via simple random sampling with one pass over the RDD, to produce a sample of size that's approximately equal to the sum of math.ceil(numItems * samplingRate) over all key values.
 Use Utils.random.nextLong as the default seed for the random number generator.
 
 Parameters:
 
 withReplacement - (undocumented)
 
 fractions - (undocumented)
 
 Returns:
 
 (undocumented)
 - sampleByKeyExact
```
public JavaPairRDD<K,V> sampleByKeyExact(boolean withReplacement,
 java.util.Map<K,Double> fractions,
 long seed)
```
 Return a subset of this RDD sampled by key (via stratified sampling) containing exactly math.ceil(numItems * samplingRate) for each stratum (group of pairs with the same key).
 This method differs from sampleByKey in that we make additional passes over the RDD to create a sample size that's exactly equal to the sum of math.ceil(numItems * samplingRate) over all key values with a 99.99% confidence. When sampling without replacement, we need one additional pass over the RDD to guarantee sample size; when sampling with replacement, we need two additional passes.
 
 Parameters:
 
 withReplacement - (undocumented)
 
 fractions - (undocumented)
 
 seed - (undocumented)
 
 Returns:
 
 (undocumented)
 - sampleByKeyExact
```
public JavaPairRDD<K,V> sampleByKeyExact(boolean withReplacement,
 java.util.Map<K,Double> fractions)
```
 Return a subset of this RDD sampled by key (via stratified sampling) containing exactly math.ceil(numItems * samplingRate) for each stratum (group of pairs with the same key).
 This method differs from sampleByKey in that we make additional passes over the RDD to create a sample size that's exactly equal to the sum of math.ceil(numItems * samplingRate) over all key values with a 99.99% confidence. When sampling without replacement, we need one additional pass over the RDD to guarantee sample size; when sampling with replacement, we need two additional passes.
 Use Utils.random.nextLong as the default seed for the random number generator.
 
 Parameters:
 
 withReplacement - (undocumented)
 
 fractions - (undocumented)
 
 Returns:
 
 (undocumented)
 - union
```
public JavaPairRDD<K,V> union(JavaPairRDD<K,V> other)
```
 Return the union of this RDD and another one. Any identical elements will appear multiple times (use .distinct() to eliminate them).
 
 Parameters:
 
 other - (undocumented)
 
 Returns:
 
 (undocumented)
 - intersection
```
public JavaPairRDD<K,V> intersection(JavaPairRDD<K,V> other)
```
 Return the intersection of this RDD and another one. The output will not contain any duplicate elements, even if the input RDDs did.
 
 Parameters:
 
 other - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Note:
 
 This method performs a shuffle internally.
 - first
```
public scala.Tuple2<K,V> first()
```
 Description copied from interface: JavaRDDLike
 
 Return the first element in this RDD.
 
 Returns:
 
 (undocumented)
 - combineByKey
```
public <C> JavaPairRDD<K,C> combineByKey(Function<V,C> createCombiner,
 Function2<C,V,C> mergeValue,
 Function2<C,C,C> mergeCombiners,
 Partitioner partitioner,
 boolean mapSideCombine,
 Serializer serializer)
```
 Generic function to combine the elements for each key using a custom set of aggregation functions. Turns a JavaPairRDD[(K, V)] into a result of type JavaPairRDD[(K, C)], for a "combined type" C.
 Users provide three functions:
 - createCombiner, which turns a V into a C (e.g., creates a one-element list) - mergeValue, to merge a V into a C (e.g., adds it to the end of a list) - mergeCombiners, to combine two C's into a single one.
 In addition, users can control the partitioning of the output RDD, the serializer that is use for the shuffle, and whether to perform map-side aggregation (if a mapper can produce multiple items with the same key).
 
 Parameters:
 
 createCombiner - (undocumented)
 
 mergeValue - (undocumented)
 
 mergeCombiners - (undocumented)
 
 partitioner - (undocumented)
 
 mapSideCombine - (undocumented)
 
 serializer - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Note:
 
 V and C can be different -- for example, one might group an RDD of type (Int, Int) into an RDD of type (Int, List[Int]).
 - combineByKey
```
public <C> JavaPairRDD<K,C> combineByKey(Function<V,C> createCombiner,
 Function2<C,V,C> mergeValue,
 Function2<C,C,C> mergeCombiners,
 Partitioner partitioner)
```
 Generic function to combine the elements for each key using a custom set of aggregation functions. Turns a JavaPairRDD[(K, V)] into a result of type JavaPairRDD[(K, C)], for a "combined type" C.
 Users provide three functions:
 - createCombiner, which turns a V into a C (e.g., creates a one-element list) - mergeValue, to merge a V into a C (e.g., adds it to the end of a list) - mergeCombiners, to combine two C's into a single one.
 In addition, users can control the partitioning of the output RDD. This method automatically uses map-side aggregation in shuffling the RDD.
 
 Parameters:
 
 createCombiner - (undocumented)
 
 mergeValue - (undocumented)
 
 mergeCombiners - (undocumented)
 
 partitioner - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Note:
 
 V and C can be different -- for example, one might group an RDD of type (Int, Int) into an RDD of type (Int, List[Int]).
 - combineByKey
```
public <C> JavaPairRDD<K,C> combineByKey(Function<V,C> createCombiner,
 Function2<C,V,C> mergeValue,
 Function2<C,C,C> mergeCombiners,
 int numPartitions)
```
 Simplified version of combineByKey that hash-partitions the output RDD and uses map-side aggregation.
 
 Parameters:
 
 createCombiner - (undocumented)
 
 mergeValue - (undocumented)
 
 mergeCombiners - (undocumented)
 
 numPartitions - (undocumented)
 
 Returns:
 
 (undocumented)
 - reduceByKey
```
public JavaPairRDD<K,V> reduceByKey(Partitioner partitioner,
 Function2<V,V,V> func)
```
 Merge the values for each key using an associative and commutative reduce function. This will also perform the merging locally on each mapper before sending results to a reducer, similarly to a "combiner" in MapReduce.
 
 Parameters:
 
 partitioner - (undocumented)
 
 func - (undocumented)
 
 Returns:
 
 (undocumented)
 - reduceByKeyLocally
```
public java.util.Map<K,V> reduceByKeyLocally(Function2<V,V,V> func)
```
 Merge the values for each key using an associative and commutative reduce function, but return the result immediately to the master as a Map. This will also perform the merging locally on each mapper before sending results to a reducer, similarly to a "combiner" in MapReduce.
 
 Parameters:
 
 func - (undocumented)
 
 Returns:
 
 (undocumented)
 - countByKey
```
public java.util.Map<K,Long> countByKey()
```
 Count the number of elements for each key, and return the result to the master as a Map.
 - countByKeyApprox
```
public PartialResult<java.util.Map<K,BoundedDouble>> countByKeyApprox(long timeout)
```
 Approximate version of countByKey that can return a partial result if it does not finish within a timeout.
 
 Parameters:
 
 timeout - (undocumented)
 
 Returns:
 
 (undocumented)
 - countByKeyApprox
```
public PartialResult<java.util.Map<K,BoundedDouble>> countByKeyApprox(long timeout,
 double confidence)
```
 Approximate version of countByKey that can return a partial result if it does not finish within a timeout.
 
 Parameters:
 
 timeout - (undocumented)
 
 confidence - (undocumented)
 
 Returns:
 
 (undocumented)
 - aggregateByKey
```
public  JavaPairRDD<K,U> aggregateByKey(U zeroValue,
 Partitioner partitioner,
 Function2<U,V,U> seqFunc,
 Function2<U,U,U> combFunc)
```
 Aggregate the values of each key, using given combine functions and a neutral "zero value". This function can return a different result type, U, than the type of the values in this RDD, V. Thus, we need one operation for merging a V into a U and one operation for merging two U's, as in scala.TraversableOnce. The former operation is used for merging values within a partition, and the latter is used for merging values between partitions. To avoid memory allocation, both of these functions are allowed to modify and return their first argument instead of creating a new U.
 
 Parameters:
 
 zeroValue - (undocumented)
 
 partitioner - (undocumented)
 
 seqFunc - (undocumented)
 
 combFunc - (undocumented)
 
 Returns:
 
 (undocumented)
 - aggregateByKey
```
public  JavaPairRDD<K,U> aggregateByKey(U zeroValue,
 int numPartitions,
 Function2<U,V,U> seqFunc,
 Function2<U,U,U> combFunc)
```
 Aggregate the values of each key, using given combine functions and a neutral "zero value". This function can return a different result type, U, than the type of the values in this RDD, V. Thus, we need one operation for merging a V into a U and one operation for merging two U's, as in scala.TraversableOnce. The former operation is used for merging values within a partition, and the latter is used for merging values between partitions. To avoid memory allocation, both of these functions are allowed to modify and return their first argument instead of creating a new U.
 
 Parameters:
 
 zeroValue - (undocumented)
 
 numPartitions - (undocumented)
 
 seqFunc - (undocumented)
 
 combFunc - (undocumented)
 
 Returns:
 
 (undocumented)
 - aggregateByKey
```
public  JavaPairRDD<K,U> aggregateByKey(U zeroValue,
 Function2<U,V,U> seqFunc,
 Function2<U,U,U> combFunc)
```
 Aggregate the values of each key, using given combine functions and a neutral "zero value". This function can return a different result type, U, than the type of the values in this RDD, V. Thus, we need one operation for merging a V into a U and one operation for merging two U's. The former operation is used for merging values within a partition, and the latter is used for merging values between partitions. To avoid memory allocation, both of these functions are allowed to modify and return their first argument instead of creating a new U.
 
 Parameters:
 
 zeroValue - (undocumented)
 
 seqFunc - (undocumented)
 
 combFunc - (undocumented)
 
 Returns:
 
 (undocumented)
 - foldByKey
```
public JavaPairRDD<K,V> foldByKey(V zeroValue,
 Partitioner partitioner,
 Function2<V,V,V> func)
```
 Merge the values for each key using an associative function and a neutral "zero value" which may be added to the result an arbitrary number of times, and must not change the result (e.g ., Nil for list concatenation, 0 for addition, or 1 for multiplication.).
 
 Parameters:
 
 zeroValue - (undocumented)
 
 partitioner - (undocumented)
 
 func - (undocumented)
 
 Returns:
 
 (undocumented)
 - foldByKey
```
public JavaPairRDD<K,V> foldByKey(V zeroValue,
 int numPartitions,
 Function2<V,V,V> func)
```
 Merge the values for each key using an associative function and a neutral "zero value" which may be added to the result an arbitrary number of times, and must not change the result (e.g ., Nil for list concatenation, 0 for addition, or 1 for multiplication.).
 
 Parameters:
 
 zeroValue - (undocumented)
 
 numPartitions - (undocumented)
 
 func - (undocumented)
 
 Returns:
 
 (undocumented)
 - foldByKey
```
public JavaPairRDD<K,V> foldByKey(V zeroValue,
 Function2<V,V,V> func)
```
 Merge the values for each key using an associative function and a neutral "zero value" which may be added to the result an arbitrary number of times, and must not change the result (e.g., Nil for list concatenation, 0 for addition, or 1 for multiplication.).
 
 Parameters:
 
 zeroValue - (undocumented)
 
 func - (undocumented)
 
 Returns:
 
 (undocumented)
 - reduceByKey
```
public JavaPairRDD<K,V> reduceByKey(Function2<V,V,V> func,
 int numPartitions)
```
 Merge the values for each key using an associative and commutative reduce function. This will also perform the merging locally on each mapper before sending results to a reducer, similarly to a "combiner" in MapReduce. Output will be hash-partitioned with numPartitions partitions.
 
 Parameters:
 
 func - (undocumented)
 
 numPartitions - (undocumented)
 
 Returns:
 
 (undocumented)
 - groupByKey
```
public JavaPairRDD<K,Iterable<V>> groupByKey(Partitioner partitioner)
```
 Group the values for each key in the RDD into a single sequence. Allows controlling the partitioning of the resulting key-value pair RDD by passing a Partitioner.
 
 Parameters:
 
 partitioner - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Note:
 
 If you are grouping in order to perform an aggregation (such as a sum or average) over each key, using JavaPairRDD.reduceByKey or JavaPairRDD.combineByKey will provide much better performance.
 - groupByKey
```
public JavaPairRDD<K,Iterable<V>> groupByKey(int numPartitions)
```
 Group the values for each key in the RDD into a single sequence. Hash-partitions the resulting RDD with into numPartitions partitions.
 
 Parameters:
 
 numPartitions - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Note:
 
 If you are grouping in order to perform an aggregation (such as a sum or average) over each key, using JavaPairRDD.reduceByKey or JavaPairRDD.combineByKey will provide much better performance.
 - subtract
```
public JavaPairRDD<K,V> subtract(JavaPairRDD<K,V> other)
```
 Return an RDD with the elements from this that are not in other.
 Uses this partitioner/partition size, because even if other is huge, the resulting RDD will be <= us.
 
 Parameters:
 
 other - (undocumented)
 
 Returns:
 
 (undocumented)
 - subtract
```
public JavaPairRDD<K,V> subtract(JavaPairRDD<K,V> other,
 int numPartitions)
```
 Return an RDD with the elements from this that are not in other.
 
 Parameters:
 
 other - (undocumented)
 
 numPartitions - (undocumented)
 
 Returns:
 
 (undocumented)
 - subtract
```
public JavaPairRDD<K,V> subtract(JavaPairRDD<K,V> other,
 Partitioner p)
```
 Return an RDD with the elements from this that are not in other.
 
 Parameters:
 
 other - (undocumented)
 
 p - (undocumented)
 
 Returns:
 
 (undocumented)
 - subtractByKey
```
public <W> JavaPairRDD<K,V> subtractByKey(JavaPairRDD<K,W> other)
```
 Return an RDD with the pairs from this whose keys are not in other.
 Uses this partitioner/partition size, because even if other is huge, the resulting RDD will be <= us.
 
 Parameters:
 
 other - (undocumented)
 
 Returns:
 
 (undocumented)
 - subtractByKey
```
public <W> JavaPairRDD<K,V> subtractByKey(JavaPairRDD<K,W> other,
 int numPartitions)
```
 Return an RDD with the pairs from this whose keys are not in other.
 
 Parameters:
 
 other - (undocumented)
 
 numPartitions - (undocumented)
 
 Returns:
 
 (undocumented)
 - subtractByKey
```
public <W> JavaPairRDD<K,V> subtractByKey(JavaPairRDD<K,W> other,
 Partitioner p)
```
 Return an RDD with the pairs from this whose keys are not in other.
 
 Parameters:
 
 other - (undocumented)
 
 p - (undocumented)
 
 Returns:
 
 (undocumented)
 - partitionBy
```
public JavaPairRDD<K,V> partitionBy(Partitioner partitioner)
```
 Return a copy of the RDD partitioned using the specified partitioner.
 
 Parameters:
 
 partitioner - (undocumented)
 
 Returns:
 
 (undocumented)
 - join
```
public <W> JavaPairRDD<K,scala.Tuple2<V,W>> join(JavaPairRDD<K,W> other,
 Partitioner partitioner)
```
 Return an RDD containing all pairs of elements with matching keys in this and other. Each pair of elements will be returned as a (k, (v1, v2)) tuple, where (k, v1) is in this and (k, v2) is in other. Uses the given Partitioner to partition the output RDD.
 
 Parameters:
 
 other - (undocumented)
 
 partitioner - (undocumented)
 
 Returns:
 
 (undocumented)
 - leftOuterJoin
```
public <W> JavaPairRDD<K,scala.Tuple2<V,Optional<W>>> leftOuterJoin(JavaPairRDD<K,W> other,
 Partitioner partitioner)
```
 Perform a left outer join of this and other. For each element (k, v) in this, the resulting RDD will either contain all pairs (k, (v, Some(w))) for w in other, or the pair (k, (v, None)) if no elements in other have key k. Uses the given Partitioner to partition the output RDD.
 
 Parameters:
 
 other - (undocumented)
 
 partitioner - (undocumented)
 
 Returns:
 
 (undocumented)
 - rightOuterJoin
```
public <W> JavaPairRDD<K,scala.Tuple2<Optional<V>,W>> rightOuterJoin(JavaPairRDD<K,W> other,
 Partitioner partitioner)
```
 Perform a right outer join of this and other. For each element (k, w) in other, the resulting RDD will either contain all pairs (k, (Some(v), w)) for v in this, or the pair (k, (None, w)) if no elements in this have key k. Uses the given Partitioner to partition the output RDD.
 
 Parameters:
 
 other - (undocumented)
 
 partitioner - (undocumented)
 
 Returns:
 
 (undocumented)
 - fullOuterJoin
```
public <W> JavaPairRDD<K,scala.Tuple2<Optional<V>,Optional<W>>> fullOuterJoin(JavaPairRDD<K,W> other,
 Partitioner partitioner)
```
 Perform a full outer join of this and other. For each element (k, v) in this, the resulting RDD will either contain all pairs (k, (Some(v), Some(w))) for w in other, or the pair (k, (Some(v), None)) if no elements in other have key k. Similarly, for each element (k, w) in other, the resulting RDD will either contain all pairs (k, (Some(v), Some(w))) for v in this, or the pair (k, (None, Some(w))) if no elements in this have key k. Uses the given Partitioner to partition the output RDD.
 
 Parameters:
 
 other - (undocumented)
 
 partitioner - (undocumented)
 
 Returns:
 
 (undocumented)
 - combineByKey
```
public <C> JavaPairRDD<K,C> combineByKey(Function<V,C> createCombiner,
 Function2<C,V,C> mergeValue,
 Function2<C,C,C> mergeCombiners)
```
 Simplified version of combineByKey that hash-partitions the resulting RDD using the existing partitioner/parallelism level and using map-side aggregation.
 
 Parameters:
 
 createCombiner - (undocumented)
 
 mergeValue - (undocumented)
 
 mergeCombiners - (undocumented)
 
 Returns:
 
 (undocumented)
 - reduceByKey
```
public JavaPairRDD<K,V> reduceByKey(Function2<V,V,V> func)
```
 Merge the values for each key using an associative and commutative reduce function. This will also perform the merging locally on each mapper before sending results to a reducer, similarly to a "combiner" in MapReduce. Output will be hash-partitioned with the existing partitioner/ parallelism level.
 
 Parameters:
 
 func - (undocumented)
 
 Returns:
 
 (undocumented)
 - groupByKey
```
public JavaPairRDD<K,Iterable<V>> groupByKey()
```
 Group the values for each key in the RDD into a single sequence. Hash-partitions the resulting RDD with the existing partitioner/parallelism level.
 
 Returns:
 
 (undocumented)
 
 Note:
 
 If you are grouping in order to perform an aggregation (such as a sum or average) over each key, using JavaPairRDD.reduceByKey or JavaPairRDD.combineByKey will provide much better performance.
 - join
```
public <W> JavaPairRDD<K,scala.Tuple2<V,W>> join(JavaPairRDD<K,W> other)
```
 Return an RDD containing all pairs of elements with matching keys in this and other. Each pair of elements will be returned as a (k, (v1, v2)) tuple, where (k, v1) is in this and (k, v2) is in other. Performs a hash join across the cluster.
 
 Parameters:
 
 other - (undocumented)
 
 Returns:
 
 (undocumented)
 - join
```
public <W> JavaPairRDD<K,scala.Tuple2<V,W>> join(JavaPairRDD<K,W> other,
 int numPartitions)
```
 Return an RDD containing all pairs of elements with matching keys in this and other. Each pair of elements will be returned as a (k, (v1, v2)) tuple, where (k, v1) is in this and (k, v2) is in other. Performs a hash join across the cluster.
 
 Parameters:
 
 other - (undocumented)
 
 numPartitions - (undocumented)
 
 Returns:
 
 (undocumented)
 - leftOuterJoin
```
public <W> JavaPairRDD<K,scala.Tuple2<V,Optional<W>>> leftOuterJoin(JavaPairRDD<K,W> other)
```
 Perform a left outer join of this and other. For each element (k, v) in this, the resulting RDD will either contain all pairs (k, (v, Some(w))) for w in other, or the pair (k, (v, None)) if no elements in other have key k. Hash-partitions the output using the existing partitioner/parallelism level.
 
 Parameters:
 
 other - (undocumented)
 
 Returns:
 
 (undocumented)
 - leftOuterJoin
```
public <W> JavaPairRDD<K,scala.Tuple2<V,Optional<W>>> leftOuterJoin(JavaPairRDD<K,W> other,
 int numPartitions)
```
 Perform a left outer join of this and other. For each element (k, v) in this, the resulting RDD will either contain all pairs (k, (v, Some(w))) for w in other, or the pair (k, (v, None)) if no elements in other have key k. Hash-partitions the output into numPartitions partitions.
 
 Parameters:
 
 other - (undocumented)
 
 numPartitions - (undocumented)
 
 Returns:
 
 (undocumented)
 - rightOuterJoin
```
public <W> JavaPairRDD<K,scala.Tuple2<Optional<V>,W>> rightOuterJoin(JavaPairRDD<K,W> other)
```
 Perform a right outer join of this and other. For each element (k, w) in other, the resulting RDD will either contain all pairs (k, (Some(v), w)) for v in this, or the pair (k, (None, w)) if no elements in this have key k. Hash-partitions the resulting RDD using the existing partitioner/parallelism level.
 
 Parameters:
 
 other - (undocumented)
 
 Returns:
 
 (undocumented)
 - rightOuterJoin
```
public <W> JavaPairRDD<K,scala.Tuple2<Optional<V>,W>> rightOuterJoin(JavaPairRDD<K,W> other,
 int numPartitions)
```
 Perform a right outer join of this and other. For each element (k, w) in other, the resulting RDD will either contain all pairs (k, (Some(v), w)) for v in this, or the pair (k, (None, w)) if no elements in this have key k. Hash-partitions the resulting RDD into the given number of partitions.
 
 Parameters:
 
 other - (undocumented)
 
 numPartitions - (undocumented)
 
 Returns:
 
 (undocumented)
 - fullOuterJoin
```
public <W> JavaPairRDD<K,scala.Tuple2<Optional<V>,Optional<W>>> fullOuterJoin(JavaPairRDD<K,W> other)
```
 Perform a full outer join of this and other. For each element (k, v) in this, the resulting RDD will either contain all pairs (k, (Some(v), Some(w))) for w in other, or the pair (k, (Some(v), None)) if no elements in other have key k. Similarly, for each element (k, w) in other, the resulting RDD will either contain all pairs (k, (Some(v), Some(w))) for v in this, or the pair (k, (None, Some(w))) if no elements in this have key k. Hash-partitions the resulting RDD using the existing partitioner/ parallelism level.
 
 Parameters:
 
 other - (undocumented)
 
 Returns:
 
 (undocumented)
 - fullOuterJoin
```
public <W> JavaPairRDD<K,scala.Tuple2<Optional<V>,Optional<W>>> fullOuterJoin(JavaPairRDD<K,W> other,
 int numPartitions)
```
 Perform a full outer join of this and other. For each element (k, v) in this, the resulting RDD will either contain all pairs (k, (Some(v), Some(w))) for w in other, or the pair (k, (Some(v), None)) if no elements in other have key k. Similarly, for each element (k, w) in other, the resulting RDD will either contain all pairs (k, (Some(v), Some(w))) for v in this, or the pair (k, (None, Some(w))) if no elements in this have key k. Hash-partitions the resulting RDD into the given number of partitions.
 
 Parameters:
 
 other - (undocumented)
 
 numPartitions - (undocumented)
 
 Returns:
 
 (undocumented)
 - collectAsMap
```
public java.util.Map<K,V> collectAsMap()
```
 Return the key-value pairs in this RDD to the master as a Map.
 
 Returns:
 
 (undocumented)
 
 Note:
 
 this method should only be used if the resulting data is expected to be small, as all the data is loaded into the driver's memory.
 - mapValues
```
public  JavaPairRDD<K,U> mapValues(Function<V,U> f)
```
 Pass each value in the key-value pair RDD through a map function without changing the keys; this also retains the original RDD's partitioning.
 
 Parameters:
 
 f - (undocumented)
 
 Returns:
 
 (undocumented)
 - flatMapValues
```
public  JavaPairRDD<K,U> flatMapValues(FlatMapFunction<V,U> f)
```
 Pass each value in the key-value pair RDD through a flatMap function without changing the keys; this also retains the original RDD's partitioning.
 
 Parameters:
 
 f - (undocumented)
 
 Returns:
 
 (undocumented)
 - cogroup
```
public <W> JavaPairRDD<K,scala.Tuple2<Iterable<V>,Iterable<W>>> cogroup(JavaPairRDD<K,W> other,
 Partitioner partitioner)
```
 For each key k in this or other, return a resulting RDD that contains a tuple with the list of values for that key in this as well as other.
 
 Parameters:
 
 other - (undocumented)
 
 partitioner - (undocumented)
 
 Returns:
 
 (undocumented)
 - cogroup
```
public <W1,W2> JavaPairRDD<K,scala.Tuple3<Iterable<V>,Iterable<W1>,Iterable<W2>>> cogroup(JavaPairRDD<K,W1> other1,
 JavaPairRDD<K,W2> other2,
 Partitioner partitioner)
```
 For each key k in this or other1 or other2, return a resulting RDD that contains a tuple with the list of values for that key in this, other1 and other2.
 
 Parameters:
 
 other1 - (undocumented)
 
 other2 - (undocumented)
 
 partitioner - (undocumented)
 
 Returns:
 
 (undocumented)
 - cogroup
```
public <W1,W2,W3> JavaPairRDD<K,scala.Tuple4<Iterable<V>,Iterable<W1>,Iterable<W2>,Iterable<W3>>> cogroup(JavaPairRDD<K,W1> other1,
 JavaPairRDD<K,W2> other2,
 JavaPairRDD<K,W3> other3,
 Partitioner partitioner)
```
 For each key k in this or other1 or other2 or other3, return a resulting RDD that contains a tuple with the list of values for that key in this, other1, other2 and other3.
 
 Parameters:
 
 other1 - (undocumented)
 
 other2 - (undocumented)
 
 other3 - (undocumented)
 
 partitioner - (undocumented)
 
 Returns:
 
 (undocumented)
 - cogroup
```
public <W> JavaPairRDD<K,scala.Tuple2<Iterable<V>,Iterable<W>>> cogroup(JavaPairRDD<K,W> other)
```
 For each key k in this or other, return a resulting RDD that contains a tuple with the list of values for that key in this as well as other.
 
 Parameters:
 
 other - (undocumented)
 
 Returns:
 
 (undocumented)
 - cogroup
```
public <W1,W2> JavaPairRDD<K,scala.Tuple3<Iterable<V>,Iterable<W1>,Iterable<W2>>> cogroup(JavaPairRDD<K,W1> other1,
 JavaPairRDD<K,W2> other2)
```
 For each key k in this or other1 or other2, return a resulting RDD that contains a tuple with the list of values for that key in this, other1 and other2.
 
 Parameters:
 
 other1 - (undocumented)
 
 other2 - (undocumented)
 
 Returns:
 
 (undocumented)
 - cogroup
```
public <W1,W2,W3> JavaPairRDD<K,scala.Tuple4<Iterable<V>,Iterable<W1>,Iterable<W2>,Iterable<W3>>> cogroup(JavaPairRDD<K,W1> other1,
 JavaPairRDD<K,W2> other2,
 JavaPairRDD<K,W3> other3)
```
 For each key k in this or other1 or other2 or other3, return a resulting RDD that contains a tuple with the list of values for that key in this, other1, other2 and other3.
 
 Parameters:
 
 other1 - (undocumented)
 
 other2 - (undocumented)
 
 other3 - (undocumented)
 
 Returns:
 
 (undocumented)
 - cogroup
```
public <W> JavaPairRDD<K,scala.Tuple2<Iterable<V>,Iterable<W>>> cogroup(JavaPairRDD<K,W> other,
 int numPartitions)
```
 For each key k in this or other, return a resulting RDD that contains a tuple with the list of values for that key in this as well as other.
 
 Parameters:
 
 other - (undocumented)
 
 numPartitions - (undocumented)
 
 Returns:
 
 (undocumented)
 - cogroup
```
public <W1,W2> JavaPairRDD<K,scala.Tuple3<Iterable<V>,Iterable<W1>,Iterable<W2>>> cogroup(JavaPairRDD<K,W1> other1,
 JavaPairRDD<K,W2> other2,
 int numPartitions)
```
 For each key k in this or other1 or other2, return a resulting RDD that contains a tuple with the list of values for that key in this, other1 and other2.
 
 Parameters:
 
 other1 - (undocumented)
 
 other2 - (undocumented)
 
 numPartitions - (undocumented)
 
 Returns:
 
 (undocumented)
 - cogroup
```
public <W1,W2,W3> JavaPairRDD<K,scala.Tuple4<Iterable<V>,Iterable<W1>,Iterable<W2>,Iterable<W3>>> cogroup(JavaPairRDD<K,W1> other1,
 JavaPairRDD<K,W2> other2,
 JavaPairRDD<K,W3> other3,
 int numPartitions)
```
 For each key k in this or other1 or other2 or other3, return a resulting RDD that contains a tuple with the list of values for that key in this, other1, other2 and other3.
 
 Parameters:
 
 other1 - (undocumented)
 
 other2 - (undocumented)
 
 other3 - (undocumented)
 
 numPartitions - (undocumented)
 
 Returns:
 
 (undocumented)
 - groupWith
```
public <W> JavaPairRDD<K,scala.Tuple2<Iterable<V>,Iterable<W>>> groupWith(JavaPairRDD<K,W> other)
```
 Alias for cogroup.
 - groupWith
```
public <W1,W2> JavaPairRDD<K,scala.Tuple3<Iterable<V>,Iterable<W1>,Iterable<W2>>> groupWith(JavaPairRDD<K,W1> other1,
 JavaPairRDD<K,W2> other2)
```
 Alias for cogroup.
 - groupWith
```
public <W1,W2,W3> JavaPairRDD<K,scala.Tuple4<Iterable<V>,Iterable<W1>,Iterable<W2>,Iterable<W3>>> groupWith(JavaPairRDD<K,W1> other1,
 JavaPairRDD<K,W2> other2,
 JavaPairRDD<K,W3> other3)
```
 Alias for cogroup.
 - lookup
```
public java.util.List<V> lookup(K key)
```
 Return the list of values in the RDD for key key. This operation is done efficiently if the RDD has a known partitioner by only searching the partition that the key maps to.
 
 Parameters:
 
 key - (undocumented)
 
 Returns:
 
 (undocumented)
 - saveAsHadoopFile
```
public <F extends org.apache.hadoop.mapred.OutputFormat<?,?>> void saveAsHadoopFile(String path,
 Class<?> keyClass,
 Class<?> valueClass,
 Class<F> outputFormatClass,
 org.apache.hadoop.mapred.JobConf conf)
```
 Output the RDD to any Hadoop-supported file system.
 - saveAsHadoopFile
```
public <F extends org.apache.hadoop.mapred.OutputFormat<?,?>> void saveAsHadoopFile(String path,
 Class<?> keyClass,
 Class<?> valueClass,
 Class<F> outputFormatClass)
```
 Output the RDD to any Hadoop-supported file system.
 - saveAsHadoopFile
```
public <F extends org.apache.hadoop.mapred.OutputFormat<?,?>> void saveAsHadoopFile(String path,
 Class<?> keyClass,
 Class<?> valueClass,
 Class<F> outputFormatClass,
 Class<? extends org.apache.hadoop.io.compress.CompressionCodec> codec)
```
 Output the RDD to any Hadoop-supported file system, compressing with the supplied codec.
 - saveAsNewAPIHadoopFile
```
public <F extends org.apache.hadoop.mapreduce.OutputFormat<?,?>> void saveAsNewAPIHadoopFile(String path,
 Class<?> keyClass,
 Class<?> valueClass,
 Class<F> outputFormatClass,
 org.apache.hadoop.conf.Configuration conf)
```
 Output the RDD to any Hadoop-supported file system.
 - saveAsNewAPIHadoopDataset
```
public void saveAsNewAPIHadoopDataset(org.apache.hadoop.conf.Configuration conf)
```
 Output the RDD to any Hadoop-supported storage system, using a Configuration object for that storage system.
 
 Parameters:
 
 conf - (undocumented)
 - saveAsNewAPIHadoopFile
```
public <F extends org.apache.hadoop.mapreduce.OutputFormat<?,?>> void saveAsNewAPIHadoopFile(String path,
 Class<?> keyClass,
 Class<?> valueClass,
 Class<F> outputFormatClass)
```
 Output the RDD to any Hadoop-supported file system.
 - saveAsHadoopDataset
```
public void saveAsHadoopDataset(org.apache.hadoop.mapred.JobConf conf)
```
 Output the RDD to any Hadoop-supported storage system, using a Hadoop JobConf object for that storage system. The JobConf should set an OutputFormat and any output paths required (e.g. a table name to write to) in the same way as it would be configured for a Hadoop MapReduce job.
 
 Parameters:
 
 conf - (undocumented)
 - repartitionAndSortWithinPartitions
```
public JavaPairRDD<K,V> repartitionAndSortWithinPartitions(Partitioner partitioner)
```
 Repartition the RDD according to the given partitioner and, within each resulting partition, sort records by their keys.
 This is more efficient than calling repartition and then sorting within each partition because it can push the sorting down into the shuffle machinery.
 
 Parameters:
 
 partitioner - (undocumented)
 
 Returns:
 
 (undocumented)
 - repartitionAndSortWithinPartitions
```
public JavaPairRDD<K,V> repartitionAndSortWithinPartitions(Partitioner partitioner,
 java.util.Comparator<K> comp)
```
 Repartition the RDD according to the given partitioner and, within each resulting partition, sort records by their keys.
 This is more efficient than calling repartition and then sorting within each partition because it can push the sorting down into the shuffle machinery.
 
 Parameters:
 
 partitioner - (undocumented)
 
 comp - (undocumented)
 
 Returns:
 
 (undocumented)
 - sortByKey
```
public JavaPairRDD<K,V> sortByKey()
```
 Sort the RDD by key, so that each partition contains a sorted range of the elements in ascending order. Calling collect or save on the resulting RDD will return or output an ordered list of records (in the save case, they will be written to multiple part-X files in the filesystem, in order of the keys).
 
 Returns:
 
 (undocumented)
 - sortByKey
```
public JavaPairRDD<K,V> sortByKey(boolean ascending)
```
 Sort the RDD by key, so that each partition contains a sorted range of the elements. Calling collect or save on the resulting RDD will return or output an ordered list of records (in the save case, they will be written to multiple part-X files in the filesystem, in order of the keys).
 
 Parameters:
 
 ascending - (undocumented)
 
 Returns:
 
 (undocumented)
 - sortByKey
```
public JavaPairRDD<K,V> sortByKey(boolean ascending,
 int numPartitions)
```
 Sort the RDD by key, so that each partition contains a sorted range of the elements. Calling collect or save on the resulting RDD will return or output an ordered list of records (in the save case, they will be written to multiple part-X files in the filesystem, in order of the keys).
 
 Parameters:
 
 ascending - (undocumented)
 
 numPartitions - (undocumented)
 
 Returns:
 
 (undocumented)
 - sortByKey
```
public JavaPairRDD<K,V> sortByKey(java.util.Comparator<K> comp)
```
 Sort the RDD by key, so that each partition contains a sorted range of the elements. Calling collect or save on the resulting RDD will return or output an ordered list of records (in the save case, they will be written to multiple part-X files in the filesystem, in order of the keys).
 
 Parameters:
 
 comp - (undocumented)
 
 Returns:
 
 (undocumented)
 - sortByKey
```
public JavaPairRDD<K,V> sortByKey(java.util.Comparator<K> comp,
 boolean ascending)
```
 Sort the RDD by key, so that each partition contains a sorted range of the elements. Calling collect or save on the resulting RDD will return or output an ordered list of records (in the save case, they will be written to multiple part-X files in the filesystem, in order of the keys).
 
 Parameters:
 
 comp - (undocumented)
 
 ascending - (undocumented)
 
 Returns:
 
 (undocumented)
 - sortByKey
```
public JavaPairRDD<K,V> sortByKey(java.util.Comparator<K> comp,
 boolean ascending,
 int numPartitions)
```
 Sort the RDD by key, so that each partition contains a sorted range of the elements. Calling collect or save on the resulting RDD will return or output an ordered list of records (in the save case, they will be written to multiple part-X files in the filesystem, in order of the keys).
 
 Parameters:
 
 comp - (undocumented)
 
 ascending - (undocumented)
 
 numPartitions - (undocumented)
 
 Returns:
 
 (undocumented)
 - filterByRange
```
public JavaPairRDD<K,V> filterByRange(K lower,
 K upper)
```
 Return a RDD containing only the elements in the inclusive range lower to upper. If the RDD has been partitioned using a RangePartitioner, then this operation can be performed efficiently by only scanning the partitions that might contain matching elements. Otherwise, a standard filter is applied to all partitions.
 
 Parameters:
 
 lower - (undocumented)
 
 upper - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 3.1.0
 - filterByRange
```
public JavaPairRDD<K,V> filterByRange(java.util.Comparator<K> comp,
 K lower,
 K upper)
```
 Return a RDD containing only the elements in the inclusive range lower to upper. If the RDD has been partitioned using a RangePartitioner, then this operation can be performed efficiently by only scanning the partitions that might contain matching elements. Otherwise, a standard filter is applied to all partitions.
 
 Parameters:
 
 comp - (undocumented)
 
 lower - (undocumented)
 
 upper - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Since:
 
 3.1.0
 - keys
```
public JavaRDD<K> keys()
```
 Return an RDD with the keys of each tuple.
 
 Returns:
 
 (undocumented)
 - values
```
public JavaRDD<V> values()
```
 Return an RDD with the values of each tuple.
 
 Returns:
 
 (undocumented)
 - countApproxDistinctByKey
```
public JavaPairRDD<K,Long> countApproxDistinctByKey(double relativeSD,
 Partitioner partitioner)
```
 Return approximate number of distinct values for each key in this RDD.
 The algorithm used is based on streamlib's implementation of "HyperLogLog in Practice: Algorithmic Engineering of a State of The Art Cardinality Estimation Algorithm", available here.
 
 Parameters:
 
 relativeSD - Relative accuracy. Smaller values create counters that require more space. It must be greater than 0.000017.
 
 partitioner - partitioner of the resulting RDD.
 
 Returns:
 
 (undocumented)
 - countApproxDistinctByKey
```
public JavaPairRDD<K,Long> countApproxDistinctByKey(double relativeSD,
 int numPartitions)
```
 Return approximate number of distinct values for each key in this RDD.
 The algorithm used is based on streamlib's implementation of "HyperLogLog in Practice: Algorithmic Engineering of a State of The Art Cardinality Estimation Algorithm", available here.
 
 Parameters:
 
 relativeSD - Relative accuracy. Smaller values create counters that require more space. It must be greater than 0.000017.
 
 numPartitions - number of partitions of the resulting RDD.
 
 Returns:
 
 (undocumented)
 - countApproxDistinctByKey
```
public JavaPairRDD<K,Long> countApproxDistinctByKey(double relativeSD)
```
 Return approximate number of distinct values for each key in this RDD.
 The algorithm used is based on streamlib's implementation of "HyperLogLog in Practice: Algorithmic Engineering of a State of The Art Cardinality Estimation Algorithm", available here.
 
 Parameters:
 
 relativeSD - Relative accuracy. Smaller values create counters that require more space. It must be greater than 0.000017.
 
 Returns:
 
 (undocumented)
 - setName
```
public JavaPairRDD<K,V> setName(String name)
```
 Assign a name to this RDD

Class JavaPairRDD<K,V>

Constructor Summary

Method Summary

Methods inherited from class Object

Methods inherited from interface org.apache.spark.api.java.JavaRDDLike

Constructor Detail

JavaPairRDD

Method Detail

fromRDD

toRDD

fromJavaRDD

rdd

kClassTag

vClassTag

wrapRDD

classTag

cache

persist

unpersist

unpersist

distinct

distinct

filter

coalesce

coalesce

repartition

sample

sample

sampleByKey

sampleByKey

sampleByKeyExact

sampleByKeyExact

union

intersection

first

combineByKey

combineByKey

combineByKey

reduceByKey

reduceByKeyLocally

countByKey

countByKeyApprox

countByKeyApprox

aggregateByKey

aggregateByKey

aggregateByKey

foldByKey

foldByKey

foldByKey

reduceByKey

groupByKey

groupByKey

subtract

subtract

subtract

subtractByKey

subtractByKey

subtractByKey

partitionBy

join

leftOuterJoin

rightOuterJoin

fullOuterJoin

combineByKey

reduceByKey

groupByKey

join

join

leftOuterJoin

leftOuterJoin

rightOuterJoin

rightOuterJoin

fullOuterJoin

fullOuterJoin

collectAsMap

mapValues

flatMapValues

cogroup

cogroup

cogroup