PairRDDFunctions (Spark 2.4.3 JavaDoc)

Object
- org.apache.spark.rdd.PairRDDFunctions<K,V>

All Implemented Interfaces:

java.io.Serializable, Logging
```
public class PairRDDFunctions<K,V>
extends Object
implements Logging, scala.Serializable
```
Extra functions available on RDDs of (key, value) pairs through an implicit conversion.

See Also:

Serialized Form

Constructor Summary

Constructors
Constructor and Description
`PairRDDFunctions(RDD<scala.Tuple2<K,V>> self, scala.reflect.ClassTag<K> kt, scala.reflect.ClassTag<V> vt, scala.math.Ordering<K> ord)`

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`<U> RDD<scala.Tuple2<K,U>>`	`aggregateByKey(U zeroValue, scala.Function2<U,V,U> seqOp, scala.Function2<U,U,U> combOp, scala.reflect.ClassTag<U> evidence$3)` Aggregate the values of each key, using given combine functions and a neutral "zero value".
`<U> RDD<scala.Tuple2<K,U>>`	`aggregateByKey(U zeroValue, int numPartitions, scala.Function2<U,V,U> seqOp, scala.Function2<U,U,U> combOp, scala.reflect.ClassTag<U> evidence$2)` Aggregate the values of each key, using given combine functions and a neutral "zero value".
`<U> RDD<scala.Tuple2<K,U>>`	`aggregateByKey(U zeroValue, Partitioner partitioner, scala.Function2<U,V,U> seqOp, scala.Function2<U,U,U> combOp, scala.reflect.ClassTag<U> evidence$1)` Aggregate the values of each key, using given combine functions and a neutral "zero value".
`<W> RDD<scala.Tuple2<K,scala.Tuple2<scala.collection.Iterable<V>,scala.collection.Iterable<W>>>>`	`cogroup(RDD<scala.Tuple2<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> RDD<scala.Tuple2<K,scala.Tuple2<scala.collection.Iterable<V>,scala.collection.Iterable<W>>>>`	`cogroup(RDD<scala.Tuple2<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> RDD<scala.Tuple2<K,scala.Tuple2<scala.collection.Iterable<V>,scala.collection.Iterable<W>>>>`	`cogroup(RDD<scala.Tuple2<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> RDD<scala.Tuple2<K,scala.Tuple3<scala.collection.Iterable<V>,scala.collection.Iterable<W1>,scala.collection.Iterable<W2>>>>`	`cogroup(RDD<scala.Tuple2<K,W1>> other1, RDD<scala.Tuple2<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> RDD<scala.Tuple2<K,scala.Tuple3<scala.collection.Iterable<V>,scala.collection.Iterable<W1>,scala.collection.Iterable<W2>>>>`	`cogroup(RDD<scala.Tuple2<K,W1>> other1, RDD<scala.Tuple2<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> RDD<scala.Tuple2<K,scala.Tuple3<scala.collection.Iterable<V>,scala.collection.Iterable<W1>,scala.collection.Iterable<W2>>>>`	`cogroup(RDD<scala.Tuple2<K,W1>> other1, RDD<scala.Tuple2<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`.
`<W1,W2,W3> RDD<scala.Tuple2<K,scala.Tuple4<scala.collection.Iterable<V>,scala.collection.Iterable<W1>,scala.collection.Iterable<W2>,scala.collection.Iterable<W3>>>>`	`cogroup(RDD<scala.Tuple2<K,W1>> other1, RDD<scala.Tuple2<K,W2>> other2, RDD<scala.Tuple2<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> RDD<scala.Tuple2<K,scala.Tuple4<scala.collection.Iterable<V>,scala.collection.Iterable<W1>,scala.collection.Iterable<W2>,scala.collection.Iterable<W3>>>>`	`cogroup(RDD<scala.Tuple2<K,W1>> other1, RDD<scala.Tuple2<K,W2>> other2, RDD<scala.Tuple2<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> RDD<scala.Tuple2<K,scala.Tuple4<scala.collection.Iterable<V>,scala.collection.Iterable<W1>,scala.collection.Iterable<W2>,scala.collection.Iterable<W3>>>>`	`cogroup(RDD<scala.Tuple2<K,W1>> other1, RDD<scala.Tuple2<K,W2>> other2, RDD<scala.Tuple2<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`.
`scala.collection.Map<K,V>`	`collectAsMap()` Return the key-value pairs in this RDD to the master as a Map.
`<C> RDD<scala.Tuple2<K,C>>`	`combineByKey(scala.Function1<V,C> createCombiner, scala.Function2<C,V,C> mergeValue, scala.Function2<C,C,C> mergeCombiners)` Simplified version of combineByKeyWithClassTag that hash-partitions the resulting RDD using the existing partitioner/parallelism level.
`<C> RDD<scala.Tuple2<K,C>>`	`combineByKey(scala.Function1<V,C> createCombiner, scala.Function2<C,V,C> mergeValue, scala.Function2<C,C,C> mergeCombiners, int numPartitions)` Simplified version of combineByKeyWithClassTag that hash-partitions the output RDD.
`<C> RDD<scala.Tuple2<K,C>>`	`combineByKey(scala.Function1<V,C> createCombiner, scala.Function2<C,V,C> mergeValue, scala.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.
`<C> RDD<scala.Tuple2<K,C>>`	`combineByKeyWithClassTag(scala.Function1<V,C> createCombiner, scala.Function2<C,V,C> mergeValue, scala.Function2<C,C,C> mergeCombiners, scala.reflect.ClassTag<C> ct)` :: Experimental :: Simplified version of combineByKeyWithClassTag that hash-partitions the resulting RDD using the existing partitioner/parallelism level.
`<C> RDD<scala.Tuple2<K,C>>`	`combineByKeyWithClassTag(scala.Function1<V,C> createCombiner, scala.Function2<C,V,C> mergeValue, scala.Function2<C,C,C> mergeCombiners, int numPartitions, scala.reflect.ClassTag<C> ct)` :: Experimental :: Simplified version of combineByKeyWithClassTag that hash-partitions the output RDD.
`<C> RDD<scala.Tuple2<K,C>>`	`combineByKeyWithClassTag(scala.Function1<V,C> createCombiner, scala.Function2<C,V,C> mergeValue, scala.Function2<C,C,C> mergeCombiners, Partitioner partitioner, boolean mapSideCombine, Serializer serializer, scala.reflect.ClassTag<C> ct)` :: Experimental :: Generic function to combine the elements for each key using a custom set of aggregation functions.
`RDD<scala.Tuple2<K,Object>>`	`countApproxDistinctByKey(double relativeSD)` Return approximate number of distinct values for each key in this RDD.
`RDD<scala.Tuple2<K,Object>>`	`countApproxDistinctByKey(double relativeSD, int numPartitions)` Return approximate number of distinct values for each key in this RDD.
`RDD<scala.Tuple2<K,Object>>`	`countApproxDistinctByKey(double relativeSD, Partitioner partitioner)` Return approximate number of distinct values for each key in this RDD.
`RDD<scala.Tuple2<K,Object>>`	`countApproxDistinctByKey(int p, int sp, Partitioner partitioner)` Return approximate number of distinct values for each key in this RDD.
`scala.collection.Map<K,Object>`	`countByKey()` Count the number of elements for each key, collecting the results to a local Map.
`PartialResult<scala.collection.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.
`<U> RDD<scala.Tuple2<K,U>>`	`flatMapValues(scala.Function1<V,scala.collection.TraversableOnce<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.
`RDD<scala.Tuple2<K,V>>`	`foldByKey(V zeroValue, scala.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.).
`RDD<scala.Tuple2<K,V>>`	`foldByKey(V zeroValue, int numPartitions, scala.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.).
`RDD<scala.Tuple2<K,V>>`	`foldByKey(V zeroValue, Partitioner partitioner, scala.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.).
`<W> RDD<scala.Tuple2<K,scala.Tuple2<scala.Option<V>,scala.Option<W>>>>`	`fullOuterJoin(RDD<scala.Tuple2<K,W>> other)` Perform a full outer join of `this` and `other`.
`<W> RDD<scala.Tuple2<K,scala.Tuple2<scala.Option<V>,scala.Option<W>>>>`	`fullOuterJoin(RDD<scala.Tuple2<K,W>> other, int numPartitions)` Perform a full outer join of `this` and `other`.
`<W> RDD<scala.Tuple2<K,scala.Tuple2<scala.Option<V>,scala.Option<W>>>>`	`fullOuterJoin(RDD<scala.Tuple2<K,W>> other, Partitioner partitioner)` Perform a full outer join of `this` and `other`.
`RDD<scala.Tuple2<K,scala.collection.Iterable<V>>>`	`groupByKey()` Group the values for each key in the RDD into a single sequence.
`RDD<scala.Tuple2<K,scala.collection.Iterable<V>>>`	`groupByKey(int numPartitions)` Group the values for each key in the RDD into a single sequence.
`RDD<scala.Tuple2<K,scala.collection.Iterable<V>>>`	`groupByKey(Partitioner partitioner)` Group the values for each key in the RDD into a single sequence.
`<W> RDD<scala.Tuple2<K,scala.Tuple2<scala.collection.Iterable<V>,scala.collection.Iterable<W>>>>`	`groupWith(RDD<scala.Tuple2<K,W>> other)` Alias for cogroup.
`<W1,W2> RDD<scala.Tuple2<K,scala.Tuple3<scala.collection.Iterable<V>,scala.collection.Iterable<W1>,scala.collection.Iterable<W2>>>>`	`groupWith(RDD<scala.Tuple2<K,W1>> other1, RDD<scala.Tuple2<K,W2>> other2)` Alias for cogroup.
`<W1,W2,W3> RDD<scala.Tuple2<K,scala.Tuple4<scala.collection.Iterable<V>,scala.collection.Iterable<W1>,scala.collection.Iterable<W2>,scala.collection.Iterable<W3>>>>`	`groupWith(RDD<scala.Tuple2<K,W1>> other1, RDD<scala.Tuple2<K,W2>> other2, RDD<scala.Tuple2<K,W3>> other3)` Alias for cogroup.
`<W> RDD<scala.Tuple2<K,scala.Tuple2<V,W>>>`	`join(RDD<scala.Tuple2<K,W>> other)` Return an RDD containing all pairs of elements with matching keys in `this` and `other`.
`<W> RDD<scala.Tuple2<K,scala.Tuple2<V,W>>>`	`join(RDD<scala.Tuple2<K,W>> other, int numPartitions)` Return an RDD containing all pairs of elements with matching keys in `this` and `other`.
`<W> RDD<scala.Tuple2<K,scala.Tuple2<V,W>>>`	`join(RDD<scala.Tuple2<K,W>> other, Partitioner partitioner)` Return an RDD containing all pairs of elements with matching keys in `this` and `other`.
`RDD<K>`	`keys()` Return an RDD with the keys of each tuple.
`<W> RDD<scala.Tuple2<K,scala.Tuple2<V,scala.Option<W>>>>`	`leftOuterJoin(RDD<scala.Tuple2<K,W>> other)` Perform a left outer join of `this` and `other`.
`<W> RDD<scala.Tuple2<K,scala.Tuple2<V,scala.Option<W>>>>`	`leftOuterJoin(RDD<scala.Tuple2<K,W>> other, int numPartitions)` Perform a left outer join of `this` and `other`.
`<W> RDD<scala.Tuple2<K,scala.Tuple2<V,scala.Option<W>>>>`	`leftOuterJoin(RDD<scala.Tuple2<K,W>> other, Partitioner partitioner)` Perform a left outer join of `this` and `other`.
`scala.collection.Seq<V>`	`lookup(K key)` Return the list of values in the RDD for key `key`.
`<U> RDD<scala.Tuple2<K,U>>`	`mapValues(scala.Function1<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.
`RDD<scala.Tuple2<K,V>>`	`partitionBy(Partitioner partitioner)` Return a copy of the RDD partitioned using the specified partitioner.
`RDD<scala.Tuple2<K,V>>`	`reduceByKey(scala.Function2<V,V,V> func)` Merge the values for each key using an associative and commutative reduce function.
`RDD<scala.Tuple2<K,V>>`	`reduceByKey(scala.Function2<V,V,V> func, int numPartitions)` Merge the values for each key using an associative and commutative reduce function.
`RDD<scala.Tuple2<K,V>>`	`reduceByKey(Partitioner partitioner, scala.Function2<V,V,V> func)` Merge the values for each key using an associative and commutative reduce function.
`scala.collection.Map<K,V>`	`reduceByKeyLocally(scala.Function2<V,V,V> func)` Merge the values for each key using an associative and commutative reduce function, but return the results immediately to the master as a Map.
`<W> RDD<scala.Tuple2<K,scala.Tuple2<scala.Option<V>,W>>>`	`rightOuterJoin(RDD<scala.Tuple2<K,W>> other)` Perform a right outer join of `this` and `other`.
`<W> RDD<scala.Tuple2<K,scala.Tuple2<scala.Option<V>,W>>>`	`rightOuterJoin(RDD<scala.Tuple2<K,W>> other, int numPartitions)` Perform a right outer join of `this` and `other`.
`<W> RDD<scala.Tuple2<K,scala.Tuple2<scala.Option<V>,W>>>`	`rightOuterJoin(RDD<scala.Tuple2<K,W>> other, Partitioner partitioner)` Perform a right outer join of `this` and `other`.
`RDD<scala.Tuple2<K,V>>`	`sampleByKey(boolean withReplacement, scala.collection.Map<K,Object> fractions, long seed)` Return a subset of this RDD sampled by key (via stratified sampling).
`RDD<scala.Tuple2<K,V>>`	`sampleByKeyExact(boolean withReplacement, scala.collection.Map<K,Object> 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.
`void`	`saveAsHadoopFile(String path, Class<?> keyClass, Class<?> valueClass, Class<? extends org.apache.hadoop.mapred.OutputFormat<?,?>> outputFormatClass, Class<? extends org.apache.hadoop.io.compress.CompressionCodec> codec)` Output the RDD to any Hadoop-supported file system, using a Hadoop `OutputFormat` class supporting the key and value types K and V in this RDD.
`void`	`saveAsHadoopFile(String path, Class<?> keyClass, Class<?> valueClass, Class<? extends org.apache.hadoop.mapred.OutputFormat<?,?>> outputFormatClass, org.apache.hadoop.mapred.JobConf conf, scala.Option<Class<? extends org.apache.hadoop.io.compress.CompressionCodec>> codec)` Output the RDD to any Hadoop-supported file system, using a Hadoop `OutputFormat` class supporting the key and value types K and V in this RDD.
`<F extends org.apache.hadoop.mapred.OutputFormat<K,V>> void`	`saveAsHadoopFile(String path, Class<? extends org.apache.hadoop.io.compress.CompressionCodec> codec, scala.reflect.ClassTag<F> fm)` Output the RDD to any Hadoop-supported file system, using a Hadoop `OutputFormat` class supporting the key and value types K and V in this RDD.
`<F extends org.apache.hadoop.mapred.OutputFormat<K,V>> void`	`saveAsHadoopFile(String path, scala.reflect.ClassTag<F> fm)` Output the RDD to any Hadoop-supported file system, using a Hadoop `OutputFormat` class supporting the key and value types K and V in this RDD.
`void`	`saveAsNewAPIHadoopDataset(org.apache.hadoop.conf.Configuration conf)` Output the RDD to any Hadoop-supported storage system with new Hadoop API, using a Hadoop Configuration object for that storage system.
`void`	`saveAsNewAPIHadoopFile(String path, Class<?> keyClass, Class<?> valueClass, Class<? extends org.apache.hadoop.mapreduce.OutputFormat<?,?>> outputFormatClass, org.apache.hadoop.conf.Configuration conf)` Output the RDD to any Hadoop-supported file system, using a new Hadoop API `OutputFormat` (mapreduce.OutputFormat) object supporting the key and value types K and V in this RDD.
`<F extends org.apache.hadoop.mapreduce.OutputFormat<K,V>> void`	`saveAsNewAPIHadoopFile(String path, scala.reflect.ClassTag<F> fm)` Output the RDD to any Hadoop-supported file system, using a new Hadoop API `OutputFormat` (mapreduce.OutputFormat) object supporting the key and value types K and V in this RDD.
`<W> RDD<scala.Tuple2<K,V>>`	`subtractByKey(RDD<scala.Tuple2<K,W>> other, scala.reflect.ClassTag<W> evidence$4)` Return an RDD with the pairs from `this` whose keys are not in `other`.
`<W> RDD<scala.Tuple2<K,V>>`	`subtractByKey(RDD<scala.Tuple2<K,W>> other, int numPartitions, scala.reflect.ClassTag<W> evidence$5)` Return an RDD with the pairs from `this` whose keys are not in `other`.
`<W> RDD<scala.Tuple2<K,V>>`	`subtractByKey(RDD<scala.Tuple2<K,W>> other, Partitioner p, scala.reflect.ClassTag<W> evidence$6)` Return an RDD with the pairs from `this` whose keys are not in `other`.
`RDD<V>`	`values()` Return an RDD with the values of each tuple.

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

Methods inherited from interface org.apache.spark.internal.Logging
initializeLogging, initializeLogIfNecessary, initializeLogIfNecessary, isTraceEnabled, log_, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning

- Constructor Detail
 - PairRDDFunctions
```
public PairRDDFunctions(RDD<scala.Tuple2<K,V>> self,
 scala.reflect.ClassTag<K> kt,
 scala.reflect.ClassTag<V> vt,
 scala.math.Ordering<K> ord)
```
- Method Detail
 - combineByKeyWithClassTag
```
public <C> RDD<scala.Tuple2<K,C>> combineByKeyWithClassTag(scala.Function1<V,C> createCombiner,
 scala.Function2<C,V,C> mergeValue,
 scala.Function2<C,C,C> mergeCombiners,
 Partitioner partitioner,
 boolean mapSideCombine,
 Serializer serializer,
 scala.reflect.ClassTag<C> ct)
```
 :: Experimental :: Generic function to combine the elements for each key using a custom set of aggregation functions. Turns an RDD[(K, V)] into a result of type RDD[(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, 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)
 
 ct - (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, Seq[Int]).
 - combineByKey
```
public <C> RDD<scala.Tuple2<K,C>> combineByKey(scala.Function1<V,C> createCombiner,
 scala.Function2<C,V,C> mergeValue,
 scala.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. This method is here for backward compatibility. It does not provide combiner classtag information to the shuffle.
 
 Parameters:
 
 createCombiner - (undocumented)
 
 mergeValue - (undocumented)
 
 mergeCombiners - (undocumented)
 
 partitioner - (undocumented)
 
 mapSideCombine - (undocumented)
 
 serializer - (undocumented)
 
 Returns:
 
 (undocumented)
 
 See Also:
 
 combineByKeyWithClassTag
 - combineByKey
```
public <C> RDD<scala.Tuple2<K,C>> combineByKey(scala.Function1<V,C> createCombiner,
 scala.Function2<C,V,C> mergeValue,
 scala.Function2<C,C,C> mergeCombiners,
 int numPartitions)
```
 Simplified version of combineByKeyWithClassTag that hash-partitions the output RDD. This method is here for backward compatibility. It does not provide combiner classtag information to the shuffle.
 
 Parameters:
 
 createCombiner - (undocumented)
 
 mergeValue - (undocumented)
 
 mergeCombiners - (undocumented)
 
 numPartitions - (undocumented)
 
 Returns:
 
 (undocumented)
 
 See Also:
 
 combineByKeyWithClassTag
 - combineByKeyWithClassTag
```
public <C> RDD<scala.Tuple2<K,C>> combineByKeyWithClassTag(scala.Function1<V,C> createCombiner,
 scala.Function2<C,V,C> mergeValue,
 scala.Function2<C,C,C> mergeCombiners,
 int numPartitions,
 scala.reflect.ClassTag<C> ct)
```
 :: Experimental :: Simplified version of combineByKeyWithClassTag that hash-partitions the output RDD.
 
 Parameters:
 
 createCombiner - (undocumented)
 
 mergeValue - (undocumented)
 
 mergeCombiners - (undocumented)
 
 numPartitions - (undocumented)
 
 ct - (undocumented)
 
 Returns:
 
 (undocumented)
 - aggregateByKey
```
public  RDD<scala.Tuple2<K,U>> aggregateByKey(U zeroValue,
 Partitioner partitioner,
 scala.Function2<U,V,U> seqOp,
 scala.Function2<U,U,U> combOp,
 scala.reflect.ClassTag evidence$1)
```
 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)
 
 seqOp - (undocumented)
 
 combOp - (undocumented)
 
 evidence$1 - (undocumented)
 
 Returns:
 
 (undocumented)
 - aggregateByKey
```
public  RDD<scala.Tuple2<K,U>> aggregateByKey(U zeroValue,
 int numPartitions,
 scala.Function2<U,V,U> seqOp,
 scala.Function2<U,U,U> combOp,
 scala.reflect.ClassTag evidence$2)
```
 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)
 
 seqOp - (undocumented)
 
 combOp - (undocumented)
 
 evidence$2 - (undocumented)
 
 Returns:
 
 (undocumented)
 - aggregateByKey
```
public  RDD<scala.Tuple2<K,U>> aggregateByKey(U zeroValue,
 scala.Function2<U,V,U> seqOp,
 scala.Function2<U,U,U> combOp,
 scala.reflect.ClassTag evidence$3)
```
 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)
 
 seqOp - (undocumented)
 
 combOp - (undocumented)
 
 evidence$3 - (undocumented)
 
 Returns:
 
 (undocumented)
 - foldByKey
```
public RDD<scala.Tuple2<K,V>> foldByKey(V zeroValue,
 Partitioner partitioner,
 scala.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 RDD<scala.Tuple2<K,V>> foldByKey(V zeroValue,
 int numPartitions,
 scala.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 RDD<scala.Tuple2<K,V>> foldByKey(V zeroValue,
 scala.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)
 - sampleByKey
```
public RDD<scala.Tuple2<K,V>> sampleByKey(boolean withReplacement,
 scala.collection.Map<K,Object> 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 - whether to sample with or without replacement
 
 fractions - map of specific keys to sampling rates
 
 seed - seed for the random number generator
 
 Returns:
 
 RDD containing the sampled subset
 - sampleByKeyExact
```
public RDD<scala.Tuple2<K,V>> sampleByKeyExact(boolean withReplacement,
 scala.collection.Map<K,Object> 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 - whether to sample with or without replacement
 
 fractions - map of specific keys to sampling rates
 
 seed - seed for the random number generator
 
 Returns:
 
 RDD containing the sampled subset
 - reduceByKey
```
public RDD<scala.Tuple2<K,V>> reduceByKey(Partitioner partitioner,
 scala.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)
 - reduceByKey
```
public RDD<scala.Tuple2<K,V>> reduceByKey(scala.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)
 - reduceByKey
```
public RDD<scala.Tuple2<K,V>> reduceByKey(scala.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)
 - reduceByKeyLocally
```
public scala.collection.Map<K,V> reduceByKeyLocally(scala.Function2<V,V,V> func)
```
 Merge the values for each key using an associative and commutative reduce function, but return the results 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 scala.collection.Map<K,Object> countByKey()
```
 Count the number of elements for each key, collecting the results to a local Map.
 
 Returns:
 
 (undocumented)
 
 Note:
 
 This method should only be used if the resulting map is expected to be small, as the whole thing is loaded into the driver's memory. To handle very large results, consider using rdd.mapValues(_ => 1L).reduceByKey(_ + _), which returns an RDD[T, Long] instead of a map.
 - countByKeyApprox
```
public PartialResult<scala.collection.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.
 The confidence is the probability that the error bounds of the result will contain the true value. That is, if countApprox were called repeatedly with confidence 0.9, we would expect 90% of the results to contain the true count. The confidence must be in the range [0,1] or an exception will be thrown.
 
 Parameters:
 
 timeout - maximum time to wait for the job, in milliseconds
 
 confidence - the desired statistical confidence in the result
 
 Returns:
 
 a potentially incomplete result, with error bounds
 - countApproxDistinctByKey
```
public RDD<scala.Tuple2<K,Object>> countApproxDistinctByKey(int p,
 int sp,
 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.
 The relative accuracy is approximately 1.054 / sqrt(2^p). Setting a nonzero (sp is greater than p) would trigger sparse representation of registers, which may reduce the memory consumption and increase accuracy when the cardinality is small.
 
 Parameters:
 
 p - The precision value for the normal set. p must be a value between 4 and sp if sp is not zero (32 max).
 
 sp - The precision value for the sparse set, between 0 and 32. If sp equals 0, the sparse representation is skipped.
 
 partitioner - Partitioner to use for the resulting RDD.
 
 Returns:
 
 (undocumented)
 - countApproxDistinctByKey
```
public RDD<scala.Tuple2<K,Object>> 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 RDD<scala.Tuple2<K,Object>> 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 RDD<scala.Tuple2<K,Object>> 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)
 - groupByKey
```
public RDD<scala.Tuple2<K,scala.collection.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. The ordering of elements within each group is not guaranteed, and may even differ each time the resulting RDD is evaluated.
 
 Parameters:
 
 partitioner - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Note:
 
 This operation may be very expensive. If you are grouping in order to perform an aggregation (such as a sum or average) over each key, using PairRDDFunctions.aggregateByKey or PairRDDFunctions.reduceByKey will provide much better performance.
 , As currently implemented, groupByKey must be able to hold all the key-value pairs for any key in memory. If a key has too many values, it can result in an OutOfMemoryError.
 - groupByKey
```
public RDD<scala.Tuple2<K,scala.collection.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. The ordering of elements within each group is not guaranteed, and may even differ each time the resulting RDD is evaluated.
 
 Parameters:
 
 numPartitions - (undocumented)
 
 Returns:
 
 (undocumented)
 
 Note:
 
 This operation may be very expensive. If you are grouping in order to perform an aggregation (such as a sum or average) over each key, using PairRDDFunctions.aggregateByKey or PairRDDFunctions.reduceByKey will provide much better performance.
 , As currently implemented, groupByKey must be able to hold all the key-value pairs for any key in memory. If a key has too many values, it can result in an OutOfMemoryError.
 - partitionBy
```
public RDD<scala.Tuple2<K,V>> partitionBy(Partitioner partitioner)
```
 Return a copy of the RDD partitioned using the specified partitioner.
 
 Parameters:
 
 partitioner - (undocumented)
 
 Returns:
 
 (undocumented)
 - join
```
public <W> RDD<scala.Tuple2<K,scala.Tuple2<V,W>>> join(RDD<scala.Tuple2<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> RDD<scala.Tuple2<K,scala.Tuple2<V,scala.Option<W>>>> leftOuterJoin(RDD<scala.Tuple2<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> RDD<scala.Tuple2<K,scala.Tuple2<scala.Option<V>,W>>> rightOuterJoin(RDD<scala.Tuple2<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> RDD<scala.Tuple2<K,scala.Tuple2<scala.Option<V>,scala.Option<W>>>> fullOuterJoin(RDD<scala.Tuple2<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> RDD<scala.Tuple2<K,C>> combineByKey(scala.Function1<V,C> createCombiner,
 scala.Function2<C,V,C> mergeValue,
 scala.Function2<C,C,C> mergeCombiners)
```
 Simplified version of combineByKeyWithClassTag that hash-partitions the resulting RDD using the existing partitioner/parallelism level. This method is here for backward compatibility. It does not provide combiner classtag information to the shuffle.
 
 Parameters:
 
 createCombiner - (undocumented)
 
 mergeValue - (undocumented)
 
 mergeCombiners - (undocumented)
 
 Returns:
 
 (undocumented)
 
 See Also:
 
 combineByKeyWithClassTag
 - combineByKeyWithClassTag
```
public <C> RDD<scala.Tuple2<K,C>> combineByKeyWithClassTag(scala.Function1<V,C> createCombiner,
 scala.Function2<C,V,C> mergeValue,
 scala.Function2<C,C,C> mergeCombiners,
 scala.reflect.ClassTag<C> ct)
```
 :: Experimental :: Simplified version of combineByKeyWithClassTag that hash-partitions the resulting RDD using the existing partitioner/parallelism level.
 
 Parameters:
 
 createCombiner - (undocumented)
 
 mergeValue - (undocumented)
 
 mergeCombiners - (undocumented)
 
 ct - (undocumented)
 
 Returns:
 
 (undocumented)
 - groupByKey
```
public RDD<scala.Tuple2<K,scala.collection.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. The ordering of elements within each group is not guaranteed, and may even differ each time the resulting RDD is evaluated.
 
 Returns:
 
 (undocumented)
 
 Note:
 
 This operation may be very expensive. If you are grouping in order to perform an aggregation (such as a sum or average) over each key, using PairRDDFunctions.aggregateByKey or PairRDDFunctions.reduceByKey will provide much better performance.
 - join
```
public <W> RDD<scala.Tuple2<K,scala.Tuple2<V,W>>> join(RDD<scala.Tuple2<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> RDD<scala.Tuple2<K,scala.Tuple2<V,W>>> join(RDD<scala.Tuple2<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> RDD<scala.Tuple2<K,scala.Tuple2<V,scala.Option<W>>>> leftOuterJoin(RDD<scala.Tuple2<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> RDD<scala.Tuple2<K,scala.Tuple2<V,scala.Option<W>>>> leftOuterJoin(RDD<scala.Tuple2<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> RDD<scala.Tuple2<K,scala.Tuple2<scala.Option<V>,W>>> rightOuterJoin(RDD<scala.Tuple2<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> RDD<scala.Tuple2<K,scala.Tuple2<scala.Option<V>,W>>> rightOuterJoin(RDD<scala.Tuple2<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> RDD<scala.Tuple2<K,scala.Tuple2<scala.Option<V>,scala.Option<W>>>> fullOuterJoin(RDD<scala.Tuple2<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> RDD<scala.Tuple2<K,scala.Tuple2<scala.Option<V>,scala.Option<W>>>> fullOuterJoin(RDD<scala.Tuple2<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 scala.collection.Map<K,V> collectAsMap()
```
 Return the key-value pairs in this RDD to the master as a Map.
 Warning: this doesn't return a multimap (so if you have multiple values to the same key, only one value per key is preserved in the map returned)
 
 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  RDD<scala.Tuple2<K,U>> mapValues(scala.Function1<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  RDD<scala.Tuple2<K,U>> flatMapValues(scala.Function1<V,scala.collection.TraversableOnce> 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 <W1,W2,W3> RDD<scala.Tuple2<K,scala.Tuple4<scala.collection.Iterable<V>,scala.collection.Iterable<W1>,scala.collection.Iterable<W2>,scala.collection.Iterable<W3>>>> cogroup(RDD<scala.Tuple2<K,W1>> other1,
 RDD<scala.Tuple2<K,W2>> other2,
 RDD<scala.Tuple2<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> RDD<scala.Tuple2<K,scala.Tuple2<scala.collection.Iterable<V>,scala.collection.Iterable<W>>>> cogroup(RDD<scala.Tuple2<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> RDD<scala.Tuple2<K,scala.Tuple3<scala.collection.Iterable<V>,scala.collection.Iterable<W1>,scala.collection.Iterable<W2>>>> cogroup(RDD<scala.Tuple2<K,W1>> other1,
 RDD<scala.Tuple2<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> RDD<scala.Tuple2<K,scala.Tuple4<scala.collection.Iterable<V>,scala.collection.Iterable<W1>,scala.collection.Iterable<W2>,scala.collection.Iterable<W3>>>> cogroup(RDD<scala.Tuple2<K,W1>> other1,
 RDD<scala.Tuple2<K,W2>> other2,
 RDD<scala.Tuple2<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> RDD<scala.Tuple2<K,scala.Tuple2<scala.collection.Iterable<V>,scala.collection.Iterable<W>>>> cogroup(RDD<scala.Tuple2<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> RDD<scala.Tuple2<K,scala.Tuple3<scala.collection.Iterable<V>,scala.collection.Iterable<W1>,scala.collection.Iterable<W2>>>> cogroup(RDD<scala.Tuple2<K,W1>> other1,
 RDD<scala.Tuple2<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 <W> RDD<scala.Tuple2<K,scala.Tuple2<scala.collection.Iterable<V>,scala.collection.Iterable<W>>>> cogroup(RDD<scala.Tuple2<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> RDD<scala.Tuple2<K,scala.Tuple3<scala.collection.Iterable<V>,scala.collection.Iterable<W1>,scala.collection.Iterable<W2>>>> cogroup(RDD<scala.Tuple2<K,W1>> other1,
 RDD<scala.Tuple2<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> RDD<scala.Tuple2<K,scala.Tuple4<scala.collection.Iterable<V>,scala.collection.Iterable<W1>,scala.collection.Iterable<W2>,scala.collection.Iterable<W3>>>> cogroup(RDD<scala.Tuple2<K,W1>> other1,
 RDD<scala.Tuple2<K,W2>> other2,
 RDD<scala.Tuple2<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> RDD<scala.Tuple2<K,scala.Tuple2<scala.collection.Iterable<V>,scala.collection.Iterable<W>>>> groupWith(RDD<scala.Tuple2<K,W>> other)
```
 Alias for cogroup.
 - groupWith
```
public <W1,W2> RDD<scala.Tuple2<K,scala.Tuple3<scala.collection.Iterable<V>,scala.collection.Iterable<W1>,scala.collection.Iterable<W2>>>> groupWith(RDD<scala.Tuple2<K,W1>> other1,
 RDD<scala.Tuple2<K,W2>> other2)
```
 Alias for cogroup.
 - groupWith
```
public <W1,W2,W3> RDD<scala.Tuple2<K,scala.Tuple4<scala.collection.Iterable<V>,scala.collection.Iterable<W1>,scala.collection.Iterable<W2>,scala.collection.Iterable<W3>>>> groupWith(RDD<scala.Tuple2<K,W1>> other1,
 RDD<scala.Tuple2<K,W2>> other2,
 RDD<scala.Tuple2<K,W3>> other3)
```
 Alias for cogroup.
 - subtractByKey
```
public <W> RDD<scala.Tuple2<K,V>> subtractByKey(RDD<scala.Tuple2<K,W>> other,
 scala.reflect.ClassTag<W> evidence$4)
```
 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 less than or equal to us.
 
 Parameters:
 
 other - (undocumented)
 
 evidence$4 - (undocumented)
 
 Returns:
 
 (undocumented)
 - subtractByKey
```
public <W> RDD<scala.Tuple2<K,V>> subtractByKey(RDD<scala.Tuple2<K,W>> other,
 int numPartitions,
 scala.reflect.ClassTag<W> evidence$5)
```
 Return an RDD with the pairs from this whose keys are not in other.
 
 Parameters:
 
 other - (undocumented)
 
 numPartitions - (undocumented)
 
 evidence$5 - (undocumented)
 
 Returns:
 
 (undocumented)
 - subtractByKey
```
public <W> RDD<scala.Tuple2<K,V>> subtractByKey(RDD<scala.Tuple2<K,W>> other,
 Partitioner p,
 scala.reflect.ClassTag<W> evidence$6)
```
 Return an RDD with the pairs from this whose keys are not in other.
 
 Parameters:
 
 other - (undocumented)
 
 p - (undocumented)
 
 evidence$6 - (undocumented)
 
 Returns:
 
 (undocumented)
 - lookup
```
public scala.collection.Seq<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<K,V>> void saveAsHadoopFile(String path,
 scala.reflect.ClassTag<F> fm)
```
 Output the RDD to any Hadoop-supported file system, using a Hadoop OutputFormat class supporting the key and value types K and V in this RDD.
 
 Parameters:
 
 path - (undocumented)
 
 fm - (undocumented)
 - saveAsHadoopFile
```
public <F extends org.apache.hadoop.mapred.OutputFormat<K,V>> void saveAsHadoopFile(String path,
 Class<? extends org.apache.hadoop.io.compress.CompressionCodec> codec,
 scala.reflect.ClassTag<F> fm)
```
 Output the RDD to any Hadoop-supported file system, using a Hadoop OutputFormat class supporting the key and value types K and V in this RDD. Compress the result with the supplied codec.
 
 Parameters:
 
 path - (undocumented)
 
 codec - (undocumented)
 
 fm - (undocumented)
 - saveAsNewAPIHadoopFile
```
public <F extends org.apache.hadoop.mapreduce.OutputFormat<K,V>> void saveAsNewAPIHadoopFile(String path,
 scala.reflect.ClassTag<F> fm)
```
 Output the RDD to any Hadoop-supported file system, using a new Hadoop API OutputFormat (mapreduce.OutputFormat) object supporting the key and value types K and V in this RDD.
 
 Parameters:
 
 path - (undocumented)
 
 fm - (undocumented)
 - saveAsNewAPIHadoopFile
```
public void saveAsNewAPIHadoopFile(String path,
 Class<?> keyClass,
 Class<?> valueClass,
 Class<? extends org.apache.hadoop.mapreduce.OutputFormat<?,?>> outputFormatClass,
 org.apache.hadoop.conf.Configuration conf)
```
 Output the RDD to any Hadoop-supported file system, using a new Hadoop API OutputFormat (mapreduce.OutputFormat) object supporting the key and value types K and V in this RDD.
 
 Parameters:
 
 path - (undocumented)
 
 keyClass - (undocumented)
 
 valueClass - (undocumented)
 
 outputFormatClass - (undocumented)
 
 conf - (undocumented)
 - saveAsHadoopFile
```
public void saveAsHadoopFile(String path,
 Class<?> keyClass,
 Class<?> valueClass,
 Class<? extends org.apache.hadoop.mapred.OutputFormat<?,?>> outputFormatClass,
 Class<? extends org.apache.hadoop.io.compress.CompressionCodec> codec)
```
 Output the RDD to any Hadoop-supported file system, using a Hadoop OutputFormat class supporting the key and value types K and V in this RDD. Compress with the supplied codec.
 
 Parameters:
 
 path - (undocumented)
 
 keyClass - (undocumented)
 
 valueClass - (undocumented)
 
 outputFormatClass - (undocumented)
 
 codec - (undocumented)
 - saveAsHadoopFile
```
public void saveAsHadoopFile(String path,
 Class<?> keyClass,
 Class<?> valueClass,
 Class<? extends org.apache.hadoop.mapred.OutputFormat<?,?>> outputFormatClass,
 org.apache.hadoop.mapred.JobConf conf,
 scala.Option<Class<? extends org.apache.hadoop.io.compress.CompressionCodec>> codec)
```
 Output the RDD to any Hadoop-supported file system, using a Hadoop OutputFormat class supporting the key and value types K and V in this RDD.
 
 Parameters:
 
 path - (undocumented)
 
 keyClass - (undocumented)
 
 valueClass - (undocumented)
 
 outputFormatClass - (undocumented)
 
 conf - (undocumented)
 
 codec - (undocumented)
 
 Note:
 
 We should make sure our tasks are idempotent when speculation is enabled, i.e. do not use output committer that writes data directly. There is an example in https://issues.apache.org/jira/browse/SPARK-10063 to show the bad result of using direct output committer with speculation enabled.
 - saveAsNewAPIHadoopDataset
```
public void saveAsNewAPIHadoopDataset(org.apache.hadoop.conf.Configuration conf)
```
 Output the RDD to any Hadoop-supported storage system with new Hadoop API, using a Hadoop Configuration object for that storage system. The Conf 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)
 
 Note:
 
 We should make sure our tasks are idempotent when speculation is enabled, i.e. do not use output committer that writes data directly. There is an example in https://issues.apache.org/jira/browse/SPARK-10063 to show the bad result of using direct output committer with speculation enabled.
 - 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)
 - keys
```
public RDD<K> keys()
```
 Return an RDD with the keys of each tuple.
 
 Returns:
 
 (undocumented)
 - values
```
public RDD<V> values()
```
 Return an RDD with the values of each tuple.
 
 Returns:
 
 (undocumented)

Class PairRDDFunctions<K,V>

Constructor Summary

Method Summary

Methods inherited from class Object

Methods inherited from interface org.apache.spark.internal.Logging

Constructor Detail

PairRDDFunctions

Method Detail

combineByKeyWithClassTag

combineByKey

combineByKey

combineByKeyWithClassTag

aggregateByKey

aggregateByKey

aggregateByKey

foldByKey

foldByKey

foldByKey

sampleByKey

sampleByKeyExact

reduceByKey

reduceByKey

reduceByKey

reduceByKeyLocally

countByKey

countByKeyApprox

countApproxDistinctByKey

countApproxDistinctByKey

countApproxDistinctByKey

countApproxDistinctByKey

groupByKey

groupByKey

partitionBy

join

leftOuterJoin

rightOuterJoin

fullOuterJoin

combineByKey

combineByKeyWithClassTag

groupByKey

join

join

leftOuterJoin

leftOuterJoin

rightOuterJoin

rightOuterJoin

fullOuterJoin

fullOuterJoin

collectAsMap

mapValues

flatMapValues

cogroup

cogroup

cogroup

cogroup

cogroup

cogroup

cogroup

cogroup

cogroup

groupWith

groupWith

groupWith

subtractByKey

subtractByKey

subtractByKey

lookup

saveAsHadoopFile

saveAsHadoopFile

saveAsNewAPIHadoopFile

saveAsNewAPIHadoopFile

saveAsHadoopFile

saveAsHadoopFile

saveAsNewAPIHadoopDataset

saveAsHadoopDataset

keys

values