Value Members

1.  final def !=(arg0: Any): Boolean

   Definition Classes

   AnyRef → Any

2.  final def ##(): Int

   Definition Classes

   AnyRef → Any

3.  final def ==(arg0: Any): Boolean

   Definition Classes

   AnyRef → Any

4.  def aggregate[U](zeroValue: U)(seqOp: Function2[U, (K, V), U], combOp: Function2[U, U, U]): U

   Aggregate the elements of each partition, and then the results for all the partitions, using given combine functions and a neutral "zero value".

   Aggregate the elements of each partition, and then the results for all the partitions, using given combine functions and a neutral "zero value". This function can return a different result type, U, than the type of this RDD, T. Thus, we need one operation for merging a T into an U and one operation for merging two U's, as in scala.TraversableOnce. Both of these functions are allowed to modify and return their first argument instead of creating a new U to avoid memory allocation.

   Definition Classes

   JavaRDDLike

5.  def aggregateByKey[U](zeroValue: U, seqFunc: Function2[U, V, U], combFunc: Function2[U, U, U]): JavaPairRDD[K, U]

   Aggregate the values of each key, using given combine functions and a neutral "zero value".

   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.

   Definition Classes

   JavaPairRDD

6.  def aggregateByKey[U](zeroValue: U, numPartitions: Int, seqFunc: Function2[U, V, U], combFunc: Function2[U, U, U]): JavaPairRDD[K, U]

   Aggregate the values of each key, using given combine functions and a neutral "zero value".

   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.

   Definition Classes

   JavaPairRDD

7.  def aggregateByKey[U](zeroValue: U, partitioner: Partitioner, seqFunc: Function2[U, V, U], combFunc: Function2[U, U, U]): JavaPairRDD[K, U]

   Aggregate the values of each key, using given combine functions and a neutral "zero value".

   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.

   Definition Classes

   JavaPairRDD

8.  final def asInstanceOf[T0]: T0

   Definition Classes

   Any

9.  def cache(): JavaPairRDD[K, V]

   Persist this RDD with the default storage level (MEMORY_ONLY).

   Persist this RDD with the default storage level (MEMORY_ONLY).

   Definition Classes

   JavaPairRDD

10.  def cartesian[U](other: JavaRDDLike[U, _]): JavaPairRDD[(K, V), U]

    Return the Cartesian product of this RDD and another one, that is, the RDD of all pairs of elements (a, b) where a is in this and b is in other.

    Return the Cartesian product of this RDD and another one, that is, the RDD of all pairs of elements (a, b) where a is in this and b is in other.

    Definition Classes

    JavaRDDLike

11.  def checkpoint(): Unit

    Mark this RDD for checkpointing.

    Mark this RDD for checkpointing. It will be saved to a file inside the checkpoint directory set with SparkContext.setCheckpointDir() and all references to its parent RDDs will be removed. This function must be called before any job has been executed on this RDD. It is strongly recommended that this RDD is persisted in memory, otherwise saving it on a file will require recomputation.

    Definition Classes

    JavaRDDLike

12.  val classTag: ClassTag[(K, V)]

    Definition Classes

    JavaPairRDD → JavaRDDLike

13.  def clone(): AnyRef

    Attributes

    protected[lang]

    Definition Classes

    AnyRef

    Annotations

    @throws( ... ) @native()

14.  def coalesce(numPartitions: Int, shuffle: Boolean): JavaPairRDD[K, V]

    Return a new RDD that is reduced into numPartitions partitions.

    Return a new RDD that is reduced into numPartitions partitions.

    Definition Classes

    JavaPairRDD

15.  def coalesce(numPartitions: Int): JavaPairRDD[K, V]

    Return a new RDD that is reduced into numPartitions partitions.

    Return a new RDD that is reduced into numPartitions partitions.

    Definition Classes

    JavaPairRDD

16.  def cogroup[W1, W2, W3](other1: JavaPairRDD[K, W1], other2: JavaPairRDD[K, W2], other3: JavaPairRDD[K, W3], numPartitions: Int): JavaPairRDD[K, (Iterable[V], Iterable[W1], Iterable[W2], Iterable[W3])]

    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.

    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.

    Definition Classes

    JavaPairRDD

17.  def cogroup[W1, W2](other1: JavaPairRDD[K, W1], other2: JavaPairRDD[K, W2], numPartitions: Int): JavaPairRDD[K, (Iterable[V], Iterable[W1], Iterable[W2])]

    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.

    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.

    Definition Classes

    JavaPairRDD

18.  def cogroup[W](other: JavaPairRDD[K, W], numPartitions: Int): JavaPairRDD[K, (Iterable[V], Iterable[W])]

    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.

    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.

    Definition Classes

    JavaPairRDD

19.  def cogroup[W1, W2, W3](other1: JavaPairRDD[K, W1], other2: JavaPairRDD[K, W2], other3: JavaPairRDD[K, W3]): JavaPairRDD[K, (Iterable[V], Iterable[W1], Iterable[W2], Iterable[W3])]

    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.

    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.

    Definition Classes

    JavaPairRDD

20.  def cogroup[W1, W2](other1: JavaPairRDD[K, W1], other2: JavaPairRDD[K, W2]): JavaPairRDD[K, (Iterable[V], Iterable[W1], Iterable[W2])]

    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.

    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.

    Definition Classes

    JavaPairRDD

21.  def cogroup[W](other: JavaPairRDD[K, W]): JavaPairRDD[K, (Iterable[V], Iterable[W])]

    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.

    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.

    Definition Classes

    JavaPairRDD

22.  def cogroup[W1, W2, W3](other1: JavaPairRDD[K, W1], other2: JavaPairRDD[K, W2], other3: JavaPairRDD[K, W3], partitioner: Partitioner): JavaPairRDD[K, (Iterable[V], Iterable[W1], Iterable[W2], Iterable[W3])]

    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.

    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.

    Definition Classes

    JavaPairRDD

23.  def cogroup[W1, W2](other1: JavaPairRDD[K, W1], other2: JavaPairRDD[K, W2], partitioner: Partitioner): JavaPairRDD[K, (Iterable[V], Iterable[W1], Iterable[W2])]

    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.

    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.

    Definition Classes

    JavaPairRDD

24.  def cogroup[W](other: JavaPairRDD[K, W], partitioner: Partitioner): JavaPairRDD[K, (Iterable[V], Iterable[W])]

    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.

    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.

    Definition Classes

    JavaPairRDD

25.  def collect(): List[(K, V)]

    Return an array that contains all of the elements in this RDD.

    Return an array that contains all of the elements in this RDD.

    Definition Classes

    JavaRDDLike

    Note

    this method should only be used if the resulting array is expected to be small, as all the data is loaded into the driver's memory.

26.  def collectAsMap(): Map[K, V]

    Return the key-value pairs in this RDD to the master as a Map.

    Return the key-value pairs in this RDD to the master as a Map.

    Definition Classes

    JavaPairRDD

    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.

27.  def collectAsync(): JavaFutureAction[List[(K, V)]]

    The asynchronous version of collect, which returns a future for retrieving an array containing all of the elements in this RDD.

    The asynchronous version of collect, which returns a future for retrieving an array containing all of the elements in this RDD.

    Definition Classes

    JavaRDDLike

    Note

    this method should only be used if the resulting array is expected to be small, as all the data is loaded into the driver's memory.

28.  def collectPartitions(partitionIds: Array[Int]): Array[List[(K, V)]]

    Return an array that contains all of the elements in a specific partition of this RDD.

    Return an array that contains all of the elements in a specific partition of this RDD.

    Definition Classes

    JavaRDDLike

29.  def combineByKey[C](createCombiner: Function[V, C], mergeValue: Function2[C, V, C], mergeCombiners: Function2[C, C, C]): JavaPairRDD[K, C]

    Simplified version of combineByKey that hash-partitions the resulting RDD using the existing partitioner/parallelism level and using map-side aggregation.

    Simplified version of combineByKey that hash-partitions the resulting RDD using the existing partitioner/parallelism level and using map-side aggregation.

    Definition Classes

    JavaPairRDD

30.  def combineByKey[C](createCombiner: Function[V, C], mergeValue: Function2[C, V, C], mergeCombiners: Function2[C, C, C], numPartitions: Int): JavaPairRDD[K, C]

    Simplified version of combineByKey that hash-partitions the output RDD and uses map-side aggregation.

    Simplified version of combineByKey that hash-partitions the output RDD and uses map-side aggregation.

    Definition Classes

    JavaPairRDD

31.  def combineByKey[C](createCombiner: Function[V, C], mergeValue: Function2[C, V, C], mergeCombiners: Function2[C, C, C], partitioner: Partitioner): JavaPairRDD[K, C]

    Generic function to combine the elements for each key using a custom set of aggregation functions.

    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.

    Definition Classes

    JavaPairRDD

    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]).

32.  def combineByKey[C](createCombiner: Function[V, C], mergeValue: Function2[C, V, C], mergeCombiners: Function2[C, C, C], partitioner: Partitioner, mapSideCombine: Boolean, serializer: Serializer): JavaPairRDD[K, C]

    Generic function to combine the elements for each key using a custom set of aggregation functions.

    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).

    Definition Classes

    JavaPairRDD

    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]).

33.  def context: SparkContext

    The org.apache.spark.SparkContext that this RDD was created on.

    The org.apache.spark.SparkContext that this RDD was created on.

    Definition Classes

    JavaRDDLike

34.  def count(): Long

    Return the number of elements in the RDD.

    Return the number of elements in the RDD.

    Definition Classes

    JavaRDDLike

35.  def countApprox(timeout: Long): PartialResult[BoundedDouble]

    Approximate version of count() that returns a potentially incomplete result within a timeout, even if not all tasks have finished.

    Approximate version of count() that returns a potentially incomplete result within a timeout, even if not all tasks have finished.

    timeout

    maximum time to wait for the job, in milliseconds

    Definition Classes

    JavaRDDLike

36.  def countApprox(timeout: Long, confidence: Double): PartialResult[BoundedDouble]

    Approximate version of count() that returns a potentially incomplete result within a timeout, even if not all tasks have finished.

    Approximate version of count() that returns a potentially incomplete result within a timeout, even if not all tasks have finished.

    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.

    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

    Definition Classes

    JavaRDDLike

37.  def countApproxDistinct(relativeSD: Double): Long

    Return approximate number of distinct elements in the RDD.

    Return approximate number of distinct elements in the 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.

    relativeSD

    Relative accuracy. Smaller values create counters that require more space. It must be greater than 0.000017.

    Definition Classes

    JavaRDDLike

38.  def countApproxDistinctByKey(relativeSD: Double): JavaPairRDD[K, Long]

    Return approximate number of distinct values for each key in this RDD.

    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.

    relativeSD

    Relative accuracy. Smaller values create counters that require more space. It must be greater than 0.000017.

    Definition Classes

    JavaPairRDD

39.  def countApproxDistinctByKey(relativeSD: Double, numPartitions: Int): JavaPairRDD[K, Long]

    Return approximate number of distinct values for each key in this RDD.

    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.

    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.

    Definition Classes

    JavaPairRDD

40.  def countApproxDistinctByKey(relativeSD: Double, partitioner: Partitioner): JavaPairRDD[K, Long]

    Return approximate number of distinct values for each key in this RDD.

    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.

    relativeSD

    Relative accuracy. Smaller values create counters that require more space. It must be greater than 0.000017.

    partitioner

    partitioner of the resulting RDD.

    Definition Classes

    JavaPairRDD

41.  def countAsync(): JavaFutureAction[Long]

    The asynchronous version of count, which returns a future for counting the number of elements in this RDD.

    The asynchronous version of count, which returns a future for counting the number of elements in this RDD.

    Definition Classes

    JavaRDDLike

42.  def countByKey(): Map[K, Long]

    Count the number of elements for each key, and return the result to the master as a Map.

    Count the number of elements for each key, and return the result to the master as a Map.

    Definition Classes

    JavaPairRDD

43.  def countByKeyApprox(timeout: Long, confidence: Double = 0.95): PartialResult[Map[K, BoundedDouble]]

    Approximate version of countByKey that can return a partial result if it does not finish within a timeout.

    Approximate version of countByKey that can return a partial result if it does not finish within a timeout.

    Definition Classes

    JavaPairRDD

44.  def countByKeyApprox(timeout: Long): PartialResult[Map[K, BoundedDouble]]

    Approximate version of countByKey that can return a partial result if it does not finish within a timeout.

    Approximate version of countByKey that can return a partial result if it does not finish within a timeout.

    Definition Classes

    JavaPairRDD

45.  def countByValue(): Map[(K, V), Long]

    Return the count of each unique value in this RDD as a map of (value, count) pairs.

    Return the count of each unique value in this RDD as a map of (value, count) pairs. The final combine step happens locally on the master, equivalent to running a single reduce task.

    Definition Classes

    JavaRDDLike

46.  def countByValueApprox(timeout: Long): PartialResult[Map[(K, V), BoundedDouble]]

    Approximate version of countByValue().

    Approximate version of countByValue().

    timeout

    maximum time to wait for the job, in milliseconds

    returns

    a potentially incomplete result, with error bounds

    Definition Classes

    JavaRDDLike

47.  def countByValueApprox(timeout: Long, confidence: Double): PartialResult[Map[(K, V), BoundedDouble]]

    Approximate version of countByValue().

    Approximate version of countByValue().

    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.

    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

    Definition Classes

    JavaRDDLike

48.  def distinct(numPartitions: Int): JavaPairRDD[K, V]

    Return a new RDD containing the distinct elements in this RDD.

    Return a new RDD containing the distinct elements in this RDD.

    Definition Classes

    JavaPairRDD

49.  def distinct(): JavaPairRDD[K, V]

    Return a new RDD containing the distinct elements in this RDD.

    Return a new RDD containing the distinct elements in this RDD.

    Definition Classes

    JavaPairRDD

50.  final def eq(arg0: AnyRef): Boolean

    Definition Classes

    AnyRef

51.  def equals(arg0: Any): Boolean

    Definition Classes

    AnyRef → Any

52.  def filter(f: Function[(K, V), Boolean]): JavaPairRDD[K, V]

    Return a new RDD containing only the elements that satisfy a predicate.

    Return a new RDD containing only the elements that satisfy a predicate.

    Definition Classes

    JavaPairRDD

53.  def filterByRange(comp: Comparator[K], lower: K, upper: K): JavaPairRDD[K, V]

    Return a RDD containing only the elements in the inclusive range lower to 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.

    Definition Classes

    JavaPairRDD

    Annotations

    @Since( "3.1.0" )

    Since

    3.1.0

54.  def filterByRange(lower: K, upper: K): JavaPairRDD[K, V]

    Return a RDD containing only the elements in the inclusive range lower to 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.

    Definition Classes

    JavaPairRDD

    Annotations

    @Since( "3.1.0" )

    Since

    3.1.0

55.  def finalize(): Unit

    Attributes

    protected[lang]

    Definition Classes

    AnyRef

    Annotations

    @throws( classOf[java.lang.Throwable] )

56.  def first(): (K, V)

    Return the first element in this RDD.

    Return the first element in this RDD.

    Definition Classes

    JavaPairRDD → JavaRDDLike

57.  def flatMap[U](f: FlatMapFunction[(K, V), U]): JavaRDD[U]

    Return a new RDD by first applying a function to all elements of this RDD, and then flattening the results.

    Return a new RDD by first applying a function to all elements of this RDD, and then flattening the results.

    Definition Classes

    JavaRDDLike

58.  def flatMapToDouble(f: DoubleFlatMapFunction[(K, V)]): JavaDoubleRDD

    Return a new RDD by first applying a function to all elements of this RDD, and then flattening the results.

    Return a new RDD by first applying a function to all elements of this RDD, and then flattening the results.

    Definition Classes

    JavaRDDLike

59.  def flatMapToPair[K2, V2](f: PairFlatMapFunction[(K, V), K2, V2]): JavaPairRDD[K2, V2]

    Return a new RDD by first applying a function to all elements of this RDD, and then flattening the results.

    Return a new RDD by first applying a function to all elements of this RDD, and then flattening the results.

    Definition Classes

    JavaRDDLike

60.  def flatMapValues[U](f: FlatMapFunction[V, U]): JavaPairRDD[K, U]

    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.

    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.

    Definition Classes

    JavaPairRDD

61.  def fold(zeroValue: (K, V))(f: Function2[(K, V), (K, V), (K, V)]): (K, V)

    Aggregate the elements of each partition, and then the results for all the partitions, using a given associative function and a neutral "zero value".

    Aggregate the elements of each partition, and then the results for all the partitions, using a given associative function and a neutral "zero value". The function op(t1, t2) is allowed to modify t1 and return it as its result value to avoid object allocation; however, it should not modify t2.

    This behaves somewhat differently from fold operations implemented for non-distributed collections in functional languages like Scala. This fold operation may be applied to partitions individually, and then fold those results into the final result, rather than apply the fold to each element sequentially in some defined ordering. For functions that are not commutative, the result may differ from that of a fold applied to a non-distributed collection.

    Definition Classes

    JavaRDDLike

62.  def foldByKey(zeroValue: V, func: Function2[V, V, V]): JavaPairRDD[K, V]

    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.).

    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.).

    Definition Classes

    JavaPairRDD

63.  def foldByKey(zeroValue: V, numPartitions: Int, func: Function2[V, V, V]): JavaPairRDD[K, V]

    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.).

    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.).

    Definition Classes

    JavaPairRDD

64.  def foldByKey(zeroValue: V, partitioner: Partitioner, func: Function2[V, V, V]): JavaPairRDD[K, V]

    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.).

    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.).

    Definition Classes

    JavaPairRDD

65.  def foreach(f: VoidFunction[(K, V)]): Unit

    Applies a function f to all elements of this RDD.

    Applies a function f to all elements of this RDD.

    Definition Classes

    JavaRDDLike

66.  def foreachAsync(f: VoidFunction[(K, V)]): JavaFutureAction[Void]

    The asynchronous version of the foreach action, which applies a function f to all the elements of this RDD.

    The asynchronous version of the foreach action, which applies a function f to all the elements of this RDD.

    Definition Classes

    JavaRDDLike

67.  def foreachPartition(f: VoidFunction[Iterator[(K, V)]]): Unit

    Applies a function f to each partition of this RDD.

    Applies a function f to each partition of this RDD.

    Definition Classes

    JavaRDDLike

68.  def foreachPartitionAsync(f: VoidFunction[Iterator[(K, V)]]): JavaFutureAction[Void]

    The asynchronous version of the foreachPartition action, which applies a function f to each partition of this RDD.

    The asynchronous version of the foreachPartition action, which applies a function f to each partition of this RDD.

    Definition Classes

    JavaRDDLike

69.  def fullOuterJoin[W](other: JavaPairRDD[K, W], numPartitions: Int): JavaPairRDD[K, (Optional[V], Optional[W])]

    Perform a full outer join of this and 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 into the given number of partitions.

    Definition Classes

    JavaPairRDD

70.  def fullOuterJoin[W](other: JavaPairRDD[K, W]): JavaPairRDD[K, (Optional[V], Optional[W])]

    Perform a full outer join of this and 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.

    Definition Classes

    JavaPairRDD

71.  def fullOuterJoin[W](other: JavaPairRDD[K, W], partitioner: Partitioner): JavaPairRDD[K, (Optional[V], Optional[W])]

    Perform a full outer join of this and 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. Uses the given Partitioner to partition the output RDD.

    Definition Classes

    JavaPairRDD

72.  def getCheckpointFile(): Optional[String]

    Gets the name of the file to which this RDD was checkpointed

    Gets the name of the file to which this RDD was checkpointed

    Definition Classes

    JavaRDDLike

73.  final def getClass(): Class[_]

    Definition Classes

    AnyRef → Any

    Annotations

    @native()

74.  def getNumPartitions: Int

    Return the number of partitions in this RDD.

    Return the number of partitions in this RDD.

    Definition Classes

    JavaRDDLike

    Annotations

    @Since( "1.6.0" )

75.  def getStorageLevel: StorageLevel

    Get the RDD's current storage level, or StorageLevel.NONE if none is set.

    Get the RDD's current storage level, or StorageLevel.NONE if none is set.

    Definition Classes

    JavaRDDLike

76.  def glom(): JavaRDD[List[(K, V)]]

    Return an RDD created by coalescing all elements within each partition into an array.

    Return an RDD created by coalescing all elements within each partition into an array.

    Definition Classes

    JavaRDDLike

77.  def groupBy[U](f: Function[(K, V), U], numPartitions: Int): JavaPairRDD[U, Iterable[(K, V)]]

    Return an RDD of grouped elements.

    Return an RDD of grouped elements. Each group consists of a key and a sequence of elements mapping to that key.

    Definition Classes

    JavaRDDLike

78.  def groupBy[U](f: Function[(K, V), U]): JavaPairRDD[U, Iterable[(K, V)]]

    Return an RDD of grouped elements.

    Return an RDD of grouped elements. Each group consists of a key and a sequence of elements mapping to that key.

    Definition Classes

    JavaRDDLike

79.  def groupByKey(): JavaPairRDD[K, Iterable[V]]

    Group the values for each key in the RDD into a single sequence.

    Group the values for each key in the RDD into a single sequence. Hash-partitions the resulting RDD with the existing partitioner/parallelism level.

    Definition Classes

    JavaPairRDD

    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.

80.  def groupByKey(numPartitions: Int): JavaPairRDD[K, Iterable[V]]

    Group the values for each key in the RDD into a single sequence.

    Group the values for each key in the RDD into a single sequence. Hash-partitions the resulting RDD with into numPartitions partitions.

    Definition Classes

    JavaPairRDD

    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.

81.  def groupByKey(partitioner: Partitioner): JavaPairRDD[K, Iterable[V]]

    Group the values for each key in the RDD into a single sequence.

    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.

    Definition Classes

    JavaPairRDD

    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.

82.  def groupWith[W1, W2, W3](other1: JavaPairRDD[K, W1], other2: JavaPairRDD[K, W2], other3: JavaPairRDD[K, W3]): JavaPairRDD[K, (Iterable[V], Iterable[W1], Iterable[W2], Iterable[W3])]

    Alias for cogroup.

    Alias for cogroup.

    Definition Classes

    JavaPairRDD

83.  def groupWith[W1, W2](other1: JavaPairRDD[K, W1], other2: JavaPairRDD[K, W2]): JavaPairRDD[K, (Iterable[V], Iterable[W1], Iterable[W2])]

    Alias for cogroup.

    Alias for cogroup.

    Definition Classes

    JavaPairRDD

84.  def groupWith[W](other: JavaPairRDD[K, W]): JavaPairRDD[K, (Iterable[V], Iterable[W])]

    Alias for cogroup.

    Alias for cogroup.

    Definition Classes

    JavaPairRDD

85.  def hashCode(): Int

    Definition Classes

    AnyRef → Any

    Annotations

    @native()

86.  def id: Int

    A unique ID for this RDD (within its SparkContext).

    A unique ID for this RDD (within its SparkContext).

    Definition Classes

    JavaRDDLike

87.  def intersection(other: JavaPairRDD[K, V]): JavaPairRDD[K, V]

    Return the intersection of this RDD and another one.

    Return the intersection of this RDD and another one. The output will not contain any duplicate elements, even if the input RDDs did.

    Definition Classes

    JavaPairRDD

    Note

    This method performs a shuffle internally.

88.  def isCheckpointed: Boolean

    Return whether this RDD has been checkpointed or not

    Return whether this RDD has been checkpointed or not

    Definition Classes

    JavaRDDLike

89.  def isEmpty(): Boolean
    returns

    true if and only if the RDD contains no elements at all. Note that an RDD may be empty even when it has at least 1 partition.

    Definition Classes

    JavaRDDLike

90.  final def isInstanceOf[T0]: Boolean

    Definition Classes

    Any

91.  def iterator(split: Partition, taskContext: TaskContext): Iterator[(K, V)]

    Internal method to this RDD; will read from cache if applicable, or otherwise compute it.

    Internal method to this RDD; will read from cache if applicable, or otherwise compute it. This should not be called by users directly, but is available for implementers of custom subclasses of RDD.

    Definition Classes

    JavaRDDLike

92.  def join[W](other: JavaPairRDD[K, W], numPartitions: Int): JavaPairRDD[K, (V, W)]

    Return an RDD containing all pairs of elements with matching keys in this and 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.

    Definition Classes

    JavaPairRDD

93.  def join[W](other: JavaPairRDD[K, W]): JavaPairRDD[K, (V, W)]

    Return an RDD containing all pairs of elements with matching keys in this and 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.

    Definition Classes

    JavaPairRDD

94.  def join[W](other: JavaPairRDD[K, W], partitioner: Partitioner): JavaPairRDD[K, (V, W)]

    Return an RDD containing all pairs of elements with matching keys in this and 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. Uses the given Partitioner to partition the output RDD.

    Definition Classes

    JavaPairRDD

95.  implicit val kClassTag: ClassTag[K]

    Definition Classes

    JavaNewHadoopRDD → JavaPairRDD

96.  def keyBy[U](f: Function[(K, V), U]): JavaPairRDD[U, (K, V)]

    Creates tuples of the elements in this RDD by applying f.

    Creates tuples of the elements in this RDD by applying f.

    Definition Classes

    JavaRDDLike

97.  def keys(): JavaRDD[K]

    Return an RDD with the keys of each tuple.

    Return an RDD with the keys of each tuple.

    Definition Classes

    JavaPairRDD

98.  def leftOuterJoin[W](other: JavaPairRDD[K, W], numPartitions: Int): JavaPairRDD[K, (V, Optional[W])]

    Perform a left outer join of this and 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 into numPartitions partitions.

    Definition Classes

    JavaPairRDD

99.  def leftOuterJoin[W](other: JavaPairRDD[K, W]): JavaPairRDD[K, (V, Optional[W])]

    Perform a left outer join of this and 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.

    Definition Classes

    JavaPairRDD

100.  def leftOuterJoin[W](other: JavaPairRDD[K, W], partitioner: Partitioner): JavaPairRDD[K, (V, Optional[W])]

     Perform a left outer join of this and 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. Uses the given Partitioner to partition the output RDD.

     Definition Classes

     JavaPairRDD

101.  def lookup(key: K): List[V]

     Return the list of values in the RDD for key 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.

     Definition Classes

     JavaPairRDD

102.  def map[R](f: Function[(K, V), R]): JavaRDD[R]

     Return a new RDD by applying a function to all elements of this RDD.

     Return a new RDD by applying a function to all elements of this RDD.

     Definition Classes

     JavaRDDLike

103.  def mapPartitions[U](f: FlatMapFunction[Iterator[(K, V)], U], preservesPartitioning: Boolean): JavaRDD[U]

     Return a new RDD by applying a function to each partition of this RDD.

     Return a new RDD by applying a function to each partition of this RDD.

     Definition Classes

     JavaRDDLike

104.  def mapPartitions[U](f: FlatMapFunction[Iterator[(K, V)], U]): JavaRDD[U]

     Return a new RDD by applying a function to each partition of this RDD.

     Return a new RDD by applying a function to each partition of this RDD.

     Definition Classes

     JavaRDDLike

105.  def mapPartitionsToDouble(f: DoubleFlatMapFunction[Iterator[(K, V)]], preservesPartitioning: Boolean): JavaDoubleRDD

     Return a new RDD by applying a function to each partition of this RDD.

     Return a new RDD by applying a function to each partition of this RDD.

     Definition Classes

     JavaRDDLike

106.  def mapPartitionsToDouble(f: DoubleFlatMapFunction[Iterator[(K, V)]]): JavaDoubleRDD

     Return a new RDD by applying a function to each partition of this RDD.

     Return a new RDD by applying a function to each partition of this RDD.

     Definition Classes

     JavaRDDLike

107.  def mapPartitionsToPair[K2, V2](f: PairFlatMapFunction[Iterator[(K, V)], K2, V2], preservesPartitioning: Boolean): JavaPairRDD[K2, V2]

     Return a new RDD by applying a function to each partition of this RDD.

     Return a new RDD by applying a function to each partition of this RDD.

     Definition Classes

     JavaRDDLike

108.  def mapPartitionsToPair[K2, V2](f: PairFlatMapFunction[Iterator[(K, V)], K2, V2]): JavaPairRDD[K2, V2]

     Return a new RDD by applying a function to each partition of this RDD.

     Return a new RDD by applying a function to each partition of this RDD.

     Definition Classes

     JavaRDDLike

109.  def mapPartitionsWithIndex[R](f: Function2[Integer, Iterator[(K, V)], Iterator[R]], preservesPartitioning: Boolean = false): JavaRDD[R]

     Return a new RDD by applying a function to each partition of this RDD, while tracking the index of the original partition.

     Return a new RDD by applying a function to each partition of this RDD, while tracking the index of the original partition.

     Definition Classes

     JavaRDDLike

110.  def mapPartitionsWithInputSplit[R](f: Function2[InputSplit, Iterator[(K, V)], Iterator[R]], preservesPartitioning: Boolean = false): JavaRDD[R]

     Maps over a partition, providing the InputSplit that was used as the base of the partition.

     Maps over a partition, providing the InputSplit that was used as the base of the partition.

     Annotations

     @DeveloperApi()

111.  def mapToDouble[R](f: DoubleFunction[(K, V)]): JavaDoubleRDD

     Return a new RDD by applying a function to all elements of this RDD.

     Return a new RDD by applying a function to all elements of this RDD.

     Definition Classes

     JavaRDDLike

112.  def mapToPair[K2, V2](f: PairFunction[(K, V), K2, V2]): JavaPairRDD[K2, V2]

     Return a new RDD by applying a function to all elements of this RDD.

     Return a new RDD by applying a function to all elements of this RDD.

     Definition Classes

     JavaRDDLike

113.  def mapValues[U](f: Function[V, U]): JavaPairRDD[K, U]

     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.

     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.

     Definition Classes

     JavaPairRDD

114.  def max(comp: Comparator[(K, V)]): (K, V)

     Returns the maximum element from this RDD as defined by the specified Comparator[T].

     Returns the maximum element from this RDD as defined by the specified Comparator[T].

     comp

     the comparator that defines ordering

     returns

     the maximum of the RDD

     Definition Classes

     JavaRDDLike

115.  def min(comp: Comparator[(K, V)]): (K, V)

     Returns the minimum element from this RDD as defined by the specified Comparator[T].

     Returns the minimum element from this RDD as defined by the specified Comparator[T].

     comp

     the comparator that defines ordering

     returns

     the minimum of the RDD

     Definition Classes

     JavaRDDLike

116.  def name(): String

     Definition Classes

     JavaRDDLike

117.  final def ne(arg0: AnyRef): Boolean

     Definition Classes

     AnyRef

118.  final def notify(): Unit

     Definition Classes

     AnyRef

     Annotations

     @native()

119.  final def notifyAll(): Unit

     Definition Classes

     AnyRef

     Annotations

     @native()

120.  def partitionBy(partitioner: Partitioner): JavaPairRDD[K, V]

     Return a copy of the RDD partitioned using the specified partitioner.

     Return a copy of the RDD partitioned using the specified partitioner.

     Definition Classes

     JavaPairRDD

121.  def partitioner: Optional[Partitioner]

     The partitioner of this RDD.

     The partitioner of this RDD.

     Definition Classes

     JavaRDDLike

122.  def partitions: List[Partition]

     Set of partitions in this RDD.

     Set of partitions in this RDD.

     Definition Classes

     JavaRDDLike

123.  def persist(newLevel: StorageLevel): JavaPairRDD[K, V]

     Set this RDD's storage level to persist its values across operations after the first time it is computed.

     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.

     Definition Classes

     JavaPairRDD

124.  def pipe(command: List[String], env: Map[String, String], separateWorkingDir: Boolean, bufferSize: Int, encoding: String): JavaRDD[String]

     Return an RDD created by piping elements to a forked external process.

     Return an RDD created by piping elements to a forked external process.

     Definition Classes

     JavaRDDLike

125.  def pipe(command: List[String], env: Map[String, String], separateWorkingDir: Boolean, bufferSize: Int): JavaRDD[String]

     Return an RDD created by piping elements to a forked external process.

     Return an RDD created by piping elements to a forked external process.

     Definition Classes

     JavaRDDLike

126.  def pipe(command: List[String], env: Map[String, String]): JavaRDD[String]

     Return an RDD created by piping elements to a forked external process.

     Return an RDD created by piping elements to a forked external process.

     Definition Classes

     JavaRDDLike

127.  def pipe(command: List[String]): JavaRDD[String]

     Return an RDD created by piping elements to a forked external process.

     Return an RDD created by piping elements to a forked external process.

     Definition Classes

     JavaRDDLike

128.  def pipe(command: String): JavaRDD[String]

     Return an RDD created by piping elements to a forked external process.

     Return an RDD created by piping elements to a forked external process.

     Definition Classes

     JavaRDDLike

129.  val rdd: RDD[(K, V)]

     Definition Classes

     JavaPairRDD → JavaRDDLike

130.  def reduce(f: Function2[(K, V), (K, V), (K, V)]): (K, V)

     Reduces the elements of this RDD using the specified commutative and associative binary operator.

     Reduces the elements of this RDD using the specified commutative and associative binary operator.

     Definition Classes

     JavaRDDLike

131.  def reduceByKey(func: Function2[V, V, V]): JavaPairRDD[K, V]

     Merge the values for each key using an associative and commutative reduce function.

     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.

     Definition Classes

     JavaPairRDD

132.  def reduceByKey(func: Function2[V, V, V], numPartitions: Int): JavaPairRDD[K, V]

     Merge the values for each key using an associative and commutative reduce function.

     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.

     Definition Classes

     JavaPairRDD

133.  def reduceByKey(partitioner: Partitioner, func: Function2[V, V, V]): JavaPairRDD[K, V]

     Merge the values for each key using an associative and commutative reduce function.

     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.

     Definition Classes

     JavaPairRDD

134.  def reduceByKeyLocally(func: Function2[V, V, V]): Map[K, V]

     Merge the values for each key using an associative and commutative reduce function, but return the result immediately to the master as a Map.

     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.

     Definition Classes

     JavaPairRDD

135.  def repartition(numPartitions: Int): JavaPairRDD[K, V]

     Return a new RDD that has exactly numPartitions partitions.

     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.

     Definition Classes

     JavaPairRDD

136.  def repartitionAndSortWithinPartitions(partitioner: Partitioner, comp: Comparator[K]): JavaPairRDD[K, V]

     Repartition the RDD according to the given partitioner and, within each resulting partition, sort records by their keys.

     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.

     Definition Classes

     JavaPairRDD

137.  def repartitionAndSortWithinPartitions(partitioner: Partitioner): JavaPairRDD[K, V]

     Repartition the RDD according to the given partitioner and, within each resulting partition, sort records by their keys.

     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.

     Definition Classes

     JavaPairRDD

138.  def rightOuterJoin[W](other: JavaPairRDD[K, W], numPartitions: Int): JavaPairRDD[K, (Optional[V], W)]

     Perform a right outer join of this and 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 into the given number of partitions.

     Definition Classes

     JavaPairRDD

139.  def rightOuterJoin[W](other: JavaPairRDD[K, W]): JavaPairRDD[K, (Optional[V], W)]

     Perform a right outer join of this and 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.

     Definition Classes

     JavaPairRDD

140.  def rightOuterJoin[W](other: JavaPairRDD[K, W], partitioner: Partitioner): JavaPairRDD[K, (Optional[V], W)]

     Perform a right outer join of this and 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. Uses the given Partitioner to partition the output RDD.

     Definition Classes

     JavaPairRDD

141.  def sample(withReplacement: Boolean, fraction: Double, seed: Long): JavaPairRDD[K, V]

     Return a sampled subset of this RDD.

     Return a sampled subset of this RDD.

     Definition Classes

     JavaPairRDD

142.  def sample(withReplacement: Boolean, fraction: Double): JavaPairRDD[K, V]

     Return a sampled subset of this RDD.

     Return a sampled subset of this RDD.

     Definition Classes

     JavaPairRDD

143.  def sampleByKey(withReplacement: Boolean, fractions: Map[K, Double]): JavaPairRDD[K, V]

     Return a subset of this RDD sampled by key (via stratified sampling).

     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.

     Definition Classes

     JavaPairRDD

144.  def sampleByKey(withReplacement: Boolean, fractions: Map[K, Double], seed: Long): JavaPairRDD[K, V]

     Return a subset of this RDD sampled by key (via stratified sampling).

     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.

     Definition Classes

     JavaPairRDD

145.  def sampleByKeyExact(withReplacement: Boolean, fractions: Map[K, Double]): JavaPairRDD[K, V]

     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).

     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.

     Definition Classes

     JavaPairRDD

146.  def sampleByKeyExact(withReplacement: Boolean, fractions: Map[K, Double], seed: Long): JavaPairRDD[K, V]

     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).

     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.

     Definition Classes

     JavaPairRDD

147.  def saveAsHadoopDataset(conf: JobConf): Unit

     Output the RDD to any Hadoop-supported storage system, using a Hadoop JobConf object for that storage system.

     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.

     Definition Classes

     JavaPairRDD

148.  def saveAsHadoopFile[F <: OutputFormat[_, _]](path: String, keyClass: Class[_], valueClass: Class[_], outputFormatClass: Class[F], codec: Class[_ <: CompressionCodec]): Unit

     Output the RDD to any Hadoop-supported file system, compressing with the supplied codec.

     Output the RDD to any Hadoop-supported file system, compressing with the supplied codec.

     Definition Classes

     JavaPairRDD

149.  def saveAsHadoopFile[F <: OutputFormat[_, _]](path: String, keyClass: Class[_], valueClass: Class[_], outputFormatClass: Class[F]): Unit

     Output the RDD to any Hadoop-supported file system.

     Output the RDD to any Hadoop-supported file system.

     Definition Classes

     JavaPairRDD

150.  def saveAsHadoopFile[F <: OutputFormat[_, _]](path: String, keyClass: Class[_], valueClass: Class[_], outputFormatClass: Class[F], conf: JobConf): Unit

     Output the RDD to any Hadoop-supported file system.

     Output the RDD to any Hadoop-supported file system.

     Definition Classes

     JavaPairRDD

151.  def saveAsNewAPIHadoopDataset(conf: Configuration): Unit

     Output the RDD to any Hadoop-supported storage system, using a Configuration object for that storage system.

     Output the RDD to any Hadoop-supported storage system, using a Configuration object for that storage system.

     Definition Classes

     JavaPairRDD

152.  def saveAsNewAPIHadoopFile[F <: OutputFormat[_, _]](path: String, keyClass: Class[_], valueClass: Class[_], outputFormatClass: Class[F]): Unit

     Output the RDD to any Hadoop-supported file system.

     Output the RDD to any Hadoop-supported file system.

     Definition Classes

     JavaPairRDD

153.  def saveAsNewAPIHadoopFile[F <: OutputFormat[_, _]](path: String, keyClass: Class[_], valueClass: Class[_], outputFormatClass: Class[F], conf: Configuration): Unit

     Output the RDD to any Hadoop-supported file system.

     Output the RDD to any Hadoop-supported file system.

     Definition Classes

     JavaPairRDD

154.  def saveAsObjectFile(path: String): Unit

     Save this RDD as a SequenceFile of serialized objects.

     Save this RDD as a SequenceFile of serialized objects.

     Definition Classes

     JavaRDDLike

155.  def saveAsTextFile(path: String, codec: Class[_ <: CompressionCodec]): Unit

     Save this RDD as a compressed text file, using string representations of elements.

     Save this RDD as a compressed text file, using string representations of elements.

     Definition Classes

     JavaRDDLike

156.  def saveAsTextFile(path: String): Unit

     Save this RDD as a text file, using string representations of elements.

     Save this RDD as a text file, using string representations of elements.

     Definition Classes

     JavaRDDLike

157.  def setName(name: String): JavaPairRDD[K, V]

     Assign a name to this RDD

     Assign a name to this RDD

     Definition Classes

     JavaPairRDD

158.  def sortByKey(comp: Comparator[K], ascending: Boolean, numPartitions: Int): JavaPairRDD[K, V]

     Sort the RDD by key, so that each partition contains a sorted range of the elements.

     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).

     Definition Classes

     JavaPairRDD

159.  def sortByKey(comp: Comparator[K], ascending: Boolean): JavaPairRDD[K, V]

     Sort the RDD by key, so that each partition contains a sorted range of the elements.

     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).

     Definition Classes

     JavaPairRDD

160.  def sortByKey(comp: Comparator[K]): JavaPairRDD[K, V]

     Sort the RDD by key, so that each partition contains a sorted range of the elements.

     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).

     Definition Classes

     JavaPairRDD

161.  def sortByKey(ascending: Boolean, numPartitions: Int): JavaPairRDD[K, V]

     Sort the RDD by key, so that each partition contains a sorted range of the elements.

     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).

     Definition Classes

     JavaPairRDD

162.  def sortByKey(ascending: Boolean): JavaPairRDD[K, V]

     Sort the RDD by key, so that each partition contains a sorted range of the elements.

     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).

     Definition Classes

     JavaPairRDD

163.  def sortByKey(): JavaPairRDD[K, V]

     Sort the RDD by key, so that each partition contains a sorted range of the elements in ascending order.

     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).

     Definition Classes

     JavaPairRDD

164.  def subtract(other: JavaPairRDD[K, V], p: Partitioner): JavaPairRDD[K, V]

     Return an RDD with the elements from this that are not in other.

     Return an RDD with the elements from this that are not in other.

     Definition Classes

     JavaPairRDD

165.  def subtract(other: JavaPairRDD[K, V], numPartitions: Int): JavaPairRDD[K, V]

     Return an RDD with the elements from this that are not in other.

     Return an RDD with the elements from this that are not in other.

     Definition Classes

     JavaPairRDD

166.  def subtract(other: JavaPairRDD[K, V]): JavaPairRDD[K, V]

     Return an RDD with the elements from this that are not in 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.

     Definition Classes

     JavaPairRDD

167.  def subtractByKey[W](other: JavaPairRDD[K, W], p: Partitioner): JavaPairRDD[K, V]

     Return an RDD with the pairs from this whose keys are not in other.

     Return an RDD with the pairs from this whose keys are not in other.

     Definition Classes

     JavaPairRDD

168.  def subtractByKey[W](other: JavaPairRDD[K, W], numPartitions: Int): JavaPairRDD[K, V]

     Return an RDD with the pairs from this whose keys are not in other.

     Return an RDD with the pairs from this whose keys are not in other.

     Definition Classes

     JavaPairRDD

169.  def subtractByKey[W](other: JavaPairRDD[K, W]): JavaPairRDD[K, V]

     Return an RDD with the pairs from this whose keys are not in 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.

     Definition Classes

     JavaPairRDD

170.  final def synchronized[T0](arg0: ⇒ T0): T0

     Definition Classes

     AnyRef

171.  def take(num: Int): List[(K, V)]

     Take the first num elements of the RDD.

     Take the first num elements of the RDD. This currently scans the partitions *one by one*, so it will be slow if a lot of partitions are required. In that case, use collect() to get the whole RDD instead.

     Definition Classes

     JavaRDDLike

     Note

     this method should only be used if the resulting array is expected to be small, as all the data is loaded into the driver's memory.

172.  def takeAsync(num: Int): JavaFutureAction[List[(K, V)]]

     The asynchronous version of the take action, which returns a future for retrieving the first num elements of this RDD.

     The asynchronous version of the take action, which returns a future for retrieving the first num elements of this RDD.

     Definition Classes

     JavaRDDLike

     Note

     this method should only be used if the resulting array is expected to be small, as all the data is loaded into the driver's memory.

173.  def takeOrdered(num: Int): List[(K, V)]

     Returns the first k (smallest) elements from this RDD using the natural ordering for T while maintain the order.

     Returns the first k (smallest) elements from this RDD using the natural ordering for T while maintain the order.

     num

     k, the number of top elements to return

     returns

     an array of top elements

     Definition Classes

     JavaRDDLike

     Note

     this method should only be used if the resulting array is expected to be small, as all the data is loaded into the driver's memory.

174.  def takeOrdered(num: Int, comp: Comparator[(K, V)]): List[(K, V)]

     Returns the first k (smallest) elements from this RDD as defined by the specified Comparator[T] and maintains the order.

     Returns the first k (smallest) elements from this RDD as defined by the specified Comparator[T] and maintains the order.

     num

     k, the number of elements to return

     comp

     the comparator that defines the order

     returns

     an array of top elements

     Definition Classes

     JavaRDDLike

     Note

     this method should only be used if the resulting array is expected to be small, as all the data is loaded into the driver's memory.

175.  def takeSample(withReplacement: Boolean, num: Int, seed: Long): List[(K, V)]

     Definition Classes

     JavaRDDLike

176.  def takeSample(withReplacement: Boolean, num: Int): List[(K, V)]

     Definition Classes

     JavaRDDLike

177.  def toDebugString(): String

     A description of this RDD and its recursive dependencies for debugging.

     A description of this RDD and its recursive dependencies for debugging.

     Definition Classes

     JavaRDDLike

178.  def toLocalIterator(): Iterator[(K, V)]

     Return an iterator that contains all of the elements in this RDD.

     Return an iterator that contains all of the elements in this RDD.

     The iterator will consume as much memory as the largest partition in this RDD.

     Definition Classes

     JavaRDDLike

179.  def toString(): String

     Definition Classes

     AnyRef → Any

180.  def top(num: Int): List[(K, V)]

     Returns the top k (largest) elements from this RDD using the natural ordering for T and maintains the order.

     Returns the top k (largest) elements from this RDD using the natural ordering for T and maintains the order.

     num

     k, the number of top elements to return

     returns

     an array of top elements

     Definition Classes

     JavaRDDLike

     Note

     this method should only be used if the resulting array is expected to be small, as all the data is loaded into the driver's memory.

181.  def top(num: Int, comp: Comparator[(K, V)]): List[(K, V)]

     Returns the top k (largest) elements from this RDD as defined by the specified Comparator[T] and maintains the order.

     Returns the top k (largest) elements from this RDD as defined by the specified Comparator[T] and maintains the order.

     num

     k, the number of top elements to return

     comp

     the comparator that defines the order

     returns

     an array of top elements

     Definition Classes

     JavaRDDLike

     Note

     this method should only be used if the resulting array is expected to be small, as all the data is loaded into the driver's memory.

182.  def treeAggregate[U](zeroValue: U, seqOp: Function2[U, (K, V), U], combOp: Function2[U, U, U], depth: Int, finalAggregateOnExecutor: Boolean): U

     org.apache.spark.api.java.JavaRDDLike.treeAggregate with a parameter to do the final aggregation on the executor.

     org.apache.spark.api.java.JavaRDDLike.treeAggregate with a parameter to do the final aggregation on the executor.

     Definition Classes

     JavaRDDLike

183.  def treeAggregate[U](zeroValue: U, seqOp: Function2[U, (K, V), U], combOp: Function2[U, U, U]): U

     org.apache.spark.api.java.JavaRDDLike.treeAggregate with suggested depth 2.

     org.apache.spark.api.java.JavaRDDLike.treeAggregate with suggested depth 2.

     Definition Classes

     JavaRDDLike

184.  def treeAggregate[U](zeroValue: U, seqOp: Function2[U, (K, V), U], combOp: Function2[U, U, U], depth: Int): U

     Aggregates the elements of this RDD in a multi-level tree pattern.

     Aggregates the elements of this RDD in a multi-level tree pattern.

     depth

     suggested depth of the tree

     Definition Classes

     JavaRDDLike

     See also

     org.apache.spark.api.java.JavaRDDLike#aggregate

185.  def treeReduce(f: Function2[(K, V), (K, V), (K, V)]): (K, V)

     org.apache.spark.api.java.JavaRDDLike.treeReduce with suggested depth 2.

     org.apache.spark.api.java.JavaRDDLike.treeReduce with suggested depth 2.

     Definition Classes

     JavaRDDLike

186.  def treeReduce(f: Function2[(K, V), (K, V), (K, V)], depth: Int): (K, V)

     Reduces the elements of this RDD in a multi-level tree pattern.

     Reduces the elements of this RDD in a multi-level tree pattern.

     depth

     suggested depth of the tree

     Definition Classes

     JavaRDDLike

     See also

     org.apache.spark.api.java.JavaRDDLike#reduce

187.  def union(other: JavaPairRDD[K, V]): JavaPairRDD[K, V]

     Return the union of this RDD and another one.

     Return the union of this RDD and another one. Any identical elements will appear multiple times (use .distinct() to eliminate them).

     Definition Classes

     JavaPairRDD

188.  def unpersist(blocking: Boolean): JavaPairRDD[K, V]

     Mark the RDD as non-persistent, and remove all blocks for it from memory and disk.

     Mark the RDD as non-persistent, and remove all blocks for it from memory and disk.

     blocking

     Whether to block until all blocks are deleted.

     Definition Classes

     JavaPairRDD

189.  def unpersist(): JavaPairRDD[K, V]

     Mark the RDD as non-persistent, and remove all blocks for it from memory and disk.

     Mark the RDD as non-persistent, and remove all blocks for it from memory and disk. This method blocks until all blocks are deleted.

     Definition Classes

     JavaPairRDD

190.  implicit val vClassTag: ClassTag[V]

     Definition Classes

     JavaNewHadoopRDD → JavaPairRDD

191.  def values(): JavaRDD[V]

     Return an RDD with the values of each tuple.

     Return an RDD with the values of each tuple.

     Definition Classes

     JavaPairRDD

192.  final def wait(): Unit

     Definition Classes

     AnyRef

     Annotations

     @throws( ... )

193.  final def wait(arg0: Long, arg1: Int): Unit

     Definition Classes

     AnyRef

     Annotations

     @throws( ... )

194.  final def wait(arg0: Long): Unit

     Definition Classes

     AnyRef

     Annotations

     @throws( ... ) @native()

195.  def wrapRDD(rdd: RDD[(K, V)]): JavaPairRDD[K, V]

     Definition Classes

     JavaPairRDD → JavaRDDLike

196.  def zip[U](other: JavaRDDLike[U, _]): JavaPairRDD[(K, V), U]

     Zips this RDD with another one, returning key-value pairs with the first element in each RDD, second element in each RDD, etc.

     Zips this RDD with another one, returning key-value pairs with the first element in each RDD, second element in each RDD, etc. Assumes that the two RDDs have the *same number of partitions* and the *same number of elements in each partition* (e.g. one was made through a map on the other).

     Definition Classes

     JavaRDDLike

197.  def zipPartitions[U, V](other: JavaRDDLike[U, _], f: FlatMapFunction2[Iterator[(K, V)], Iterator[U], V]): JavaRDD[V]

     Zip this RDD's partitions with one (or more) RDD(s) and return a new RDD by applying a function to the zipped partitions.

     Zip this RDD's partitions with one (or more) RDD(s) and return a new RDD by applying a function to the zipped partitions. Assumes that all the RDDs have the *same number of partitions*, but does *not* require them to have the same number of elements in each partition.

     Definition Classes

     JavaRDDLike

198.  def zipWithIndex(): JavaPairRDD[(K, V), Long]

     Zips this RDD with its element indices.

     Zips this RDD with its element indices. The ordering is first based on the partition index and then the ordering of items within each partition. So the first item in the first partition gets index 0, and the last item in the last partition receives the largest index. This is similar to Scala's zipWithIndex but it uses Long instead of Int as the index type. This method needs to trigger a spark job when this RDD contains more than one partitions.

     Definition Classes

     JavaRDDLike

199.  def zipWithUniqueId(): JavaPairRDD[(K, V), Long]

     Zips this RDD with generated unique Long ids.

     Zips this RDD with generated unique Long ids. Items in the kth partition will get ids k, n+k, 2*n+k, ..., where n is the number of partitions. So there may exist gaps, but this method won't trigger a spark job, which is different from org.apache.spark.rdd.RDD#zipWithIndex.

     Definition Classes

     JavaRDDLike