org.apache.spark.rdd

PartitionPruningRDD

class PartitionPruningRDD[T] extends RDD[T]

:: DeveloperApi :: A RDD used to prune RDD partitions/partitions so we can avoid launching tasks on all partitions. An example use case: If we know the RDD is partitioned by range, and the execution DAG has a filter on the key, we can avoid launching tasks on partitions that don't have the range covering the key.

Annotations
@DeveloperApi()
Linear Supertypes
RDD[T], Logging, Serializable, Serializable, AnyRef, Any
Ordering
  1. Alphabetic
  2. By inheritance
Inherited
  1. PartitionPruningRDD
  2. RDD
  3. Logging
  4. Serializable
  5. Serializable
  6. AnyRef
  7. Any
  1. Hide All
  2. Show all
Learn more about member selection
Visibility
  1. Public
  2. All

Instance Constructors

  1. new PartitionPruningRDD(prev: RDD[T], partitionFilterFunc: (Int) ⇒ Boolean)(implicit arg0: ClassTag[T])

Value Members

  1. final def !=(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  2. final def !=(arg0: Any): Boolean

    Definition Classes
    Any
  3. final def ##(): Int

    Definition Classes
    AnyRef → Any
  4. def ++(other: RDD[T]): RDD[T]

    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
    RDD
  5. final def ==(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  6. final def ==(arg0: Any): Boolean

    Definition Classes
    Any
  7. def aggregate[U](zeroValue: U)(seqOp: (U, T) ⇒ U, combOp: (U, U) ⇒ U)(implicit arg0: ClassTag[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
    RDD
  8. final def asInstanceOf[T0]: T0

    Definition Classes
    Any
  9. def cache(): PartitionPruningRDD.this.type

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

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

    Definition Classes
    RDD
  10. def cartesian[U](other: RDD[U])(implicit arg0: ClassTag[U]): RDD[(T, 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
    RDD
  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
    RDD
  12. def clearDependencies(): Unit

    Clears the dependencies of this RDD.

    Clears the dependencies of this RDD. This method must ensure that all references to the original parent RDDs is removed to enable the parent RDDs to be garbage collected. Subclasses of RDD may override this method for implementing their own cleaning logic. See org.apache.spark.rdd.UnionRDD for an example.

    Attributes
    protected
    Definition Classes
    RDD
  13. def clone(): AnyRef

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  14. def coalesce(numPartitions: Int, shuffle: Boolean = false)(implicit ord: Ordering[T] = null): RDD[T]

    Return a new RDD that is reduced into numPartitions partitions.

    Return a new RDD that is reduced into numPartitions partitions.

    This results in a narrow dependency, e.g. if you go from 1000 partitions to 100 partitions, there will not be a shuffle, instead each of the 100 new partitions will claim 10 of the current partitions.

    However, if you're doing a drastic coalesce, e.g. to numPartitions = 1, this may result in your computation taking place on fewer nodes than you like (e.g. one node in the case of numPartitions = 1). To avoid this, you can pass shuffle = true. This will add a shuffle step, but means the current upstream partitions will be executed in parallel (per whatever the current partitioning is).

    Note: With shuffle = true, you can actually coalesce to a larger number of partitions. This is useful if you have a small number of partitions, say 100, potentially with a few partitions being abnormally large. Calling coalesce(1000, shuffle = true) will result in 1000 partitions with the data distributed using a hash partitioner.

    Definition Classes
    RDD
  15. def collect[U](f: PartialFunction[T, U])(implicit arg0: ClassTag[U]): RDD[U]

    Return an RDD that contains all matching values by applying f.

    Return an RDD that contains all matching values by applying f.

    Definition Classes
    RDD
  16. def collect(): Array[T]

    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
    RDD
  17. def compute(split: Partition, context: TaskContext): Iterator[T]

    :: DeveloperApi :: Implemented by subclasses to compute a given partition.

    :: DeveloperApi :: Implemented by subclasses to compute a given partition.

    Definition Classes
    PartitionPruningRDDRDD
  18. 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
    RDD
  19. def count(): Long

    Return the number of elements in the RDD.

    Return the number of elements in the RDD.

    Definition Classes
    RDD
  20. def countApprox(timeout: Long, confidence: Double = 0.95): PartialResult[BoundedDouble]

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

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

    Definition Classes
    RDD
    Annotations
    @Experimental()
  21. def countApproxDistinct(relativeSD: Double = 0.05): Long

    :: Experimental :: Return approximate number of distinct elements in the RDD.

    :: Experimental :: Return approximate number of distinct elements in the RDD.

    The accuracy of approximation can be controlled through the relative standard deviation (relativeSD) parameter, which also controls the amount of memory used. Lower values result in more accurate counts but increase the memory footprint and vise versa. The default value of relativeSD is 0.05.

    Definition Classes
    RDD
    Annotations
    @Experimental()
  22. def countByValue()(implicit ord: Ordering[T] = null): Map[T, 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
    RDD
  23. def countByValueApprox(timeout: Long, confidence: Double = 0.95)(implicit ord: Ordering[T] = null): PartialResult[Map[T, BoundedDouble]]

    :: Experimental :: Approximate version of countByValue().

    :: Experimental :: Approximate version of countByValue().

    Definition Classes
    RDD
    Annotations
    @Experimental()
  24. final def dependencies: Seq[Dependency[_]]

    Get the list of dependencies of this RDD, taking into account whether the RDD is checkpointed or not.

    Get the list of dependencies of this RDD, taking into account whether the RDD is checkpointed or not.

    Definition Classes
    RDD
  25. def distinct(): RDD[T]

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

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

    Definition Classes
    RDD
  26. def distinct(numPartitions: Int)(implicit ord: Ordering[T] = null): RDD[T]

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

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

    Definition Classes
    RDD
  27. final def eq(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  28. def equals(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any
  29. def filter(f: (T) ⇒ Boolean): RDD[T]

    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
    RDD
  30. def finalize(): Unit

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  31. def first(): T

    Return the first element in this RDD.

    Return the first element in this RDD.

    Definition Classes
    RDD
  32. def firstParent[U](implicit arg0: ClassTag[U]): RDD[U]

    Returns the first parent RDD

    Returns the first parent RDD

    Attributes
    protected[org.apache.spark]
    Definition Classes
    RDD
  33. def flatMap[U](f: (T) ⇒ TraversableOnce[U])(implicit arg0: ClassTag[U]): RDD[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
    RDD
  34. def fold(zeroValue: T)(op: (T, T) ⇒ T): T

    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.

    Definition Classes
    RDD
  35. def foreach(f: (T) ⇒ Unit): Unit

    Applies a function f to all elements of this RDD.

    Applies a function f to all elements of this RDD.

    Definition Classes
    RDD
  36. def foreachPartition(f: (Iterator[T]) ⇒ Unit): Unit

    Applies a function f to each partition of this RDD.

    Applies a function f to each partition of this RDD.

    Definition Classes
    RDD
  37. def getCheckpointFile: Option[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
    RDD
  38. final def getClass(): Class[_]

    Definition Classes
    AnyRef → Any
  39. def getDependencies: Seq[Dependency[_]]

    Implemented by subclasses to return how this RDD depends on parent RDDs.

    Implemented by subclasses to return how this RDD depends on parent RDDs. This method will only be called once, so it is safe to implement a time-consuming computation in it.

    Attributes
    protected
    Definition Classes
    RDD
  40. def getPartitions: Array[Partition]

    Implemented by subclasses to return the set of partitions in this RDD.

    Implemented by subclasses to return the set of partitions in this RDD. This method will only be called once, so it is safe to implement a time-consuming computation in it.

    Attributes
    protected
    Definition Classes
    PartitionPruningRDDRDD
  41. def getPreferredLocations(split: Partition): Seq[String]

    Optionally overridden by subclasses to specify placement preferences.

    Optionally overridden by subclasses to specify placement preferences.

    Attributes
    protected
    Definition Classes
    RDD
  42. def getStorageLevel: StorageLevel

    Get the RDD's current storage level, or StorageLevel.

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

    Definition Classes
    RDD
  43. def glom(): RDD[Array[T]]

    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
    RDD
  44. def groupBy[K](f: (T) ⇒ K, p: Partitioner)(implicit kt: ClassTag[K], ord: Ordering[K] = null): RDD[(K, Iterable[T])]

    Return an RDD of grouped items.

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

    Definition Classes
    RDD
  45. def groupBy[K](f: (T) ⇒ K, numPartitions: Int)(implicit kt: ClassTag[K]): RDD[(K, Iterable[T])]

    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
    RDD
  46. def groupBy[K](f: (T) ⇒ K)(implicit kt: ClassTag[K]): RDD[(K, Iterable[T])]

    Return an RDD of grouped items.

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

    Definition Classes
    RDD
  47. def hashCode(): Int

    Definition Classes
    AnyRef → Any
  48. val id: Int

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

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

    Definition Classes
    RDD
  49. def intersection(other: RDD[T], numPartitions: Int): RDD[T]

    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. Performs a hash partition across the cluster

    Note that this method performs a shuffle internally.

    numPartitions

    How many partitions to use in the resulting RDD

    Definition Classes
    RDD
  50. def intersection(other: RDD[T], partitioner: Partitioner)(implicit ord: Ordering[T] = null): RDD[T]

    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.

    Note that this method performs a shuffle internally.

    partitioner

    Partitioner to use for the resulting RDD

    Definition Classes
    RDD
  51. def intersection(other: RDD[T]): RDD[T]

    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.

    Note that this method performs a shuffle internally.

    Definition Classes
    RDD
  52. def isCheckpointed: Boolean

    Return whether this RDD has been checkpointed or not

    Return whether this RDD has been checkpointed or not

    Definition Classes
    RDD
  53. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any
  54. def isTraceEnabled(): Boolean

    Attributes
    protected
    Definition Classes
    Logging
  55. final def iterator(split: Partition, context: TaskContext): Iterator[T]

    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 implementors of custom subclasses of RDD.

    Definition Classes
    RDD
  56. def keyBy[K](f: (T) ⇒ K): RDD[(K, T)]

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

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

    Definition Classes
    RDD
  57. def log: Logger

    Attributes
    protected
    Definition Classes
    Logging
  58. def logDebug(msg: ⇒ String, throwable: Throwable): Unit

    Attributes
    protected
    Definition Classes
    Logging
  59. def logDebug(msg: ⇒ String): Unit

    Attributes
    protected
    Definition Classes
    Logging
  60. def logError(msg: ⇒ String, throwable: Throwable): Unit

    Attributes
    protected
    Definition Classes
    Logging
  61. def logError(msg: ⇒ String): Unit

    Attributes
    protected
    Definition Classes
    Logging
  62. def logInfo(msg: ⇒ String, throwable: Throwable): Unit

    Attributes
    protected
    Definition Classes
    Logging
  63. def logInfo(msg: ⇒ String): Unit

    Attributes
    protected
    Definition Classes
    Logging
  64. def logTrace(msg: ⇒ String, throwable: Throwable): Unit

    Attributes
    protected
    Definition Classes
    Logging
  65. def logTrace(msg: ⇒ String): Unit

    Attributes
    protected
    Definition Classes
    Logging
  66. def logWarning(msg: ⇒ String, throwable: Throwable): Unit

    Attributes
    protected
    Definition Classes
    Logging
  67. def logWarning(msg: ⇒ String): Unit

    Attributes
    protected
    Definition Classes
    Logging
  68. def map[U](f: (T) ⇒ U)(implicit arg0: ClassTag[U]): RDD[U]

    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
    RDD
  69. def mapPartitions[U](f: (Iterator[T]) ⇒ Iterator[U], preservesPartitioning: Boolean = false)(implicit arg0: ClassTag[U]): RDD[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
    RDD
  70. def mapPartitionsWithContext[U](f: (TaskContext, Iterator[T]) ⇒ Iterator[U], preservesPartitioning: Boolean = false)(implicit arg0: ClassTag[U]): RDD[U]

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

    :: DeveloperApi :: Return a new RDD by applying a function to each partition of this RDD. This is a variant of mapPartitions that also passes the TaskContext into the closure.

    Definition Classes
    RDD
    Annotations
    @DeveloperApi()
  71. def mapPartitionsWithIndex[U](f: (Int, Iterator[T]) ⇒ Iterator[U], preservesPartitioning: Boolean = false)(implicit arg0: ClassTag[U]): RDD[U]

    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
    RDD
  72. def max()(implicit ord: Ordering[T]): T

    Returns the max of this RDD as defined by the implicit Ordering[T].

    Returns the max of this RDD as defined by the implicit Ordering[T].

    returns

    the maximum element of the RDD

    Definition Classes
    RDD
  73. def min()(implicit ord: Ordering[T]): T

    Returns the min of this RDD as defined by the implicit Ordering[T].

    Returns the min of this RDD as defined by the implicit Ordering[T].

    returns

    the minimum element of the RDD

    Definition Classes
    RDD
  74. var name: String

    A friendly name for this RDD

    A friendly name for this RDD

    Definition Classes
    RDD
  75. final def ne(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  76. final def notify(): Unit

    Definition Classes
    AnyRef
  77. final def notifyAll(): Unit

    Definition Classes
    AnyRef
  78. val partitioner: Option[Partitioner]

    Optionally overridden by subclasses to specify how they are partitioned.

    Optionally overridden by subclasses to specify how they are partitioned.

    Definition Classes
    RDD
  79. final def partitions: Array[Partition]

    Get the array of partitions of this RDD, taking into account whether the RDD is checkpointed or not.

    Get the array of partitions of this RDD, taking into account whether the RDD is checkpointed or not.

    Definition Classes
    RDD
  80. def persist(): PartitionPruningRDD.this.type

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

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

    Definition Classes
    RDD
  81. def persist(newLevel: StorageLevel): PartitionPruningRDD.this.type

    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. This can only be used to assign a new storage level if the RDD does not have a storage level set yet..

    Definition Classes
    RDD
  82. def pipe(command: Seq[String], env: Map[String, String] = Map(), printPipeContext: ((String) ⇒ Unit) ⇒ Unit = null, printRDDElement: (T, (String) ⇒ Unit) ⇒ Unit = null, separateWorkingDir: Boolean = false): RDD[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. The print behavior can be customized by providing two functions.

    command

    command to run in forked process.

    env

    environment variables to set.

    printPipeContext

    Before piping elements, this function is called as an oppotunity to pipe context data. Print line function (like out.println) will be passed as printPipeContext's parameter.

    printRDDElement

    Use this function to customize how to pipe elements. This function will be called with each RDD element as the 1st parameter, and the print line function (like out.println()) as the 2nd parameter. An example of pipe the RDD data of groupBy() in a streaming way, instead of constructing a huge String to concat all the elements: def printRDDElement(record:(String, Seq[String]), f:String=>Unit) = for (e <- record._2){f(e)}

    separateWorkingDir

    Use separate working directories for each task.

    returns

    the result RDD

    Definition Classes
    RDD
  83. def pipe(command: String, env: Map[String, String]): RDD[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
    RDD
  84. def pipe(command: String): RDD[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
    RDD
  85. final def preferredLocations(split: Partition): Seq[String]

    Get the preferred locations of a partition (as hostnames), taking into account whether the RDD is checkpointed.

    Get the preferred locations of a partition (as hostnames), taking into account whether the RDD is checkpointed.

    Definition Classes
    RDD
  86. def randomSplit(weights: Array[Double], seed: Long = Utils.random.nextLong): Array[RDD[T]]

    Randomly splits this RDD with the provided weights.

    Randomly splits this RDD with the provided weights.

    weights

    weights for splits, will be normalized if they don't sum to 1

    seed

    random seed

    returns

    split RDDs in an array

    Definition Classes
    RDD
  87. def reduce(f: (T, T) ⇒ T): T

    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
    RDD
  88. def repartition(numPartitions: Int)(implicit ord: Ordering[T] = null): RDD[T]

    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
    RDD
  89. def sample(withReplacement: Boolean, fraction: Double, seed: Long = Utils.random.nextLong): RDD[T]

    Return a sampled subset of this RDD.

    Return a sampled subset of this RDD.

    Definition Classes
    RDD
  90. 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
    RDD
  91. 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
    RDD
  92. 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
    RDD
  93. def setName(_name: String): PartitionPruningRDD.this.type

    Assign a name to this RDD

    Assign a name to this RDD

    Definition Classes
    RDD
  94. def sparkContext: SparkContext

    The SparkContext that created this RDD.

    The SparkContext that created this RDD.

    Definition Classes
    RDD
  95. def subtract(other: RDD[T], p: Partitioner)(implicit ord: Ordering[T] = null): RDD[T]

    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
    RDD
  96. def subtract(other: RDD[T], numPartitions: Int): RDD[T]

    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
    RDD
  97. def subtract(other: RDD[T]): RDD[T]

    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
    RDD
  98. final def synchronized[T0](arg0: ⇒ T0): T0

    Definition Classes
    AnyRef
  99. def take(num: Int): Array[T]

    Take the first num elements of the RDD.

    Take the first num elements of the RDD. It works by first scanning one partition, and use the results from that partition to estimate the number of additional partitions needed to satisfy the limit.

    Definition Classes
    RDD
  100. def takeOrdered(num: Int)(implicit ord: Ordering[T]): Array[T]

    Returns the first K (smallest) elements from this RDD as defined by the specified implicit Ordering[T] and maintains the ordering.

    Returns the first K (smallest) elements from this RDD as defined by the specified implicit Ordering[T] and maintains the ordering. This does the opposite of top. For example:

    sc.parallelize(Seq(10, 4, 2, 12, 3)).takeOrdered(1)
    // returns Array(2)
    
    sc.parallelize(Seq(2, 3, 4, 5, 6)).takeOrdered(2)
    // returns Array(2, 3)
    num

    the number of top elements to return

    ord

    the implicit ordering for T

    returns

    an array of top elements

    Definition Classes
    RDD
  101. def takeSample(withReplacement: Boolean, num: Int, seed: Long = Utils.random.nextLong): Array[T]

    Definition Classes
    RDD
  102. 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
    RDD
  103. def toJavaRDD(): JavaRDD[T]

    Definition Classes
    RDD
  104. def toLocalIterator: Iterator[T]

    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
    RDD
  105. def toString(): String

    Definition Classes
    RDD → AnyRef → Any
  106. def top(num: Int)(implicit ord: Ordering[T]): Array[T]

    Returns the top K (largest) elements from this RDD as defined by the specified implicit Ordering[T].

    Returns the top K (largest) elements from this RDD as defined by the specified implicit Ordering[T]. This does the opposite of takeOrdered. For example:

    sc.parallelize(Seq(10, 4, 2, 12, 3)).top(1)
    // returns Array(12)
    
    sc.parallelize(Seq(2, 3, 4, 5, 6)).top(2)
    // returns Array(6, 5)
    num

    the number of top elements to return

    ord

    the implicit ordering for T

    returns

    an array of top elements

    Definition Classes
    RDD
  107. def union(other: RDD[T]): RDD[T]

    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
    RDD
  108. def unpersist(blocking: Boolean = true): PartitionPruningRDD.this.type

    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.

    returns

    This RDD.

    Definition Classes
    RDD
  109. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  110. final def wait(arg0: Long, arg1: Int): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  111. final def wait(arg0: Long): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  112. def zip[U](other: RDD[U])(implicit arg0: ClassTag[U]): RDD[(T, 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
    RDD
  113. def zipPartitions[B, C, D, V](rdd2: RDD[B], rdd3: RDD[C], rdd4: RDD[D])(f: (Iterator[T], Iterator[B], Iterator[C], Iterator[D]) ⇒ Iterator[V])(implicit arg0: ClassTag[B], arg1: ClassTag[C], arg2: ClassTag[D], arg3: ClassTag[V]): RDD[V]

    Definition Classes
    RDD
  114. def zipPartitions[B, C, D, V](rdd2: RDD[B], rdd3: RDD[C], rdd4: RDD[D], preservesPartitioning: Boolean)(f: (Iterator[T], Iterator[B], Iterator[C], Iterator[D]) ⇒ Iterator[V])(implicit arg0: ClassTag[B], arg1: ClassTag[C], arg2: ClassTag[D], arg3: ClassTag[V]): RDD[V]

    Definition Classes
    RDD
  115. def zipPartitions[B, C, V](rdd2: RDD[B], rdd3: RDD[C])(f: (Iterator[T], Iterator[B], Iterator[C]) ⇒ Iterator[V])(implicit arg0: ClassTag[B], arg1: ClassTag[C], arg2: ClassTag[V]): RDD[V]

    Definition Classes
    RDD
  116. def zipPartitions[B, C, V](rdd2: RDD[B], rdd3: RDD[C], preservesPartitioning: Boolean)(f: (Iterator[T], Iterator[B], Iterator[C]) ⇒ Iterator[V])(implicit arg0: ClassTag[B], arg1: ClassTag[C], arg2: ClassTag[V]): RDD[V]

    Definition Classes
    RDD
  117. def zipPartitions[B, V](rdd2: RDD[B])(f: (Iterator[T], Iterator[B]) ⇒ Iterator[V])(implicit arg0: ClassTag[B], arg1: ClassTag[V]): RDD[V]

    Definition Classes
    RDD
  118. def zipPartitions[B, V](rdd2: RDD[B], preservesPartitioning: Boolean)(f: (Iterator[T], Iterator[B]) ⇒ Iterator[V])(implicit arg0: ClassTag[B], arg1: ClassTag[V]): RDD[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
    RDD
  119. def zipWithIndex(): RDD[(T, 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
    RDD
  120. def zipWithUniqueId(): RDD[(T, 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
    RDD

Deprecated Value Members

  1. def filterWith[A](constructA: (Int) ⇒ A)(p: (T, A) ⇒ Boolean): RDD[T]

    Filters this RDD with p, where p takes an additional parameter of type A.

    Filters this RDD with p, where p takes an additional parameter of type A. This additional parameter is produced by constructA, which is called in each partition with the index of that partition.

    Definition Classes
    RDD
    Annotations
    @deprecated
    Deprecated

    (Since version 1.0.0) use mapPartitionsWithIndex and filter

  2. def flatMapWith[A, U](constructA: (Int) ⇒ A, preservesPartitioning: Boolean = false)(f: (T, A) ⇒ Seq[U])(implicit arg0: ClassTag[U]): RDD[U]

    FlatMaps f over this RDD, where f takes an additional parameter of type A.

    FlatMaps f over this RDD, where f takes an additional parameter of type A. This additional parameter is produced by constructA, which is called in each partition with the index of that partition.

    Definition Classes
    RDD
    Annotations
    @deprecated
    Deprecated

    (Since version 1.0.0) use mapPartitionsWithIndex and flatMap

  3. def foreachWith[A](constructA: (Int) ⇒ A)(f: (T, A) ⇒ Unit): Unit

    Applies f to each element of this RDD, where f takes an additional parameter of type A.

    Applies f to each element of this RDD, where f takes an additional parameter of type A. This additional parameter is produced by constructA, which is called in each partition with the index of that partition.

    Definition Classes
    RDD
    Annotations
    @deprecated
    Deprecated

    (Since version 1.0.0) use mapPartitionsWithIndex and foreach

  4. def mapPartitionsWithSplit[U](f: (Int, Iterator[T]) ⇒ Iterator[U], preservesPartitioning: Boolean = false)(implicit arg0: ClassTag[U]): RDD[U]

    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
    RDD
    Annotations
    @deprecated
    Deprecated

    (Since version 0.7.0) use mapPartitionsWithIndex

  5. def mapWith[A, U](constructA: (Int) ⇒ A, preservesPartitioning: Boolean = false)(f: (T, A) ⇒ U)(implicit arg0: ClassTag[U]): RDD[U]

    Maps f over this RDD, where f takes an additional parameter of type A.

    Maps f over this RDD, where f takes an additional parameter of type A. This additional parameter is produced by constructA, which is called in each partition with the index of that partition.

    Definition Classes
    RDD
    Annotations
    @deprecated
    Deprecated

    (Since version 1.0.0) use mapPartitionsWithIndex

  6. def toArray(): Array[T]

    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
    RDD
    Annotations
    @deprecated
    Deprecated

    (Since version 1.0.0) use collect

Inherited from RDD[T]

Inherited from Logging

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Ungrouped