Packages

c

org.apache.spark.rdd

OrderedRDDFunctions

class OrderedRDDFunctions[K, V, P <: Product2[K, V]] extends Logging with Serializable

Extra functions available on RDDs of (key, value) pairs where the key is sortable through an implicit conversion. They will work with any key type K that has an implicit Ordering[K] in scope. Ordering objects already exist for all of the standard primitive types. Users can also define their own orderings for custom types, or to override the default ordering. The implicit ordering that is in the closest scope will be used.

import org.apache.spark.SparkContext._

val rdd: RDD[(String, Int)] = ...
implicit val caseInsensitiveOrdering = new Ordering[String] {
  override def compare(a: String, b: String) =
    a.toLowerCase(Locale.ROOT).compare(b.toLowerCase(Locale.ROOT))
}

// Sort by key, using the above case insensitive ordering.
rdd.sortByKey()
Source
OrderedRDDFunctions.scala
Linear Supertypes
Serializable, Logging, AnyRef, Any
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Inherited
  1. OrderedRDDFunctions
  2. Serializable
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Visibility
  1. Public
  2. Protected

Instance Constructors

  1. new OrderedRDDFunctions(self: RDD[P])(implicit arg0: Ordering[K], arg1: ClassTag[K], arg2: ClassTag[V], arg3: ClassTag[P])
    Annotations
    @DeveloperApi()

Type Members

  1. implicit class LogStringContext extends AnyRef
    Definition Classes
    Logging

Value Members

  1. def filterByRange(lower: K, upper: K): RDD[P]

    Returns an RDD containing only the elements in the inclusive range lower to upper.

    Returns an 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.

  2. def repartitionAndSortWithinPartitions(partitioner: Partitioner): RDD[(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.

  3. def sortByKey(ascending: Boolean = true, numPartitions: Int = self.partitions.length): RDD[(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).