Packages

  • package root
    Definition Classes
    root
  • package org
    Definition Classes
    root
  • package apache
    Definition Classes
    org
  • package spark

    Core Spark functionality.

    Core Spark functionality. org.apache.spark.SparkContext serves as the main entry point to Spark, while org.apache.spark.rdd.RDD is the data type representing a distributed collection, and provides most parallel operations.

    In addition, org.apache.spark.rdd.PairRDDFunctions contains operations available only on RDDs of key-value pairs, such as groupByKey and join; org.apache.spark.rdd.DoubleRDDFunctions contains operations available only on RDDs of Doubles; and org.apache.spark.rdd.SequenceFileRDDFunctions contains operations available on RDDs that can be saved as SequenceFiles. These operations are automatically available on any RDD of the right type (e.g. RDD[(Int, Int)] through implicit conversions.

    Java programmers should reference the org.apache.spark.api.java package for Spark programming APIs in Java.

    Classes and methods marked with Experimental are user-facing features which have not been officially adopted by the Spark project. These are subject to change or removal in minor releases.

    Classes and methods marked with Developer API are intended for advanced users want to extend Spark through lower level interfaces. These are subject to changes or removal in minor releases.

    Definition Classes
    apache
  • package util

    Spark utilities.

    Spark utilities.

    Definition Classes
    spark
  • package random

    Utilities for random number generation.

    Utilities for random number generation.

    Definition Classes
    util
  • package sketch
    Definition Classes
    util
  • BloomFilter
  • CountMinSketch
  • IncompatibleMergeException

package sketch

Type Members

  1. abstract class BloomFilter extends AnyRef

    A Bloom filter is a space-efficient probabilistic data structure that offers an approximate containment test with one-sided error: if it claims that an item is contained in it, this might be in error, but if it claims that an item is not contained in it, then this is definitely true.

    A Bloom filter is a space-efficient probabilistic data structure that offers an approximate containment test with one-sided error: if it claims that an item is contained in it, this might be in error, but if it claims that an item is not contained in it, then this is definitely true. Currently supported data types include:

    • Byte
    • Short
    • Integer
    • Long
    • String

    The false positive probability (FPP) of a Bloom filter is defined as the probability that #mightContain(Object) will erroneously return true for an object that has not actually been put in the BloomFilter.

    The implementation is largely based on the BloomFilter class from Guava.

  2. abstract class CountMinSketch extends AnyRef

    A Count-min sketch is a probabilistic data structure used for cardinality estimation using sub-linear space.

    A Count-min sketch is a probabilistic data structure used for cardinality estimation using sub-linear space. Currently, supported data types include:

    • Byte
    • Short
    • Integer
    • Long
    • String

    A CountMinSketch is initialized with a random seed, and a pair of parameters:

    • relative error (or eps), and
    • confidence (or delta)

    Suppose you want to estimate the number of times an element x has appeared in a data stream so far. With probability delta, the estimate of this frequency is within the range true frequency <= estimate <= true frequency + eps * N, where N is the total count of items have appeared the data stream so far.

    Under the cover, a CountMinSketch is essentially a two-dimensional long array with depth d and width w, where

    • d = ceil(2 / eps)
    • w = ceil(-log(1 - confidence) / log(2))

    This implementation is largely based on the CountMinSketch class from stream-lib.

  3. class IncompatibleMergeException extends Exception

Ungrouped