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 mllib

    RDD-based machine learning APIs (in maintenance mode).

    RDD-based machine learning APIs (in maintenance mode).

    The spark.mllib package is in maintenance mode as of the Spark 2.0.0 release to encourage migration to the DataFrame-based APIs under the org.apache.spark.ml package. While in maintenance mode,

    • no new features in the RDD-based spark.mllib package will be accepted, unless they block implementing new features in the DataFrame-based spark.ml package;
    • bug fixes in the RDD-based APIs will still be accepted.

    The developers will continue adding more features to the DataFrame-based APIs in the 2.x series to reach feature parity with the RDD-based APIs. And once we reach feature parity, this package will be deprecated.

    Definition Classes
    spark
    See also

    SPARK-4591 to track the progress of feature parity

  • package classification
    Definition Classes
    mllib
  • package clustering
    Definition Classes
    mllib
  • package evaluation
    Definition Classes
    mllib
  • package feature
    Definition Classes
    mllib
  • package fpm
    Definition Classes
    mllib
  • package linalg
    Definition Classes
    mllib
  • package distributed
  • DenseMatrix
  • DenseVector
  • Matrices
  • Matrix
  • QRDecomposition
  • SingularValueDecomposition
  • SparseMatrix
  • SparseVector
  • Vector
  • VectorUDT
  • Vectors
  • package optimization
    Definition Classes
    mllib
  • package pmml
    Definition Classes
    mllib
  • package random
    Definition Classes
    mllib
  • package rdd
    Definition Classes
    mllib
  • package recommendation
    Definition Classes
    mllib
  • package regression
    Definition Classes
    mllib
  • package stat
    Definition Classes
    mllib
  • package tree

    This package contains the default implementation of the decision tree algorithm, which supports:

    This package contains the default implementation of the decision tree algorithm, which supports:

    • binary classification,
    • regression,
    • information loss calculation with entropy and Gini for classification and variance for regression,
    • both continuous and categorical features.
    Definition Classes
    mllib
  • package util
    Definition Classes
    mllib

package linalg

Ordering
  1. Alphabetic
Visibility
  1. Public
  2. Protected

Package Members

  1. package distributed

Type Members

  1. class DenseMatrix extends Matrix

    Column-major dense matrix.

    Column-major dense matrix. The entry values are stored in a single array of doubles with columns listed in sequence. For example, the following matrix

    1.0 2.0
3.0 4.0
5.0 6.0

    is stored as [1.0, 3.0, 5.0, 2.0, 4.0, 6.0].

    Annotations
    @Since("1.0.0") @SQLUserDefinedType()
  2. class DenseVector extends Vector

    A dense vector represented by a value array.

    A dense vector represented by a value array.

    Annotations
    @Since("1.0.0") @SQLUserDefinedType()
  3. sealed trait Matrix extends Serializable

    Trait for a local matrix.

    Trait for a local matrix.

    Annotations
    @SQLUserDefinedType() @Since("1.0.0")
  4. case class QRDecomposition[QType, RType](Q: QType, R: RType) extends Product with Serializable

    Represents QR factors.

    Represents QR factors.

    Annotations
    @Since("1.5.0")
  5. case class SingularValueDecomposition[UType, VType](U: UType, s: Vector, V: VType) extends Product with Serializable

    Represents singular value decomposition (SVD) factors.

    Represents singular value decomposition (SVD) factors.

    Annotations
    @Since("1.0.0")
  6. class SparseMatrix extends Matrix

    Column-major sparse matrix.

    Column-major sparse matrix. The entry values are stored in Compressed Sparse Column (CSC) format. For example, the following matrix

    1.0 0.0 4.0
0.0 3.0 5.0
2.0 0.0 6.0

    is stored as values: [1.0, 2.0, 3.0, 4.0, 5.0, 6.0], rowIndices=[0, 2, 1, 0, 1, 2], colPointers=[0, 2, 3, 6].

    Annotations
    @Since("1.2.0") @SQLUserDefinedType()
  7. class SparseVector extends Vector

    A sparse vector represented by an index array and a value array.

    A sparse vector represented by an index array and a value array.

    Annotations
    @Since("1.0.0") @SQLUserDefinedType()
  8. sealed trait Vector extends Serializable

    Represents a numeric vector, whose index type is Int and value type is Double.

    Represents a numeric vector, whose index type is Int and value type is Double.

    Annotations
    @SQLUserDefinedType() @Since("1.0.0")
    Note

    Users should not implement this interface.

  9. class VectorUDT extends UserDefinedType[Vector]

    :: AlphaComponent ::

    :: AlphaComponent ::

    User-defined type for Vector which allows easy interaction with SQL via org.apache.spark.sql.Dataset.

    Annotations
    @AlphaComponent()

Value Members

  1. object DenseMatrix extends Serializable

    Factory methods for org.apache.spark.mllib.linalg.DenseMatrix.

    Factory methods for org.apache.spark.mllib.linalg.DenseMatrix.

    Annotations
    @Since("1.3.0")
  2. object DenseVector extends Serializable
    Annotations
    @Since("1.3.0")
  3. object Matrices

    Factory methods for org.apache.spark.mllib.linalg.Matrix.

    Factory methods for org.apache.spark.mllib.linalg.Matrix.

    Annotations
    @Since("1.0.0")
  4. object SparseMatrix extends Serializable

    Factory methods for org.apache.spark.mllib.linalg.SparseMatrix.

    Factory methods for org.apache.spark.mllib.linalg.SparseMatrix.

    Annotations
    @Since("1.3.0")
  5. object SparseVector extends Serializable
    Annotations
    @Since("1.3.0")
  6. object Vectors

    Factory methods for org.apache.spark.mllib.linalg.Vector.

    Factory methods for org.apache.spark.mllib.linalg.Vector. We don't use the name Vector because Scala imports scala.collection.immutable.Vector by default.

    Annotations
    @Since("1.0.0")

Ungrouped