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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 clustering

Definition Classes
mllib
BisectingKMeans
BisectingKMeansModel
DistanceMeasure
DistributedLDAModel
EMLDAOptimizer
GaussianMixture
GaussianMixtureModel
KMeans
KMeansModel
LDA
LDAModel
LDAOptimizer
LocalLDAModel
OnlineLDAOptimizer
PowerIterationClustering
PowerIterationClusteringModel
StreamingKMeans
StreamingKMeansModel

org.apache.spark.mllib.clustering

PowerIterationClusteringModel

Companion object PowerIterationClusteringModel

class PowerIterationClusteringModel extends Saveable with Serializable

Model produced by PowerIterationClustering.

Annotations: @Since( "1.3.0" )

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Serializable, Serializable, Saveable, AnyRef, Any

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PowerIterationClusteringModel
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new PowerIterationClusteringModel(k: Int, assignments: RDD[Assignment])
k
number of clusters
assignments
an RDD of clustering PowerIterationClustering#Assignments

Annotations
@Since( "1.3.0" )

Value Members

final def !=(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def ##(): Int

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

Definition Classes
AnyRef → Any
final def asInstanceOf[T0]: T0

Definition Classes
Any
val assignments: RDD[Assignment]

Annotations
@Since( "1.3.0" )
def clone(): AnyRef

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protected[lang]
Definition Classes
AnyRef
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@throws( ... ) @native()
final def eq(arg0: AnyRef): Boolean

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def equals(arg0: Any): Boolean

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def finalize(): Unit

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protected[lang]
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@throws( classOf[java.lang.Throwable] )
final def getClass(): Class[_]

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@native()
def hashCode(): Int

Definition Classes
AnyRef → Any
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@native()
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
val k: Int

Annotations
@Since( "1.3.0" )
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
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@native()
final def notifyAll(): Unit

Definition Classes
AnyRef
Annotations
@native()
def save(sc: SparkContext, path: String): Unit
Save this model to the given path.
Save this model to the given path.
This saves:
- human-readable (JSON) model metadata to path/metadata/
- Parquet formatted data to path/data/
The model may be loaded using Loader.load.
sc
Spark context used to save model data.
path
Path specifying the directory in which to save this model. If the directory already exists, this method throws an exception.

Definition Classes
PowerIterationClusteringModel → Saveable
Annotations
@Since( "1.4.0" )
final def synchronized[T0](arg0: ⇒ T0): T0

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AnyRef
def toString(): String

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final def wait(): Unit

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@throws( ... )
final def wait(arg0: Long, arg1: Int): Unit

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@throws( ... )
final def wait(arg0: Long): Unit

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Packages

PowerIterationClusteringModel

Companion object PowerIterationClusteringModel

class PowerIterationClusteringModel extends Saveable with Serializable

Instance Constructors

Value Members

Inherited from Serializable

Inherited from Serializable

Inherited from Saveable

Inherited from AnyRef

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PowerIterationClusteringModel 

Companion object PowerIterationClusteringModel

class PowerIterationClusteringModel extends Saveable with Serializable

Instance Constructors

Value Members

Inherited from Serializable

Inherited from Serializable

Inherited from Saveable

Inherited from AnyRef

Inherited from Any

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PowerIterationClusteringModel