Packages

package root

Definition Classes: root

package com

Definition Classes: root

package here

Definition Classes: com

package platform

Definition Classes: here

package data

Definition Classes: platform

package processing

This package provides the Data Processing Library for building distributed data processing applications.

A Runner both implements the interface with the environment for an application to run, and starts the application. The application, in turn, is driven by a Driver, that controls and performs the distributed processing.

Choose a Runner best suited for the environment where the application runs.

The Driver performs one of more tasks which read layers from input catalogs and write to one or more layers of an output catalog.

The main entry point in the processing library is the com.here.platform.data.processing.driver.DriverBuilder class where you can add different kinds of tasks to the driver. The driver runs the tasks, and commits the final results to the output catalog.

Tasks are implemented using one or more compilers.

The simplest compiler is the direct compiler which maps each input tile to N output tiles. The application needs to define com.here.platform.data.processing.compiler.Direct1ToNCompiler.

Other more complex compilation patterns are based on some kind of dependency tracking between input partitions and output partitions.

The processing Library supports the following patterns:

- com.here.platform.data.processing.compiler.NonIncrementalCompiler: non-incremental compilation only - com.here.platform.data.processing.compiler.DepCompiler: non-incremental dependency calculation and incremental compilation - com.here.platform.data.processing.compiler.IncrementalDepCompiler: incremental dependency calculation and compilation - com.here.platform.data.processing.compiler.Direct1ToNCompiler: incremental compilation where every output tile depends only on one input tile, and this mapping is independent from tile content - com.here.platform.data.processing.compiler.DirectMToNCompiler: incremental compilation where every output tile depends on multiple input tiles, and this mapping is independent from tile content - com.here.platform.data.processing.compiler.MapGroupCompiler: incremental compilation where every output tile can depend on multiple input tiles, and this mapping depend on the tile content - com.here.platform.data.processing.compiler.RefTreeCompiler: fully-managed two phases incremental compilation that can resolve references between input partitions. Input/Output dependency management is implemented and the developer doesn't need to provide this logic

The application's main object normally mixes in the a runner trait (like PipelineRunner) to setup the Driver, and interfaces with the environment where the application is run. See the Main classes in the example compilers for more details.

com.here.platform.data.processing.catalog, com.here.platform.data.processing.blobstore, and com.here.platform.data.processing.publisher contain utilities for accessing catalogs and payloads in a Spark-friendly way, providing an RDD-based abstraction over data and metadata. These classes are used by the processing library, but can also be used independently.

Definition Classes: data

package java

This package provides Java bindings for the Data Processing Library, to build distributed data processing applications in Java.

Choose a Runner best suited for the environment where the application runs.

The Driver performs one of more tasks which read layers from input catalogs and write to one or more layers of an output catalog.

The main entry point in the processing library is the com.here.platform.data.processing.java.driver.DriverBuilder class where you can add different kinds of tasks to the driver. The driver runs the tasks, and commits the final results to the output catalog.

Tasks are implemented using one or more compilers.

The simplest compiler is the direct compiler which maps each input tile to N output tiles. The application needs to implement a com.here.platform.data.processing.java.compiler.Direct1ToNCompiler.

Other more complex compilation patterns are based on different types of dependency tracking between input partitions and output partitions.

The processing Library supports the following patterns:

- com.here.platform.data.processing.java.compiler.NonIncrementalCompiler: non-incremental compilation only - com.here.platform.data.processing.java.compiler.DepCompiler: non-incremental dependency calculation and incremental compilation - com.here.platform.data.processing.java.compiler.IncrementalDepCompiler: incremental dependency calculation and compilation - com.here.platform.data.processing.java.compiler.Direct1ToNCompiler: incremental compilation where every output tile depends only on one input tile, and this mapping is independent from tile content - com.here.platform.data.processing.java.compiler.DirectMToNCompiler: incremental compilation where every output tile can depend on multiple input tiles, and this mapping is independent from tile content - com.here.platform.data.processing.java.compiler.MapGroupCompiler: incremental compilation where every output tile can depend on multiple input tiles, and this mapping depend on the tile content - com.here.platform.data.processing.java.compiler.RefTreeCompiler: fully-managed two phases incremental compilation that can resolve references between input partitions. Input/Output dependency management is implemented and the developer doesn't need to provide this logic

The application's main object normally extends a runner class (like PipelineRunner) to setup the Driver, and interfaces with the environment where the application runs. For more details, see the Main classes in the example compilers.

com.here.platform.data.processing.java.catalog, com.here.platform.data.processing.java.blobstore, and com.here.platform.data.processing.java.publisher contain utilities for accessing catalogs and payloads in a Spark-friendly way, providing an RDD-based abstraction over data and metadata. These classes are used by the processing library, but can also be used independently.

Definition Classes: processing

package spark

Definition Classes: java

package partitioner

Definition Classes: spark

AdaptiveLevelingPartitioner

HashPartitioner

LocalityAwarePartitioner

NameHashPartitioner

PartitionNamePartitioner

PartitionerOfKey

com.here.platform.data.processing.java.spark.partitioner

AdaptiveLevelingPartitioner

final class AdaptiveLevelingPartitioner extends PartitionNamePartitioner with ScalaPartitionNamePartitionerWrapper

A PartitionerOfKey that uses a precalculated leveling com.here.platform.data.processing.java.leveling.Pattern.

Keys are distributed to Spark partitions strictly following the leveling points that the pattern specifies. Keys left not aggregated by the pattern are distributed among a disjoint set of Spark partitions using a fallback partitioner, if specified. Otherwise they are uniformly distributed over the existing partitions.

The number of partitions used for aggregated keys is fixed and matches the number of leveling points of the pattern.

Linear Supertypes

ScalaPartitionNamePartitionerWrapper, Wrapper[spark.partitioner.PartitionNamePartitioner], PartitionNamePartitioner, PartitionerOfKey, Serializable, Serializable, AnyRef, Any

Ordering

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By Inheritance

Inherited

AdaptiveLevelingPartitioner
ScalaPartitionNamePartitionerWrapper
Wrapper
PartitionNamePartitioner
PartitionerOfKey
Serializable
Serializable
AnyRef
Any

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Visibility

Public
All

Instance Constructors

new AdaptiveLevelingPartitioner(pattern: AdaptivePattern, fallbackPartitioner: PartitionNamePartitioner)
pattern
The adaptive leveling pattern that controls the partitioning.
fallbackPartitioner
The partitioner used for non-aggregated keys.
new AdaptiveLevelingPartitioner(pattern: AdaptivePattern)
pattern
The adaptive leveling pattern that controls the partitioning.

Note
With this constructor, non aggregated keys are distributed over the existing Spark partitions of the pattern.
new AdaptiveLevelingPartitioner(impl: spark.partitioner.AdaptiveLevelingPartitioner)

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
def clone(): AnyRef

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

Definition Classes
AnyRef
def equals(o: Any): Boolean

Definition Classes
Wrapper → AnyRef → Any
def finalize(): Unit

Attributes
protected[lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
Annotations
@native()
final def getPartitionForKey(key: Key): Int
Gets the partition for a given key.
Gets the partition for a given key.
key
the key for which the partition must be calculated
returns
the partition, identified by one scala.Int, in which the key should be located

Definition Classes
PartitionNamePartitioner → PartitionerOfKey
def getPartitionForName(name: Name): Int

Definition Classes
ScalaPartitionNamePartitionerWrapper → PartitionNamePartitioner
def hashCode(): Int

Definition Classes
Wrapper → AnyRef → Any
val impl: spark.partitioner.AdaptiveLevelingPartitioner

Definition Classes
AdaptiveLevelingPartitioner → Wrapper
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

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

Definition Classes
AnyRef
Annotations
@native()
def numPartitions: Int
Gets the total number of partitions defined by the partitioner.
Gets the total number of partitions defined by the partitioner.
returns
the total number of partitions defined

Definition Classes
ScalaPartitionNamePartitionerWrapper → PartitionerOfKey
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def toSpark: Partitioner
Gets a standard Spark org.apache.spark.Partitioner wrapping this one.
Gets a standard Spark org.apache.spark.Partitioner wrapping this one.
returns
this partitioner wrapped inside a standard Spark interface

Definition Classes
ScalaPartitionNamePartitionerWrapper → PartitionerOfKey
def toString(): String

Definition Classes
Wrapper → AnyRef → Any
final def wait(): Unit

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

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

Definition Classes
AnyRef
Annotations
@throws( ... ) @native()

Packages

AdaptiveLevelingPartitioner

final class AdaptiveLevelingPartitioner extends PartitionNamePartitioner with ScalaPartitionNamePartitionerWrapper

Instance Constructors

Value Members

Inherited from ScalaPartitionNamePartitionerWrapper

Inherited from Wrapper[spark.partitioner.PartitionNamePartitioner]

Inherited from PartitionNamePartitioner

Inherited from PartitionerOfKey

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Ungrouped

Packages

AdaptiveLevelingPartitioner 

final class AdaptiveLevelingPartitioner extends PartitionNamePartitioner with ScalaPartitionNamePartitionerWrapper

Instance Constructors

Value Members

Inherited from ScalaPartitionNamePartitionerWrapper

Inherited from Wrapper[spark.partitioner.PartitionNamePartitioner]

Inherited from PartitionNamePartitioner

Inherited from PartitionerOfKey

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Ungrouped

AdaptiveLevelingPartitioner