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

LocalityAwarePartitioner

final class LocalityAwarePartitioner extends PartitionNamePartitioner with ScalaPartitionNamePartitionerWrapper

Implements a PartitionerOfKey that is aware of the geographic location of the keys and can therefore put keys that are close to each other in the same Spark partition. This increases the data locality and speeds up the processing of Spark worker nodes.

The partitioner detects which com.here.platform.data.processing.java.catalog.partition.Keys are actually HereTiles: in this case keys are grouped at a fixed quadtree level, that is generally higher that the level of the keys (= lower number). Keys that are at a level even higher that the one specified, or keys that are not HERE tiles are partitioned using their hashcode and spread uniformly across all the available partitions. These partitions do not have data locality.

Linear Supertypes

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

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Inherited

LocalityAwarePartitioner
ScalaPartitionNamePartitionerWrapper
Wrapper
PartitionNamePartitioner
PartitionerOfKey
Serializable
AnyRef
Any

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Visibility

Public
Protected

Instance Constructors

new LocalityAwarePartitioner(numPartitions: Int, level: Int)
numPartitions
The overall number of partitions.
level
The level by which to group HereTiles in the same Spark partition.
new LocalityAwarePartitioner(impl: spark.partitioner.LocalityAwarePartitioner)

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(classOf[java.lang.CloneNotSupportedException]) @IntrinsicCandidate() @native()
final def eq(arg0: AnyRef): Boolean
Definition Classes
AnyRef
def equals(o: Any): Boolean
Definition Classes
Wrapper → AnyRef → Any
final def getClass(): Class[_ <: AnyRef]
Definition Classes
AnyRef → Any
Annotations
@IntrinsicCandidate() @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.LocalityAwarePartitioner
Definition Classes
LocalityAwarePartitioner → 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
@IntrinsicCandidate() @native()
final def notifyAll(): Unit
Definition Classes
AnyRef
Annotations
@IntrinsicCandidate() @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(arg0: Long, arg1: Int): Unit
Definition Classes
AnyRef
Annotations
@throws(classOf[java.lang.InterruptedException])
final def wait(arg0: Long): Unit
Definition Classes
AnyRef
Annotations
@throws(classOf[java.lang.InterruptedException]) @native()
final def wait(): Unit
Definition Classes
AnyRef
Annotations
@throws(classOf[java.lang.InterruptedException])

Deprecated Value Members

def finalize(): Unit
Attributes
protected[lang]
Definition Classes
AnyRef
Annotations
@throws(classOf[java.lang.Throwable]) @Deprecated
Deprecated
(Since version 9)

Packages

LocalityAwarePartitioner

final class LocalityAwarePartitioner extends PartitionNamePartitioner with ScalaPartitionNamePartitionerWrapper

Instance Constructors

Value Members

Deprecated Value Members

Inherited from ScalaPartitionNamePartitionerWrapper

Inherited from Wrapper[spark.partitioner.PartitionNamePartitioner]

Inherited from PartitionNamePartitioner

Inherited from PartitionerOfKey

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Ungrouped

Packages

LocalityAwarePartitioner

final class LocalityAwarePartitioner extends PartitionNamePartitioner with ScalaPartitionNamePartitionerWrapper

Instance Constructors

Value Members

Deprecated Value Members

Inherited from ScalaPartitionNamePartitionerWrapper

Inherited from Wrapper[spark.partitioner.PartitionNamePartitioner]

Inherited from PartitionNamePartitioner

Inherited from PartitionerOfKey

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Ungrouped

LocalityAwarePartitioner