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 impl

Definition Classes: java

package leveling

Definition Classes: impl

WrapperEstimateFn

WrapperPattern

com.here.platform.data.processing.java.impl.leveling

WrapperEstimateFn

class WrapperEstimateFn extends AdaptivePatternEstimateFn with WrapperInputLayers[AdaptivePatternEstimateFn] with WrapperInputOptPartitioner[AdaptivePatternEstimateFn]

Linear Supertypes

WrapperInputOptPartitioner[AdaptivePatternEstimateFn], WrapperInputLayers[AdaptivePatternEstimateFn], Wrapper[AdaptivePatternEstimateFn], AdaptivePatternEstimateFn, Serializable, Serializable, InputOptPartitioner, InputLayers, AnyRef, Any

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Inherited

WrapperEstimateFn
WrapperInputOptPartitioner
WrapperInputLayers
Wrapper
AdaptivePatternEstimateFn
Serializable
Serializable
InputOptPartitioner
InputLayers
AnyRef
Any

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Visibility

Public
All

Instance Constructors

new WrapperEstimateFn(impl: AdaptivePatternEstimateFn)

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 estimateFn(src: (InKey, InMeta)): Iterable[(HereTile, Long)]
Estimate the weights of zero, one or more output HereTiles
Estimate the weights of zero, one or more output HereTiles
src
Input key and metadata, it can be used to retrieve the data with a com.here.platform.data.processing.blobstore.Retriever, although this method is not suggested.
returns
Output HereTiles with their estimated weights. Weights must be positive.

Definition Classes
WrapperEstimateFn → AdaptivePatternEstimateFn
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()
def hashCode(): Int

Definition Classes
Wrapper → AnyRef → Any
val impl: AdaptivePatternEstimateFn

Definition Classes
WrapperEstimateFn → Wrapper
def inLayers: Map[Id, Set[Id]]
Represents layers of the input catalogs that you should query and provide to the compiler.
Represents layers of the input catalogs that you should query and provide to the compiler. These layers are grouped by input catalog and identified by catalog ID and layer ID.

Definition Classes
WrapperInputLayers → InputLayers
def inPartitioner(parallelism: Int): Option[Partitioner[InKey]]
Specifies the partitioner to use when querying the input catalogs.
Specifies the partitioner to use when querying the input catalogs. If no partitioner is provided, by returning None from this function, then the Executor uses the default partitioner.
parallelism
The number of partitions the partitioner should partition the catalog into, this should match the parallelism of the Spark RDD containing the input partitions.
returns
The optional input partitioner with the parallelism specified.

Definition Classes
WrapperInputOptPartitioner → InputOptPartitioner
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()
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
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

WrapperEstimateFn

class WrapperEstimateFn extends AdaptivePatternEstimateFn with WrapperInputLayers[AdaptivePatternEstimateFn] with WrapperInputOptPartitioner[AdaptivePatternEstimateFn]

Instance Constructors

Value Members

Inherited from WrapperInputOptPartitioner[AdaptivePatternEstimateFn]

Inherited from WrapperInputLayers[AdaptivePatternEstimateFn]

Inherited from Wrapper[AdaptivePatternEstimateFn]

Inherited from AdaptivePatternEstimateFn

Inherited from Serializable

Inherited from Serializable

Inherited from InputOptPartitioner

Inherited from InputLayers

Inherited from AnyRef

Inherited from Any

Ungrouped

Packages

WrapperEstimateFn 

class WrapperEstimateFn extends AdaptivePatternEstimateFn with WrapperInputLayers[AdaptivePatternEstimateFn] with WrapperInputOptPartitioner[AdaptivePatternEstimateFn]

Instance Constructors

Value Members

Inherited from WrapperInputOptPartitioner[AdaptivePatternEstimateFn]

Inherited from WrapperInputLayers[AdaptivePatternEstimateFn]

Inherited from Wrapper[AdaptivePatternEstimateFn]

Inherited from AdaptivePatternEstimateFn

Inherited from Serializable

Inherited from Serializable

Inherited from InputOptPartitioner

Inherited from InputLayers

Inherited from AnyRef

Inherited from Any

Ungrouped

WrapperEstimateFn