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 leveling

Definition Classes: java

AdaptivePattern

AdaptivePatternEstimateFn

AdaptivePatternEstimator

FixedPattern

Pattern

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

AdaptivePatternEstimateFn

trait AdaptivePatternEstimateFn extends InputLayers with InputOptPartitioner with Serializable

Main interface the user has to implement to calculate an com.here.platform.data.processing.leveling.AdaptivePattern.

The users estimate the contribution of input partitions to different tile, by returning a weight for each tile involved. An com.here.platform.data.processing.java.leveling.AdaptivePattern is then calculated by accumulating weights to find out leveling points whose total weights (the sum of the weights of their children) does not exceed a given threshold. Users are free to give any custom meaning to weights and thresholds.

Used by AdaptivePatternEstimator, it is invoked in a distributed fashion on every input partition of the layers mentioned as input. com.here.platform.data.processing.java.blobstore.Retriever may be passed in constructor and used internally, however this pattern is discouraged.

AdaptivePatternEstimator applies this function to the whole input layers at every run non-incrementally so, unless the input layers are few and small, using a com.here.platform.data.processing.java.blobstore.Retriever would download the whole input data every time.

Estimates don't have to be precise, so the suggested pattern is to use the payload size present in the metadata as indication of the data size, without retrieving the payload.

Linear Supertypes

Serializable, Serializable, InputOptPartitioner, InputLayers, AnyRef, Any

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Abstract Value Members

abstract def estimateFn(src: Java.Pair[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.java.blobstore.Retriever, although this method is not suggested.
returns
Output HereTiles with their estimated weights. Weights must be positive.
abstract def inLayers: Map[String, Set[String]]
Layers of the input catalogs that should be queried and provided to the compiler, grouped by input catalog and identified by catalog id and layer ID.
Layers of the input catalogs that should be queried and provided to the compiler, grouped by input catalog and identified by catalog id and layer ID.

Definition Classes
InputLayers
abstract def inPartitioner(parallelism: Int): Option[PartitionerOfKey]
If the returned com.here.platform.data.processing.java.Java.Option is Empty, the Executor will use a default partitioner, if it is defined then the given partitioner will be applied when querying the input catalogs.
If the returned com.here.platform.data.processing.java.Java.Option is Empty, the Executor will use a default partitioner, if it is defined then the given partitioner will be applied when querying the input catalogs.
parallelism
the parallelism of the partitioner
returns
the input optional partitioner with the given parallelism

Definition Classes
InputOptPartitioner

Concrete 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(arg0: Any): Boolean

Definition Classes
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()
def hashCode(): Int

Definition Classes
AnyRef → Any
Annotations
@native()
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
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

AdaptivePatternEstimateFn

trait AdaptivePatternEstimateFn extends InputLayers with InputOptPartitioner with Serializable

Abstract Value Members

Concrete Value Members

Inherited from Serializable

Inherited from Serializable

Inherited from InputOptPartitioner

Inherited from InputLayers

Inherited from AnyRef

Inherited from Any

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Packages

AdaptivePatternEstimateFn 

trait AdaptivePatternEstimateFn extends InputLayers with InputOptPartitioner with Serializable

Abstract Value Members

Concrete Value Members

Inherited from Serializable

Inherited from Serializable

Inherited from InputOptPartitioner

Inherited from InputLayers

Inherited from AnyRef

Inherited from Any

Ungrouped

AdaptivePatternEstimateFn