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

AdaptivePatternEstimator

class AdaptivePatternEstimator extends Wrapper[leveling.AdaptivePatternEstimator]

Computes an AdaptivePattern given an AdaptivePatternEstimateFn and a threshold.

Linear Supertypes

Wrapper[leveling.AdaptivePatternEstimator], AnyRef, Any

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new AdaptivePatternEstimator(estimateFn: AdaptivePatternEstimateFn, context: DriverContext)
estimateFn
The estimate function.
context
The com.here.platform.data.processing.java.driver.DriverContext object that the estimator is running in.

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
def adaptivePattern(threshold: Long): Broadcast[AdaptivePattern]
Estimate an AdaptivePattern given a threshold are returns it wrapped in a org.apache.spark.broadcast.Broadcast.
Estimate an AdaptivePattern given a threshold are returns it wrapped in a org.apache.spark.broadcast.Broadcast. The resulting pattern can be used to output tiles at a lower level in geographic areas where the content is sparse or at a higher level in geographic areas where the content is dense.
Changes to the estimated pattern *do prevent* the Driver to perform incremental compilation.
threshold
The maximum weight a leveling point may carry.
returns
The estimated adaptive leveling pattern, wrapped in a org.apache.spark.broadcast.Broadcast.
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()
def hashCode(): Int
Definition Classes
Wrapper → AnyRef → Any
val impl: leveling.AdaptivePatternEstimator
Definition Classes
AdaptivePatternEstimator → 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 partitioner(threshold: Long, fallbackPartitioner: PartitionNamePartitioner): AdaptiveLevelingPartitioner
Estimate an AdaptivePattern given a threshold and use it to construct an com.here.platform.data.processing.java.spark.partitioner.AdaptiveLevelingPartitioner.
Estimate an AdaptivePattern given a threshold and use it to construct an com.here.platform.data.processing.java.spark.partitioner.AdaptiveLevelingPartitioner.
This partitioner can be used to balance the sizes of Spark partitions to obtain a more even, uniformed distribution of content inside them, avoiding cases where the partitions are too heavy to process, or there are too many light partitions.
Changes to the estimated pattern *do not prevent* the Driver to perform incremental compilation.
threshold
The maximum weight a leveling point may carry.
fallbackPartitioner
The partitioner used for non-aggregated keys.
returns
An com.here.platform.data.processing.spark.partitioner.AdaptiveLevelingPartitioners.
def partitioner(threshold: Long): AdaptiveLevelingPartitioner
Estimate an AdaptivePattern given a threshold and use it to construct an com.here.platform.data.processing.java.spark.partitioner.AdaptiveLevelingPartitioner.
Estimate an AdaptivePattern given a threshold and use it to construct an com.here.platform.data.processing.java.spark.partitioner.AdaptiveLevelingPartitioner.
This partitioner can be used to balance the sizes of Spark partitions to obtain a more even, uniformed distribution of content inside them, avoiding cases where the partitions are too heavy to process, or there are too many light partitions.
Changes to the estimated pattern *do not prevent* the Driver to perform incremental compilation.
threshold
The maximum weight a leveling point may carry.
returns
An com.here.platform.data.processing.spark.partitioner.AdaptiveLevelingPartitioners.
Note
With this factory, non aggregated keys are distributed over the existing Spark partitions of the pattern.
final def synchronized[T0](arg0: => T0): T0
Definition Classes
AnyRef
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
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@throws(classOf[java.lang.InterruptedException]) @native()
final def wait(): Unit
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@throws(classOf[java.lang.InterruptedException])

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

Packages

AdaptivePatternEstimator

class AdaptivePatternEstimator extends Wrapper[leveling.AdaptivePatternEstimator]

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Inherited from Wrapper[leveling.AdaptivePatternEstimator]

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AdaptivePatternEstimator

class AdaptivePatternEstimator extends Wrapper[leveling.AdaptivePatternEstimator]

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Inherited from Wrapper[leveling.AdaptivePatternEstimator]

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AdaptivePatternEstimator