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

Pattern

trait Pattern extends Serializable

Represents a leveling pattern.

A Pattern controls a leveling algorithm by deciding if a given partition should be mapped to another one, usually one of its parent and at a different level. Partitions are mapped to other partitions to balance the density of the content, such as map many small partitions to one single partition at a lower level; big partitions remain unmapped. Content is rebalanced and leveled, at the "leveling point".

Typically a leveling point may not be defined for every partition.

A Pattern can be used in multiple ways: - In com.here.platform.data.processing.java.compiler.direct.CompileInFn.mappingFn, com.here.platform.data.processing.java.compiler.mapgroup.CompileInFn.compileInFn, com.here.platform.data.processing.java.compiler.reftree.CompileInFnWithRefs.compileInFn or com.here.platform.data.processing.java.compiler.reftree.CompileInFnWithRefsReturnsReferences.compileInRefsFn to produce com.here.platform.data.processing.java.compiler.OutKeys that are functions of the density of the input, thus resulting in a density-based leveling of output partitions. - In the specialized com.here.platform.data.processing.java.spark.partitioner.AdaptiveLevelingPartitioner to define Spark partitions based on content density and distribute processing more uniformly across Spark partitions. This does not affect the output catalog but only the runtime characteristics of the process.

Leveling patterns are defined on com.here.platform.data.processing.java.catalog.partition.Name. This concept is applicable to both the Generic and the HereTile partitioning scheme.

A Pattern must be scala.Serializable: it is usually calculated in the com.here.platform.data.processing.driver.Driver and transferred to worker nodes to implement the distributed adaptive leveling and/or the density-aware Spark partitioning.

Given the size and complexity of some Patterns, the org.apache.spark.broadcast.Broadcast mechanism should be used when capturing a Pattern. This happens for example when a Pattern is passed as parameter in the constructor of your implementation of com.here.platform.data.processing.java.compiler.direct.CompileInFn, com.here.platform.data.processing.java.compiler.mapgroup.CompileInFn or com.here.platform.data.processing.java.compiler.reftree.CompileInFn.

Linear Supertypes

Serializable, Serializable, AnyRef, Any

Known Subclasses

WrapperPattern, AdaptivePattern, FixedPattern

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Abstract Value Members

abstract def levelingTargetFor(partition: Name): Option[Name]
Finds the leveling point to which a partition should be mapped.
Finds the leveling point to which a partition should be mapped.
partition
The name of a partition.
returns
The partition to which the given partition should be mapped, if any.

Concrete Value Members

final def !=(arg0: Any): Boolean

Definition Classes
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final def ##(): Int

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final def ==(arg0: Any): Boolean

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final def asInstanceOf[T0]: T0

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def clone(): AnyRef

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def finalize(): Unit

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trait Pattern extends Serializable

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trait Pattern extends Serializable

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