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

PartitionerOfKey

trait PartitionerOfKey extends Serializable

Abstract class implementing in Java a type-safe Spark partitioner. This class is usually employed in interfaces of the processing library to better specify the kind of partitioner needed; compiler developers must provide this class.

A PartitionerOfKey is similar to a Spark org.apache.spark.Partitioner, but the function that has to be implemented to provide the code requires the key of type com.here.platform.data.processing.java.catalog.partition.Key instead of a generic Object required by the Spark org.apache.spark.Partitioner.

equals and hashCode methods must be implemented properly otherwise Spark may introduce unnecessary shuffle operations or assertions in the data processing algorithms may fail.

Linear Supertypes

Serializable, Serializable, AnyRef, Any

Known Subclasses

AdaptiveLevelingPartitioner, LocalityAwarePartitioner, NameHashPartitioner, PartitionNamePartitioner

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Abstract Value Members

abstract 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
abstract 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
abstract 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

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

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protected[lang]
Definition Classes
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Annotations
@throws( ... ) @native()
final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
def equals(arg0: Any): Boolean

Definition Classes
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def finalize(): Unit

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protected[lang]
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@throws( classOf[java.lang.Throwable] )
final def getClass(): Class[_]

Definition Classes
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Annotations
@native()
def hashCode(): Int

Definition Classes
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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
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Annotations
@native()
final def notifyAll(): Unit

Definition Classes
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@native()
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
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def toString(): String

Definition Classes
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final def wait(): Unit

Definition Classes
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@throws( ... )
final def wait(arg0: Long, arg1: Int): Unit

Definition Classes
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@throws( ... )
final def wait(arg0: Long): Unit

Definition Classes
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Packages

PartitionerOfKey

trait PartitionerOfKey extends Serializable

Abstract Value Members

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Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

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PartitionerOfKey 

trait PartitionerOfKey extends Serializable

Abstract Value Members

Concrete Value Members

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

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PartitionerOfKey