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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.
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.
This package provides Java bindings for the Data Processing Library, to build distributed data processing applications in Java.
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.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 compiler

Definition Classes
java
package direct

Definition Classes
compiler
package mapgroup

Definition Classes
compiler
package reftree

Definition Classes
compiler
CompileOut1To1Fn
CompileOut1ToNFn
CompileOutFn
DepCompiler
Direct1ToNCompiler
DirectMToNCompiler
IncrementalDepCompiler
InputLayers
InputOptPartitioner
InputPartitioner
MapGroupCompiler
NonIncrementalCompiler
OutputLayers
OutputOptPartitioner
OutputPartitioner
RefTreeCompiler

com.here.platform.data.processing.java.compiler

Direct1ToNCompiler

trait Direct1ToNCompiler[T] extends InputLayers with InputOptPartitioner with CompileInFn[T] with OutputLayers with OutputOptPartitioner with compiler.direct.CompileOutFn[T]

This compiler allows a stateless incremental compilation, in the case the keys of output partitions are a function of the input keys only, so where the input/output mapping does not depend on the input content but only on the input keys.

This compiler is applicable only when each output partition is affected by one single input partition. This is restriction enforced but allows a very efficient implementation of the incremental compilation case. The limitation is on the output side only: one input partition may be mapped to zero, one or more output partitions but each output partition can be mapped to by one input partition only, without overlaps.

In case multiple input partitions may affect the same output partition, the DirectMToNCompiler should be used instead.

T: the type of the values passed between front-end and back-end

Note: the implementation must be scala.Serializable as this is copied to workers and run inside Spark map functions
,
This is a Java friendly version of com.here.platform.data.processing.compiler.Direct1ToNCompiler.
See also: extended interfaces for more details

Linear Supertypes

direct.CompileOutFn[T], OutputOptPartitioner, OutputLayers, CompileInFn[T], Serializable, Serializable, InputOptPartitioner, InputLayers, AnyRef, Any

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CompileOutFn
OutputOptPartitioner
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Abstract Value Members

abstract def compileInFn(in: Java.Pair[InKey, InMeta]): T
Calculates the intermediate result from a single input partition.
Calculates the intermediate result from a single input partition. The result will be provided together with the input key in the CompileOutFn.
in
the input partition to process
returns
the value of intermediate data of type T for this partition. This value will be passed in CompileOutFn to all output keys impacted by the in partition.

Definition Classes
CompileInFn
abstract def compileOutFn(outKey: OutKey, intermediate: T): Option[Payload]
Compiles a single output partition from intermediate data of a single input partition.
Compiles a single output partition from intermediate data of a single input partition. This function will only be called for output keys that were returned in CompileInFn.
Output keys which are no longer mapped to the mapping function calls, for example due to deleted input partitions in incremental processing, get deleted automatically.
outKey
Key of the output partition to generate
intermediate
Intermediate value calculated by CompileInFn.compileInFn for the input partition that was mapped to this output key
returns
com.here.platform.data.processing.java.Java.Options of com.here.platform.data.processing.java.blobstore.Payload, with either the partition content, or Empty if there is no data to be published.

Definition Classes
CompileOutFn
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
abstract def mappingFn(inKey: InKey): Iterable[OutKey]
Calculates which output partitions, if any, are affected by the given single input partition.
Calculates which output partitions, if any, are affected by the given single input partition. The mapping must be function of only the input InKey.
The metadata is intentionally not provided, because the result of this call cannot be function of the metadata and therefore the data. This is because the direct.CompileInFn implementation does not perform a stateful dependency-tracking for incremental compilation. If the function would use additional information from the tile content, it would break incremental compilation use case.
inKey
the input partition being mapped
returns
the output partitions that the given input partition maps to

Definition Classes
CompileInFn
abstract def outLayers: Set[String]
Layers that are expected to be produced by the compiler.
Layers that are expected to be produced by the compiler.

Definition Classes
OutputLayers
abstract def outPartitioner(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 output catalog and producing data to be published.
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 output catalog and producing data to be published.
parallelism
the parallelism of the partitioner
returns
the output optional partitioner with the given parallelism

Definition Classes
OutputOptPartitioner

Concrete Value Members

final def !=(arg0: Any): Boolean

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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 equals(arg0: Any): Boolean

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Packages

Direct1ToNCompiler

trait Direct1ToNCompiler[T] extends InputLayers with InputOptPartitioner with CompileInFn[T] with OutputLayers with OutputOptPartitioner with compiler.direct.CompileOutFn[T]

Abstract Value Members

Concrete Value Members

Inherited from direct.CompileOutFn[T]

Inherited from OutputOptPartitioner

Inherited from OutputLayers

Inherited from CompileInFn[T]

Inherited from Serializable

Inherited from Serializable

Inherited from InputOptPartitioner

Inherited from InputLayers

Inherited from AnyRef

Inherited from Any

Ungrouped

Packages

Direct1ToNCompiler 

trait Direct1ToNCompiler[T] extends InputLayers with InputOptPartitioner with CompileInFn[T] with OutputLayers with OutputOptPartitioner with compiler.direct.CompileOutFn[T]

Abstract Value Members

Concrete Value Members

Inherited from direct.CompileOutFn[T]

Inherited from OutputOptPartitioner

Inherited from OutputLayers

Inherited from CompileInFn[T]

Inherited from Serializable

Inherited from Serializable

Inherited from InputOptPartitioner

Inherited from InputLayers

Inherited from AnyRef

Inherited from Any

Ungrouped

Direct1ToNCompiler