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 broadcast

Provides the basic functionality to perform org.apache.spark.broadcast.Broadcast creation.

To use a broadcast variable in the processing library, developers are required to add the variable's hash to the fingerprints. This ensures that incremental compilations are not compromised. This package object provides the method toBroadcast() to use for that purpose. The package also offers a functionality to query catalogs by properly managing versions. The functionality is based on the org.apache.spark.broadcast.Broadcast which offers a self-contained mechanism to create a single broadcast variable of a generic type.

This compiler class is the preferred way of working with broadcast variables in the Data Processing Library.

Definition Classes: java

BroadcastCompiler

com.here.platform.data.processing.java.broadcast

BroadcastCompiler

abstract class BroadcastCompiler[T] extends InputLayers with InputOptPartitioner

Offers the interface for the data compilation supposed to be stored on a Spark org.apache.spark.broadcast.Broadcast variable.

Compiled data is an object instance of type T. The suggested use of this functionality is to: - perform the broadcast creations during the driver setting up. - send the broadcast variables to the compiler which is under creation as a parameter.

T: the type of the object to be stored in the broadcast variable

Linear Supertypes

InputOptPartitioner, InputLayers, AnyRef, Any

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BroadcastCompiler
InputOptPartitioner
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Instance Constructors

new BroadcastCompiler(context: DriverContext, clazz: Class[T], id: String)
BroadcastCompiler constructor that specifies the compiler ID.
BroadcastCompiler constructor that specifies the compiler ID.
context
The com.here.platform.data.processing.java.driver.DriverContext object that the compiler is running in.
clazz
The class of type T.
id
The ID for the compiler. Used to stored the fingerprint of the broadcast variable with an ID, which makes it independent of creation order.
new BroadcastCompiler(context: DriverContext, clazz: Class[T])
BroadcastCompiler constructor.
BroadcastCompiler constructor.
context
The com.here.platform.data.processing.java.driver.DriverContext object that the compiler is running in.
clazz
The class of type T.

Abstract Value Members

abstract def calculateFingerprint(t: T): Int
Calculate the fingerprint of the compiled data.
Calculate the fingerprint of the compiled data.
t
the data to calculate the fingerprint of
returns
the fingerprint

Attributes
protected
Note
fingerprint calculation needs to be stable to not compromise incremental compilation.
abstract def compile(input: JavaPairRDD[InKey, InMeta], parallelism: Int): T
Compiles an input metadata RDD into an object of data T.
Compiles an input metadata RDD into an object of data T.
This function needs to be overridden to perform the compilation of the input meta RDD.
input
the input metadata RDD
parallelism
the parallelism of the RDDs. This parameter is normally needed to get partitioners from com.here.platform.data.processing.java.compiler.InputOptPartitioner and/or from com.here.platform.data.processing.java.compiler.OutputOptPartitioner traits
returns
the compilation result

Attributes
protected
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 compileBroadcast(parallelism: Int): Broadcast[T]
Performs the compilation and the related operations to create the broadcast variable and to update the fingerprints.
Performs the compilation and the related operations to create the broadcast variable and to update the fingerprints. The id passed to the constructor, if specified, is used as fingerprint ID.
This is the method to be called by the class users. Please note that it updates a mutable content in the com.here.platform.data.processing.java.driver.DriverContext object: it changes a shared status. Multiple calls to this functionality should then happen in a deterministic order.
parallelism
the parallelism of the RDDs. This parameter is normally needed to get partitioners from com.here.platform.data.processing.java.compiler.InputOptPartitioner and/or from com.here.platform.data.processing.java.compiler.OutputOptPartitioner traits
returns
the broadcast variable
final def compileBroadcastNoUpdateFingerprints(parallelism: Int): Broadcast[T]
Performs the compilation and the related operations to create the broadcast variable without updating the fingerprints.
Performs the compilation and the related operations to create the broadcast variable without updating the fingerprints. WARNING: the recompilation won't be triggered if a change of the broadcast object is detected.
parallelism
the parallelism of the RDDs. This parameter is normally needed to get partitioners from com.here.platform.data.processing.java.compiler.InputOptPartitioner and/or from com.here.platform.data.processing.java.compiler.OutputOptPartitioner traits
returns
the broadcast variable
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

BroadcastCompiler

abstract class BroadcastCompiler[T] extends InputLayers with InputOptPartitioner

Instance Constructors

Abstract Value Members

Concrete Value Members

Inherited from InputOptPartitioner

Inherited from InputLayers

Inherited from AnyRef

Inherited from Any

Ungrouped

Packages

BroadcastCompiler 

abstract class BroadcastCompiler[T] extends InputLayers with InputOptPartitioner

Instance Constructors

Abstract Value Members

Concrete Value Members

Inherited from InputOptPartitioner

Inherited from InputLayers

Inherited from AnyRef

Inherited from Any

Ungrouped

BroadcastCompiler