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package root

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root
package com

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root
package here

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com
package platform

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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 driver

Definition Classes
processing
package runner

Definition Classes
driver
package pipeline

Definition Classes
runner
package java

Definition Classes
pipeline
package impl

Definition Classes
java
PipelineRunner

com.here.platform.data.processing.driver.runner.pipeline.java

PipelineRunner

abstract class PipelineRunner extends AnyRef

Implements the main entry points of applications based on the Data Processing Library.

The command line is parsed, a SparkContext is created, a com.here.platform.data.processing.driver.config.DriverConfig is constructed and passed to a new com.here.platform.data.processing.driver.Driver. Finally that com.here.platform.data.processing.driver.Driver is run with the version parameters.

Note: This is a Java friendly version of com.here.platform.data.processing.driver.runner.pipeline.PipelineRunner mixed in with com.here.platform.data.processing.driver.DriverSetupWithBuilder.

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new PipelineRunner()

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abstract def applicationVersion: String
The running application version.
abstract def configureCompiler(completeConfig: CompleteConfig, context: java.driver.DriverContext, builder: java.driver.DriverBuilder): java.driver.DriverBuilder
Configures the compiler the processing library runs using a com.here.platform.data.processing.java.driver.DriverBuilder.
Configures the compiler the processing library runs using a com.here.platform.data.processing.java.driver.DriverBuilder.
completeConfig
the full configuration
context
the com.here.platform.data.processing.java.driver.DriverContext in which the com.here.platform.data.processing.driver.Driver being built runs
builder
the com.here.platform.data.processing.java.driver.DriverBuilder object
returns
a fully-configured com.here.platform.data.processing.java.driver.DriverBuilder, that must include one or more tasks

See also
com.here.platform.data.processing.java.driver.DriverBuilder to build a com.here.platform.data.processing.driver.Driver

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final def !=(arg0: Any): Boolean

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@throws( ... ) @native()
val delegate: pipeline.PipelineRunner

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final def isInstanceOf[T0]: Boolean

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final def main(args: Array[String]): Unit
Runs an application based on the Data Processing Library.
final def ne(arg0: AnyRef): Boolean

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final def notify(): Unit

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def runWith(args: Array[String], sparkContext: JavaSparkContext, pipelineContext: PipelineContext): Unit
Runs the runner with the provided Spark context and pipeline context.
Runs the runner with the provided Spark context and pipeline context. This method is useful for testing.
args
The command line arguments.
sparkContext
The Spark context.
pipelineContext
The pipeline context.
def runWith(args: Array[String], pipelineContext: PipelineContext): Unit
Runs the runner with the provided pipeline context.
Runs the runner with the provided pipeline context. This method is useful for testing.
args
The command line arguments.
pipelineContext
The pipeline context.
def runWith(args: Array[String], sparkContext: JavaSparkContext): Unit
Runs the runner with the provided Spark context.
Runs the runner with the provided Spark context. This method is useful for testing.
args
The command line arguments.
sparkContext
The Spark context,.
final def synchronized[T0](arg0: ⇒ T0): T0

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PipelineRunner

abstract class PipelineRunner extends AnyRef

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PipelineRunner 

abstract class PipelineRunner extends AnyRef

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PipelineRunner