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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 deltasets
Definition Classes
driver
package transformations
Definition Classes
deltasets
BaseSetOps
Publishing
RDDTransformations
ReadingBack
Referencing
Stateful
Stateless

com.here.platform.data.processing.driver.deltasets.transformations

Publishing

trait Publishing extends AnyRef

Groups transformations for DeltaSet[Partition.Key, Payload, P] or DeltaSet[Partition.Key, Option[Payload], P] that publish the payload.

Linear Supertypes

AnyRef, Any

Known Subclasses

OptionPublishingFactory, PublishingFactory

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abstract val autoIdAssigner: BaseSetIdAssigner
The ID assigner used to automatically assign DeltaSet IDs if not provided.
The ID assigner used to automatically assign DeltaSet IDs if not provided.
Attributes
protected
abstract def publish(layers: Set[Id], id: Id = autoIdAssigner("publish"), configOverride: Override = DeltaSetConfig.noOverride): PublishedSet
Publishes the payloads in this DeltaSet.
Publishes the payloads in this DeltaSet.
This function does not shuffle any data any data.
layers
The set of layers in the output catalog to publish to. Must be a superset of the layers in the layer component the partition keys in the upstream DeltaSet.
id
Deprecated.
configOverride
Deprecated.
returns
A PublishedSet that reflects the metadata of the published partitions.
Note
All arguments must be serializable, since this transformation is serialized and sent to workers nodes.
abstract def publishPart(layers: Set[Id], partMapper: PublishedPartMapper, part: Int, id: Id = autoIdAssigner("partialPublish"), configOverride: Override = DeltaSetConfig.noOverride): PublishedPart
Publishes the payloads in this DeltaSet using multi part publishing.
Publishes the payloads in this DeltaSet using multi part publishing. Use this transformation if you need to publish multiple times to the same layer (or set of layers) disjoint parts of the output data. This is especially useful if you need to read back each part individually to compute the remaining parts. The returned PublishedParts need to be combined using DeltaContext.unionPublishedParts to get the final PublishedSet.
This function does not shuffle any data any data.
layers
The set of layers in the output catalog to publish to. Must be a superset of the layers in the layer component the partition keys in the upstream DeltaSet.
partMapper
The PublishedPartMapper used in this multi part publishing.
part
The part number.
id
Deprecated.
configOverride
Deprecated.
returns
A PublishedPart that reflects the metadata of the published partitions.
Note
All arguments must be serializable, since this transformation is serialized and sent to workers nodes.

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Deprecated
(Since version 9)

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Publishing

trait Publishing extends AnyRef

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trait Publishing extends AnyRef

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Publishing