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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

Referencing

trait Referencing extends AnyRef

The transformation applicable to input metadata.

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ReferencingFactory

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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 mapValuesWithResolver[V](mapFn: (Resolver, Key, Meta) ⇒ V, strategies: Seq[ResolutionStrategy[Key, Meta]], id: Id = ..., configOverride: Override = DeltaSetConfig.noOverride)(implicit arg0: ClassTag[V]): DeltaSet[Key, V]
Transforms the DeltaSet, the _subjects_, while having access to a Resolver that allows finding the metadata for related partitions, the _references_.
Transforms the DeltaSet, the _subjects_, while having access to a Resolver that allows finding the metadata for related partitions, the _references_.
mapFn
The function to transform the values of the subject DeltaSet. Takes the Resolver, the key, and the original value of the subject as input; returns the new value of the subject.
strategies
Defines a list of strategies to find the metadata corresponding to a reference key. See ResolutionStrategy.
id
Deprecated.
configOverride
Deprecated.

Note
This will use the partitioner of the upstream DeltaSet also for partitioning references.
,
The mapFn must be scala.Serializable as it is copied to workers and run inside Spark map functions.
abstract def resolveReferences(refStructure: RefTree, resolveFn: (Key, Meta) ⇒ Map[RefName, Set[Key]], id: Id = autoIdAssigner("resolveReferences"), configOverride: Override = DeltaSetConfig.noOverride): DeltaSet[Key, (Meta, Map[Key, Meta])]
Resolves references from one partition to other partitions.
Resolves references from one partition to other partitions. This transformation requires you to specify the shape of all reference relations in the form of a RefTree, and provide a function that determines all possible references for each partition.
One condition to apply this transformation is that all references of a layer can be calculated purely from the source data of this layer using the resolve function, which takes the metadata of one partition as input. The structure of the references must be predefined in a RefTree object.
The result contains every subject partition of the input DeltaSet with the full list of metadata for all of its referenced partitions.
For this transformation to be efficient in incremental mode, it is best to apply it as early as possible in the computation.
This transformation will shuffle data whenever there are references for one key belonging to a different partition.
refStructure
The RefTree that defines the structure of the resolution.
resolveFn
The function that produces all references for each partition.
id
Deprecated.
configOverride
Deprecated.

Note
The resolveFn must be scala.Serializable as it is copied to workers and run inside Spark map functions.
See also
com.here.platform.data.processing.compiler.RefTreeCompiler, which uses the same concepts in a fixed compiler pattern.

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trait Referencing extends AnyRef

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trait Referencing extends AnyRef

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