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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 leveling
Definition Classes
processing
AdaptivePattern
AdaptivePatternEstimateFn
AdaptivePatternEstimator
FixedPattern
Implicits
Pattern

com.here.platform.data.processing.leveling

AdaptivePatternEstimateFn

trait AdaptivePatternEstimateFn extends InputLayers with InputOptPartitioner with Serializable

Main interface the user has to implement to calculate an com.here.platform.data.processing.leveling.AdaptivePattern.

The users estimate the contribution of input partitions to different tile, by returning a weight for each tile involved. An com.here.platform.data.processing.leveling.AdaptivePattern is then calculated by accumulating weights to find out leveling points whose total weights (the sum of the weights of their children) does not exceed a given threshold. Users are free to give any custom meaning to weights and thresholds.

Used by AdaptivePatternEstimator, it is invoked in a distributed fashion on every input partition of the layers mentioned as input. com.here.platform.data.processing.blobstore.Retriever may be passed in constructor and used internally, however this pattern is discouraged.

AdaptivePatternEstimator applies this function to the whole input layers at every run non-incrementally so, unless the input layers are few and small, using a com.here.platform.data.processing.blobstore.Retriever would download the whole input data every time.

Estimates don't have to be precise, so the suggested pattern is to use the payload size present in the metadata as indication of the data size, without retrieving the payload.

Linear Supertypes

Serializable, InputOptPartitioner, InputLayers, AnyRef, Any

Known Subclasses

WrapperEstimateFn

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abstract def estimateFn(src: (InKey, InMeta)): Iterable[(HereTile, Long)]
Estimate the weights of zero, one or more output HereTiles
Estimate the weights of zero, one or more output HereTiles
src
Input key and metadata, it can be used to retrieve the data with a com.here.platform.data.processing.blobstore.Retriever, although this method is not suggested.
returns
Output HereTiles with their estimated weights. Weights must be positive.
abstract def inLayers: Map[Id, Set[Id]]
Represents layers of the input catalogs that you should query and provide to the compiler.
Represents layers of the input catalogs that you should query and provide to the compiler. These layers are grouped by input catalog and identified by catalog ID and layer ID.
Definition Classes
InputLayers
abstract def inPartitioner(parallelism: Int): Option[Partitioner[InKey]]
Specifies the partitioner to use when querying the input catalogs.
Specifies the partitioner to use when querying the input catalogs. If no partitioner is provided, by returning None from this function, then the Executor uses the default partitioner.
parallelism
The number of partitions the partitioner should partition the catalog into, this should match the parallelism of the Spark RDD containing the input partitions.
returns
The optional input partitioner with the parallelism specified.
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
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protected[lang]
Definition Classes
AnyRef
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@throws(classOf[java.lang.CloneNotSupportedException]) @IntrinsicCandidate() @native()
final def eq(arg0: AnyRef): Boolean
Definition Classes
AnyRef
def equals(arg0: AnyRef): Boolean
Definition Classes
AnyRef → Any
final def getClass(): Class[_ <: AnyRef]
Definition Classes
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def hashCode(): Int
Definition Classes
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Definition Classes
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final def ne(arg0: AnyRef): Boolean
Definition Classes
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final def notify(): Unit
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Definition Classes
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Definition Classes
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Definition Classes
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Deprecated Value Members

def finalize(): Unit
Attributes
protected[lang]
Definition Classes
AnyRef
Annotations
@throws(classOf[java.lang.Throwable]) @Deprecated
Deprecated
(Since version 9)

Packages

AdaptivePatternEstimateFn

trait AdaptivePatternEstimateFn extends InputLayers with InputOptPartitioner with Serializable

Abstract Value Members

Concrete Value Members

Deprecated Value Members

Inherited from Serializable

Inherited from InputOptPartitioner

Inherited from InputLayers

Inherited from AnyRef

Inherited from Any

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Packages

AdaptivePatternEstimateFn

trait AdaptivePatternEstimateFn extends InputLayers with InputOptPartitioner with Serializable

Abstract Value Members

Concrete Value Members

Deprecated Value Members

Inherited from Serializable

Inherited from InputOptPartitioner

Inherited from InputLayers

Inherited from AnyRef

Inherited from Any

Ungrouped

AdaptivePatternEstimateFn