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package com

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package here

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package platform

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package data

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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

AdaptivePatternEstimator

Companion object AdaptivePatternEstimator

class AdaptivePatternEstimator extends AnyRef

Computes an AdaptivePattern given an AdaptivePatternEstimateFn and a threshold.

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new AdaptivePatternEstimator(estimateFn: AdaptivePatternEstimateFn, context: DriverContext)
estimateFn
The estimate function.
context
The com.here.platform.data.processing.driver.DriverContext object that the estimator is running in.

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def adaptivePattern(threshold: Long): Broadcast[AdaptivePattern]
Estimate an AdaptivePattern given a threshold are returns it wrapped in a org.apache.spark.broadcast.Broadcast.
Estimate an AdaptivePattern given a threshold are returns it wrapped in a org.apache.spark.broadcast.Broadcast. The resulting pattern can be used to output tiles at a lower level in geographic areas where the content is sparse or at a higher level in geographic areas where the content is dense.
Changes to the estimated pattern *do prevent* the Driver to perform incremental compilation.
threshold
The maximum weight a leveling point may carry.
returns
The estimated adaptive leveling pattern, wrapped in a org.apache.spark.broadcast.Broadcast.
final def asInstanceOf[T0]: T0

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def partitioner(threshold: Long, fallbackPartitioner: Option[PartitionNamePartitioner] = None): AdaptiveLevelingPartitioner
Estimate an AdaptivePattern given a threshold and use it to construct an com.here.platform.data.processing.spark.partitioner.AdaptiveLevelingPartitioner.
Estimate an AdaptivePattern given a threshold and use it to construct an com.here.platform.data.processing.spark.partitioner.AdaptiveLevelingPartitioner.
This partitioner can be used to balance the sizes of Spark partitions to obtain a more even, uniformed distribution of content inside them, avoiding cases where the partitions are too heavy to process, or there are too many light partitions.
Changes to the estimated pattern *do not prevent* the Driver to perform incremental compilation.
threshold
The maximum weight a leveling point may carry.
fallbackPartitioner
The optional partitioner used for non-aggregated keys. If undefined, non-aggregated keys are uniformly distributed over the existing partitions.
returns
An com.here.platform.data.processing.spark.partitioner.AdaptiveLevelingPartitioners.
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AdaptivePatternEstimator

Companion object AdaptivePatternEstimator

class AdaptivePatternEstimator extends AnyRef

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AdaptivePatternEstimator 

Companion object AdaptivePatternEstimator

class AdaptivePatternEstimator extends AnyRef

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AdaptivePatternEstimator