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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 spark

Definition Classes
processing
package rdd

Definition Classes
spark
object Implicits

Definition Classes
rdd
CoalesceWrapper
Describer
FlatMapPartitionWrapper
KeyValueOpsWrapper
KeyValueWrapper
LazyJoinWrapper
MapKeyValueWrapper
MapPartitionWrapper
PersistenceWrapper

com.here.platform.data.processing.spark.rdd.Implicits

MapPartitionWrapper

implicit final class MapPartitionWrapper[V] extends AnyVal

Maps partitions of RDDs in parallel.

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new MapPartitionWrapper(rdd: RDD[V])

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final def ==(arg0: Any): Boolean

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final def asInstanceOf[T0]: T0

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def mapParallel[U](numThreads: Int)(f: (V) ⇒ U)(implicit arg0: ClassTag[U], vt: ClassTag[V]): RDD[U]
Applies map to an RDD, but it can use a thread pool if numThreads is >1, in that case partitions are computed in parallel.
Applies map to an RDD, but it can use a thread pool if numThreads is >1, in that case partitions are computed in parallel.
U
the type of the new values
numThreads
the number of threads to use for parallel invocations of f(). Can only be set to a value larger than 1 if the operations include IO operations, or if multiple cores are available per executor
f
the mapping function, providing the new value for the element
returns
an RDD with mapping function applied to each element

Note
calling this function loses partitioning for key-value pair RDD
def mapParallel[U](f: (V) ⇒ U, numThreads: Int)(implicit arg0: ClassTag[U], vt: ClassTag[V]): RDD[U]
Applies map to an RDD, but it can use a thread pool if numThreads is >1, in that case partitions are computed in parallel.
Applies map to an RDD, but it can use a thread pool if numThreads is >1, in that case partitions are computed in parallel.
U
the type of the new values
f
the mapping function, providing the new value for the element
numThreads
the number of threads to use for parallel invocations of f(). Can only be set to a value larger than 1 if the operations include IO operations, or if multiple cores are available per executor
returns
an RDD with mapping function applied to each element

Note
calling this function loses partitioning for key-value pair RDD
def mapParallelUnordered[U](numThreads: Int)(f: (V) ⇒ U)(implicit arg0: ClassTag[U], vt: ClassTag[V]): RDD[U]
Applies map to an RDD, but it can use a thread pool if numThreads is >1, in that case partitions are computed in parallel.
Applies map to an RDD, but it can use a thread pool if numThreads is >1, in that case partitions are computed in parallel. Elements are emitted as soon as they are ready, therefore it is possible that the transformed elements in each Spark partition are not in the same order as in the upstream RDD.
U
the type of the new values
numThreads
the number of threads to use for parallel invocations of f(). Can only be set to a value larger than 1 if the operations include IO operations, or if multiple cores are available per executor
f
the mapping function, providing the new value for the element
returns
an RDD with mapping function applied to each element

Note
calling this function loses partitioning for key-value pair RDD
def mapParallelUnordered[U](f: (V) ⇒ U, numThreads: Int)(implicit arg0: ClassTag[U], vt: ClassTag[V]): RDD[U]
Applies map to an RDD, but it can use a thread pool if numThreads is >1, in that case partitions are computed in parallel.
Applies map to an RDD, but it can use a thread pool if numThreads is >1, in that case partitions are computed in parallel. Elements are emitted as soon as they are ready, therefore it is possible that the transformed elements in each Spark partition are not in the same order as in the upstream RDD.
U
the type of the new values
f
the mapping function, providing the new value for the element
numThreads
the number of threads to use for parallel invocations of f(). Can only be set to a value larger than 1 if the operations include IO operations, or if multiple cores are available per executor
returns
an RDD with mapping function applied to each element

Note
calling this function loses partitioning for key-value pair RDD
def toString(): String

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MapPartitionWrapper

implicit final class MapPartitionWrapper[V] extends AnyVal

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implicit final class MapPartitionWrapper[V] extends AnyVal

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MapPartitionWrapper