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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

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 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

LazyJoinWrapper

implicit final class LazyJoinWrapper[K, V] extends AnyVal

Adds lazy joins to key/value-based RDDs.

K: The type of the key.
V: The type of the value.

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new LazyJoinWrapper(rdd: RDD[(K, V)])
rdd
The key/value-based RDD. The transformations will be lazy if the rdd is not repartitioned.

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final def !=(arg0: Any): Boolean

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final def ##(): Int

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final def ==(arg0: Any): Boolean

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final def asInstanceOf[T0]: T0

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def getClass(): Class[_ <: AnyVal]

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final def isInstanceOf[T0]: Boolean

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def lazyFullOuterJoin[W](other: RDD[(K, W)], partitioner: Partitioner = defaultPartitioner)(implicit arg0: ClassTag[W], kt: ClassTag[K], vt: ClassTag[V]): RDD[(K, (Option[V], Option[W]))]
Like org.apache.spark.rdd.PairRDDFunctions.fullOuterJoin, but all the elements in the Spark partitions of the left operand are evaluated lazily if the RDD uses the same partitioner as the result.
Like org.apache.spark.rdd.PairRDDFunctions.fullOuterJoin, but all the elements in the Spark partitions of the left operand are evaluated lazily if the RDD uses the same partitioner as the result.
W
The value-type of the right operand.
other
The right operand. It will be fully evaluated.
partitioner
The partitioner used for the resulting RDD. If the same partitioner of the left operand is used, the operation will not cause the left RDD to be fully evaluated. Default value is the left operand's partitioner, if defined, or a HashPartitioner, otherwise.
returns
an RDD with the result of the full outer join.
def lazyLeftOuterJoin[W](other: RDD[(K, W)], partitioner: Partitioner = defaultPartitioner)(implicit arg0: ClassTag[W], kt: ClassTag[K], vt: ClassTag[V]): RDD[(K, (V, Option[W]))]
Like org.apache.spark.rdd.PairRDDFunctions.leftOuterJoin, but all the elements in the Spark partitions of the left operand are evaluated lazily if the RDD uses the same partitioner as the result.
Like org.apache.spark.rdd.PairRDDFunctions.leftOuterJoin, but all the elements in the Spark partitions of the left operand are evaluated lazily if the RDD uses the same partitioner as the result.
W
The value-type of the right operand.
other
The right operand. It will be fully evaluated.
partitioner
The partitioner used for the resulting RDD. If the same partitioner of the left operand is used, the operation will not cause the left RDD to be fully evaluated. Default value is the left operand's partitioner, if defined, or a HashPartitioner, otherwise.
returns
an RDD with the result of the left outer join.
def lazyRightOuterJoin[W](other: RDD[(K, W)], partitioner: Partitioner = defaultPartitioner)(implicit arg0: ClassTag[W], kt: ClassTag[K], vt: ClassTag[V]): RDD[(K, (Option[V], W))]
Like org.apache.spark.rdd.PairRDDFunctions.rightOuterJoin, but all the elements in the Spark partitions of the left operand are evaluated lazily if the RDD uses the same partitioner as the result.
Like org.apache.spark.rdd.PairRDDFunctions.rightOuterJoin, but all the elements in the Spark partitions of the left operand are evaluated lazily if the RDD uses the same partitioner as the result.
W
The value-type of the right operand.
other
The right operand. It will be fully evaluated.
partitioner
The partitioner used for the resulting RDD. If the same partitioner of the left operand is used, the operation will not cause the left RDD to be fully evaluated. Default value is the left operand's partitioner, if defined, or a HashPartitioner, otherwise.
returns
an RDD with the result of the right outer join.
def toString(): String

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LazyJoinWrapper

implicit final class LazyJoinWrapper[K, V] extends AnyVal

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implicit final class LazyJoinWrapper[K, V] extends AnyVal

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LazyJoinWrapper