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package root
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package com
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package here
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com
package platform
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here
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 driver
Definition Classes
processing
package impl
Definition Classes
driver
package state
Definition Classes
impl
package partition
Definition Classes
state
FingerprintsProducer
StateSerializer

com.here.platform.data.processing.driver.impl.state

StateSerializer

Companion object StateSerializer

class StateSerializer[K, V] extends AnyRef

Serializes the compiler state represented as RDD[K, V] to/from an RDD[K, Payload]. The compiler-specific serializer for a single spark partition needs to be defined when instantiating class. This class saves tha state in a deterministic way by:

- partitioning by key - sorting by key inside the partitions

K: the type of the state key, to write the state in a stable way this type must have an ordering
V: the type of the state values

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new StateSerializer(statePartitionSerializer: StatePartitionSerializer[K, V], partitions: Int)(implicit arg0: Ordering[K], arg1: ClassTag[K], arg2: ClassTag[V])
statePartitionSerializer
the partition.StatePartitionSerializer to serialize state information
partitions
the maximum number of partitions in which to group the state entries

Value Members

final def !=(arg0: Any): Boolean
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final def ==(arg0: Any): Boolean
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final def asInstanceOf[T0]: T0
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@throws(classOf[java.lang.CloneNotSupportedException]) @IntrinsicCandidate() @native()
def deserializeRDD(prefixKey: Key, rdd: RDD[(Key, Payload)]): RDD[(K, V)]
Deserializes an RDD of prefix keys and com.here.platform.data.processing.blobstore.Payload.
Deserializes an RDD of prefix keys and com.here.platform.data.processing.blobstore.Payload. Each Payload consisting of a scala.List of items of type (K,V) serialized using a given object serializer.
prefixKey
the prefix key to select the partitions
rdd
the RDD to be deserialized
returns
the deserialized RDD
final def eq(arg0: AnyRef): Boolean
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final def isInstanceOf[T0]: Boolean
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final def notify(): Unit
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@IntrinsicCandidate() @native()
final def notifyAll(): Unit
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@IntrinsicCandidate() @native()
val partitioner: Partitioner[Key]
def serializeRDD(prefixKey: Key, rdd: RDD[(K, V)]): RDD[(Key, Payload)]
Serializes an RDD of (K,V) in another RDD of com.here.platform.data.processing.blobstore.Payload consisting of a Iterable[(K, V)] serialized using specific serializer.
Serializes an RDD of (K,V) in another RDD of com.here.platform.data.processing.blobstore.Payload consisting of a Iterable[(K, V)] serialized using specific serializer. The RDD is serialized by partitions. Each RDD partition is encoded into a single com.here.platform.data.processing.blobstore.Payload and a catalog partition key is generated out of a template key, so that the result set of payloads can be committed to a catalog.
prefixKey
the catalog template key where the serialized RDD is going to be published
rdd
the RDD to be serialized
returns
the serialized RDD ready to be uploaded
Note
an hash based repartitioning happens in this function
final def synchronized[T0](arg0: => T0): T0
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final def wait(arg0: Long, arg1: Int): Unit
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final def wait(): Unit
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def finalize(): Unit
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Deprecated
(Since version 9)

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StateSerializer

Companion object StateSerializer

class StateSerializer[K, V] extends AnyRef

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StateSerializer

Companion object StateSerializer

class StateSerializer[K, V] extends AnyRef

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StateSerializer