Spark 3.1.2 ScalaDoc - org.apache.spark.rdd.CoGroupedRDD

final def !=(arg0: Any): Boolean

Definition Classes: AnyRef → Any

final def ##(): Int

Definition Classes: AnyRef → Any

def ++(other: RDD[(K, Array[Iterable[_]])]): RDD[(K, Array[Iterable[_]])]

Return the union of this RDD and another one.

Return the union of this RDD and another one. Any identical elements will appear multiple times (use .distinct() to eliminate them).

Definition Classes: RDD

final def ==(arg0: Any): Boolean

Definition Classes: AnyRef → Any

def aggregate[U](zeroValue: U)(seqOp: (U, (K, Array[Iterable[_]])) ⇒ U, combOp: (U, U) ⇒ U)(implicit arg0: ClassTag[U]): U

Aggregate the elements of each partition, and then the results for all the partitions, using given combine functions and a neutral "zero value".

Aggregate the elements of each partition, and then the results for all the partitions, using given combine functions and a neutral "zero value". This function can return a different result type, U, than the type of this RDD, T. Thus, we need one operation for merging a T into an U and one operation for merging two U's, as in scala.TraversableOnce. Both of these functions are allowed to modify and return their first argument instead of creating a new U to avoid memory allocation.

zeroValue: the initial value for the accumulated result of each partition for the seqOp operator, and also the initial value for the combine results from different partitions for the combOp operator - this will typically be the neutral element (e.g. Nil for list concatenation or 0 for summation)
seqOp: an operator used to accumulate results within a partition
combOp: an associative operator used to combine results from different partitions

Definition Classes: RDD

final def asInstanceOf[T0]: T0

Definition Classes: Any

def barrier(): RDDBarrier[(K, Array[Iterable[_]])]

:: Experimental :: Marks the current stage as a barrier stage, where Spark must launch all tasks together.

:: Experimental :: Marks the current stage as a barrier stage, where Spark must launch all tasks together. In case of a task failure, instead of only restarting the failed task, Spark will abort the entire stage and re-launch all tasks for this stage. The barrier execution mode feature is experimental and it only handles limited scenarios. Please read the linked SPIP and design docs to understand the limitations and future plans.

returns: an RDDBarrier instance that provides actions within a barrier stage

Definition Classes: RDD
Annotations: @Experimental() @Since( "2.4.0" )
See also: org.apache.spark.BarrierTaskContext
SPIP: Barrier Execution Mode
Design Doc

def cache(): CoGroupedRDD.this.type

Persist this RDD with the default storage level (MEMORY_ONLY).

Definition Classes: RDD

def cartesian[U](other: RDD[U])(implicit arg0: ClassTag[U]): RDD[((K, Array[Iterable[_]]), U)]

Return the Cartesian product of this RDD and another one, that is, the RDD of all pairs of elements (a, b) where a is in this and b is in other.

Definition Classes: RDD

def checkpoint(): Unit

Mark this RDD for checkpointing.

Mark this RDD for checkpointing. It will be saved to a file inside the checkpoint directory set with SparkContext#setCheckpointDir and all references to its parent RDDs will be removed. This function must be called before any job has been executed on this RDD. It is strongly recommended that this RDD is persisted in memory, otherwise saving it on a file will require recomputation.

Definition Classes: RDD

def cleanShuffleDependencies(blocking: Boolean = false): Unit

:: Experimental :: Removes an RDD's shuffles and it's non-persisted ancestors.

:: Experimental :: Removes an RDD's shuffles and it's non-persisted ancestors. When running without a shuffle service, cleaning up shuffle files enables downscaling. If you use the RDD after this call, you should checkpoint and materialize it first. If you are uncertain of what you are doing, please do not use this feature. Additional techniques for mitigating orphaned shuffle files: * Tuning the driver GC to be more aggressive, so the regular context cleaner is triggered * Setting an appropriate TTL for shuffle files to be auto cleaned

Definition Classes: RDD
Annotations: @Experimental() @DeveloperApi() @Since( "3.1.0" )

def clearDependencies(): Unit

Clears the dependencies of this RDD.

Clears the dependencies of this RDD. This method must ensure that all references to the original parent RDDs are removed to enable the parent RDDs to be garbage collected. Subclasses of RDD may override this method for implementing their own cleaning logic. See org.apache.spark.rdd.UnionRDD for an example.

Definition Classes: CoGroupedRDD → RDD

def clone(): AnyRef

Attributes: protected[lang]
Definition Classes: AnyRef
Annotations: @throws( ... ) @native()

def coalesce(numPartitions: Int, shuffle: Boolean = false, partitionCoalescer: Option[PartitionCoalescer] = Option.empty)(implicit ord: Ordering[(K, Array[Iterable[_]])] = null): RDD[(K, Array[Iterable[_]])]

Return a new RDD that is reduced into numPartitions partitions.

This results in a narrow dependency, e.g. if you go from 1000 partitions to 100 partitions, there will not be a shuffle, instead each of the 100 new partitions will claim 10 of the current partitions. If a larger number of partitions is requested, it will stay at the current number of partitions.

However, if you're doing a drastic coalesce, e.g. to numPartitions = 1, this may result in your computation taking place on fewer nodes than you like (e.g. one node in the case of numPartitions = 1). To avoid this, you can pass shuffle = true. This will add a shuffle step, but means the current upstream partitions will be executed in parallel (per whatever the current partitioning is).

Definition Classes: RDD
Note: With shuffle = true, you can actually coalesce to a larger number of partitions. This is useful if you have a small number of partitions, say 100, potentially with a few partitions being abnormally large. Calling coalesce(1000, shuffle = true) will result in 1000 partitions with the data distributed using a hash partitioner. The optional partition coalescer passed in must be serializable.

def collect[U](f: PartialFunction[(K, Array[Iterable[_]]), U])(implicit arg0: ClassTag[U]): RDD[U]

Return an RDD that contains all matching values by applying f.

Definition Classes: RDD

def collect(): Array[(K, Array[Iterable[_]])]

Return an array that contains all of the elements in this RDD.

Definition Classes: RDD
Note: This method should only be used if the resulting array is expected to be small, as all the data is loaded into the driver's memory.

def compute(s: Partition, context: TaskContext): Iterator[(K, Array[Iterable[_]])]

:: DeveloperApi :: Implemented by subclasses to compute a given partition.

Definition Classes: CoGroupedRDD → RDD

def context: SparkContext

The org.apache.spark.SparkContext that this RDD was created on.

Definition Classes: RDD

def count(): Long

Return the number of elements in the RDD.

Definition Classes: RDD

def countApprox(timeout: Long, confidence: Double = 0.95): PartialResult[BoundedDouble]

Approximate version of count() that returns a potentially incomplete result within a timeout, even if not all tasks have finished.

The confidence is the probability that the error bounds of the result will contain the true value. That is, if countApprox were called repeatedly with confidence 0.9, we would expect 90% of the results to contain the true count. The confidence must be in the range [0,1] or an exception will be thrown.

timeout: maximum time to wait for the job, in milliseconds
confidence: the desired statistical confidence in the result
returns: a potentially incomplete result, with error bounds

Definition Classes: RDD

def countApproxDistinct(relativeSD: Double = 0.05): Long

Return approximate number of distinct elements in the RDD.

The algorithm used is based on streamlib's implementation of "HyperLogLog in Practice: Algorithmic Engineering of a State of The Art Cardinality Estimation Algorithm", available here.

relativeSD: Relative accuracy. Smaller values create counters that require more space. It must be greater than 0.000017.

Definition Classes: RDD

def countApproxDistinct(p: Int, sp: Int): Long

Return approximate number of distinct elements in the RDD.

The algorithm used is based on streamlib's implementation of "HyperLogLog in Practice: Algorithmic Engineering of a State of The Art Cardinality Estimation Algorithm", available here.

The relative accuracy is approximately 1.054 / sqrt(2^p). Setting a nonzero (sp is greater than p) would trigger sparse representation of registers, which may reduce the memory consumption and increase accuracy when the cardinality is small.

p: The precision value for the normal set. p must be a value between 4 and sp if sp is not zero (32 max).
sp: The precision value for the sparse set, between 0 and 32. If sp equals 0, the sparse representation is skipped.

Definition Classes: RDD

def countByValue()(implicit ord: Ordering[(K, Array[Iterable[_]])] = null): Map[(K, Array[Iterable[_]]), Long]

Return the count of each unique value in this RDD as a local map of (value, count) pairs.

Definition Classes

RDD

Note

This method should only be used if the resulting map is expected to be small, as the whole thing is loaded into the driver's memory. To handle very large results, consider using

rdd.map(x => (x, 1L)).reduceByKey(_ + _)

, which returns an RDD[T, Long] instead of a map.

def countByValueApprox(timeout: Long, confidence: Double = 0.95)(implicit ord: Ordering[(K, Array[Iterable[_]])] = null): PartialResult[Map[(K, Array[Iterable[_]]), BoundedDouble]]

Approximate version of countByValue().

timeout: maximum time to wait for the job, in milliseconds
confidence: the desired statistical confidence in the result
returns: a potentially incomplete result, with error bounds

Definition Classes: RDD

final def dependencies: Seq[Dependency[_]]

Get the list of dependencies of this RDD, taking into account whether the RDD is checkpointed or not.

Definition Classes: RDD

def distinct(): RDD[(K, Array[Iterable[_]])]

Return a new RDD containing the distinct elements in this RDD.

Definition Classes: RDD

def distinct(numPartitions: Int)(implicit ord: Ordering[(K, Array[Iterable[_]])] = null): RDD[(K, Array[Iterable[_]])]

Return a new RDD containing the distinct elements in this RDD.

Definition Classes: RDD

final def eq(arg0: AnyRef): Boolean

Definition Classes: AnyRef

def equals(arg0: Any): Boolean

Definition Classes: AnyRef → Any

def filter(f: ((K, Array[Iterable[_]])) ⇒ Boolean): RDD[(K, Array[Iterable[_]])]

Return a new RDD containing only the elements that satisfy a predicate.

Definition Classes: RDD

def finalize(): Unit

Attributes: protected[lang]
Definition Classes: AnyRef
Annotations: @throws( classOf[java.lang.Throwable] )

def first(): (K, Array[Iterable[_]])

Return the first element in this RDD.

Definition Classes: RDD

def firstParent[U](implicit arg0: ClassTag[U]): RDD[U]

Returns the first parent RDD

Attributes: protected[spark]
Definition Classes: RDD

def flatMap[U](f: ((K, Array[Iterable[_]])) ⇒ TraversableOnce[U])(implicit arg0: ClassTag[U]): RDD[U]

Return a new RDD by first applying a function to all elements of this RDD, and then flattening the results.

Definition Classes: RDD

def fold(zeroValue: (K, Array[Iterable[_]]))(op: ((K, Array[Iterable[_]]), (K, Array[Iterable[_]])) ⇒ (K, Array[Iterable[_]])): (K, Array[Iterable[_]])

Aggregate the elements of each partition, and then the results for all the partitions, using a given associative function and a neutral "zero value".

Aggregate the elements of each partition, and then the results for all the partitions, using a given associative function and a neutral "zero value". The function op(t1, t2) is allowed to modify t1 and return it as its result value to avoid object allocation; however, it should not modify t2.

This behaves somewhat differently from fold operations implemented for non-distributed collections in functional languages like Scala. This fold operation may be applied to partitions individually, and then fold those results into the final result, rather than apply the fold to each element sequentially in some defined ordering. For functions that are not commutative, the result may differ from that of a fold applied to a non-distributed collection.

zeroValue: the initial value for the accumulated result of each partition for the op operator, and also the initial value for the combine results from different partitions for the op operator - this will typically be the neutral element (e.g. Nil for list concatenation or 0 for summation)
op: an operator used to both accumulate results within a partition and combine results from different partitions

Definition Classes: RDD

def foreach(f: ((K, Array[Iterable[_]])) ⇒ Unit): Unit

Applies a function f to all elements of this RDD.

Definition Classes: RDD

def foreachPartition(f: (Iterator[(K, Array[Iterable[_]])]) ⇒ Unit): Unit

Applies a function f to each partition of this RDD.

Definition Classes: RDD

def getCheckpointFile: Option[String]

Gets the name of the directory to which this RDD was checkpointed.

Gets the name of the directory to which this RDD was checkpointed. This is not defined if the RDD is checkpointed locally.

Definition Classes: RDD

final def getClass(): Class[_]

Definition Classes: AnyRef → Any
Annotations: @native()

def getDependencies: Seq[Dependency[_]]

Implemented by subclasses to return how this RDD depends on parent RDDs.

Implemented by subclasses to return how this RDD depends on parent RDDs. This method will only be called once, so it is safe to implement a time-consuming computation in it.

Definition Classes: CoGroupedRDD → RDD

final def getNumPartitions: Int

Returns the number of partitions of this RDD.

Definition Classes: RDD
Annotations: @Since( "1.6.0" )

def getOutputDeterministicLevel: DeterministicLevel.Value

Attributes: protected
Definition Classes: RDD
Annotations: @DeveloperApi()

def getPartitions: Array[Partition]

Implemented by subclasses to return the set of partitions in this RDD.

Implemented by subclasses to return the set of partitions in this RDD. This method will only be called once, so it is safe to implement a time-consuming computation in it.

The partitions in this array must satisfy the following property: rdd.partitions.zipWithIndex.forall { case (partition, index) => partition.index == index }

Definition Classes: CoGroupedRDD → RDD

def getPreferredLocations(split: Partition): Seq[String]

Optionally overridden by subclasses to specify placement preferences.

Attributes: protected
Definition Classes: RDD

def getResourceProfile(): ResourceProfile

Get the ResourceProfile specified with this RDD or null if it wasn't specified.

returns: the user specified ResourceProfile or null (for Java compatibility) if none was specified

Definition Classes: RDD
Annotations: @Experimental() @Since( "3.1.0" )

def getStorageLevel: StorageLevel

Get the RDD's current storage level, or StorageLevel.NONE if none is set.

Definition Classes: RDD

def glom(): RDD[Array[(K, Array[Iterable[_]])]]

Return an RDD created by coalescing all elements within each partition into an array.

Definition Classes: RDD

def groupBy[K](f: ((K, Array[Iterable[_]])) ⇒ K, p: Partitioner)(implicit kt: ClassTag[K], ord: Ordering[K] = null): RDD[(K, Iterable[(K, Array[Iterable[_]])])]

Return an RDD of grouped items.

Return an RDD of grouped items. Each group consists of a key and a sequence of elements mapping to that key. The ordering of elements within each group is not guaranteed, and may even differ each time the resulting RDD is evaluated.

Definition Classes: RDD
Note: This operation may be very expensive. If you are grouping in order to perform an aggregation (such as a sum or average) over each key, using PairRDDFunctions.aggregateByKey or PairRDDFunctions.reduceByKey will provide much better performance.

def groupBy[K](f: ((K, Array[Iterable[_]])) ⇒ K, numPartitions: Int)(implicit kt: ClassTag[K]): RDD[(K, Iterable[(K, Array[Iterable[_]])])]

Return an RDD of grouped elements.

Return an RDD of grouped elements. Each group consists of a key and a sequence of elements mapping to that key. The ordering of elements within each group is not guaranteed, and may even differ each time the resulting RDD is evaluated.

Definition Classes: RDD
Note: This operation may be very expensive. If you are grouping in order to perform an aggregation (such as a sum or average) over each key, using PairRDDFunctions.aggregateByKey or PairRDDFunctions.reduceByKey will provide much better performance.

def groupBy[K](f: ((K, Array[Iterable[_]])) ⇒ K)(implicit kt: ClassTag[K]): RDD[(K, Iterable[(K, Array[Iterable[_]])])]

Return an RDD of grouped items.

Return an RDD of grouped items. Each group consists of a key and a sequence of elements mapping to that key. The ordering of elements within each group is not guaranteed, and may even differ each time the resulting RDD is evaluated.

Definition Classes: RDD
Note: This operation may be very expensive. If you are grouping in order to perform an aggregation (such as a sum or average) over each key, using PairRDDFunctions.aggregateByKey or PairRDDFunctions.reduceByKey will provide much better performance.

def hashCode(): Int

Definition Classes: AnyRef → Any
Annotations: @native()

val id: Int

A unique ID for this RDD (within its SparkContext).

Definition Classes: RDD

def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean

Attributes: protected
Definition Classes: Logging

def initializeLogIfNecessary(isInterpreter: Boolean): Unit

Attributes: protected
Definition Classes: Logging

def intersection(other: RDD[(K, Array[Iterable[_]])], numPartitions: Int): RDD[(K, Array[Iterable[_]])]

Return the intersection of this RDD and another one.

Return the intersection of this RDD and another one. The output will not contain any duplicate elements, even if the input RDDs did. Performs a hash partition across the cluster

numPartitions: How many partitions to use in the resulting RDD

Definition Classes: RDD
Note: This method performs a shuffle internally.

def intersection(other: RDD[(K, Array[Iterable[_]])], partitioner: Partitioner)(implicit ord: Ordering[(K, Array[Iterable[_]])] = null): RDD[(K, Array[Iterable[_]])]

Return the intersection of this RDD and another one.

Return the intersection of this RDD and another one. The output will not contain any duplicate elements, even if the input RDDs did.

partitioner: Partitioner to use for the resulting RDD

Definition Classes: RDD
Note: This method performs a shuffle internally.

def intersection(other: RDD[(K, Array[Iterable[_]])]): RDD[(K, Array[Iterable[_]])]

Return the intersection of this RDD and another one.

Return the intersection of this RDD and another one. The output will not contain any duplicate elements, even if the input RDDs did.

Definition Classes: RDD
Note: This method performs a shuffle internally.

lazy val isBarrier_: Boolean

Attributes: protected
Definition Classes: RDD
Annotations: @transient()

def isCheckpointed: Boolean

Return whether this RDD is checkpointed and materialized, either reliably or locally.

Definition Classes: RDD

def isEmpty(): Boolean

returns: true if and only if the RDD contains no elements at all. Note that an RDD may be empty even when it has at least 1 partition.

Definition Classes: RDD
Note: Due to complications in the internal implementation, this method will raise an exception if called on an RDD of Nothing or Null. This may be come up in practice because, for example, the type of parallelize(Seq()) is RDD[Nothing]. (parallelize(Seq()) should be avoided anyway in favor of parallelize(Seq[T]()).)

final def isInstanceOf[T0]: Boolean

Definition Classes: Any

def isTraceEnabled(): Boolean

Attributes: protected
Definition Classes: Logging

final def iterator(split: Partition, context: TaskContext): Iterator[(K, Array[Iterable[_]])]

Internal method to this RDD; will read from cache if applicable, or otherwise compute it.

Internal method to this RDD; will read from cache if applicable, or otherwise compute it. This should not be called by users directly, but is available for implementers of custom subclasses of RDD.

Definition Classes: RDD

def keyBy[K](f: ((K, Array[Iterable[_]])) ⇒ K): RDD[(K, (K, Array[Iterable[_]]))]

Creates tuples of the elements in this RDD by applying f.

Definition Classes: RDD

def localCheckpoint(): CoGroupedRDD.this.type

Mark this RDD for local checkpointing using Spark's existing caching layer.

This method is for users who wish to truncate RDD lineages while skipping the expensive step of replicating the materialized data in a reliable distributed file system. This is useful for RDDs with long lineages that need to be truncated periodically (e.g. GraphX).

Local checkpointing sacrifices fault-tolerance for performance. In particular, checkpointed data is written to ephemeral local storage in the executors instead of to a reliable, fault-tolerant storage. The effect is that if an executor fails during the computation, the checkpointed data may no longer be accessible, causing an irrecoverable job failure.

This is NOT safe to use with dynamic allocation, which removes executors along with their cached blocks. If you must use both features, you are advised to set spark.dynamicAllocation.cachedExecutorIdleTimeout to a high value.

The checkpoint directory set through SparkContext#setCheckpointDir is not used.

Definition Classes: RDD

def log: Logger

Attributes: protected
Definition Classes: Logging

def logDebug(msg: ⇒ String, throwable: Throwable): Unit

Attributes: protected
Definition Classes: Logging

def logDebug(msg: ⇒ String): Unit

Attributes: protected
Definition Classes: Logging

def logError(msg: ⇒ String, throwable: Throwable): Unit

Attributes: protected
Definition Classes: Logging

def logError(msg: ⇒ String): Unit

Attributes: protected
Definition Classes: Logging

def logInfo(msg: ⇒ String, throwable: Throwable): Unit

Attributes: protected
Definition Classes: Logging

def logInfo(msg: ⇒ String): Unit

Attributes: protected
Definition Classes: Logging

def logName: String

Attributes: protected
Definition Classes: Logging

def logTrace(msg: ⇒ String, throwable: Throwable): Unit

Attributes: protected
Definition Classes: Logging

def logTrace(msg: ⇒ String): Unit

Attributes: protected
Definition Classes: Logging

def logWarning(msg: ⇒ String, throwable: Throwable): Unit

Attributes: protected
Definition Classes: Logging

def logWarning(msg: ⇒ String): Unit

Attributes: protected
Definition Classes: Logging

def map[U](f: ((K, Array[Iterable[_]])) ⇒ U)(implicit arg0: ClassTag[U]): RDD[U]

Return a new RDD by applying a function to all elements of this RDD.

Definition Classes: RDD

def mapPartitions[U](f: (Iterator[(K, Array[Iterable[_]])]) ⇒ Iterator[U], preservesPartitioning: Boolean = false)(implicit arg0: ClassTag[U]): RDD[U]

Return a new RDD by applying a function to each partition of this RDD.

preservesPartitioning indicates whether the input function preserves the partitioner, which should be false unless this is a pair RDD and the input function doesn't modify the keys.

Definition Classes: RDD

def mapPartitionsWithIndex[U](f: (Int, Iterator[(K, Array[Iterable[_]])]) ⇒ Iterator[U], preservesPartitioning: Boolean = false)(implicit arg0: ClassTag[U]): RDD[U]

Return a new RDD by applying a function to each partition of this RDD, while tracking the index of the original partition.

preservesPartitioning indicates whether the input function preserves the partitioner, which should be false unless this is a pair RDD and the input function doesn't modify the keys.

Definition Classes: RDD

def max()(implicit ord: Ordering[(K, Array[Iterable[_]])]): (K, Array[Iterable[_]])

Returns the max of this RDD as defined by the implicit Ordering[T].

returns: the maximum element of the RDD

Definition Classes: RDD

def min()(implicit ord: Ordering[(K, Array[Iterable[_]])]): (K, Array[Iterable[_]])

Returns the min of this RDD as defined by the implicit Ordering[T].

returns: the minimum element of the RDD

Definition Classes: RDD

var name: String

A friendly name for this RDD

Definition Classes: RDD

final def ne(arg0: AnyRef): Boolean

Definition Classes: AnyRef

final def notify(): Unit

Definition Classes: AnyRef
Annotations: @native()

final def notifyAll(): Unit

Definition Classes: AnyRef
Annotations: @native()

def parent[U](j: Int)(implicit arg0: ClassTag[U]): RDD[U]

Returns the jth parent RDD: e.g.

Returns the jth parent RDD: e.g. rdd.parent[T](0) is equivalent to rdd.firstParent[T]

Attributes: protected[spark]
Definition Classes: RDD

val partitioner: Some[Partitioner]

Definition Classes: CoGroupedRDD → RDD

final def partitions: Array[Partition]

Get the array of partitions of this RDD, taking into account whether the RDD is checkpointed or not.

Definition Classes: RDD

def persist(): CoGroupedRDD.this.type

Persist this RDD with the default storage level (MEMORY_ONLY).

Definition Classes: RDD

def persist(newLevel: StorageLevel): CoGroupedRDD.this.type

Set this RDD's storage level to persist its values across operations after the first time it is computed.

Set this RDD's storage level to persist its values across operations after the first time it is computed. This can only be used to assign a new storage level if the RDD does not have a storage level set yet. Local checkpointing is an exception.

Definition Classes: RDD

def pipe(command: Seq[String], env: Map[String, String] = Map(), printPipeContext: ((String) ⇒ Unit) ⇒ Unit = null, printRDDElement: ((K, Array[Iterable[_]]), (String) ⇒ Unit) ⇒ Unit = null, separateWorkingDir: Boolean = false, bufferSize: Int = 8192, encoding: String = Codec.defaultCharsetCodec.name): RDD[String]

Return an RDD created by piping elements to a forked external process.

Return an RDD created by piping elements to a forked external process. The resulting RDD is computed by executing the given process once per partition. All elements of each input partition are written to a process's stdin as lines of input separated by a newline. The resulting partition consists of the process's stdout output, with each line of stdout resulting in one element of the output partition. A process is invoked even for empty partitions.

The print behavior can be customized by providing two functions.

command

command to run in forked process.

env

environment variables to set.

printPipeContext

Before piping elements, this function is called as an opportunity to pipe context data. Print line function (like out.println) will be passed as printPipeContext's parameter.

printRDDElement

Use this function to customize how to pipe elements. This function will be called with each RDD element as the 1st parameter, and the print line function (like out.println()) as the 2nd parameter. An example of pipe the RDD data of groupBy() in a streaming way, instead of constructing a huge String to concat all the elements:

def printRDDElement(record:(String, Seq[String]), f:String=>Unit) =
  for (e <- record._2) {f(e)}

separateWorkingDir

Use separate working directories for each task.

bufferSize

Buffer size for the stdin writer for the piped process.

encoding

Char encoding used for interacting (via stdin, stdout and stderr) with the piped process

returns

the result RDD

Definition Classes: RDD

def pipe(command: String, env: Map[String, String]): RDD[String]

Return an RDD created by piping elements to a forked external process.

Definition Classes: RDD

def pipe(command: String): RDD[String]

Return an RDD created by piping elements to a forked external process.

Definition Classes: RDD

final def preferredLocations(split: Partition): Seq[String]

Get the preferred locations of a partition, taking into account whether the RDD is checkpointed.

Definition Classes: RDD

def randomSplit(weights: Array[Double], seed: Long = Utils.random.nextLong): Array[RDD[(K, Array[Iterable[_]])]]

Randomly splits this RDD with the provided weights.

weights: weights for splits, will be normalized if they don't sum to 1
seed: random seed
returns: split RDDs in an array

Definition Classes: RDD

var rdds: Seq[RDD[_ <: Product2[K, _]]]

def reduce(f: ((K, Array[Iterable[_]]), (K, Array[Iterable[_]])) ⇒ (K, Array[Iterable[_]])): (K, Array[Iterable[_]])

Reduces the elements of this RDD using the specified commutative and associative binary operator.

Definition Classes: RDD

def repartition(numPartitions: Int)(implicit ord: Ordering[(K, Array[Iterable[_]])] = null): RDD[(K, Array[Iterable[_]])]

Return a new RDD that has exactly numPartitions partitions.

Can increase or decrease the level of parallelism in this RDD. Internally, this uses a shuffle to redistribute data.

If you are decreasing the number of partitions in this RDD, consider using coalesce, which can avoid performing a shuffle.

Definition Classes: RDD

def sample(withReplacement: Boolean, fraction: Double, seed: Long = Utils.random.nextLong): RDD[(K, Array[Iterable[_]])]

Return a sampled subset of this RDD.

withReplacement: can elements be sampled multiple times (replaced when sampled out)
fraction: expected size of the sample as a fraction of this RDD's size without replacement: probability that each element is chosen; fraction must be [0, 1] with replacement: expected number of times each element is chosen; fraction must be greater than or equal to 0
seed: seed for the random number generator

Definition Classes: RDD
Note: This is NOT guaranteed to provide exactly the fraction of the count of the given RDD.

def saveAsObjectFile(path: String): Unit

Save this RDD as a SequenceFile of serialized objects.

Definition Classes: RDD

def saveAsTextFile(path: String, codec: Class[_ <: CompressionCodec]): Unit

Save this RDD as a compressed text file, using string representations of elements.

Definition Classes: RDD

def saveAsTextFile(path: String): Unit

Save this RDD as a text file, using string representations of elements.

Definition Classes: RDD

def setName(_name: String): CoGroupedRDD.this.type

Assign a name to this RDD

Definition Classes: RDD

def setSerializer(serializer: Serializer): CoGroupedRDD[K]

Set a serializer for this RDD's shuffle, or null to use the default (spark.serializer)

def sortBy[K](f: ((K, Array[Iterable[_]])) ⇒ K, ascending: Boolean = true, numPartitions: Int = this.partitions.length)(implicit ord: Ordering[K], ctag: ClassTag[K]): RDD[(K, Array[Iterable[_]])]

Return this RDD sorted by the given key function.

Definition Classes: RDD

def sparkContext: SparkContext

The SparkContext that created this RDD.

Definition Classes: RDD

def subtract(other: RDD[(K, Array[Iterable[_]])], p: Partitioner)(implicit ord: Ordering[(K, Array[Iterable[_]])] = null): RDD[(K, Array[Iterable[_]])]

Return an RDD with the elements from this that are not in other.

Definition Classes: RDD

def subtract(other: RDD[(K, Array[Iterable[_]])], numPartitions: Int): RDD[(K, Array[Iterable[_]])]

Return an RDD with the elements from this that are not in other.

Definition Classes: RDD

def subtract(other: RDD[(K, Array[Iterable[_]])]): RDD[(K, Array[Iterable[_]])]

Return an RDD with the elements from this that are not in other.

Uses this partitioner/partition size, because even if other is huge, the resulting RDD will be <= us.

Definition Classes: RDD

final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes: AnyRef

def take(num: Int): Array[(K, Array[Iterable[_]])]

Take the first num elements of the RDD.

Take the first num elements of the RDD. It works by first scanning one partition, and use the results from that partition to estimate the number of additional partitions needed to satisfy the limit.

Definition Classes: RDD
Note: This method should only be used if the resulting array is expected to be small, as all the data is loaded into the driver's memory.
,
Due to complications in the internal implementation, this method will raise an exception if called on an RDD of Nothing or Null.

def takeOrdered(num: Int)(implicit ord: Ordering[(K, Array[Iterable[_]])]): Array[(K, Array[Iterable[_]])]

Returns the first k (smallest) elements from this RDD as defined by the specified implicit Ordering[T] and maintains the ordering.

Returns the first k (smallest) elements from this RDD as defined by the specified implicit Ordering[T] and maintains the ordering. This does the opposite of top. For example:

sc.parallelize(Seq(10, 4, 2, 12, 3)).takeOrdered(1)
// returns Array(2)

sc.parallelize(Seq(2, 3, 4, 5, 6)).takeOrdered(2)
// returns Array(2, 3)

num: k, the number of elements to return
ord: the implicit ordering for T
returns: an array of top elements

Definition Classes: RDD
Note: This method should only be used if the resulting array is expected to be small, as all the data is loaded into the driver's memory.

def takeSample(withReplacement: Boolean, num: Int, seed: Long = Utils.random.nextLong): Array[(K, Array[Iterable[_]])]

Return a fixed-size sampled subset of this RDD in an array

withReplacement: whether sampling is done with replacement
num: size of the returned sample
seed: seed for the random number generator
returns: sample of specified size in an array

Definition Classes: RDD
Note: this method should only be used if the resulting array is expected to be small, as all the data is loaded into the driver's memory.

def toDebugString: String

A description of this RDD and its recursive dependencies for debugging.

Definition Classes: RDD

def toJavaRDD(): JavaRDD[(K, Array[Iterable[_]])]

Definition Classes: RDD

def toLocalIterator: Iterator[(K, Array[Iterable[_]])]

Return an iterator that contains all of the elements in this RDD.

The iterator will consume as much memory as the largest partition in this RDD.

Definition Classes: RDD
Note: This results in multiple Spark jobs, and if the input RDD is the result of a wide transformation (e.g. join with different partitioners), to avoid recomputing the input RDD should be cached first.

def toString(): String

Definition Classes: RDD → AnyRef → Any

def top(num: Int)(implicit ord: Ordering[(K, Array[Iterable[_]])]): Array[(K, Array[Iterable[_]])]

Returns the top k (largest) elements from this RDD as defined by the specified implicit Ordering[T] and maintains the ordering.

Returns the top k (largest) elements from this RDD as defined by the specified implicit Ordering[T] and maintains the ordering. This does the opposite of takeOrdered. For example:

sc.parallelize(Seq(10, 4, 2, 12, 3)).top(1)
// returns Array(12)

sc.parallelize(Seq(2, 3, 4, 5, 6)).top(2)
// returns Array(6, 5)

num: k, the number of top elements to return
ord: the implicit ordering for T
returns: an array of top elements

Definition Classes: RDD
Note: This method should only be used if the resulting array is expected to be small, as all the data is loaded into the driver's memory.

def treeAggregate[U](zeroValue: U)(seqOp: (U, (K, Array[Iterable[_]])) ⇒ U, combOp: (U, U) ⇒ U, depth: Int = 2)(implicit arg0: ClassTag[U]): U

Aggregates the elements of this RDD in a multi-level tree pattern.

Aggregates the elements of this RDD in a multi-level tree pattern. This method is semantically identical to org.apache.spark.rdd.RDD#aggregate.

depth: suggested depth of the tree (default: 2)

Definition Classes: RDD

def treeReduce(f: ((K, Array[Iterable[_]]), (K, Array[Iterable[_]])) ⇒ (K, Array[Iterable[_]]), depth: Int = 2): (K, Array[Iterable[_]])

Reduces the elements of this RDD in a multi-level tree pattern.

depth: suggested depth of the tree (default: 2)

Definition Classes: RDD
See also: org.apache.spark.rdd.RDD#reduce

def union(other: RDD[(K, Array[Iterable[_]])]): RDD[(K, Array[Iterable[_]])]

Return the union of this RDD and another one.

Return the union of this RDD and another one. Any identical elements will appear multiple times (use .distinct() to eliminate them).

Definition Classes: RDD

def unpersist(blocking: Boolean = false): CoGroupedRDD.this.type

Mark the RDD as non-persistent, and remove all blocks for it from memory and disk.

blocking: Whether to block until all blocks are deleted (default: false)
returns: This RDD.

Definition Classes: RDD

final def wait(): Unit

Definition Classes: AnyRef
Annotations: @throws( ... )

final def wait(arg0: Long, arg1: Int): Unit

Definition Classes: AnyRef
Annotations: @throws( ... )

final def wait(arg0: Long): Unit

Definition Classes: AnyRef
Annotations: @throws( ... ) @native()

def withResources(rp: ResourceProfile): CoGroupedRDD.this.type

Specify a ResourceProfile to use when calculating this RDD.

Specify a ResourceProfile to use when calculating this RDD. This is only supported on certain cluster managers and currently requires dynamic allocation to be enabled. It will result in new executors with the resources specified being acquired to calculate the RDD.

Definition Classes: RDD
Annotations: @Experimental() @Since( "3.1.0" )

def zip[U](other: RDD[U])(implicit arg0: ClassTag[U]): RDD[((K, Array[Iterable[_]]), U)]

Zips this RDD with another one, returning key-value pairs with the first element in each RDD, second element in each RDD, etc.

Zips this RDD with another one, returning key-value pairs with the first element in each RDD, second element in each RDD, etc. Assumes that the two RDDs have the *same number of partitions* and the *same number of elements in each partition* (e.g. one was made through a map on the other).

Definition Classes: RDD

def zipPartitions[B, C, D, V](rdd2: RDD[B], rdd3: RDD[C], rdd4: RDD[D])(f: (Iterator[(K, Array[Iterable[_]])], Iterator[B], Iterator[C], Iterator[D]) ⇒ Iterator[V])(implicit arg0: ClassTag[B], arg1: ClassTag[C], arg2: ClassTag[D], arg3: ClassTag[V]): RDD[V]

Definition Classes: RDD

def zipPartitions[B, C, D, V](rdd2: RDD[B], rdd3: RDD[C], rdd4: RDD[D], preservesPartitioning: Boolean)(f: (Iterator[(K, Array[Iterable[_]])], Iterator[B], Iterator[C], Iterator[D]) ⇒ Iterator[V])(implicit arg0: ClassTag[B], arg1: ClassTag[C], arg2: ClassTag[D], arg3: ClassTag[V]): RDD[V]

Definition Classes: RDD

def zipPartitions[B, C, V](rdd2: RDD[B], rdd3: RDD[C])(f: (Iterator[(K, Array[Iterable[_]])], Iterator[B], Iterator[C]) ⇒ Iterator[V])(implicit arg0: ClassTag[B], arg1: ClassTag[C], arg2: ClassTag[V]): RDD[V]

Definition Classes: RDD

def zipPartitions[B, C, V](rdd2: RDD[B], rdd3: RDD[C], preservesPartitioning: Boolean)(f: (Iterator[(K, Array[Iterable[_]])], Iterator[B], Iterator[C]) ⇒ Iterator[V])(implicit arg0: ClassTag[B], arg1: ClassTag[C], arg2: ClassTag[V]): RDD[V]

Definition Classes: RDD

def zipPartitions[B, V](rdd2: RDD[B])(f: (Iterator[(K, Array[Iterable[_]])], Iterator[B]) ⇒ Iterator[V])(implicit arg0: ClassTag[B], arg1: ClassTag[V]): RDD[V]

Definition Classes: RDD

def zipPartitions[B, V](rdd2: RDD[B], preservesPartitioning: Boolean)(f: (Iterator[(K, Array[Iterable[_]])], Iterator[B]) ⇒ Iterator[V])(implicit arg0: ClassTag[B], arg1: ClassTag[V]): RDD[V]

Zip this RDD's partitions with one (or more) RDD(s) and return a new RDD by applying a function to the zipped partitions.

Zip this RDD's partitions with one (or more) RDD(s) and return a new RDD by applying a function to the zipped partitions. Assumes that all the RDDs have the *same number of partitions*, but does *not* require them to have the same number of elements in each partition.

Definition Classes: RDD

def zipWithIndex(): RDD[((K, Array[Iterable[_]]), Long)]

Zips this RDD with its element indices.

Zips this RDD with its element indices. The ordering is first based on the partition index and then the ordering of items within each partition. So the first item in the first partition gets index 0, and the last item in the last partition receives the largest index.

This is similar to Scala's zipWithIndex but it uses Long instead of Int as the index type. This method needs to trigger a spark job when this RDD contains more than one partitions.

Definition Classes: RDD
Note: Some RDDs, such as those returned by groupBy(), do not guarantee order of elements in a partition. The index assigned to each element is therefore not guaranteed, and may even change if the RDD is reevaluated. If a fixed ordering is required to guarantee the same index assignments, you should sort the RDD with sortByKey() or save it to a file.

def zipWithUniqueId(): RDD[((K, Array[Iterable[_]]), Long)]

Zips this RDD with generated unique Long ids.

Zips this RDD with generated unique Long ids. Items in the kth partition will get ids k, n+k, 2*n+k, ..., where n is the number of partitions. So there may exist gaps, but this method won't trigger a spark job, which is different from org.apache.spark.rdd.RDD#zipWithIndex.

Definition Classes: RDD
Note: Some RDDs, such as those returned by groupBy(), do not guarantee order of elements in a partition. The unique ID assigned to each element is therefore not guaranteed, and may even change if the RDD is reevaluated. If a fixed ordering is required to guarantee the same index assignments, you should sort the RDD with sortByKey() or save it to a file.

Packages

CoGroupedRDD

class CoGroupedRDD[K] extends RDD[(K, Array[Iterable[_]])]

Instance Constructors

Value Members

Inherited from RDD[(K, Array[Iterable[_]])]

Inherited from Logging

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Ungrouped

Packages

CoGroupedRDD 

class CoGroupedRDD[K] extends RDD[(K, Array[Iterable[_]])]

Instance Constructors

Value Members

Inherited from RDD[(K, Array[Iterable[_]])]

Inherited from Logging

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Ungrouped

CoGroupedRDD