class JavaRDD[T] extends AbstractJavaRDDLike[T, JavaRDD[T]]
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- JavaRDD.scala
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final
def
!=(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
-
final
def
##(): Int
- Definition Classes
- AnyRef → Any
-
final
def
==(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
-
def
aggregate[U](zeroValue: U)(seqOp: Function2[U, T, U], combOp: Function2[U, U, 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.
- Definition Classes
- JavaRDDLike
-
final
def
asInstanceOf[T0]: T0
- Definition Classes
- Any
-
def
cache(): JavaRDD[T]
Persist this RDD with the default storage level (
MEMORY_ONLY). -
def
cartesian[U](other: JavaRDDLike[U, _]): JavaPairRDD[T, 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
thisand b is inother.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
thisand b is inother.- Definition Classes
- JavaRDDLike
-
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
- JavaRDDLike
-
implicit
val
classTag: ClassTag[T]
- Definition Classes
- JavaRDD → JavaRDDLike
-
def
clone(): AnyRef
- Attributes
- protected[lang]
- Definition Classes
- AnyRef
- Annotations
- @throws( ... ) @native()
-
def
coalesce(numPartitions: Int, shuffle: Boolean): JavaRDD[T]
Return a new RDD that is reduced into
numPartitionspartitions. -
def
coalesce(numPartitions: Int): JavaRDD[T]
Return a new RDD that is reduced into
numPartitionspartitions. -
def
collect(): List[T]
Return an array that contains all of the elements in this RDD.
Return an array that contains all of the elements in this RDD.
- Definition Classes
- JavaRDDLike
- 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
collectAsync(): JavaFutureAction[List[T]]
The asynchronous version of
collect, which returns a future for retrieving an array containing all of the elements in this RDD.The asynchronous version of
collect, which returns a future for retrieving an array containing all of the elements in this RDD.- Definition Classes
- JavaRDDLike
- 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
collectPartitions(partitionIds: Array[Int]): Array[List[T]]
Return an array that contains all of the elements in a specific partition of this RDD.
Return an array that contains all of the elements in a specific partition of this RDD.
- Definition Classes
- JavaRDDLike
-
def
context: SparkContext
The org.apache.spark.SparkContext that this RDD was created on.
The org.apache.spark.SparkContext that this RDD was created on.
- Definition Classes
- JavaRDDLike
-
def
count(): Long
Return the number of elements in the RDD.
Return the number of elements in the RDD.
- Definition Classes
- JavaRDDLike
-
def
countApprox(timeout: Long): PartialResult[BoundedDouble]
Approximate version of count() that returns a potentially incomplete result within a timeout, even if not all tasks have finished.
Approximate version of count() that returns a potentially incomplete result within a timeout, even if not all tasks have finished.
- timeout
maximum time to wait for the job, in milliseconds
- Definition Classes
- JavaRDDLike
-
def
countApprox(timeout: Long, confidence: Double): PartialResult[BoundedDouble]
Approximate version of count() that returns a potentially incomplete result within a timeout, even if not all tasks have finished.
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
- JavaRDDLike
-
def
countApproxDistinct(relativeSD: Double): Long
Return approximate number of distinct elements in the RDD.
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
- JavaRDDLike
-
def
countAsync(): JavaFutureAction[Long]
The asynchronous version of
count, which returns a future for counting the number of elements in this RDD.The asynchronous version of
count, which returns a future for counting the number of elements in this RDD.- Definition Classes
- JavaRDDLike
-
def
countByValue(): Map[T, Long]
Return the count of each unique value in this RDD as a map of (value, count) pairs.
Return the count of each unique value in this RDD as a map of (value, count) pairs. The final combine step happens locally on the master, equivalent to running a single reduce task.
- Definition Classes
- JavaRDDLike
-
def
countByValueApprox(timeout: Long): PartialResult[Map[T, BoundedDouble]]
Approximate version of countByValue().
Approximate version of countByValue().
- timeout
maximum time to wait for the job, in milliseconds
- returns
a potentially incomplete result, with error bounds
- Definition Classes
- JavaRDDLike
-
def
countByValueApprox(timeout: Long, confidence: Double): PartialResult[Map[T, BoundedDouble]]
Approximate version of countByValue().
Approximate version of countByValue().
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
- JavaRDDLike
-
def
distinct(numPartitions: Int): JavaRDD[T]
Return a new RDD containing the distinct elements in this RDD.
-
def
distinct(): JavaRDD[T]
Return a new RDD containing the distinct elements in this RDD.
-
final
def
eq(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
-
def
equals(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
-
def
filter(f: Function[T, Boolean]): JavaRDD[T]
Return a new RDD containing only the elements that satisfy a predicate.
-
def
finalize(): Unit
- Attributes
- protected[lang]
- Definition Classes
- AnyRef
- Annotations
- @throws( classOf[java.lang.Throwable] )
-
def
first(): T
Return the first element in this RDD.
Return the first element in this RDD.
- Definition Classes
- JavaRDDLike
-
def
flatMap[U](f: FlatMapFunction[T, U]): JavaRDD[U]
Return a new RDD by first applying a function to all elements of this RDD, and then flattening the results.
Return a new RDD by first applying a function to all elements of this RDD, and then flattening the results.
- Definition Classes
- JavaRDDLike
-
def
flatMapToDouble(f: DoubleFlatMapFunction[T]): JavaDoubleRDD
Return a new RDD by first applying a function to all elements of this RDD, and then flattening the results.
Return a new RDD by first applying a function to all elements of this RDD, and then flattening the results.
- Definition Classes
- JavaRDDLike
-
def
flatMapToPair[K2, V2](f: PairFlatMapFunction[T, K2, V2]): JavaPairRDD[K2, V2]
Return a new RDD by first applying a function to all elements of this RDD, and then flattening the results.
Return a new RDD by first applying a function to all elements of this RDD, and then flattening the results.
- Definition Classes
- JavaRDDLike
-
def
fold(zeroValue: T)(f: Function2[T, T, T]): T
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.
- Definition Classes
- JavaRDDLike
-
def
foreach(f: VoidFunction[T]): Unit
Applies a function f to all elements of this RDD.
Applies a function f to all elements of this RDD.
- Definition Classes
- JavaRDDLike
-
def
foreachAsync(f: VoidFunction[T]): JavaFutureAction[Void]
The asynchronous version of the
foreachaction, which applies a function f to all the elements of this RDD.The asynchronous version of the
foreachaction, which applies a function f to all the elements of this RDD.- Definition Classes
- JavaRDDLike
-
def
foreachPartition(f: VoidFunction[Iterator[T]]): Unit
Applies a function f to each partition of this RDD.
Applies a function f to each partition of this RDD.
- Definition Classes
- JavaRDDLike
-
def
foreachPartitionAsync(f: VoidFunction[Iterator[T]]): JavaFutureAction[Void]
The asynchronous version of the
foreachPartitionaction, which applies a function f to each partition of this RDD.The asynchronous version of the
foreachPartitionaction, which applies a function f to each partition of this RDD.- Definition Classes
- JavaRDDLike
-
def
getCheckpointFile(): Optional[String]
Gets the name of the file to which this RDD was checkpointed
Gets the name of the file to which this RDD was checkpointed
- Definition Classes
- JavaRDDLike
-
final
def
getClass(): Class[_]
- Definition Classes
- AnyRef → Any
- Annotations
- @native()
-
def
getNumPartitions: Int
Return the number of partitions in this RDD.
Return the number of partitions in this RDD.
- Definition Classes
- JavaRDDLike
- Annotations
- @Since( "1.6.0" )
-
def
getResourceProfile(): ResourceProfile
Get the ResourceProfile specified with this RDD or None if it wasn't specified.
Get the ResourceProfile specified with this RDD or None if it wasn't specified.
- returns
the user specified ResourceProfile or null if none was specified
-
def
getStorageLevel: StorageLevel
Get the RDD's current storage level, or StorageLevel.NONE if none is set.
Get the RDD's current storage level, or StorageLevel.NONE if none is set.
- Definition Classes
- JavaRDDLike
-
def
glom(): JavaRDD[List[T]]
Return an RDD created by coalescing all elements within each partition into an array.
Return an RDD created by coalescing all elements within each partition into an array.
- Definition Classes
- JavaRDDLike
-
def
groupBy[U](f: Function[T, U], numPartitions: Int): JavaPairRDD[U, Iterable[T]]
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.
- Definition Classes
- JavaRDDLike
-
def
groupBy[U](f: Function[T, U]): JavaPairRDD[U, Iterable[T]]
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.
- Definition Classes
- JavaRDDLike
-
def
hashCode(): Int
- Definition Classes
- AnyRef → Any
- Annotations
- @native()
-
def
id: Int
A unique ID for this RDD (within its SparkContext).
A unique ID for this RDD (within its SparkContext).
- Definition Classes
- JavaRDDLike
-
def
intersection(other: JavaRDD[T]): JavaRDD[T]
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.
- Note
This method performs a shuffle internally.
-
def
isCheckpointed: Boolean
Return whether this RDD has been checkpointed or not
Return whether this RDD has been checkpointed or not
- Definition Classes
- JavaRDDLike
-
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
- JavaRDDLike
-
final
def
isInstanceOf[T0]: Boolean
- Definition Classes
- Any
-
def
iterator(split: Partition, taskContext: TaskContext): Iterator[T]
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
- JavaRDDLike
-
def
keyBy[U](f: Function[T, U]): JavaPairRDD[U, T]
Creates tuples of the elements in this RDD by applying
f.Creates tuples of the elements in this RDD by applying
f.- Definition Classes
- JavaRDDLike
-
def
map[R](f: Function[T, R]): JavaRDD[R]
Return a new RDD by applying a function to all elements of this RDD.
Return a new RDD by applying a function to all elements of this RDD.
- Definition Classes
- JavaRDDLike
-
def
mapPartitions[U](f: FlatMapFunction[Iterator[T], U], preservesPartitioning: Boolean): JavaRDD[U]
Return a new RDD by applying a function to each partition of this RDD.
Return a new RDD by applying a function to each partition of this RDD.
- Definition Classes
- JavaRDDLike
-
def
mapPartitions[U](f: FlatMapFunction[Iterator[T], U]): JavaRDD[U]
Return a new RDD by applying a function to each partition of this RDD.
Return a new RDD by applying a function to each partition of this RDD.
- Definition Classes
- JavaRDDLike
-
def
mapPartitionsToDouble(f: DoubleFlatMapFunction[Iterator[T]], preservesPartitioning: Boolean): JavaDoubleRDD
Return a new RDD by applying a function to each partition of this RDD.
Return a new RDD by applying a function to each partition of this RDD.
- Definition Classes
- JavaRDDLike
-
def
mapPartitionsToDouble(f: DoubleFlatMapFunction[Iterator[T]]): JavaDoubleRDD
Return a new RDD by applying a function to each partition of this RDD.
Return a new RDD by applying a function to each partition of this RDD.
- Definition Classes
- JavaRDDLike
-
def
mapPartitionsToPair[K2, V2](f: PairFlatMapFunction[Iterator[T], K2, V2], preservesPartitioning: Boolean): JavaPairRDD[K2, V2]
Return a new RDD by applying a function to each partition of this RDD.
Return a new RDD by applying a function to each partition of this RDD.
- Definition Classes
- JavaRDDLike
-
def
mapPartitionsToPair[K2, V2](f: PairFlatMapFunction[Iterator[T], K2, V2]): JavaPairRDD[K2, V2]
Return a new RDD by applying a function to each partition of this RDD.
Return a new RDD by applying a function to each partition of this RDD.
- Definition Classes
- JavaRDDLike
-
def
mapPartitionsWithIndex[R](f: Function2[Integer, Iterator[T], Iterator[R]], preservesPartitioning: Boolean = false): JavaRDD[R]
Return a new RDD by applying a function to each partition of this RDD, while tracking the index of the original partition.
Return a new RDD by applying a function to each partition of this RDD, while tracking the index of the original partition.
- Definition Classes
- JavaRDDLike
-
def
mapToDouble[R](f: DoubleFunction[T]): JavaDoubleRDD
Return a new RDD by applying a function to all elements of this RDD.
Return a new RDD by applying a function to all elements of this RDD.
- Definition Classes
- JavaRDDLike
-
def
mapToPair[K2, V2](f: PairFunction[T, K2, V2]): JavaPairRDD[K2, V2]
Return a new RDD by applying a function to all elements of this RDD.
Return a new RDD by applying a function to all elements of this RDD.
- Definition Classes
- JavaRDDLike
-
def
max(comp: Comparator[T]): T
Returns the maximum element from this RDD as defined by the specified Comparator[T].
Returns the maximum element from this RDD as defined by the specified Comparator[T].
- comp
the comparator that defines ordering
- returns
the maximum of the RDD
- Definition Classes
- JavaRDDLike
-
def
min(comp: Comparator[T]): T
Returns the minimum element from this RDD as defined by the specified Comparator[T].
Returns the minimum element from this RDD as defined by the specified Comparator[T].
- comp
the comparator that defines ordering
- returns
the minimum of the RDD
- Definition Classes
- JavaRDDLike
-
def
name(): String
- Definition Classes
- JavaRDDLike
-
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
partitioner: Optional[Partitioner]
The partitioner of this RDD.
The partitioner of this RDD.
- Definition Classes
- JavaRDDLike
-
def
partitions: List[Partition]
Set of partitions in this RDD.
Set of partitions in this RDD.
- Definition Classes
- JavaRDDLike
-
def
persist(newLevel: StorageLevel): JavaRDD[T]
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..
-
def
pipe(command: List[String], env: Map[String, String], separateWorkingDir: Boolean, bufferSize: Int, encoding: String): JavaRDD[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.
- Definition Classes
- JavaRDDLike
-
def
pipe(command: List[String], env: Map[String, String], separateWorkingDir: Boolean, bufferSize: Int): JavaRDD[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.
- Definition Classes
- JavaRDDLike
-
def
pipe(command: List[String], env: Map[String, String]): JavaRDD[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.
- Definition Classes
- JavaRDDLike
-
def
pipe(command: List[String]): JavaRDD[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.
- Definition Classes
- JavaRDDLike
-
def
pipe(command: String): JavaRDD[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.
- Definition Classes
- JavaRDDLike
-
def
randomSplit(weights: Array[Double], seed: Long): Array[JavaRDD[T]]
Randomly splits this RDD with the provided weights.
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
-
def
randomSplit(weights: Array[Double]): Array[JavaRDD[T]]
Randomly splits this RDD with the provided weights.
Randomly splits this RDD with the provided weights.
- weights
weights for splits, will be normalized if they don't sum to 1
- returns
split RDDs in an array
-
val
rdd: RDD[T]
- Definition Classes
- JavaRDD → JavaRDDLike
-
def
reduce(f: Function2[T, T, T]): T
Reduces the elements of this RDD using the specified commutative and associative binary operator.
Reduces the elements of this RDD using the specified commutative and associative binary operator.
- Definition Classes
- JavaRDDLike
-
def
repartition(numPartitions: Int): JavaRDD[T]
Return a new RDD that has exactly numPartitions partitions.
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. -
def
sample(withReplacement: Boolean, fraction: Double, seed: Long): JavaRDD[T]
Return a sampled subset of this RDD, with a user-supplied seed.
Return a sampled subset of this RDD, with a user-supplied seed.
- 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
- Note
This is NOT guaranteed to provide exactly the fraction of the count of the given
RDD.
-
def
sample(withReplacement: Boolean, fraction: Double): JavaRDD[T]
Return a sampled subset of this RDD with a random seed.
Return a sampled subset of this RDD with a random seed.
- 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
- 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.
Save this RDD as a SequenceFile of serialized objects.
- Definition Classes
- JavaRDDLike
-
def
saveAsTextFile(path: String, codec: Class[_ <: CompressionCodec]): Unit
Save this RDD as a compressed text file, using string representations of elements.
Save this RDD as a compressed text file, using string representations of elements.
- Definition Classes
- JavaRDDLike
-
def
saveAsTextFile(path: String): Unit
Save this RDD as a text file, using string representations of elements.
Save this RDD as a text file, using string representations of elements.
- Definition Classes
- JavaRDDLike
-
def
setName(name: String): JavaRDD[T]
Assign a name to this RDD
-
def
sortBy[S](f: Function[T, S], ascending: Boolean, numPartitions: Int): JavaRDD[T]
Return this RDD sorted by the given key function.
-
def
subtract(other: JavaRDD[T], p: Partitioner): JavaRDD[T]
Return an RDD with the elements from
thisthat are not inother. -
def
subtract(other: JavaRDD[T], numPartitions: Int): JavaRDD[T]
Return an RDD with the elements from
thisthat are not inother. -
def
subtract(other: JavaRDD[T]): JavaRDD[T]
Return an RDD with the elements from
thisthat are not inother.Return an RDD with the elements from
thisthat are not inother.Uses
thispartitioner/partition size, because even ifotheris huge, the resulting RDD will be less than or equal to us. -
final
def
synchronized[T0](arg0: ⇒ T0): T0
- Definition Classes
- AnyRef
-
def
take(num: Int): List[T]
Take the first num elements of the RDD.
Take the first num elements of the RDD. This currently scans the partitions *one by one*, so it will be slow if a lot of partitions are required. In that case, use collect() to get the whole RDD instead.
- Definition Classes
- JavaRDDLike
- 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
takeAsync(num: Int): JavaFutureAction[List[T]]
The asynchronous version of the
takeaction, which returns a future for retrieving the firstnumelements of this RDD.The asynchronous version of the
takeaction, which returns a future for retrieving the firstnumelements of this RDD.- Definition Classes
- JavaRDDLike
- 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
takeOrdered(num: Int): List[T]
Returns the first k (smallest) elements from this RDD using the natural ordering for T while maintain the order.
Returns the first k (smallest) elements from this RDD using the natural ordering for T while maintain the order.
- num
k, the number of top elements to return
- returns
an array of top elements
- Definition Classes
- JavaRDDLike
- 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
takeOrdered(num: Int, comp: Comparator[T]): List[T]
Returns the first k (smallest) elements from this RDD as defined by the specified Comparator[T] and maintains the order.
Returns the first k (smallest) elements from this RDD as defined by the specified Comparator[T] and maintains the order.
- num
k, the number of elements to return
- comp
the comparator that defines the order
- returns
an array of top elements
- Definition Classes
- JavaRDDLike
- 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): List[T]
- Definition Classes
- JavaRDDLike
-
def
takeSample(withReplacement: Boolean, num: Int): List[T]
- Definition Classes
- JavaRDDLike
-
def
toDebugString(): String
A description of this RDD and its recursive dependencies for debugging.
A description of this RDD and its recursive dependencies for debugging.
- Definition Classes
- JavaRDDLike
-
def
toLocalIterator(): Iterator[T]
Return an iterator that contains all of the elements in this RDD.
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
- JavaRDDLike
-
def
toString(): String
- Definition Classes
- JavaRDD → AnyRef → Any
-
def
top(num: Int): List[T]
Returns the top k (largest) elements from this RDD using the natural ordering for T and maintains the order.
Returns the top k (largest) elements from this RDD using the natural ordering for T and maintains the order.
- num
k, the number of top elements to return
- returns
an array of top elements
- Definition Classes
- JavaRDDLike
- 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
top(num: Int, comp: Comparator[T]): List[T]
Returns the top k (largest) elements from this RDD as defined by the specified Comparator[T] and maintains the order.
Returns the top k (largest) elements from this RDD as defined by the specified Comparator[T] and maintains the order.
- num
k, the number of top elements to return
- comp
the comparator that defines the order
- returns
an array of top elements
- Definition Classes
- JavaRDDLike
- 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: Function2[U, T, U], combOp: Function2[U, U, U], depth: Int, finalAggregateOnExecutor: Boolean): U
org.apache.spark.api.java.JavaRDDLike.treeAggregatewith a parameter to do the final aggregation on the executor.org.apache.spark.api.java.JavaRDDLike.treeAggregatewith a parameter to do the final aggregation on the executor.- Definition Classes
- JavaRDDLike
-
def
treeAggregate[U](zeroValue: U, seqOp: Function2[U, T, U], combOp: Function2[U, U, U]): U
org.apache.spark.api.java.JavaRDDLike.treeAggregatewith suggested depth 2.org.apache.spark.api.java.JavaRDDLike.treeAggregatewith suggested depth 2.- Definition Classes
- JavaRDDLike
-
def
treeAggregate[U](zeroValue: U, seqOp: Function2[U, T, U], combOp: Function2[U, U, U], depth: Int): 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.
- depth
suggested depth of the tree
- Definition Classes
- JavaRDDLike
- See also
-
def
treeReduce(f: Function2[T, T, T]): T
org.apache.spark.api.java.JavaRDDLike.treeReducewith suggested depth 2.org.apache.spark.api.java.JavaRDDLike.treeReducewith suggested depth 2.- Definition Classes
- JavaRDDLike
-
def
treeReduce(f: Function2[T, T, T], depth: Int): T
Reduces the elements of this RDD in a multi-level tree pattern.
Reduces the elements of this RDD in a multi-level tree pattern.
- depth
suggested depth of the tree
- Definition Classes
- JavaRDDLike
- See also
-
def
union(other: JavaRDD[T]): JavaRDD[T]
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). -
def
unpersist(blocking: Boolean): JavaRDD[T]
Mark the RDD as non-persistent, and remove all blocks for it from memory and disk.
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.
-
def
unpersist(): JavaRDD[T]
Mark the RDD as non-persistent, and remove all blocks for it from memory and disk.
Mark the RDD as non-persistent, and remove all blocks for it from memory and disk. This method blocks until all blocks are deleted.
-
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): JavaRDD[T]
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.
-
def
wrapRDD(rdd: RDD[T]): JavaRDD[T]
- Definition Classes
- JavaRDD → JavaRDDLike
-
def
zip[U](other: JavaRDDLike[U, _]): JavaPairRDD[T, 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
- JavaRDDLike
-
def
zipPartitions[U, V](other: JavaRDDLike[U, _], f: FlatMapFunction2[Iterator[T], Iterator[U], V]): JavaRDD[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
- JavaRDDLike
-
def
zipWithIndex(): JavaPairRDD[T, 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
- JavaRDDLike
-
def
zipWithUniqueId(): JavaPairRDD[T, 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
- JavaRDDLike