class JavaHadoopRDD[K, V] extends JavaPairRDD[K, V]
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- @DeveloperApi()
- Source
- JavaHadoopRDD.scala
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- JavaHadoopRDD
- JavaPairRDD
- AbstractJavaRDDLike
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- Serializable
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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, (K, V), 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
-
def
aggregateByKey[U](zeroValue: U, seqFunc: Function2[U, V, U], combFunc: Function2[U, U, U]): JavaPairRDD[K, U]
Aggregate the values of each key, using given combine functions and a neutral "zero value".
Aggregate the values of each key, using given combine functions and a neutral "zero value". This function can return a different result type, U, than the type of the values in this RDD, V. Thus, we need one operation for merging a V into a U and one operation for merging two U's. The former operation is used for merging values within a partition, and the latter is used for merging values between partitions. To avoid memory allocation, both of these functions are allowed to modify and return their first argument instead of creating a new U.
- Definition Classes
- JavaPairRDD
-
def
aggregateByKey[U](zeroValue: U, numPartitions: Int, seqFunc: Function2[U, V, U], combFunc: Function2[U, U, U]): JavaPairRDD[K, U]
Aggregate the values of each key, using given combine functions and a neutral "zero value".
Aggregate the values of each key, using given combine functions and a neutral "zero value". This function can return a different result type, U, than the type of the values in this RDD, V. Thus, we need one operation for merging a V into a U and one operation for merging two U's, as in scala.TraversableOnce. The former operation is used for merging values within a partition, and the latter is used for merging values between partitions. To avoid memory allocation, both of these functions are allowed to modify and return their first argument instead of creating a new U.
- Definition Classes
- JavaPairRDD
-
def
aggregateByKey[U](zeroValue: U, partitioner: Partitioner, seqFunc: Function2[U, V, U], combFunc: Function2[U, U, U]): JavaPairRDD[K, U]
Aggregate the values of each key, using given combine functions and a neutral "zero value".
Aggregate the values of each key, using given combine functions and a neutral "zero value". This function can return a different result type, U, than the type of the values in this RDD, V. Thus, we need one operation for merging a V into a U and one operation for merging two U's, as in scala.TraversableOnce. The former operation is used for merging values within a partition, and the latter is used for merging values between partitions. To avoid memory allocation, both of these functions are allowed to modify and return their first argument instead of creating a new U.
- Definition Classes
- JavaPairRDD
-
final
def
asInstanceOf[T0]: T0
- Definition Classes
- Any
-
def
cache(): JavaPairRDD[K, V]
Persist this RDD with the default storage level (
MEMORY_ONLY
).Persist this RDD with the default storage level (
MEMORY_ONLY
).- Definition Classes
- JavaPairRDD
-
def
cartesian[U](other: JavaRDDLike[U, _]): JavaPairRDD[(K, V), 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 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
this
and 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
-
val
classTag: ClassTag[(K, V)]
- Definition Classes
- JavaPairRDD → JavaRDDLike
-
def
clone(): AnyRef
- Attributes
- protected[lang]
- Definition Classes
- AnyRef
- Annotations
- @throws( ... ) @native()
-
def
coalesce(numPartitions: Int, shuffle: Boolean): JavaPairRDD[K, V]
Return a new RDD that is reduced into
numPartitions
partitions.Return a new RDD that is reduced into
numPartitions
partitions.- Definition Classes
- JavaPairRDD
-
def
coalesce(numPartitions: Int): JavaPairRDD[K, V]
Return a new RDD that is reduced into
numPartitions
partitions.Return a new RDD that is reduced into
numPartitions
partitions.- Definition Classes
- JavaPairRDD
-
def
cogroup[W1, W2, W3](other1: JavaPairRDD[K, W1], other2: JavaPairRDD[K, W2], other3: JavaPairRDD[K, W3], numPartitions: Int): JavaPairRDD[K, (Iterable[V], Iterable[W1], Iterable[W2], Iterable[W3])]
For each key k in
this
orother1
orother2
orother3
, return a resulting RDD that contains a tuple with the list of values for that key inthis
,other1
,other2
andother3
.For each key k in
this
orother1
orother2
orother3
, return a resulting RDD that contains a tuple with the list of values for that key inthis
,other1
,other2
andother3
.- Definition Classes
- JavaPairRDD
-
def
cogroup[W1, W2](other1: JavaPairRDD[K, W1], other2: JavaPairRDD[K, W2], numPartitions: Int): JavaPairRDD[K, (Iterable[V], Iterable[W1], Iterable[W2])]
For each key k in
this
orother1
orother2
, return a resulting RDD that contains a tuple with the list of values for that key inthis
,other1
andother2
.For each key k in
this
orother1
orother2
, return a resulting RDD that contains a tuple with the list of values for that key inthis
,other1
andother2
.- Definition Classes
- JavaPairRDD
-
def
cogroup[W](other: JavaPairRDD[K, W], numPartitions: Int): JavaPairRDD[K, (Iterable[V], Iterable[W])]
For each key k in
this
orother
, return a resulting RDD that contains a tuple with the list of values for that key inthis
as well asother
.For each key k in
this
orother
, return a resulting RDD that contains a tuple with the list of values for that key inthis
as well asother
.- Definition Classes
- JavaPairRDD
-
def
cogroup[W1, W2, W3](other1: JavaPairRDD[K, W1], other2: JavaPairRDD[K, W2], other3: JavaPairRDD[K, W3]): JavaPairRDD[K, (Iterable[V], Iterable[W1], Iterable[W2], Iterable[W3])]
For each key k in
this
orother1
orother2
orother3
, return a resulting RDD that contains a tuple with the list of values for that key inthis
,other1
,other2
andother3
.For each key k in
this
orother1
orother2
orother3
, return a resulting RDD that contains a tuple with the list of values for that key inthis
,other1
,other2
andother3
.- Definition Classes
- JavaPairRDD
-
def
cogroup[W1, W2](other1: JavaPairRDD[K, W1], other2: JavaPairRDD[K, W2]): JavaPairRDD[K, (Iterable[V], Iterable[W1], Iterable[W2])]
For each key k in
this
orother1
orother2
, return a resulting RDD that contains a tuple with the list of values for that key inthis
,other1
andother2
.For each key k in
this
orother1
orother2
, return a resulting RDD that contains a tuple with the list of values for that key inthis
,other1
andother2
.- Definition Classes
- JavaPairRDD
-
def
cogroup[W](other: JavaPairRDD[K, W]): JavaPairRDD[K, (Iterable[V], Iterable[W])]
For each key k in
this
orother
, return a resulting RDD that contains a tuple with the list of values for that key inthis
as well asother
.For each key k in
this
orother
, return a resulting RDD that contains a tuple with the list of values for that key inthis
as well asother
.- Definition Classes
- JavaPairRDD
-
def
cogroup[W1, W2, W3](other1: JavaPairRDD[K, W1], other2: JavaPairRDD[K, W2], other3: JavaPairRDD[K, W3], partitioner: Partitioner): JavaPairRDD[K, (Iterable[V], Iterable[W1], Iterable[W2], Iterable[W3])]
For each key k in
this
orother1
orother2
orother3
, return a resulting RDD that contains a tuple with the list of values for that key inthis
,other1
,other2
andother3
.For each key k in
this
orother1
orother2
orother3
, return a resulting RDD that contains a tuple with the list of values for that key inthis
,other1
,other2
andother3
.- Definition Classes
- JavaPairRDD
-
def
cogroup[W1, W2](other1: JavaPairRDD[K, W1], other2: JavaPairRDD[K, W2], partitioner: Partitioner): JavaPairRDD[K, (Iterable[V], Iterable[W1], Iterable[W2])]
For each key k in
this
orother1
orother2
, return a resulting RDD that contains a tuple with the list of values for that key inthis
,other1
andother2
.For each key k in
this
orother1
orother2
, return a resulting RDD that contains a tuple with the list of values for that key inthis
,other1
andother2
.- Definition Classes
- JavaPairRDD
-
def
cogroup[W](other: JavaPairRDD[K, W], partitioner: Partitioner): JavaPairRDD[K, (Iterable[V], Iterable[W])]
For each key k in
this
orother
, return a resulting RDD that contains a tuple with the list of values for that key inthis
as well asother
.For each key k in
this
orother
, return a resulting RDD that contains a tuple with the list of values for that key inthis
as well asother
.- Definition Classes
- JavaPairRDD
-
def
collect(): List[(K, V)]
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
collectAsMap(): Map[K, V]
Return the key-value pairs in this RDD to the master as a Map.
Return the key-value pairs in this RDD to the master as a Map.
- Definition Classes
- JavaPairRDD
- Note
this method should only be used if the resulting data is expected to be small, as all the data is loaded into the driver's memory.
-
def
collectAsync(): JavaFutureAction[List[(K, V)]]
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[(K, V)]]
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
combineByKey[C](createCombiner: Function[V, C], mergeValue: Function2[C, V, C], mergeCombiners: Function2[C, C, C]): JavaPairRDD[K, C]
Simplified version of combineByKey that hash-partitions the resulting RDD using the existing partitioner/parallelism level and using map-side aggregation.
Simplified version of combineByKey that hash-partitions the resulting RDD using the existing partitioner/parallelism level and using map-side aggregation.
- Definition Classes
- JavaPairRDD
-
def
combineByKey[C](createCombiner: Function[V, C], mergeValue: Function2[C, V, C], mergeCombiners: Function2[C, C, C], numPartitions: Int): JavaPairRDD[K, C]
Simplified version of combineByKey that hash-partitions the output RDD and uses map-side aggregation.
Simplified version of combineByKey that hash-partitions the output RDD and uses map-side aggregation.
- Definition Classes
- JavaPairRDD
-
def
combineByKey[C](createCombiner: Function[V, C], mergeValue: Function2[C, V, C], mergeCombiners: Function2[C, C, C], partitioner: Partitioner): JavaPairRDD[K, C]
Generic function to combine the elements for each key using a custom set of aggregation functions.
Generic function to combine the elements for each key using a custom set of aggregation functions. Turns a JavaPairRDD[(K, V)] into a result of type JavaPairRDD[(K, C)], for a "combined type" C.
Users provide three functions:
createCombiner
, which turns a V into a C (e.g., creates a one-element list)mergeValue
, to merge a V into a C (e.g., adds it to the end of a list)mergeCombiners
, to combine two C's into a single one.
In addition, users can control the partitioning of the output RDD. This method automatically uses map-side aggregation in shuffling the RDD.
- Definition Classes
- JavaPairRDD
- Note
V and C can be different -- for example, one might group an RDD of type (Int, Int) into an RDD of type (Int, List[Int]).
-
def
combineByKey[C](createCombiner: Function[V, C], mergeValue: Function2[C, V, C], mergeCombiners: Function2[C, C, C], partitioner: Partitioner, mapSideCombine: Boolean, serializer: Serializer): JavaPairRDD[K, C]
Generic function to combine the elements for each key using a custom set of aggregation functions.
Generic function to combine the elements for each key using a custom set of aggregation functions. Turns a JavaPairRDD[(K, V)] into a result of type JavaPairRDD[(K, C)], for a "combined type" C.
Users provide three functions:
createCombiner
, which turns a V into a C (e.g., creates a one-element list)mergeValue
, to merge a V into a C (e.g., adds it to the end of a list)mergeCombiners
, to combine two C's into a single one.
In addition, users can control the partitioning of the output RDD, the serializer that is use for the shuffle, and whether to perform map-side aggregation (if a mapper can produce multiple items with the same key).
- Definition Classes
- JavaPairRDD
- Note
V and C can be different -- for example, one might group an RDD of type (Int, Int) into an RDD of type (Int, List[Int]).
-
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
countApproxDistinctByKey(relativeSD: Double): JavaPairRDD[K, Long]
Return approximate number of distinct values for each key in this RDD.
Return approximate number of distinct values for each key in this 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
- JavaPairRDD
-
def
countApproxDistinctByKey(relativeSD: Double, numPartitions: Int): JavaPairRDD[K, Long]
Return approximate number of distinct values for each key in this RDD.
Return approximate number of distinct values for each key in this 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.
- numPartitions
number of partitions of the resulting RDD.
- Definition Classes
- JavaPairRDD
-
def
countApproxDistinctByKey(relativeSD: Double, partitioner: Partitioner): JavaPairRDD[K, Long]
Return approximate number of distinct values for each key in this RDD.
Return approximate number of distinct values for each key in this 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.
- partitioner
partitioner of the resulting RDD.
- Definition Classes
- JavaPairRDD
-
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
countByKey(): Map[K, Long]
Count the number of elements for each key, and return the result to the master as a Map.
Count the number of elements for each key, and return the result to the master as a Map.
- Definition Classes
- JavaPairRDD
-
def
countByKeyApprox(timeout: Long, confidence: Double = 0.95): PartialResult[Map[K, BoundedDouble]]
Approximate version of countByKey that can return a partial result if it does not finish within a timeout.
Approximate version of countByKey that can return a partial result if it does not finish within a timeout.
- Definition Classes
- JavaPairRDD
-
def
countByKeyApprox(timeout: Long): PartialResult[Map[K, BoundedDouble]]
Approximate version of countByKey that can return a partial result if it does not finish within a timeout.
Approximate version of countByKey that can return a partial result if it does not finish within a timeout.
- Definition Classes
- JavaPairRDD
-
def
countByValue(): Map[(K, V), 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[(K, V), 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[(K, V), 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): JavaPairRDD[K, V]
Return a new RDD containing the distinct elements in this RDD.
Return a new RDD containing the distinct elements in this RDD.
- Definition Classes
- JavaPairRDD
-
def
distinct(): JavaPairRDD[K, V]
Return a new RDD containing the distinct elements in this RDD.
Return a new RDD containing the distinct elements in this RDD.
- Definition Classes
- JavaPairRDD
-
final
def
eq(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
-
def
equals(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
-
def
filter(f: Function[(K, V), Boolean]): JavaPairRDD[K, V]
Return a new RDD containing only the elements that satisfy a predicate.
Return a new RDD containing only the elements that satisfy a predicate.
- Definition Classes
- JavaPairRDD
-
def
filterByRange(comp: Comparator[K], lower: K, upper: K): JavaPairRDD[K, V]
Return a RDD containing only the elements in the inclusive range
lower
toupper
.Return a RDD containing only the elements in the inclusive range
lower
toupper
. If the RDD has been partitioned using aRangePartitioner
, then this operation can be performed efficiently by only scanning the partitions that might contain matching elements. Otherwise, a standardfilter
is applied to all partitions.- Definition Classes
- JavaPairRDD
- Annotations
- @Since( "3.1.0" )
- Since
3.1.0
-
def
filterByRange(lower: K, upper: K): JavaPairRDD[K, V]
Return a RDD containing only the elements in the inclusive range
lower
toupper
.Return a RDD containing only the elements in the inclusive range
lower
toupper
. If the RDD has been partitioned using aRangePartitioner
, then this operation can be performed efficiently by only scanning the partitions that might contain matching elements. Otherwise, a standardfilter
is applied to all partitions.- Definition Classes
- JavaPairRDD
- Annotations
- @Since( "3.1.0" )
- Since
3.1.0
-
def
finalize(): Unit
- Attributes
- protected[lang]
- Definition Classes
- AnyRef
- Annotations
- @throws( classOf[java.lang.Throwable] )
-
def
first(): (K, V)
Return the first element in this RDD.
Return the first element in this RDD.
- Definition Classes
- JavaPairRDD → JavaRDDLike
-
def
flatMap[U](f: FlatMapFunction[(K, V), 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[(K, V)]): 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[(K, V), 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
flatMapValues[U](f: FlatMapFunction[V, U]): JavaPairRDD[K, U]
Pass each value in the key-value pair RDD through a flatMap function without changing the keys; this also retains the original RDD's partitioning.
Pass each value in the key-value pair RDD through a flatMap function without changing the keys; this also retains the original RDD's partitioning.
- Definition Classes
- JavaPairRDD
-
def
fold(zeroValue: (K, V))(f: Function2[(K, V), (K, V), (K, V)]): (K, V)
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
foldByKey(zeroValue: V, func: Function2[V, V, V]): JavaPairRDD[K, V]
Merge the values for each key using an associative function and a neutral "zero value" which may be added to the result an arbitrary number of times, and must not change the result (e.g., Nil for list concatenation, 0 for addition, or 1 for multiplication.).
Merge the values for each key using an associative function and a neutral "zero value" which may be added to the result an arbitrary number of times, and must not change the result (e.g., Nil for list concatenation, 0 for addition, or 1 for multiplication.).
- Definition Classes
- JavaPairRDD
-
def
foldByKey(zeroValue: V, numPartitions: Int, func: Function2[V, V, V]): JavaPairRDD[K, V]
Merge the values for each key using an associative function and a neutral "zero value" which may be added to the result an arbitrary number of times, and must not change the result (e.g ., Nil for list concatenation, 0 for addition, or 1 for multiplication.).
Merge the values for each key using an associative function and a neutral "zero value" which may be added to the result an arbitrary number of times, and must not change the result (e.g ., Nil for list concatenation, 0 for addition, or 1 for multiplication.).
- Definition Classes
- JavaPairRDD
-
def
foldByKey(zeroValue: V, partitioner: Partitioner, func: Function2[V, V, V]): JavaPairRDD[K, V]
Merge the values for each key using an associative function and a neutral "zero value" which may be added to the result an arbitrary number of times, and must not change the result (e.g ., Nil for list concatenation, 0 for addition, or 1 for multiplication.).
Merge the values for each key using an associative function and a neutral "zero value" which may be added to the result an arbitrary number of times, and must not change the result (e.g ., Nil for list concatenation, 0 for addition, or 1 for multiplication.).
- Definition Classes
- JavaPairRDD
-
def
foreach(f: VoidFunction[(K, V)]): 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[(K, V)]): JavaFutureAction[Void]
The asynchronous version of the
foreach
action, which applies a function f to all the elements of this RDD.The asynchronous version of the
foreach
action, which applies a function f to all the elements of this RDD.- Definition Classes
- JavaRDDLike
-
def
foreachPartition(f: VoidFunction[Iterator[(K, V)]]): 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[(K, V)]]): JavaFutureAction[Void]
The asynchronous version of the
foreachPartition
action, which applies a function f to each partition of this RDD.The asynchronous version of the
foreachPartition
action, which applies a function f to each partition of this RDD.- Definition Classes
- JavaRDDLike
-
def
fullOuterJoin[W](other: JavaPairRDD[K, W], numPartitions: Int): JavaPairRDD[K, (Optional[V], Optional[W])]
Perform a full outer join of
this
andother
.Perform a full outer join of
this
andother
. For each element (k, v) inthis
, the resulting RDD will either contain all pairs (k, (Some(v), Some(w))) for w inother
, or the pair (k, (Some(v), None)) if no elements inother
have key k. Similarly, for each element (k, w) inother
, the resulting RDD will either contain all pairs (k, (Some(v), Some(w))) for v inthis
, or the pair (k, (None, Some(w))) if no elements inthis
have key k. Hash-partitions the resulting RDD into the given number of partitions.- Definition Classes
- JavaPairRDD
-
def
fullOuterJoin[W](other: JavaPairRDD[K, W]): JavaPairRDD[K, (Optional[V], Optional[W])]
Perform a full outer join of
this
andother
.Perform a full outer join of
this
andother
. For each element (k, v) inthis
, the resulting RDD will either contain all pairs (k, (Some(v), Some(w))) for w inother
, or the pair (k, (Some(v), None)) if no elements inother
have key k. Similarly, for each element (k, w) inother
, the resulting RDD will either contain all pairs (k, (Some(v), Some(w))) for v inthis
, or the pair (k, (None, Some(w))) if no elements inthis
have key k. Hash-partitions the resulting RDD using the existing partitioner/ parallelism level.- Definition Classes
- JavaPairRDD
-
def
fullOuterJoin[W](other: JavaPairRDD[K, W], partitioner: Partitioner): JavaPairRDD[K, (Optional[V], Optional[W])]
Perform a full outer join of
this
andother
.Perform a full outer join of
this
andother
. For each element (k, v) inthis
, the resulting RDD will either contain all pairs (k, (Some(v), Some(w))) for w inother
, or the pair (k, (Some(v), None)) if no elements inother
have key k. Similarly, for each element (k, w) inother
, the resulting RDD will either contain all pairs (k, (Some(v), Some(w))) for v inthis
, or the pair (k, (None, Some(w))) if no elements inthis
have key k. Uses the given Partitioner to partition the output RDD.- Definition Classes
- JavaPairRDD
-
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
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[(K, V)]]
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[(K, V), U], numPartitions: Int): JavaPairRDD[U, Iterable[(K, V)]]
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[(K, V), U]): JavaPairRDD[U, Iterable[(K, V)]]
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
groupByKey(): JavaPairRDD[K, Iterable[V]]
Group the values for each key in the RDD into a single sequence.
Group the values for each key in the RDD into a single sequence. Hash-partitions the resulting RDD with the existing partitioner/parallelism level.
- Definition Classes
- JavaPairRDD
- Note
If you are grouping in order to perform an aggregation (such as a sum or average) over each key, using
JavaPairRDD.reduceByKey
orJavaPairRDD.combineByKey
will provide much better performance.
-
def
groupByKey(numPartitions: Int): JavaPairRDD[K, Iterable[V]]
Group the values for each key in the RDD into a single sequence.
Group the values for each key in the RDD into a single sequence. Hash-partitions the resulting RDD with into
numPartitions
partitions.- Definition Classes
- JavaPairRDD
- Note
If you are grouping in order to perform an aggregation (such as a sum or average) over each key, using
JavaPairRDD.reduceByKey
orJavaPairRDD.combineByKey
will provide much better performance.
-
def
groupByKey(partitioner: Partitioner): JavaPairRDD[K, Iterable[V]]
Group the values for each key in the RDD into a single sequence.
Group the values for each key in the RDD into a single sequence. Allows controlling the partitioning of the resulting key-value pair RDD by passing a Partitioner.
- Definition Classes
- JavaPairRDD
- Note
If you are grouping in order to perform an aggregation (such as a sum or average) over each key, using
JavaPairRDD.reduceByKey
orJavaPairRDD.combineByKey
will provide much better performance.
-
def
groupWith[W1, W2, W3](other1: JavaPairRDD[K, W1], other2: JavaPairRDD[K, W2], other3: JavaPairRDD[K, W3]): JavaPairRDD[K, (Iterable[V], Iterable[W1], Iterable[W2], Iterable[W3])]
Alias for cogroup.
Alias for cogroup.
- Definition Classes
- JavaPairRDD
-
def
groupWith[W1, W2](other1: JavaPairRDD[K, W1], other2: JavaPairRDD[K, W2]): JavaPairRDD[K, (Iterable[V], Iterable[W1], Iterable[W2])]
Alias for cogroup.
Alias for cogroup.
- Definition Classes
- JavaPairRDD
-
def
groupWith[W](other: JavaPairRDD[K, W]): JavaPairRDD[K, (Iterable[V], Iterable[W])]
Alias for cogroup.
Alias for cogroup.
- Definition Classes
- JavaPairRDD
-
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: JavaPairRDD[K, V]): JavaPairRDD[K, V]
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
- JavaPairRDD
- 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[(K, V)]
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
join[W](other: JavaPairRDD[K, W], numPartitions: Int): JavaPairRDD[K, (V, W)]
Return an RDD containing all pairs of elements with matching keys in
this
andother
.Return an RDD containing all pairs of elements with matching keys in
this
andother
. Each pair of elements will be returned as a (k, (v1, v2)) tuple, where (k, v1) is inthis
and (k, v2) is inother
. Performs a hash join across the cluster.- Definition Classes
- JavaPairRDD
-
def
join[W](other: JavaPairRDD[K, W]): JavaPairRDD[K, (V, W)]
Return an RDD containing all pairs of elements with matching keys in
this
andother
.Return an RDD containing all pairs of elements with matching keys in
this
andother
. Each pair of elements will be returned as a (k, (v1, v2)) tuple, where (k, v1) is inthis
and (k, v2) is inother
. Performs a hash join across the cluster.- Definition Classes
- JavaPairRDD
-
def
join[W](other: JavaPairRDD[K, W], partitioner: Partitioner): JavaPairRDD[K, (V, W)]
Return an RDD containing all pairs of elements with matching keys in
this
andother
.Return an RDD containing all pairs of elements with matching keys in
this
andother
. Each pair of elements will be returned as a (k, (v1, v2)) tuple, where (k, v1) is inthis
and (k, v2) is inother
. Uses the given Partitioner to partition the output RDD.- Definition Classes
- JavaPairRDD
-
implicit
val
kClassTag: ClassTag[K]
- Definition Classes
- JavaHadoopRDD → JavaPairRDD
-
def
keyBy[U](f: Function[(K, V), U]): JavaPairRDD[U, (K, V)]
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
keys(): JavaRDD[K]
Return an RDD with the keys of each tuple.
Return an RDD with the keys of each tuple.
- Definition Classes
- JavaPairRDD
-
def
leftOuterJoin[W](other: JavaPairRDD[K, W], numPartitions: Int): JavaPairRDD[K, (V, Optional[W])]
Perform a left outer join of
this
andother
.Perform a left outer join of
this
andother
. For each element (k, v) inthis
, the resulting RDD will either contain all pairs (k, (v, Some(w))) for w inother
, or the pair (k, (v, None)) if no elements inother
have key k. Hash-partitions the output intonumPartitions
partitions.- Definition Classes
- JavaPairRDD
-
def
leftOuterJoin[W](other: JavaPairRDD[K, W]): JavaPairRDD[K, (V, Optional[W])]
Perform a left outer join of
this
andother
.Perform a left outer join of
this
andother
. For each element (k, v) inthis
, the resulting RDD will either contain all pairs (k, (v, Some(w))) for w inother
, or the pair (k, (v, None)) if no elements inother
have key k. Hash-partitions the output using the existing partitioner/parallelism level.- Definition Classes
- JavaPairRDD
-
def
leftOuterJoin[W](other: JavaPairRDD[K, W], partitioner: Partitioner): JavaPairRDD[K, (V, Optional[W])]
Perform a left outer join of
this
andother
.Perform a left outer join of
this
andother
. For each element (k, v) inthis
, the resulting RDD will either contain all pairs (k, (v, Some(w))) for w inother
, or the pair (k, (v, None)) if no elements inother
have key k. Uses the given Partitioner to partition the output RDD.- Definition Classes
- JavaPairRDD
-
def
lookup(key: K): List[V]
Return the list of values in the RDD for key
key
.Return the list of values in the RDD for key
key
. This operation is done efficiently if the RDD has a known partitioner by only searching the partition that the key maps to.- Definition Classes
- JavaPairRDD
-
def
map[R](f: Function[(K, V), 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[(K, V)], 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[(K, V)], 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[(K, V)]], 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[(K, V)]]): 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[(K, V)], 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[(K, V)], 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[(K, V)], 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
mapPartitionsWithInputSplit[R](f: Function2[InputSplit, Iterator[(K, V)], Iterator[R]], preservesPartitioning: Boolean = false): JavaRDD[R]
Maps over a partition, providing the InputSplit that was used as the base of the partition.
Maps over a partition, providing the InputSplit that was used as the base of the partition.
- Annotations
- @DeveloperApi()
-
def
mapToDouble[R](f: DoubleFunction[(K, V)]): 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[(K, V), 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
mapValues[U](f: Function[V, U]): JavaPairRDD[K, U]
Pass each value in the key-value pair RDD through a map function without changing the keys; this also retains the original RDD's partitioning.
Pass each value in the key-value pair RDD through a map function without changing the keys; this also retains the original RDD's partitioning.
- Definition Classes
- JavaPairRDD
-
def
max(comp: Comparator[(K, V)]): (K, V)
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[(K, V)]): (K, V)
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
partitionBy(partitioner: Partitioner): JavaPairRDD[K, V]
Return a copy of the RDD partitioned using the specified partitioner.
Return a copy of the RDD partitioned using the specified partitioner.
- Definition Classes
- JavaPairRDD
-
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): JavaPairRDD[K, V]
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. Can only be called once on each RDD.
- Definition Classes
- JavaPairRDD
-
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
-
val
rdd: RDD[(K, V)]
- Definition Classes
- JavaPairRDD → JavaRDDLike
-
def
reduce(f: Function2[(K, V), (K, V), (K, V)]): (K, V)
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
reduceByKey(func: Function2[V, V, V]): JavaPairRDD[K, V]
Merge the values for each key using an associative and commutative reduce function.
Merge the values for each key using an associative and commutative reduce function. This will also perform the merging locally on each mapper before sending results to a reducer, similarly to a "combiner" in MapReduce. Output will be hash-partitioned with the existing partitioner/ parallelism level.
- Definition Classes
- JavaPairRDD
-
def
reduceByKey(func: Function2[V, V, V], numPartitions: Int): JavaPairRDD[K, V]
Merge the values for each key using an associative and commutative reduce function.
Merge the values for each key using an associative and commutative reduce function. This will also perform the merging locally on each mapper before sending results to a reducer, similarly to a "combiner" in MapReduce. Output will be hash-partitioned with numPartitions partitions.
- Definition Classes
- JavaPairRDD
-
def
reduceByKey(partitioner: Partitioner, func: Function2[V, V, V]): JavaPairRDD[K, V]
Merge the values for each key using an associative and commutative reduce function.
Merge the values for each key using an associative and commutative reduce function. This will also perform the merging locally on each mapper before sending results to a reducer, similarly to a "combiner" in MapReduce.
- Definition Classes
- JavaPairRDD
-
def
reduceByKeyLocally(func: Function2[V, V, V]): Map[K, V]
Merge the values for each key using an associative and commutative reduce function, but return the result immediately to the master as a Map.
Merge the values for each key using an associative and commutative reduce function, but return the result immediately to the master as a Map. This will also perform the merging locally on each mapper before sending results to a reducer, similarly to a "combiner" in MapReduce.
- Definition Classes
- JavaPairRDD
-
def
repartition(numPartitions: Int): JavaPairRDD[K, V]
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.- Definition Classes
- JavaPairRDD
-
def
repartitionAndSortWithinPartitions(partitioner: Partitioner, comp: Comparator[K]): JavaPairRDD[K, V]
Repartition the RDD according to the given partitioner and, within each resulting partition, sort records by their keys.
Repartition the RDD according to the given partitioner and, within each resulting partition, sort records by their keys.
This is more efficient than calling
repartition
and then sorting within each partition because it can push the sorting down into the shuffle machinery.- Definition Classes
- JavaPairRDD
-
def
repartitionAndSortWithinPartitions(partitioner: Partitioner): JavaPairRDD[K, V]
Repartition the RDD according to the given partitioner and, within each resulting partition, sort records by their keys.
Repartition the RDD according to the given partitioner and, within each resulting partition, sort records by their keys.
This is more efficient than calling
repartition
and then sorting within each partition because it can push the sorting down into the shuffle machinery.- Definition Classes
- JavaPairRDD
-
def
rightOuterJoin[W](other: JavaPairRDD[K, W], numPartitions: Int): JavaPairRDD[K, (Optional[V], W)]
Perform a right outer join of
this
andother
.Perform a right outer join of
this
andother
. For each element (k, w) inother
, the resulting RDD will either contain all pairs (k, (Some(v), w)) for v inthis
, or the pair (k, (None, w)) if no elements inthis
have key k. Hash-partitions the resulting RDD into the given number of partitions.- Definition Classes
- JavaPairRDD
-
def
rightOuterJoin[W](other: JavaPairRDD[K, W]): JavaPairRDD[K, (Optional[V], W)]
Perform a right outer join of
this
andother
.Perform a right outer join of
this
andother
. For each element (k, w) inother
, the resulting RDD will either contain all pairs (k, (Some(v), w)) for v inthis
, or the pair (k, (None, w)) if no elements inthis
have key k. Hash-partitions the resulting RDD using the existing partitioner/parallelism level.- Definition Classes
- JavaPairRDD
-
def
rightOuterJoin[W](other: JavaPairRDD[K, W], partitioner: Partitioner): JavaPairRDD[K, (Optional[V], W)]
Perform a right outer join of
this
andother
.Perform a right outer join of
this
andother
. For each element (k, w) inother
, the resulting RDD will either contain all pairs (k, (Some(v), w)) for v inthis
, or the pair (k, (None, w)) if no elements inthis
have key k. Uses the given Partitioner to partition the output RDD.- Definition Classes
- JavaPairRDD
-
def
sample(withReplacement: Boolean, fraction: Double, seed: Long): JavaPairRDD[K, V]
Return a sampled subset of this RDD.
Return a sampled subset of this RDD.
- Definition Classes
- JavaPairRDD
-
def
sample(withReplacement: Boolean, fraction: Double): JavaPairRDD[K, V]
Return a sampled subset of this RDD.
Return a sampled subset of this RDD.
- Definition Classes
- JavaPairRDD
-
def
sampleByKey(withReplacement: Boolean, fractions: Map[K, Double]): JavaPairRDD[K, V]
Return a subset of this RDD sampled by key (via stratified sampling).
Return a subset of this RDD sampled by key (via stratified sampling).
Create a sample of this RDD using variable sampling rates for different keys as specified by
fractions
, a key to sampling rate map, via simple random sampling with one pass over the RDD, to produce a sample of size that's approximately equal to the sum of math.ceil(numItems * samplingRate) over all key values.Use Utils.random.nextLong as the default seed for the random number generator.
- Definition Classes
- JavaPairRDD
-
def
sampleByKey(withReplacement: Boolean, fractions: Map[K, Double], seed: Long): JavaPairRDD[K, V]
Return a subset of this RDD sampled by key (via stratified sampling).
Return a subset of this RDD sampled by key (via stratified sampling).
Create a sample of this RDD using variable sampling rates for different keys as specified by
fractions
, a key to sampling rate map, via simple random sampling with one pass over the RDD, to produce a sample of size that's approximately equal to the sum of math.ceil(numItems * samplingRate) over all key values.- Definition Classes
- JavaPairRDD
-
def
sampleByKeyExact(withReplacement: Boolean, fractions: Map[K, Double]): JavaPairRDD[K, V]
Return a subset of this RDD sampled by key (via stratified sampling) containing exactly math.ceil(numItems * samplingRate) for each stratum (group of pairs with the same key).
Return a subset of this RDD sampled by key (via stratified sampling) containing exactly math.ceil(numItems * samplingRate) for each stratum (group of pairs with the same key).
This method differs from
sampleByKey
in that we make additional passes over the RDD to create a sample size that's exactly equal to the sum of math.ceil(numItems * samplingRate) over all key values with a 99.99% confidence. When sampling without replacement, we need one additional pass over the RDD to guarantee sample size; when sampling with replacement, we need two additional passes.Use Utils.random.nextLong as the default seed for the random number generator.
- Definition Classes
- JavaPairRDD
-
def
sampleByKeyExact(withReplacement: Boolean, fractions: Map[K, Double], seed: Long): JavaPairRDD[K, V]
Return a subset of this RDD sampled by key (via stratified sampling) containing exactly math.ceil(numItems * samplingRate) for each stratum (group of pairs with the same key).
Return a subset of this RDD sampled by key (via stratified sampling) containing exactly math.ceil(numItems * samplingRate) for each stratum (group of pairs with the same key).
This method differs from
sampleByKey
in that we make additional passes over the RDD to create a sample size that's exactly equal to the sum of math.ceil(numItems * samplingRate) over all key values with a 99.99% confidence. When sampling without replacement, we need one additional pass over the RDD to guarantee sample size; when sampling with replacement, we need two additional passes.- Definition Classes
- JavaPairRDD
-
def
saveAsHadoopDataset(conf: JobConf): Unit
Output the RDD to any Hadoop-supported storage system, using a Hadoop JobConf object for that storage system.
Output the RDD to any Hadoop-supported storage system, using a Hadoop JobConf object for that storage system. The JobConf should set an OutputFormat and any output paths required (e.g. a table name to write to) in the same way as it would be configured for a Hadoop MapReduce job.
- Definition Classes
- JavaPairRDD
-
def
saveAsHadoopFile[F <: OutputFormat[_, _]](path: String, keyClass: Class[_], valueClass: Class[_], outputFormatClass: Class[F], codec: Class[_ <: CompressionCodec]): Unit
Output the RDD to any Hadoop-supported file system, compressing with the supplied codec.
Output the RDD to any Hadoop-supported file system, compressing with the supplied codec.
- Definition Classes
- JavaPairRDD
-
def
saveAsHadoopFile[F <: OutputFormat[_, _]](path: String, keyClass: Class[_], valueClass: Class[_], outputFormatClass: Class[F]): Unit
Output the RDD to any Hadoop-supported file system.
Output the RDD to any Hadoop-supported file system.
- Definition Classes
- JavaPairRDD
-
def
saveAsHadoopFile[F <: OutputFormat[_, _]](path: String, keyClass: Class[_], valueClass: Class[_], outputFormatClass: Class[F], conf: JobConf): Unit
Output the RDD to any Hadoop-supported file system.
Output the RDD to any Hadoop-supported file system.
- Definition Classes
- JavaPairRDD
-
def
saveAsNewAPIHadoopDataset(conf: Configuration): Unit
Output the RDD to any Hadoop-supported storage system, using a Configuration object for that storage system.
Output the RDD to any Hadoop-supported storage system, using a Configuration object for that storage system.
- Definition Classes
- JavaPairRDD
-
def
saveAsNewAPIHadoopFile[F <: OutputFormat[_, _]](path: String, keyClass: Class[_], valueClass: Class[_], outputFormatClass: Class[F]): Unit
Output the RDD to any Hadoop-supported file system.
Output the RDD to any Hadoop-supported file system.
- Definition Classes
- JavaPairRDD
-
def
saveAsNewAPIHadoopFile[F <: OutputFormat[_, _]](path: String, keyClass: Class[_], valueClass: Class[_], outputFormatClass: Class[F], conf: Configuration): Unit
Output the RDD to any Hadoop-supported file system.
Output the RDD to any Hadoop-supported file system.
- Definition Classes
- JavaPairRDD
-
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): JavaPairRDD[K, V]
Assign a name to this RDD
Assign a name to this RDD
- Definition Classes
- JavaPairRDD
-
def
sortByKey(comp: Comparator[K], ascending: Boolean, numPartitions: Int): JavaPairRDD[K, V]
Sort the RDD by key, so that each partition contains a sorted range of the elements.
Sort the RDD by key, so that each partition contains a sorted range of the elements. Calling
collect
orsave
on the resulting RDD will return or output an ordered list of records (in thesave
case, they will be written to multiplepart-X
files in the filesystem, in order of the keys).- Definition Classes
- JavaPairRDD
-
def
sortByKey(comp: Comparator[K], ascending: Boolean): JavaPairRDD[K, V]
Sort the RDD by key, so that each partition contains a sorted range of the elements.
Sort the RDD by key, so that each partition contains a sorted range of the elements. Calling
collect
orsave
on the resulting RDD will return or output an ordered list of records (in thesave
case, they will be written to multiplepart-X
files in the filesystem, in order of the keys).- Definition Classes
- JavaPairRDD
-
def
sortByKey(comp: Comparator[K]): JavaPairRDD[K, V]
Sort the RDD by key, so that each partition contains a sorted range of the elements.
Sort the RDD by key, so that each partition contains a sorted range of the elements. Calling
collect
orsave
on the resulting RDD will return or output an ordered list of records (in thesave
case, they will be written to multiplepart-X
files in the filesystem, in order of the keys).- Definition Classes
- JavaPairRDD
-
def
sortByKey(ascending: Boolean, numPartitions: Int): JavaPairRDD[K, V]
Sort the RDD by key, so that each partition contains a sorted range of the elements.
Sort the RDD by key, so that each partition contains a sorted range of the elements. Calling
collect
orsave
on the resulting RDD will return or output an ordered list of records (in thesave
case, they will be written to multiplepart-X
files in the filesystem, in order of the keys).- Definition Classes
- JavaPairRDD
-
def
sortByKey(ascending: Boolean): JavaPairRDD[K, V]
Sort the RDD by key, so that each partition contains a sorted range of the elements.
Sort the RDD by key, so that each partition contains a sorted range of the elements. Calling
collect
orsave
on the resulting RDD will return or output an ordered list of records (in thesave
case, they will be written to multiplepart-X
files in the filesystem, in order of the keys).- Definition Classes
- JavaPairRDD
-
def
sortByKey(): JavaPairRDD[K, V]
Sort the RDD by key, so that each partition contains a sorted range of the elements in ascending order.
Sort the RDD by key, so that each partition contains a sorted range of the elements in ascending order. Calling
collect
orsave
on the resulting RDD will return or output an ordered list of records (in thesave
case, they will be written to multiplepart-X
files in the filesystem, in order of the keys).- Definition Classes
- JavaPairRDD
-
def
subtract(other: JavaPairRDD[K, V], p: Partitioner): JavaPairRDD[K, V]
Return an RDD with the elements from
this
that are not inother
.Return an RDD with the elements from
this
that are not inother
.- Definition Classes
- JavaPairRDD
-
def
subtract(other: JavaPairRDD[K, V], numPartitions: Int): JavaPairRDD[K, V]
Return an RDD with the elements from
this
that are not inother
.Return an RDD with the elements from
this
that are not inother
.- Definition Classes
- JavaPairRDD
-
def
subtract(other: JavaPairRDD[K, V]): JavaPairRDD[K, V]
Return an RDD with the elements from
this
that are not inother
.Return an RDD with the elements from
this
that are not inother
.Uses
this
partitioner/partition size, because even ifother
is huge, the resulting RDD will be <= us.- Definition Classes
- JavaPairRDD
-
def
subtractByKey[W](other: JavaPairRDD[K, W], p: Partitioner): JavaPairRDD[K, V]
Return an RDD with the pairs from
this
whose keys are not inother
.Return an RDD with the pairs from
this
whose keys are not inother
.- Definition Classes
- JavaPairRDD
-
def
subtractByKey[W](other: JavaPairRDD[K, W], numPartitions: Int): JavaPairRDD[K, V]
Return an RDD with the pairs from
this
whose keys are not inother
.Return an RDD with the pairs from
this
whose keys are not inother
.- Definition Classes
- JavaPairRDD
-
def
subtractByKey[W](other: JavaPairRDD[K, W]): JavaPairRDD[K, V]
Return an RDD with the pairs from
this
whose keys are not inother
.Return an RDD with the pairs from
this
whose keys are not inother
.Uses
this
partitioner/partition size, because even ifother
is huge, the resulting RDD will be <= us.- Definition Classes
- JavaPairRDD
-
final
def
synchronized[T0](arg0: ⇒ T0): T0
- Definition Classes
- AnyRef
-
def
take(num: Int): List[(K, V)]
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[(K, V)]]
The asynchronous version of the
take
action, which returns a future for retrieving the firstnum
elements of this RDD.The asynchronous version of the
take
action, which returns a future for retrieving the firstnum
elements 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[(K, V)]
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[(K, V)]): List[(K, V)]
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[(K, V)]
- Definition Classes
- JavaRDDLike
-
def
takeSample(withReplacement: Boolean, num: Int): List[(K, V)]
- 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[(K, V)]
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
- AnyRef → Any
-
def
top(num: Int): List[(K, V)]
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[(K, V)]): List[(K, V)]
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, (K, V), U], combOp: Function2[U, U, U], depth: Int, finalAggregateOnExecutor: Boolean): U
org.apache.spark.api.java.JavaRDDLike.treeAggregate
with a parameter to do the final aggregation on the executor.org.apache.spark.api.java.JavaRDDLike.treeAggregate
with a parameter to do the final aggregation on the executor.- Definition Classes
- JavaRDDLike
-
def
treeAggregate[U](zeroValue: U, seqOp: Function2[U, (K, V), U], combOp: Function2[U, U, U]): U
org.apache.spark.api.java.JavaRDDLike.treeAggregate
with suggested depth 2.org.apache.spark.api.java.JavaRDDLike.treeAggregate
with suggested depth 2.- Definition Classes
- JavaRDDLike
-
def
treeAggregate[U](zeroValue: U, seqOp: Function2[U, (K, V), 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[(K, V), (K, V), (K, V)]): (K, V)
org.apache.spark.api.java.JavaRDDLike.treeReduce
with suggested depth 2.org.apache.spark.api.java.JavaRDDLike.treeReduce
with suggested depth 2.- Definition Classes
- JavaRDDLike
-
def
treeReduce(f: Function2[(K, V), (K, V), (K, V)], depth: Int): (K, V)
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: JavaPairRDD[K, V]): JavaPairRDD[K, V]
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
- JavaPairRDD
-
def
unpersist(blocking: Boolean): JavaPairRDD[K, V]
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.
- Definition Classes
- JavaPairRDD
-
def
unpersist(): JavaPairRDD[K, V]
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.
- Definition Classes
- JavaPairRDD
-
implicit
val
vClassTag: ClassTag[V]
- Definition Classes
- JavaHadoopRDD → JavaPairRDD
-
def
values(): JavaRDD[V]
Return an RDD with the values of each tuple.
Return an RDD with the values of each tuple.
- Definition Classes
- JavaPairRDD
-
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
wrapRDD(rdd: RDD[(K, V)]): JavaPairRDD[K, V]
- Definition Classes
- JavaPairRDD → JavaRDDLike
-
def
zip[U](other: JavaRDDLike[U, _]): JavaPairRDD[(K, V), 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[(K, V)], 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[(K, V), 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[(K, V), 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