class JavaPairDStream[K, V] extends AbstractJavaDStreamLike[(K, V), JavaPairDStream[K, V], JavaPairRDD[K, V]]
A Javafriendly interface to a DStream of keyvalue pairs, which provides extra methods
like reduceByKey
and join
.
 Source
 JavaPairDStream.scala
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 JavaPairDStream
 AbstractJavaDStreamLike
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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

final
def
asInstanceOf[T0]: T0
 Definition Classes
 Any

def
cache(): JavaPairDStream[K, V]
Persist RDDs of this DStream with the default storage level (MEMORY_ONLY_SER)

def
checkpoint(interval: Duration): DStream[(K, V)]
Enable periodic checkpointing of RDDs of this DStream.
Enable periodic checkpointing of RDDs of this DStream.
 interval
Time interval after which generated RDD will be checkpointed
 Definition Classes
 JavaDStreamLike

val
classTag: ClassTag[(K, V)]
 Definition Classes
 JavaPairDStream → JavaDStreamLike

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

def
cogroup[W](other: JavaPairDStream[K, W], partitioner: Partitioner): JavaPairDStream[K, (Iterable[V], Iterable[W])]
Return a new DStream by applying 'cogroup' between RDDs of
this
DStream andother
DStream.Return a new DStream by applying 'cogroup' between RDDs of
this
DStream andother
DStream. Hash partitioning is used to generate the RDDs withnumPartitions
partitions. 
def
cogroup[W](other: JavaPairDStream[K, W], numPartitions: Int): JavaPairDStream[K, (Iterable[V], Iterable[W])]
Return a new DStream by applying 'cogroup' between RDDs of
this
DStream andother
DStream.Return a new DStream by applying 'cogroup' between RDDs of
this
DStream andother
DStream. Hash partitioning is used to generate the RDDs withnumPartitions
partitions. 
def
cogroup[W](other: JavaPairDStream[K, W]): JavaPairDStream[K, (Iterable[V], Iterable[W])]
Return a new DStream by applying 'cogroup' between RDDs of
this
DStream andother
DStream.Return a new DStream by applying 'cogroup' between RDDs of
this
DStream andother
DStream. Hash partitioning is used to generate the RDDs with Spark's default number of partitions. 
def
combineByKey[C](createCombiner: Function[V, C], mergeValue: Function2[C, V, C], mergeCombiners: Function2[C, C, C], partitioner: Partitioner, mapSideCombine: Boolean): JavaPairDStream[K, C]
Combine elements of each key in DStream's RDDs using custom function.
Combine elements of each key in DStream's RDDs using custom function. This is similar to the combineByKey for RDDs. Please refer to combineByKey in org.apache.spark.rdd.PairRDDFunctions for more information.

def
combineByKey[C](createCombiner: Function[V, C], mergeValue: Function2[C, V, C], mergeCombiners: Function2[C, C, C], partitioner: Partitioner): JavaPairDStream[K, C]
Combine elements of each key in DStream's RDDs using custom function.
Combine elements of each key in DStream's RDDs using custom function. This is similar to the combineByKey for RDDs. Please refer to combineByKey in org.apache.spark.rdd.PairRDDFunctions for more information.

def
compute(validTime: Time): JavaPairRDD[K, V]
Method that generates an RDD for the given Duration

def
context(): StreamingContext
Return the org.apache.spark.streaming.StreamingContext associated with this DStream
Return the org.apache.spark.streaming.StreamingContext associated with this DStream
 Definition Classes
 JavaDStreamLike

def
count(): JavaDStream[Long]
Return a new DStream in which each RDD has a single element generated by counting each RDD of this DStream.
Return a new DStream in which each RDD has a single element generated by counting each RDD of this DStream.
 Definition Classes
 JavaDStreamLike

def
countByValue(numPartitions: Int): JavaPairDStream[(K, V), Long]
Return a new DStream in which each RDD contains the counts of each distinct value in each RDD of this DStream.
Return a new DStream in which each RDD contains the counts of each distinct value in each RDD of this DStream. Hash partitioning is used to generate the RDDs with
numPartitions
partitions. numPartitions
number of partitions of each RDD in the new DStream.
 Definition Classes
 JavaDStreamLike

def
countByValue(): JavaPairDStream[(K, V), Long]
Return a new DStream in which each RDD contains the counts of each distinct value in each RDD of this DStream.
Return a new DStream in which each RDD contains the counts of each distinct value in each RDD of this DStream. Hash partitioning is used to generate the RDDs with Spark's default number of partitions.
 Definition Classes
 JavaDStreamLike

def
countByValueAndWindow(windowDuration: Duration, slideDuration: Duration, numPartitions: Int): JavaPairDStream[(K, V), Long]
Return a new DStream in which each RDD contains the count of distinct elements in RDDs in a sliding window over this DStream.
Return a new DStream in which each RDD contains the count of distinct elements in RDDs in a sliding window over this DStream. Hash partitioning is used to generate the RDDs with
numPartitions
partitions. windowDuration
width of the window; must be a multiple of this DStream's batching interval
 slideDuration
sliding interval of the window (i.e., the interval after which the new DStream will generate RDDs); must be a multiple of this DStream's batching interval
 numPartitions
number of partitions of each RDD in the new DStream.
 Definition Classes
 JavaDStreamLike

def
countByValueAndWindow(windowDuration: Duration, slideDuration: Duration): JavaPairDStream[(K, V), Long]
Return a new DStream in which each RDD contains the count of distinct elements in RDDs in a sliding window over this DStream.
Return a new DStream in which each RDD contains the count of distinct elements in RDDs in a sliding window over this DStream. Hash partitioning is used to generate the RDDs with Spark's default number of partitions.
 windowDuration
width of the window; must be a multiple of this DStream's batching interval
 slideDuration
sliding interval of the window (i.e., the interval after which the new DStream will generate RDDs); must be a multiple of this DStream's batching interval
 Definition Classes
 JavaDStreamLike

def
countByWindow(windowDuration: Duration, slideDuration: Duration): JavaDStream[Long]
Return a new DStream in which each RDD has a single element generated by counting the number of elements in a window over this DStream.
Return a new DStream in which each RDD has a single element generated by counting the number of elements in a window over this DStream. windowDuration and slideDuration are as defined in the window() operation. This is equivalent to window(windowDuration, slideDuration).count()
 Definition Classes
 JavaDStreamLike

val
dstream: DStream[(K, V)]
 Definition Classes
 JavaPairDStream → JavaDStreamLike

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]): JavaPairDStream[K, V]
Return a new DStream containing only the elements that satisfy a predicate.

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

def
flatMap[U](f: FlatMapFunction[(K, V), U]): JavaDStream[U]
Return a new DStream by applying a function to all elements of this DStream, and then flattening the results
Return a new DStream by applying a function to all elements of this DStream, and then flattening the results
 Definition Classes
 JavaDStreamLike

def
flatMapToPair[K2, V2](f: PairFlatMapFunction[(K, V), K2, V2]): JavaPairDStream[K2, V2]
Return a new DStream by applying a function to all elements of this DStream, and then flattening the results
Return a new DStream by applying a function to all elements of this DStream, and then flattening the results
 Definition Classes
 JavaDStreamLike

def
flatMapValues[U](f: FlatMapFunction[V, U]): JavaPairDStream[K, U]
Return a new DStream by applying a flatmap function to the value of each keyvalue pairs in 'this' DStream without changing the key.

def
foreachRDD(foreachFunc: VoidFunction2[JavaPairRDD[K, V], Time]): Unit
Apply a function to each RDD in this DStream.
Apply a function to each RDD in this DStream. This is an output operator, so 'this' DStream will be registered as an output stream and therefore materialized.
 Definition Classes
 JavaDStreamLike

def
foreachRDD(foreachFunc: VoidFunction[JavaPairRDD[K, V]]): Unit
Apply a function to each RDD in this DStream.
Apply a function to each RDD in this DStream. This is an output operator, so 'this' DStream will be registered as an output stream and therefore materialized.
 Definition Classes
 JavaDStreamLike

def
fullOuterJoin[W](other: JavaPairDStream[K, W], partitioner: Partitioner): JavaPairDStream[K, (Optional[V], Optional[W])]
Return a new DStream by applying 'full outer join' between RDDs of
this
DStream andother
DStream.Return a new DStream by applying 'full outer join' between RDDs of
this
DStream andother
DStream. The supplied org.apache.spark.Partitioner is used to control the partitioning of each RDD. 
def
fullOuterJoin[W](other: JavaPairDStream[K, W], numPartitions: Int): JavaPairDStream[K, (Optional[V], Optional[W])]
Return a new DStream by applying 'full outer join' between RDDs of
this
DStream andother
DStream.Return a new DStream by applying 'full outer join' between RDDs of
this
DStream andother
DStream. Hash partitioning is used to generate the RDDs withnumPartitions
partitions. 
def
fullOuterJoin[W](other: JavaPairDStream[K, W]): JavaPairDStream[K, (Optional[V], Optional[W])]
Return a new DStream by applying 'full outer join' between RDDs of
this
DStream andother
DStream.Return a new DStream by applying 'full outer join' between RDDs of
this
DStream andother
DStream. Hash partitioning is used to generate the RDDs with Spark's default number of partitions. 
final
def
getClass(): Class[_]
 Definition Classes
 AnyRef → Any
 Annotations
 @native()

def
glom(): JavaDStream[List[(K, V)]]
Return a new DStream in which each RDD is generated by applying glom() to each RDD of this DStream.
Return a new DStream in which each RDD is generated by applying glom() to each RDD of this DStream. Applying glom() to an RDD coalesces all elements within each partition into an array.
 Definition Classes
 JavaDStreamLike

def
groupByKey(partitioner: Partitioner): JavaPairDStream[K, Iterable[V]]
Return a new DStream by applying
groupByKey
on each RDD ofthis
DStream.Return a new DStream by applying
groupByKey
on each RDD ofthis
DStream. Therefore, the values for each key inthis
DStream's RDDs are grouped into a single sequence to generate the RDDs of the new DStream. org.apache.spark.Partitioner is used to control the partitioning of each RDD. 
def
groupByKey(numPartitions: Int): JavaPairDStream[K, Iterable[V]]
Return a new DStream by applying
groupByKey
to each RDD.Return a new DStream by applying
groupByKey
to each RDD. Hash partitioning is used to generate the RDDs withnumPartitions
partitions. 
def
groupByKey(): JavaPairDStream[K, Iterable[V]]
Return a new DStream by applying
groupByKey
to each RDD.Return a new DStream by applying
groupByKey
to each RDD. Hash partitioning is used to generate the RDDs with Spark's default number of partitions. 
def
groupByKeyAndWindow(windowDuration: Duration, slideDuration: Duration, partitioner: Partitioner): JavaPairDStream[K, Iterable[V]]
Return a new DStream by applying
groupByKey
over a sliding window onthis
DStream.Return a new DStream by applying
groupByKey
over a sliding window onthis
DStream. Similar toDStream.groupByKey()
, but applies it over a sliding window. windowDuration
width of the window; must be a multiple of this DStream's batching interval
 slideDuration
sliding interval of the window (i.e., the interval after which the new DStream will generate RDDs); must be a multiple of this DStream's batching interval
 partitioner
Partitioner for controlling the partitioning of each RDD in the new DStream.

def
groupByKeyAndWindow(windowDuration: Duration, slideDuration: Duration, numPartitions: Int): JavaPairDStream[K, Iterable[V]]
Return a new DStream by applying
groupByKey
over a sliding window onthis
DStream.Return a new DStream by applying
groupByKey
over a sliding window onthis
DStream. Similar toDStream.groupByKey()
, but applies it over a sliding window. Hash partitioning is used to generate the RDDs withnumPartitions
partitions. windowDuration
width of the window; must be a multiple of this DStream's batching interval
 slideDuration
sliding interval of the window (i.e., the interval after which the new DStream will generate RDDs); must be a multiple of this DStream's batching interval
 numPartitions
Number of partitions of each RDD in the new DStream.

def
groupByKeyAndWindow(windowDuration: Duration, slideDuration: Duration): JavaPairDStream[K, Iterable[V]]
Return a new DStream by applying
groupByKey
over a sliding window.Return a new DStream by applying
groupByKey
over a sliding window. Similar toDStream.groupByKey()
, but applies it over a sliding window. Hash partitioning is used to generate the RDDs with Spark's default number of partitions. windowDuration
width of the window; must be a multiple of this DStream's batching interval
 slideDuration
sliding interval of the window (i.e., the interval after which the new DStream will generate RDDs); must be a multiple of this DStream's batching interval

def
groupByKeyAndWindow(windowDuration: Duration): JavaPairDStream[K, Iterable[V]]
Return a new DStream by applying
groupByKey
over a sliding window.Return a new DStream by applying
groupByKey
over a sliding window. This is similar toDStream.groupByKey()
but applies it over a sliding window. The new DStream generates RDDs with the same interval as this DStream. Hash partitioning is used to generate the RDDs with Spark's default number of partitions. windowDuration
width of the window; must be a multiple of this DStream's batching interval

def
hashCode(): Int
 Definition Classes
 AnyRef → Any
 Annotations
 @native()

final
def
isInstanceOf[T0]: Boolean
 Definition Classes
 Any

def
join[W](other: JavaPairDStream[K, W], partitioner: Partitioner): JavaPairDStream[K, (V, W)]
Return a new DStream by applying 'join' between RDDs of
this
DStream andother
DStream.Return a new DStream by applying 'join' between RDDs of
this
DStream andother
DStream. The supplied org.apache.spark.Partitioner is used to control the partitioning of each RDD. 
def
join[W](other: JavaPairDStream[K, W], numPartitions: Int): JavaPairDStream[K, (V, W)]
Return a new DStream by applying 'join' between RDDs of
this
DStream andother
DStream.Return a new DStream by applying 'join' between RDDs of
this
DStream andother
DStream. Hash partitioning is used to generate the RDDs withnumPartitions
partitions. 
def
join[W](other: JavaPairDStream[K, W]): JavaPairDStream[K, (V, W)]
Return a new DStream by applying 'join' between RDDs of
this
DStream andother
DStream.Return a new DStream by applying 'join' between RDDs of
this
DStream andother
DStream. Hash partitioning is used to generate the RDDs with Spark's default number of partitions.  implicit val kManifest: ClassTag[K]

def
leftOuterJoin[W](other: JavaPairDStream[K, W], partitioner: Partitioner): JavaPairDStream[K, (V, Optional[W])]
Return a new DStream by applying 'left outer join' between RDDs of
this
DStream andother
DStream.Return a new DStream by applying 'left outer join' between RDDs of
this
DStream andother
DStream. The supplied org.apache.spark.Partitioner is used to control the partitioning of each RDD. 
def
leftOuterJoin[W](other: JavaPairDStream[K, W], numPartitions: Int): JavaPairDStream[K, (V, Optional[W])]
Return a new DStream by applying 'left outer join' between RDDs of
this
DStream andother
DStream.Return a new DStream by applying 'left outer join' between RDDs of
this
DStream andother
DStream. Hash partitioning is used to generate the RDDs withnumPartitions
partitions. 
def
leftOuterJoin[W](other: JavaPairDStream[K, W]): JavaPairDStream[K, (V, Optional[W])]
Return a new DStream by applying 'left outer join' between RDDs of
this
DStream andother
DStream.Return a new DStream by applying 'left outer join' between RDDs of
this
DStream andother
DStream. Hash partitioning is used to generate the RDDs with Spark's default number of partitions. 
def
map[U](f: Function[(K, V), U]): JavaDStream[U]
Return a new DStream by applying a function to all elements of this DStream.
Return a new DStream by applying a function to all elements of this DStream.
 Definition Classes
 JavaDStreamLike

def
mapPartitions[U](f: FlatMapFunction[Iterator[(K, V)], U]): JavaDStream[U]
Return a new DStream in which each RDD is generated by applying mapPartitions() to each RDDs of this DStream.
Return a new DStream in which each RDD is generated by applying mapPartitions() to each RDDs of this DStream. Applying mapPartitions() to an RDD applies a function to each partition of the RDD.
 Definition Classes
 JavaDStreamLike

def
mapPartitionsToPair[K2, V2](f: PairFlatMapFunction[Iterator[(K, V)], K2, V2]): JavaPairDStream[K2, V2]
Return a new DStream in which each RDD is generated by applying mapPartitions() to each RDDs of this DStream.
Return a new DStream in which each RDD is generated by applying mapPartitions() to each RDDs of this DStream. Applying mapPartitions() to an RDD applies a function to each partition of the RDD.
 Definition Classes
 JavaDStreamLike

def
mapToPair[K2, V2](f: PairFunction[(K, V), K2, V2]): JavaPairDStream[K2, V2]
Return a new DStream by applying a function to all elements of this DStream.
Return a new DStream by applying a function to all elements of this DStream.
 Definition Classes
 JavaDStreamLike

def
mapValues[U](f: Function[V, U]): JavaPairDStream[K, U]
Return a new DStream by applying a map function to the value of each keyvalue pairs in 'this' DStream without changing the key.

def
mapWithState[StateType, MappedType](spec: StateSpec[K, V, StateType, MappedType]): JavaMapWithStateDStream[K, V, StateType, MappedType]
Return a JavaMapWithStateDStream by applying a function to every keyvalue element of
this
stream, while maintaining some state data for each unique key.Return a JavaMapWithStateDStream by applying a function to every keyvalue element of
this
stream, while maintaining some state data for each unique key. The mapping function and other specification (e.g. partitioners, timeouts, initial state data, etc.) of this transformation can be specified usingStateSpec
class. The state data is accessible in as a parameter of typeState
in the mapping function.Example of using
mapWithState
:// A mapping function that maintains an integer state and return a string Function3<String, Optional<Integer>, State<Integer>, String> mappingFunction = new Function3<String, Optional<Integer>, State<Integer>, String>() { @Override public Optional<String> call(Optional<Integer> value, State<Integer> state) { // Use state.exists(), state.get(), state.update() and state.remove() // to manage state, and return the necessary string } }; JavaMapWithStateDStream<String, Integer, Integer, String> mapWithStateDStream = keyValueDStream.mapWithState(StateSpec.function(mappingFunc));
 StateType
Class type of the state data
 MappedType
Class type of the mapped data
 spec
Specification of this transformation

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
persist(storageLevel: StorageLevel): JavaPairDStream[K, V]
Persist the RDDs of this DStream with the given storage level

def
persist(): JavaPairDStream[K, V]
Persist RDDs of this DStream with the default storage level (MEMORY_ONLY_SER)

def
print(num: Int): Unit
Print the first num elements of each RDD generated in this DStream.
Print the first num elements of each RDD generated in this DStream. This is an output operator, so this DStream will be registered as an output stream and there materialized.
 Definition Classes
 JavaDStreamLike

def
print(): Unit
Print the first ten elements of each RDD generated in this DStream.
Print the first ten elements of each RDD generated in this DStream. This is an output operator, so this DStream will be registered as an output stream and there materialized.
 Definition Classes
 JavaDStreamLike

def
reduce(f: Function2[(K, V), (K, V), (K, V)]): JavaDStream[(K, V)]
Return a new DStream in which each RDD has a single element generated by reducing each RDD of this DStream.
Return a new DStream in which each RDD has a single element generated by reducing each RDD of this DStream.
 Definition Classes
 JavaDStreamLike

def
reduceByKey(func: Function2[V, V, V], partitioner: Partitioner): JavaPairDStream[K, V]
Return a new DStream by applying
reduceByKey
to each RDD.Return a new DStream by applying
reduceByKey
to each RDD. The values for each key are merged using the supplied reduce function. org.apache.spark.Partitioner is used to control the partitioning of each RDD. 
def
reduceByKey(func: Function2[V, V, V], numPartitions: Int): JavaPairDStream[K, V]
Return a new DStream by applying
reduceByKey
to each RDD.Return a new DStream by applying
reduceByKey
to each RDD. The values for each key are merged using the supplied reduce function. Hash partitioning is used to generate the RDDs withnumPartitions
partitions. 
def
reduceByKey(func: Function2[V, V, V]): JavaPairDStream[K, V]
Return a new DStream by applying
reduceByKey
to each RDD.Return a new DStream by applying
reduceByKey
to each RDD. The values for each key are merged using the associative and commutative reduce function. Hash partitioning is used to generate the RDDs with Spark's default number of partitions. 
def
reduceByKeyAndWindow(reduceFunc: Function2[V, V, V], invReduceFunc: Function2[V, V, V], windowDuration: Duration, slideDuration: Duration, partitioner: Partitioner, filterFunc: Function[(K, V), Boolean]): JavaPairDStream[K, V]
Return a new DStream by applying incremental
reduceByKey
over a sliding window.Return a new DStream by applying incremental
reduceByKey
over a sliding window. The reduced value of over a new window is calculated using the old window's reduce value : reduce the new values that entered the window (e.g., adding new counts) 2. "inverse reduce" the old values that left the window (e.g., subtracting old counts) This is more efficient that reduceByKeyAndWindow without "inverse reduce" function. However, it is applicable to only "invertible reduce functions".
 reduceFunc
associative and commutative reduce function
 invReduceFunc
inverse function
 windowDuration
width of the window; must be a multiple of this DStream's batching interval
 slideDuration
sliding interval of the window (i.e., the interval after which the new DStream will generate RDDs); must be a multiple of this DStream's batching interval
 partitioner
Partitioner for controlling the partitioning of each RDD in the new DStream.
 filterFunc
function to filter expired keyvalue pairs; only pairs that satisfy the function are retained set this to null if you do not want to filter

def
reduceByKeyAndWindow(reduceFunc: Function2[V, V, V], invReduceFunc: Function2[V, V, V], windowDuration: Duration, slideDuration: Duration, numPartitions: Int, filterFunc: Function[(K, V), Boolean]): JavaPairDStream[K, V]
Return a new DStream by applying incremental
reduceByKey
over a sliding window.Return a new DStream by applying incremental
reduceByKey
over a sliding window. The reduced value of over a new window is calculated using the old window's reduce value : reduce the new values that entered the window (e.g., adding new counts)
2. "inverse reduce" the old values that left the window (e.g., subtracting old counts)
This is more efficient that reduceByKeyAndWindow without "inverse reduce" function.
However, it is applicable to only "invertible reduce functions".
Hash partitioning is used to generate the RDDs with
numPartitions
partitions.
 reduceFunc
associative and commutative reduce function
 invReduceFunc
inverse function
 windowDuration
width of the window; must be a multiple of this DStream's batching interval
 slideDuration
sliding interval of the window (i.e., the interval after which the new DStream will generate RDDs); must be a multiple of this DStream's batching interval
 numPartitions
number of partitions of each RDD in the new DStream.
 filterFunc
function to filter expired keyvalue pairs; only pairs that satisfy the function are retained set this to null if you do not want to filter
 reduce the new values that entered the window (e.g., adding new counts)
2. "inverse reduce" the old values that left the window (e.g., subtracting old counts)
This is more efficient that reduceByKeyAndWindow without "inverse reduce" function.
However, it is applicable to only "invertible reduce functions".
Hash partitioning is used to generate the RDDs with

def
reduceByKeyAndWindow(reduceFunc: Function2[V, V, V], invReduceFunc: Function2[V, V, V], windowDuration: Duration, slideDuration: Duration): JavaPairDStream[K, V]
Return a new DStream by reducing over a using incremental computation.
Return a new DStream by reducing over a using incremental computation. The reduced value of over a new window is calculated using the old window's reduce value :
 reduce the new values that entered the window (e.g., adding new counts) 2. "inverse reduce" the old values that left the window (e.g., subtracting old counts) This is more efficient that reduceByKeyAndWindow without "inverse reduce" function. However, it is applicable to only "invertible reduce functions". Hash partitioning is used to generate the RDDs with Spark's default number of partitions.
 reduceFunc
associative and commutative reduce function
 invReduceFunc
inverse function; such that for all y, invertible x:
invReduceFunc(reduceFunc(x, y), x) = y
 windowDuration
width of the window; must be a multiple of this DStream's batching interval
 slideDuration
sliding interval of the window (i.e., the interval after which the new DStream will generate RDDs); must be a multiple of this DStream's batching interval

def
reduceByKeyAndWindow(reduceFunc: Function2[V, V, V], windowDuration: Duration, slideDuration: Duration, partitioner: Partitioner): JavaPairDStream[K, V]
Return a new DStream by applying
reduceByKey
over a sliding window.Return a new DStream by applying
reduceByKey
over a sliding window. Similar toDStream.reduceByKey()
, but applies it over a sliding window. reduceFunc
associative rand commutative educe function
 windowDuration
width of the window; must be a multiple of this DStream's batching interval
 slideDuration
sliding interval of the window (i.e., the interval after which the new DStream will generate RDDs); must be a multiple of this DStream's batching interval
 partitioner
Partitioner for controlling the partitioning of each RDD in the new DStream.

def
reduceByKeyAndWindow(reduceFunc: Function2[V, V, V], windowDuration: Duration, slideDuration: Duration, numPartitions: Int): JavaPairDStream[K, V]
Return a new DStream by applying
reduceByKey
over a sliding window.Return a new DStream by applying
reduceByKey
over a sliding window. This is similar toDStream.reduceByKey()
but applies it over a sliding window. Hash partitioning is used to generate the RDDs withnumPartitions
partitions. reduceFunc
associative and commutative reduce function
 windowDuration
width of the window; must be a multiple of this DStream's batching interval
 slideDuration
sliding interval of the window (i.e., the interval after which the new DStream will generate RDDs); must be a multiple of this DStream's batching interval
 numPartitions
Number of partitions of each RDD in the new DStream.

def
reduceByKeyAndWindow(reduceFunc: Function2[V, V, V], windowDuration: Duration, slideDuration: Duration): JavaPairDStream[K, V]
Return a new DStream by applying
reduceByKey
over a sliding window.Return a new DStream by applying
reduceByKey
over a sliding window. This is similar toDStream.reduceByKey()
but applies it over a sliding window. Hash partitioning is used to generate the RDDs with Spark's default number of partitions. reduceFunc
associative and commutative reduce function
 windowDuration
width of the window; must be a multiple of this DStream's batching interval
 slideDuration
sliding interval of the window (i.e., the interval after which the new DStream will generate RDDs); must be a multiple of this DStream's batching interval

def
reduceByKeyAndWindow(reduceFunc: Function2[V, V, V], windowDuration: Duration): JavaPairDStream[K, V]
Create a new DStream by applying
reduceByKey
over a sliding window onthis
DStream.Create a new DStream by applying
reduceByKey
over a sliding window onthis
DStream. Similar toDStream.reduceByKey()
, but applies it over a sliding window. The new DStream generates RDDs with the same interval as this DStream. Hash partitioning is used to generate the RDDs with Spark's default number of partitions. reduceFunc
associative and commutative reduce function
 windowDuration
width of the window; must be a multiple of this DStream's batching interval

def
reduceByWindow(reduceFunc: Function2[(K, V), (K, V), (K, V)], invReduceFunc: Function2[(K, V), (K, V), (K, V)], windowDuration: Duration, slideDuration: Duration): JavaDStream[(K, V)]
Return a new DStream in which each RDD has a single element generated by reducing all elements in a sliding window over this DStream.
Return a new DStream in which each RDD has a single element generated by reducing all elements in a sliding window over this DStream. However, the reduction is done incrementally using the old window's reduced value :
 reduce the new values that entered the window (e.g., adding new counts) 2. "inverse reduce" the old values that left the window (e.g., subtracting old counts) This is more efficient than reduceByWindow without "inverse reduce" function. However, it is applicable to only "invertible reduce functions".
 reduceFunc
associative and commutative reduce function
 invReduceFunc
inverse reduce function; such that for all y, invertible x:
invReduceFunc(reduceFunc(x, y), x) = y
 windowDuration
width of the window; must be a multiple of this DStream's batching interval
 slideDuration
sliding interval of the window (i.e., the interval after which the new DStream will generate RDDs); must be a multiple of this DStream's batching interval
 Definition Classes
 JavaDStreamLike

def
reduceByWindow(reduceFunc: Function2[(K, V), (K, V), (K, V)], windowDuration: Duration, slideDuration: Duration): JavaDStream[(K, V)]
Return a new DStream in which each RDD has a single element generated by reducing all elements in a sliding window over this DStream.
Return a new DStream in which each RDD has a single element generated by reducing all elements in a sliding window over this DStream.
 reduceFunc
associative and commutative reduce function
 windowDuration
width of the window; must be a multiple of this DStream's batching interval
 slideDuration
sliding interval of the window (i.e., the interval after which the new DStream will generate RDDs); must be a multiple of this DStream's batching interval
 Definition Classes
 JavaDStreamLike

def
repartition(numPartitions: Int): JavaPairDStream[K, V]
Return a new DStream with an increased or decreased level of parallelism.
Return a new DStream with an increased or decreased level of parallelism. Each RDD in the returned DStream has exactly numPartitions partitions.

def
rightOuterJoin[W](other: JavaPairDStream[K, W], partitioner: Partitioner): JavaPairDStream[K, (Optional[V], W)]
Return a new DStream by applying 'right outer join' between RDDs of
this
DStream andother
DStream.Return a new DStream by applying 'right outer join' between RDDs of
this
DStream andother
DStream. The supplied org.apache.spark.Partitioner is used to control the partitioning of each RDD. 
def
rightOuterJoin[W](other: JavaPairDStream[K, W], numPartitions: Int): JavaPairDStream[K, (Optional[V], W)]
Return a new DStream by applying 'right outer join' between RDDs of
this
DStream andother
DStream.Return a new DStream by applying 'right outer join' between RDDs of
this
DStream andother
DStream. Hash partitioning is used to generate the RDDs withnumPartitions
partitions. 
def
rightOuterJoin[W](other: JavaPairDStream[K, W]): JavaPairDStream[K, (Optional[V], W)]
Return a new DStream by applying 'right outer join' between RDDs of
this
DStream andother
DStream.Return a new DStream by applying 'right outer join' between RDDs of
this
DStream andother
DStream. Hash partitioning is used to generate the RDDs with Spark's default number of partitions. 
def
saveAsHadoopFiles[F <: OutputFormat[_, _]](prefix: String, suffix: String, keyClass: Class[_], valueClass: Class[_], outputFormatClass: Class[F], conf: JobConf): Unit
Save each RDD in
this
DStream as a Hadoop file.Save each RDD in
this
DStream as a Hadoop file. The file name at each batch interval is generated based onprefix
andsuffix
: "prefixTIME_IN_MS.suffix". 
def
saveAsHadoopFiles[F <: OutputFormat[_, _]](prefix: String, suffix: String, keyClass: Class[_], valueClass: Class[_], outputFormatClass: Class[F]): Unit
Save each RDD in
this
DStream as a Hadoop file.Save each RDD in
this
DStream as a Hadoop file. The file name at each batch interval is generated based onprefix
andsuffix
: "prefixTIME_IN_MS.suffix". 
def
saveAsHadoopFiles(prefix: String, suffix: String): Unit
Save each RDD in
this
DStream as a Hadoop file.Save each RDD in
this
DStream as a Hadoop file. The file name at each batch interval is generated based onprefix
andsuffix
: "prefixTIME_IN_MS.suffix". 
def
saveAsNewAPIHadoopFiles[F <: OutputFormat[_, _]](prefix: String, suffix: String, keyClass: Class[_], valueClass: Class[_], outputFormatClass: Class[F], conf: Configuration = ...): Unit
Save each RDD in
this
DStream as a Hadoop file.Save each RDD in
this
DStream as a Hadoop file. The file name at each batch interval is generated based onprefix
andsuffix
: "prefixTIME_IN_MS.suffix". 
def
saveAsNewAPIHadoopFiles[F <: OutputFormat[_, _]](prefix: String, suffix: String, keyClass: Class[_], valueClass: Class[_], outputFormatClass: Class[F]): Unit
Save each RDD in
this
DStream as a Hadoop file.Save each RDD in
this
DStream as a Hadoop file. The file name at each batch interval is generated based onprefix
andsuffix
: "prefixTIME_IN_MS.suffix". 
def
saveAsNewAPIHadoopFiles(prefix: String, suffix: String): Unit
Save each RDD in
this
DStream as a Hadoop file.Save each RDD in
this
DStream as a Hadoop file. The file name at each batch interval is generated based onprefix
andsuffix
: "prefixTIME_IN_MS.suffix". 
implicit
def
scalaIntToJavaLong(in: DStream[Long]): JavaDStream[Long]
 Definition Classes
 JavaDStreamLike

def
slice(fromTime: Time, toTime: Time): List[JavaPairRDD[K, V]]
Return all the RDDs between 'fromDuration' to 'toDuration' (both included)
Return all the RDDs between 'fromDuration' to 'toDuration' (both included)
 Definition Classes
 JavaDStreamLike

final
def
synchronized[T0](arg0: ⇒ T0): T0
 Definition Classes
 AnyRef

def
toJavaDStream(): JavaDStream[(K, V)]
Convert to a JavaDStream

def
toString(): String
 Definition Classes
 AnyRef → Any

def
transform[U](transformFunc: Function2[JavaPairRDD[K, V], Time, JavaRDD[U]]): JavaDStream[U]
Return a new DStream in which each RDD is generated by applying a function on each RDD of 'this' DStream.
Return a new DStream in which each RDD is generated by applying a function on each RDD of 'this' DStream.
 Definition Classes
 JavaDStreamLike

def
transform[U](transformFunc: Function[JavaPairRDD[K, V], JavaRDD[U]]): JavaDStream[U]
Return a new DStream in which each RDD is generated by applying a function on each RDD of 'this' DStream.
Return a new DStream in which each RDD is generated by applying a function on each RDD of 'this' DStream.
 Definition Classes
 JavaDStreamLike

def
transformToPair[K2, V2](transformFunc: Function2[JavaPairRDD[K, V], Time, JavaPairRDD[K2, V2]]): JavaPairDStream[K2, V2]
Return a new DStream in which each RDD is generated by applying a function on each RDD of 'this' DStream.
Return a new DStream in which each RDD is generated by applying a function on each RDD of 'this' DStream.
 Definition Classes
 JavaDStreamLike

def
transformToPair[K2, V2](transformFunc: Function[JavaPairRDD[K, V], JavaPairRDD[K2, V2]]): JavaPairDStream[K2, V2]
Return a new DStream in which each RDD is generated by applying a function on each RDD of 'this' DStream.
Return a new DStream in which each RDD is generated by applying a function on each RDD of 'this' DStream.
 Definition Classes
 JavaDStreamLike

def
transformWith[K2, V2, W](other: JavaPairDStream[K2, V2], transformFunc: Function3[JavaPairRDD[K, V], JavaPairRDD[K2, V2], Time, JavaRDD[W]]): JavaDStream[W]
Return a new DStream in which each RDD is generated by applying a function on each RDD of 'this' DStream and 'other' DStream.
Return a new DStream in which each RDD is generated by applying a function on each RDD of 'this' DStream and 'other' DStream.
 Definition Classes
 JavaDStreamLike

def
transformWith[U, W](other: JavaDStream[U], transformFunc: Function3[JavaPairRDD[K, V], JavaRDD[U], Time, JavaRDD[W]]): JavaDStream[W]
Return a new DStream in which each RDD is generated by applying a function on each RDD of 'this' DStream and 'other' DStream.
Return a new DStream in which each RDD is generated by applying a function on each RDD of 'this' DStream and 'other' DStream.
 Definition Classes
 JavaDStreamLike

def
transformWithToPair[K2, V2, K3, V3](other: JavaPairDStream[K2, V2], transformFunc: Function3[JavaPairRDD[K, V], JavaPairRDD[K2, V2], Time, JavaPairRDD[K3, V3]]): JavaPairDStream[K3, V3]
Return a new DStream in which each RDD is generated by applying a function on each RDD of 'this' DStream and 'other' DStream.
Return a new DStream in which each RDD is generated by applying a function on each RDD of 'this' DStream and 'other' DStream.
 Definition Classes
 JavaDStreamLike

def
transformWithToPair[U, K2, V2](other: JavaDStream[U], transformFunc: Function3[JavaPairRDD[K, V], JavaRDD[U], Time, JavaPairRDD[K2, V2]]): JavaPairDStream[K2, V2]
Return a new DStream in which each RDD is generated by applying a function on each RDD of 'this' DStream and 'other' DStream.
Return a new DStream in which each RDD is generated by applying a function on each RDD of 'this' DStream and 'other' DStream.
 Definition Classes
 JavaDStreamLike

def
union(that: JavaPairDStream[K, V]): JavaPairDStream[K, V]
Return a new DStream by unifying data of another DStream with this DStream.
Return a new DStream by unifying data of another DStream with this DStream.
 that
Another DStream having the same interval (i.e., slideDuration) as this DStream.

def
updateStateByKey[S](updateFunc: Function2[List[V], Optional[S], Optional[S]], partitioner: Partitioner, initialRDD: JavaPairRDD[K, S]): JavaPairDStream[K, S]
Return a new "state" DStream where the state for each key is updated by applying the given function on the previous state of the key and the new values of the key.
Return a new "state" DStream where the state for each key is updated by applying the given function on the previous state of the key and the new values of the key. org.apache.spark.Partitioner is used to control the partitioning of each RDD.
 S
State type
 updateFunc
State update function. If
this
function returns None, then corresponding state keyvalue pair will be eliminated. partitioner
Partitioner for controlling the partitioning of each RDD in the new DStream.
 initialRDD
initial state value of each key.

def
updateStateByKey[S](updateFunc: Function2[List[V], Optional[S], Optional[S]], partitioner: Partitioner): JavaPairDStream[K, S]
Return a new "state" DStream where the state for each key is updated by applying the given function on the previous state of the key and the new values of the key.
Return a new "state" DStream where the state for each key is updated by applying the given function on the previous state of the key and the new values of the key. org.apache.spark.Partitioner is used to control the partitioning of each RDD.
 S
State type
 updateFunc
State update function. If
this
function returns None, then corresponding state keyvalue pair will be eliminated. partitioner
Partitioner for controlling the partitioning of each RDD in the new DStream.

def
updateStateByKey[S](updateFunc: Function2[List[V], Optional[S], Optional[S]], numPartitions: Int): JavaPairDStream[K, S]
Return a new "state" DStream where the state for each key is updated by applying the given function on the previous state of the key and the new values of each key.
Return a new "state" DStream where the state for each key is updated by applying the given function on the previous state of the key and the new values of each key. Hash partitioning is used to generate the RDDs with
numPartitions
partitions. S
State type
 updateFunc
State update function. If
this
function returns None, then corresponding state keyvalue pair will be eliminated. numPartitions
Number of partitions of each RDD in the new DStream.

def
updateStateByKey[S](updateFunc: Function2[List[V], Optional[S], Optional[S]]): JavaPairDStream[K, S]
Return a new "state" DStream where the state for each key is updated by applying the given function on the previous state of the key and the new values of each key.
Return a new "state" DStream where the state for each key is updated by applying the given function on the previous state of the key and the new values of each key. Hash partitioning is used to generate the RDDs with Spark's default number of partitions.
 S
State type
 updateFunc
State update function. If
this
function returns None, then corresponding state keyvalue pair will be eliminated.
 implicit val vManifest: ClassTag[V]

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
window(windowDuration: Duration, slideDuration: Duration): JavaPairDStream[K, V]
Return a new DStream which is computed based on windowed batches of this DStream.
Return a new DStream which is computed based on windowed batches of this DStream.
 windowDuration
duration (i.e., width) of the window; must be a multiple of this DStream's interval
 slideDuration
sliding interval of the window (i.e., the interval after which the new DStream will generate RDDs); must be a multiple of this DStream's interval

def
window(windowDuration: Duration): JavaPairDStream[K, V]
Return a new DStream which is computed based on windowed batches of this DStream.
Return a new DStream which is computed based on windowed batches of this DStream. The new DStream generates RDDs with the same interval as this DStream.
 windowDuration
width of the window; must be a multiple of this DStream's interval.

def
wrapRDD(rdd: RDD[(K, V)]): JavaPairRDD[K, V]
 Definition Classes
 JavaPairDStream → JavaDStreamLike