class JavaDStream[T] extends AbstractJavaDStreamLike[T, JavaDStream[T], JavaRDD[T]]
A Java-friendly interface to org.apache.spark.streaming.dstream.DStream, the basic
abstraction in Spark Streaming that represents a continuous stream of data.
DStreams can either be created from live data (such as, data from TCP sockets, Kafka,
etc.) or it can be generated by transforming existing DStreams using operations such as map,
window. For operations applicable to key-value pair DStreams, see
org.apache.spark.streaming.api.java.JavaPairDStream.
- Source
- JavaDStream.scala
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- JavaDStream
- AbstractJavaDStreamLike
- JavaDStreamLike
- 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
- final def asInstanceOf[T0]: T0
- Definition Classes
- Any
- def cache(): JavaDStream[T]
Persist RDDs of this DStream with the default storage level (MEMORY_ONLY_SER)
- def checkpoint(interval: Duration): DStream[T]
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
- implicit val classTag: ClassTag[T]
- Definition Classes
- JavaDStream → JavaDStreamLike
- def clone(): AnyRef
- Attributes
- protected[lang]
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.CloneNotSupportedException]) @IntrinsicCandidate() @native()
- def compute(validTime: Time): JavaRDD[T]
Generate 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[T, 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
numPartitionspartitions.- numPartitions
number of partitions of each RDD in the new DStream.
- Definition Classes
- JavaDStreamLike
- def countByValue(): JavaPairDStream[T, 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[T, 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
numPartitionspartitions.- 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[T, 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[T]
- Definition Classes
- JavaDStream → JavaDStreamLike
- final def eq(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
- def equals(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef → Any
- def filter(f: Function[T, Boolean]): JavaDStream[T]
Return a new DStream containing only the elements that satisfy a predicate.
- def flatMap[U](f: FlatMapFunction[T, 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[T, 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 foreachRDD(foreachFunc: VoidFunction2[JavaRDD[T], 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[JavaRDD[T]]): 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
- final def getClass(): Class[_ <: AnyRef]
- Definition Classes
- AnyRef → Any
- Annotations
- @IntrinsicCandidate() @native()
- def glom(): JavaDStream[List[T]]
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 hashCode(): Int
- Definition Classes
- AnyRef → Any
- Annotations
- @IntrinsicCandidate() @native()
- final def isInstanceOf[T0]: Boolean
- Definition Classes
- Any
- def map[U](f: Function[T, 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[T], 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[T], 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[T, 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
- final def ne(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
- final def notify(): Unit
- Definition Classes
- AnyRef
- Annotations
- @IntrinsicCandidate() @native()
- final def notifyAll(): Unit
- Definition Classes
- AnyRef
- Annotations
- @IntrinsicCandidate() @native()
- def persist(storageLevel: StorageLevel): JavaDStream[T]
Persist the RDDs of this DStream with the given storage level
- def persist(): JavaDStream[T]
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[T, T, T]): JavaDStream[T]
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 reduceByWindow(reduceFunc: Function2[T, T, T], invReduceFunc: Function2[T, T, T], windowDuration: Duration, slideDuration: Duration): JavaDStream[T]
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[T, T, T], windowDuration: Duration, slideDuration: Duration): JavaDStream[T]
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): JavaDStream[T]
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.
- implicit def scalaIntToJavaLong(in: DStream[Long]): JavaDStream[Long]
- Definition Classes
- JavaDStreamLike
- def slice(fromTime: Time, toTime: Time): List[JavaRDD[T]]
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 toString(): String
- Definition Classes
- AnyRef → Any
- def transform[U](transformFunc: Function2[JavaRDD[T], 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[JavaRDD[T], 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[JavaRDD[T], 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[JavaRDD[T], 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[JavaRDD[T], 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[JavaRDD[T], 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[JavaRDD[T], 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[JavaRDD[T], 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: JavaDStream[T]): JavaDStream[T]
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.
- final def wait(arg0: Long, arg1: Int): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.InterruptedException])
- final def wait(arg0: Long): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.InterruptedException]) @native()
- final def wait(): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.InterruptedException])
- def window(windowDuration: Duration, slideDuration: Duration): JavaDStream[T]
Return a new DStream in which each RDD contains all the elements in seen in a sliding window of time over this DStream.
Return a new DStream in which each RDD contains all the elements in seen in a sliding window of time over this DStream.
- 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 window(windowDuration: Duration): JavaDStream[T]
Return a new DStream in which each RDD contains all the elements in seen in a sliding window of time over this DStream.
Return a new DStream in which each RDD contains all the elements in seen in a sliding window of time over 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[T]): JavaRDD[T]
- Definition Classes
- JavaDStream → JavaDStreamLike
Deprecated Value Members
- def finalize(): Unit
- Attributes
- protected[lang]
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.Throwable]) @Deprecated
- Deprecated
(Since version 9)