trait JavaDStreamLike[T, This <: JavaDStreamLike[T, This, R], R <: JavaRDDLike[T, R]] extends Serializable
- Alphabetic
- By Inheritance
- JavaDStreamLike
- Serializable
- AnyRef
- Any
- Hide All
- Show All
- Public
- Protected
Abstract Value Members
Concrete Value Members
- 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 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
- def clone(): AnyRef
- Attributes
- protected[lang]
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.CloneNotSupportedException]) @native()
- def context(): StreamingContext
Return the org.apache.spark.streaming.StreamingContext associated with this DStream
- def count(): JavaDStream[Long]
Return a new DStream in which each RDD has a single element generated by counting each RDD of this DStream.
- 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.
- 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.
- 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.
- 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
- 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()
- final def eq(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
- def equals(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef → Any
- def finalize(): Unit
- Attributes
- protected[lang]
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.Throwable])
- 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
- 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
- def foreachRDD(foreachFunc: VoidFunction2[R, 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.
- def foreachRDD(foreachFunc: VoidFunction[R]): 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.
- final def getClass(): Class[_ <: AnyRef]
- Definition Classes
- AnyRef → Any
- Annotations
- @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.
- def hashCode(): Int
- Definition Classes
- AnyRef → Any
- Annotations
- @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.
- 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.
- 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.
- 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.
- 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 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.
- 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.
- 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.
- 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
- 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
- implicit def scalaIntToJavaLong(in: DStream[Long]): JavaDStream[Long]
- def slice(fromTime: Time, toTime: Time): List[R]
Return all the RDDs between 'fromDuration' to 'toDuration' (both included)
- final def synchronized[T0](arg0: => T0): T0
- Definition Classes
- AnyRef
- def toString(): String
- Definition Classes
- AnyRef → Any
- def transform[U](transformFunc: Function2[R, 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.
- def transform[U](transformFunc: Function[R, JavaRDD[U]]): JavaDStream[U]
Return a new DStream in which each RDD is generated by applying a function on each RDD of 'this' DStream.
- def transformToPair[K2, V2](transformFunc: Function2[R, 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.
- def transformToPair[K2, V2](transformFunc: Function[R, 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.
- def transformWith[K2, V2, W](other: JavaPairDStream[K2, V2], transformFunc: Function3[R, 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.
- def transformWith[U, W](other: JavaDStream[U], transformFunc: Function3[R, 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.
- def transformWithToPair[K2, V2, K3, V3](other: JavaPairDStream[K2, V2], transformFunc: Function3[R, 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.
- def transformWithToPair[U, K2, V2](other: JavaDStream[U], transformFunc: Function3[R, 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.
- final def wait(): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.InterruptedException])
- 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()