JavaPairDStream

Companion object JavaPairDStream

class JavaPairDStream[K, V] extends AbstractJavaDStreamLike[(K, V), JavaPairDStream[K, V], JavaPairRDD[K, V]]

A Java-friendly interface to a DStream of key-value pairs, which provides extra methods like reduceByKey and join.

Source: JavaPairDStream.scala

Linear Supertypes

AbstractJavaDStreamLike[(K, V), JavaPairDStream[K, V], JavaPairRDD[K, V]], JavaDStreamLike[(K, V), JavaPairDStream[K, V], JavaPairRDD[K, V]], Serializable, AnyRef, Any

Known Subclasses

JavaPairInputDStream, JavaPairReceiverInputDStream

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Inherited

JavaPairDStream
AbstractJavaDStreamLike
JavaDStreamLike
Serializable
AnyRef
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Protected

Instance Constructors

new JavaPairDStream(dstream: DStream[(K, V)])(implicit kManifest: ClassTag[K], vManifest: ClassTag[V])

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 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(classOf[java.lang.CloneNotSupportedException]) @IntrinsicCandidate() @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 and other DStream.
Return a new DStream by applying 'cogroup' between RDDs of this DStream and other DStream. Hash partitioning is used to generate the RDDs with numPartitions 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 and other DStream.
Return a new DStream by applying 'cogroup' between RDDs of this DStream and other DStream. Hash partitioning is used to generate the RDDs with numPartitions 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 and other DStream.
Return a new DStream by applying 'cogroup' between RDDs of this DStream and other 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: AnyRef): 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 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 key-value 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 and other DStream.
Return a new DStream by applying 'full outer join' between RDDs of this DStream and other 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 and other DStream.
Return a new DStream by applying 'full outer join' between RDDs of this DStream and other DStream. Hash partitioning is used to generate the RDDs with numPartitions 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 and other DStream.
Return a new DStream by applying 'full outer join' between RDDs of this DStream and other DStream. Hash partitioning is used to generate the RDDs with Spark's default number of partitions.
final def getClass(): Class[_ <: AnyRef]
Definition Classes
AnyRef → Any
Annotations
@IntrinsicCandidate() @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 of this DStream.
Return a new DStream by applying groupByKey on each RDD of this DStream. Therefore, the values for each key in this 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 with numPartitions 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 on this DStream.
Return a new DStream by applying groupByKey over a sliding window on this DStream. Similar to DStream.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 on this DStream.
Return a new DStream by applying groupByKey over a sliding window on this DStream. Similar to DStream.groupByKey(), but applies it over a sliding window. 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.
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 to DStream.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 to DStream.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
@IntrinsicCandidate() @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 and other DStream.
Return a new DStream by applying 'join' between RDDs of this DStream and other 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 and other DStream.
Return a new DStream by applying 'join' between RDDs of this DStream and other DStream. Hash partitioning is used to generate the RDDs with numPartitions partitions.
def join[W](other: JavaPairDStream[K, W]): JavaPairDStream[K, (V, W)]
Return a new DStream by applying 'join' between RDDs of this DStream and other DStream.
Return a new DStream by applying 'join' between RDDs of this DStream and other 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 and other DStream.
Return a new DStream by applying 'left outer join' between RDDs of this DStream and other 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 and other DStream.
Return a new DStream by applying 'left outer join' between RDDs of this DStream and other DStream. Hash partitioning is used to generate the RDDs with numPartitions 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 and other DStream.
Return a new DStream by applying 'left outer join' between RDDs of this DStream and other 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 key-value 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 key-value element of this stream, while maintaining some state data for each unique key.
Return a JavaMapWithStateDStream by applying a function to every key-value 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 using StateSpec class. The state data is accessible in as a parameter of type State 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
@IntrinsicCandidate() @native()
final def notifyAll(): Unit
Definition Classes
AnyRef
Annotations
@IntrinsicCandidate() @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 with numPartitions 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 :
1. 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 key-value 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 :
1. 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 key-value 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): 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 :
1. 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 to DStream.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 to DStream.reduceByKey() but applies it over a sliding window. Hash partitioning is used to generate the RDDs with numPartitions 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 to DStream.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 on this DStream.
Create a new DStream by applying reduceByKey over a sliding window on this DStream. Similar to DStream.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 :
1. 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 and other DStream.
Return a new DStream by applying 'right outer join' between RDDs of this DStream and other 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 and other DStream.
Return a new DStream by applying 'right outer join' between RDDs of this DStream and other DStream. Hash partitioning is used to generate the RDDs with numPartitions 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 and other DStream.
Return a new DStream by applying 'right outer join' between RDDs of this DStream and other 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 on prefix and suffix: "prefix-TIME_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 on prefix and suffix: "prefix-TIME_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 on prefix and suffix: "prefix-TIME_IN_MS.suffix".
def saveAsNewAPIHadoopFiles[F <: OutputFormat[_, _]](prefix: String, suffix: String, keyClass: Class[_], valueClass: Class[_], outputFormatClass: Class[F], conf: Configuration = dstream.context.sparkContext.hadoopConfiguration): 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 on prefix and suffix: "prefix-TIME_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 on prefix and suffix: "prefix-TIME_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 on prefix and suffix: "prefix-TIME_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 key-value 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 key-value 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 key-value 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 key-value pair will be eliminated.
implicit val vManifest: ClassTag[V]
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): 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

Deprecated Value Members

def finalize(): Unit
Attributes
protected[lang]
Definition Classes
AnyRef
Annotations
@throws(classOf[java.lang.Throwable]) @Deprecated
Deprecated
(Since version 9)

Packages

JavaPairDStream

Companion object JavaPairDStream

class JavaPairDStream[K, V] extends AbstractJavaDStreamLike[(K, V), JavaPairDStream[K, V], JavaPairRDD[K, V]]

Instance Constructors

Value Members

Deprecated Value Members

Inherited from AbstractJavaDStreamLike[(K, V), JavaPairDStream[K, V], JavaPairRDD[K, V]]

Inherited from JavaDStreamLike[(K, V), JavaPairDStream[K, V], JavaPairRDD[K, V]]

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Ungrouped

Packages

JavaPairDStream

Companion object JavaPairDStream

class JavaPairDStream[K, V] extends AbstractJavaDStreamLike[(K, V), JavaPairDStream[K, V], JavaPairRDD[K, V]]

Instance Constructors

Value Members

Deprecated Value Members

Inherited from AbstractJavaDStreamLike[(K, V), JavaPairDStream[K, V], JavaPairRDD[K, V]]

Inherited from JavaDStreamLike[(K, V), JavaPairDStream[K, V], JavaPairRDD[K, V]]

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Ungrouped

JavaPairDStream