sealed abstract class MapWithStateDStream[KeyType, ValueType, StateType, MappedType] extends DStream[MappedType]
DStream representing the stream of data generated by mapWithState operation on a
pair DStream.
Additionally, it also gives access to the stream of state snapshots, that is, the state data of
all keys after a batch has updated them.
- KeyType
Class of the key
- ValueType
Class of the value
- StateType
Class of the state data
- MappedType
Class of the mapped data
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- implicit class LogStringContext extends AnyRef
- Definition Classes
- Logging
Abstract Value Members
- abstract def compute(validTime: Time): Option[RDD[MappedType]]
Method that generates an RDD for the given time
Method that generates an RDD for the given time
- Definition Classes
- DStream
- abstract def dependencies: List[DStream[_]]
List of parent DStreams on which this DStream depends on
List of parent DStreams on which this DStream depends on
- Definition Classes
- DStream
- abstract def slideDuration: Duration
Time interval after which the DStream generates an RDD
Time interval after which the DStream generates an RDD
- Definition Classes
- DStream
- abstract def stateSnapshots(): DStream[(KeyType, StateType)]
Return a pair DStream where each RDD is the snapshot of the state of all the keys.
Concrete Value Members
- final def !=(arg0: Any): Boolean
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- final def ##: Int
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- final def ==(arg0: Any): Boolean
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- def MDC(key: LogKey, value: Any): MDC
- Attributes
- protected
- Definition Classes
- Logging
- final def asInstanceOf[T0]: T0
- Definition Classes
- Any
- val baseScope: Option[String]
The base scope associated with the operation that created this DStream.
The base scope associated with the operation that created this DStream.
This is the medium through which we pass the DStream operation name (e.g. updatedStateByKey) to the RDDs created by this DStream. Note that we never use this scope directly in RDDs. Instead, we instantiate a new scope during each call to
computebased on this one.This is not defined if the DStream is created outside of one of the public DStream operations.
- def cache(): DStream[MappedType]
Persist RDDs of this DStream with the default storage level (MEMORY_ONLY_SER)
Persist RDDs of this DStream with the default storage level (MEMORY_ONLY_SER)
- Definition Classes
- DStream
- def checkpoint(interval: Duration): DStream[MappedType]
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
- DStream
- def clone(): AnyRef
- Attributes
- protected[lang]
- Definition Classes
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- Annotations
- @throws(classOf[java.lang.CloneNotSupportedException]) @IntrinsicCandidate() @native()
- def context: StreamingContext
Return the StreamingContext associated with this DStream
Return the StreamingContext associated with this DStream
- Definition Classes
- DStream
- def count(): DStream[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
- DStream
- def countByValue(numPartitions: Int = ssc.sc.defaultParallelism)(implicit ord: Ordering[MappedType] = null): DStream[(MappedType, 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 (Spark's default number of partitions ifnumPartitionsnot specified).- Definition Classes
- DStream
- def countByValueAndWindow(windowDuration: Duration, slideDuration: Duration, numPartitions: Int = ssc.sc.defaultParallelism)(implicit ord: Ordering[MappedType] = null): DStream[(MappedType, 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 (Spark's default number of partitions ifnumPartitionsnot specified).- 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
- DStream
- def countByWindow(windowDuration: Duration, slideDuration: Duration): DStream[Long]
Return a new DStream in which each RDD has a single element generated by counting the number of elements in a sliding 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 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
- DStream
- def createRDDWithLocalProperties[U](time: Time, displayInnerRDDOps: Boolean)(body: => U): U
Wrap a body of code such that the call site and operation scope information are passed to the RDDs created in this body properly.
Wrap a body of code such that the call site and operation scope information are passed to the RDDs created in this body properly.
- time
Current batch time that should be embedded in the scope names
- displayInnerRDDOps
Whether the detailed callsites and scopes of the inner RDDs generated by
bodywill be displayed in the UI; only the scope and callsite of the DStream operation that generatedthiswill be displayed.- body
RDD creation code to execute with certain local properties.
- final def eq(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
- def equals(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef → Any
- def filter(filterFunc: (MappedType) => Boolean): DStream[MappedType]
Return a new DStream containing only the elements that satisfy a predicate.
Return a new DStream containing only the elements that satisfy a predicate.
- Definition Classes
- DStream
- def flatMap[U](flatMapFunc: (MappedType) => IterableOnce[U])(implicit arg0: ClassTag[U]): DStream[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
- DStream
- def foreachRDD(foreachFunc: (RDD[MappedType], Time) => Unit): 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
- DStream
- def foreachRDD(foreachFunc: (RDD[MappedType]) => Unit): 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
- DStream
- final def getClass(): Class[_ <: AnyRef]
- Definition Classes
- AnyRef → Any
- Annotations
- @IntrinsicCandidate() @native()
- def glom(): DStream[Array[MappedType]]
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
- DStream
- def hashCode(): Int
- Definition Classes
- AnyRef → Any
- Annotations
- @IntrinsicCandidate() @native()
- def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
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- def initializeLogIfNecessary(isInterpreter: Boolean): Unit
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- final def isInstanceOf[T0]: Boolean
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- def isTraceEnabled(): Boolean
- Attributes
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- def log: Logger
- Attributes
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- def logBasedOnLevel(level: Level)(f: => MessageWithContext): Unit
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- def logDebug(msg: => String, throwable: Throwable): Unit
- Attributes
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- def logDebug(entry: LogEntry, throwable: Throwable): Unit
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- def logDebug(entry: LogEntry): Unit
- Attributes
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- def logDebug(msg: => String): Unit
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- def logError(msg: => String, throwable: Throwable): Unit
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- def logError(entry: LogEntry, throwable: Throwable): Unit
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- def logError(entry: LogEntry): Unit
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- def logError(msg: => String): Unit
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- def logInfo(msg: => String, throwable: Throwable): Unit
- Attributes
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- def logInfo(entry: LogEntry, throwable: Throwable): Unit
- Attributes
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- Definition Classes
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- def logInfo(entry: LogEntry): Unit
- Attributes
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- Definition Classes
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- def logInfo(msg: => String): Unit
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- def logName: String
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- def logTrace(msg: => String, throwable: Throwable): Unit
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- def logTrace(entry: LogEntry, throwable: Throwable): Unit
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- def logTrace(entry: LogEntry): Unit
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- def logTrace(msg: => String): Unit
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- def logWarning(msg: => String, throwable: Throwable): Unit
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- def logWarning(entry: LogEntry, throwable: Throwable): Unit
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- def logWarning(entry: LogEntry): Unit
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- def logWarning(msg: => String): Unit
- Attributes
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- Definition Classes
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- def map[U](mapFunc: (MappedType) => U)(implicit arg0: ClassTag[U]): DStream[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
- DStream
- def mapPartitions[U](mapPartFunc: (Iterator[MappedType]) => Iterator[U], preservePartitioning: Boolean = false)(implicit arg0: ClassTag[U]): DStream[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
- DStream
- 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(): DStream[MappedType]
Persist RDDs of this DStream with the default storage level (MEMORY_ONLY_SER)
Persist RDDs of this DStream with the default storage level (MEMORY_ONLY_SER)
- Definition Classes
- DStream
- def persist(level: StorageLevel): DStream[MappedType]
Persist the RDDs of this DStream with the given storage level
Persist the RDDs of this DStream with the given storage level
- Definition Classes
- DStream
- 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
- DStream
- 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
- DStream
- def reduce(reduceFunc: (MappedType, MappedType) => MappedType): DStream[MappedType]
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
- DStream
- def reduceByWindow(reduceFunc: (MappedType, MappedType) => MappedType, invReduceFunc: (MappedType, MappedType) => MappedType, windowDuration: Duration, slideDuration: Duration): DStream[MappedType]
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
- DStream
- def reduceByWindow(reduceFunc: (MappedType, MappedType) => MappedType, windowDuration: Duration, slideDuration: Duration): DStream[MappedType]
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
- DStream
- def repartition(numPartitions: Int): DStream[MappedType]
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.
- Definition Classes
- DStream
- def saveAsObjectFiles(prefix: String, suffix: String = ""): Unit
Save each RDD in this DStream as a Sequence file of serialized objects.
Save each RDD in this DStream as a Sequence file of serialized objects. The file name at each batch interval is generated based on
prefixandsuffix: "prefix-TIME_IN_MS.suffix".- Definition Classes
- DStream
- def saveAsTextFiles(prefix: String, suffix: String = ""): Unit
Save each RDD in this DStream as at text file, using string representation of elements.
Save each RDD in this DStream as at text file, using string representation of elements. The file name at each batch interval is generated based on
prefixandsuffix: "prefix-TIME_IN_MS.suffix".- Definition Classes
- DStream
- def slice(fromTime: Time, toTime: Time): Seq[RDD[MappedType]]
Return all the RDDs between 'fromTime' to 'toTime' (both included)
Return all the RDDs between 'fromTime' to 'toTime' (both included)
- Definition Classes
- DStream
- def slice(interval: Interval): Seq[RDD[MappedType]]
Return all the RDDs defined by the Interval object (both end times included)
Return all the RDDs defined by the Interval object (both end times included)
- Definition Classes
- DStream
- final def synchronized[T0](arg0: => T0): T0
- Definition Classes
- AnyRef
- def toString(): String
- Definition Classes
- AnyRef → Any
- def transform[U](transformFunc: (RDD[MappedType], Time) => RDD[U])(implicit arg0: ClassTag[U]): DStream[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
- DStream
- def transform[U](transformFunc: (RDD[MappedType]) => RDD[U])(implicit arg0: ClassTag[U]): DStream[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
- DStream
- def transformWith[U, V](other: DStream[U], transformFunc: (RDD[MappedType], RDD[U], Time) => RDD[V])(implicit arg0: ClassTag[U], arg1: ClassTag[V]): DStream[V]
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
- DStream
- def transformWith[U, V](other: DStream[U], transformFunc: (RDD[MappedType], RDD[U]) => RDD[V])(implicit arg0: ClassTag[U], arg1: ClassTag[V]): DStream[V]
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
- DStream
- def union(that: DStream[MappedType]): DStream[MappedType]
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 slideDuration as this DStream.
- Definition Classes
- 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): DStream[MappedType]
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
- Definition Classes
- DStream
- def window(windowDuration: Duration): DStream[MappedType]
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.
- Definition Classes
- DStream
- def withLogContext(context: Map[String, String])(body: => Unit): Unit
- Attributes
- protected
- Definition Classes
- Logging
Deprecated Value Members
- def finalize(): Unit
- Attributes
- protected[lang]
- Definition Classes
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- @throws(classOf[java.lang.Throwable]) @Deprecated
- Deprecated
(Since version 9)