Class DStream<T>
Object
org.apache.spark.streaming.dstream.DStream<T>
- All Implemented Interfaces:
Serializable
,org.apache.spark.internal.Logging
- Direct Known Subclasses:
InputDStream
,MapWithStateDStream
public abstract class DStream<T>
extends Object
implements Serializable, org.apache.spark.internal.Logging
A Discretized Stream (DStream), the basic abstraction in Spark Streaming, is a continuous
sequence of RDDs (of the same type) representing a continuous stream of data (see
org.apache.spark.rdd.RDD in the Spark core documentation for more details on RDDs).
DStreams can either be created from live data (such as, data from TCP sockets, Kafka,
etc.) using a
StreamingContext
or it can be generated by
transforming existing DStreams using operations such as map
,
window
and reduceByKeyAndWindow
. While a Spark Streaming program is running, each DStream
periodically generates a RDD, either from live data or by transforming the RDD generated by a
parent DStream.
This class contains the basic operations available on all DStreams, such as map
, filter
and
window
. In addition, PairDStreamFunctions
contains
operations available only on DStreams of key-value pairs, such as groupByKeyAndWindow
and
join
. These operations are automatically available on any DStream of pairs
(e.g., DStream[(Int, Int)] through implicit conversions.
A DStream internally is characterized by a few basic properties: - A list of other DStreams that the DStream depends on - A time interval at which the DStream generates an RDD - A function that is used to generate an RDD after each time interval
- See Also:
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Nested Class Summary
Nested classes/interfaces inherited from interface org.apache.spark.internal.Logging
org.apache.spark.internal.Logging.LogStringContext, org.apache.spark.internal.Logging.SparkShellLoggingFilter
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Constructor Summary
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Method Summary
Modifier and TypeMethodDescriptioncache()
Persist RDDs of this DStream with the default storage level (MEMORY_ONLY_SER)checkpoint
(Duration interval) Enable periodic checkpointing of RDDs of this DStreamMethod that generates an RDD for the given timecontext()
Return the StreamingContext associated with this DStreamcount()
Return a new DStream in which each RDD has a single element generated by counting each RDD of this DStream.countByValue
(int numPartitions, scala.math.Ordering<T> ord) Return a new DStream in which each RDD contains the counts of each distinct value in each RDD of this DStream.countByValueAndWindow
(Duration windowDuration, Duration slideDuration, int numPartitions, scala.math.Ordering<T> ord) Return a new DStream in which each RDD contains the count of distinct elements in RDDs in a sliding window over this DStream.countByWindow
(Duration windowDuration, Duration slideDuration) 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.abstract scala.collection.immutable.List<DStream<?>>
List of parent DStreams on which this DStream depends onReturn a new DStream containing only the elements that satisfy a predicate.<U> DStream<U>
flatMap
(scala.Function1<T, scala.collection.IterableOnce<U>> flatMapFunc, scala.reflect.ClassTag<U> evidence$3) Return a new DStream by applying a function to all elements of this DStream, and then flattening the resultsvoid
foreachRDD
(scala.Function1<RDD<T>, scala.runtime.BoxedUnit> foreachFunc) Apply a function to each RDD in this DStream.void
foreachRDD
(scala.Function2<RDD<T>, Time, scala.runtime.BoxedUnit> foreachFunc) Apply a function to each RDD in this DStream.glom()
Return a new DStream in which each RDD is generated by applying glom() to each RDD of this DStream.<U> DStream<U>
Return a new DStream by applying a function to all elements of this DStream.<U> DStream<U>
mapPartitions
(scala.Function1<scala.collection.Iterator<T>, scala.collection.Iterator<U>> mapPartFunc, boolean preservePartitioning, scala.reflect.ClassTag<U> evidence$4) Return a new DStream in which each RDD is generated by applying mapPartitions() to each RDDs of this DStream.persist()
Persist RDDs of this DStream with the default storage level (MEMORY_ONLY_SER)persist
(StorageLevel level) Persist the RDDs of this DStream with the given storage levelvoid
print()
Print the first ten elements of each RDD generated in this DStream.void
print
(int num) Print the first num elements of each RDD generated in this DStream.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 all elements in a sliding window over this DStream.reduceByWindow
(scala.Function2<T, T, T> reduceFunc, scala.Function2<T, T, T> invReduceFunc, Duration windowDuration, Duration slideDuration) Return a new DStream in which each RDD has a single element generated by reducing all elements in a sliding window over this DStream.repartition
(int numPartitions) Return a new DStream with an increased or decreased level of parallelism.void
saveAsObjectFiles
(String prefix, String suffix) Save each RDD in this DStream as a Sequence file of serialized objects.void
saveAsTextFiles
(String prefix, String suffix) Save each RDD in this DStream as at text file, using string representation of elements.slice
(org.apache.spark.streaming.Interval interval) Return all the RDDs defined by the Interval object (both end times included)Return all the RDDs between 'fromTime' to 'toTime' (both included)abstract Duration
Time interval after which the DStream generates an RDDstatic <K,
V> PairDStreamFunctions<K, V> toPairDStreamFunctions
(DStream<scala.Tuple2<K, V>> stream, scala.reflect.ClassTag<K> kt, scala.reflect.ClassTag<V> vt, scala.math.Ordering<K> ord) <U> DStream<U>
Return a new DStream in which each RDD is generated by applying a function on each RDD of 'this' DStream.<U> DStream<U>
Return a new DStream in which each RDD is generated by applying a function on each RDD of 'this' DStream.<U,
V> DStream<V> transformWith
(DStream<U> other, scala.Function2<RDD<T>, RDD<U>, RDD<V>> transformFunc, scala.reflect.ClassTag<U> evidence$7, scala.reflect.ClassTag<V> evidence$8) Return a new DStream in which each RDD is generated by applying a function on each RDD of 'this' DStream and 'other' DStream.<U,
V> DStream<V> transformWith
(DStream<U> other, scala.Function3<RDD<T>, RDD<U>, Time, RDD<V>> transformFunc, scala.reflect.ClassTag<U> evidence$9, scala.reflect.ClassTag<V> evidence$10) Return a new DStream in which each RDD is generated by applying a function on each RDD of 'this' DStream and 'other' DStream.Return a new DStream by unifying data of another DStream with this DStream.Return a new DStream in which each RDD contains all the elements in seen in a sliding window of time over this DStream.Return a new DStream in which each RDD contains all the elements in seen in a sliding window of time over this DStream.Methods inherited from class java.lang.Object
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
Methods inherited from interface org.apache.spark.internal.Logging
initializeForcefully, initializeLogIfNecessary, initializeLogIfNecessary, initializeLogIfNecessary$default$2, isTraceEnabled, log, logDebug, logDebug, logDebug, logDebug, logError, logError, logError, logError, logInfo, logInfo, logInfo, logInfo, logName, LogStringContext, logTrace, logTrace, logTrace, logTrace, logWarning, logWarning, logWarning, logWarning, org$apache$spark$internal$Logging$$log_, org$apache$spark$internal$Logging$$log__$eq, withLogContext
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Constructor Details
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DStream
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Method Details
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toPairDStreamFunctions
public static <K,V> PairDStreamFunctions<K,V> toPairDStreamFunctions(DStream<scala.Tuple2<K, V>> stream, scala.reflect.ClassTag<K> kt, scala.reflect.ClassTag<V> vt, scala.math.Ordering<K> ord) -
slideDuration
Time interval after which the DStream generates an RDD -
dependencies
List of parent DStreams on which this DStream depends on -
compute
Method that generates an RDD for the given time -
context
Return the StreamingContext associated with this DStream -
persist
Persist the RDDs of this DStream with the given storage level -
persist
Persist RDDs of this DStream with the default storage level (MEMORY_ONLY_SER) -
cache
Persist RDDs of this DStream with the default storage level (MEMORY_ONLY_SER) -
checkpoint
Enable periodic checkpointing of RDDs of this DStream- Parameters:
interval
- Time interval after which generated RDD will be checkpointed- Returns:
- (undocumented)
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map
Return a new DStream by applying a function to all elements of this DStream. -
flatMap
public <U> DStream<U> flatMap(scala.Function1<T, scala.collection.IterableOnce<U>> flatMapFunc, scala.reflect.ClassTag<U> evidence$3) Return a new DStream by applying a function to all elements of this DStream, and then flattening the results- Parameters:
flatMapFunc
- (undocumented)evidence$3
- (undocumented)- Returns:
- (undocumented)
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filter
Return a new DStream containing only the elements that satisfy a predicate. -
glom
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.- Returns:
- (undocumented)
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repartition
Return a new DStream with an increased or decreased level of parallelism. Each RDD in the returned DStream has exactly numPartitions partitions.- Parameters:
numPartitions
- (undocumented)- Returns:
- (undocumented)
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mapPartitions
public <U> DStream<U> mapPartitions(scala.Function1<scala.collection.Iterator<T>, scala.collection.Iterator<U>> mapPartFunc, boolean preservePartitioning, scala.reflect.ClassTag<U> evidence$4) 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.- Parameters:
mapPartFunc
- (undocumented)preservePartitioning
- (undocumented)evidence$4
- (undocumented)- Returns:
- (undocumented)
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reduce
Return a new DStream in which each RDD has a single element generated by reducing each RDD of this DStream.- Parameters:
reduceFunc
- (undocumented)- Returns:
- (undocumented)
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count
Return a new DStream in which each RDD has a single element generated by counting each RDD of this DStream.- Returns:
- (undocumented)
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countByValue
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 withnumPartitions
partitions (Spark's default number of partitions ifnumPartitions
not specified).- Parameters:
numPartitions
- (undocumented)ord
- (undocumented)- Returns:
- (undocumented)
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foreachRDD
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.- Parameters:
foreachFunc
- (undocumented)
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foreachRDD
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.- Parameters:
foreachFunc
- (undocumented)
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transform
public <U> DStream<U> transform(scala.Function1<RDD<T>, RDD<U>> transformFunc, scala.reflect.ClassTag<U> evidence$5) Return a new DStream in which each RDD is generated by applying a function on each RDD of 'this' DStream.- Parameters:
transformFunc
- (undocumented)evidence$5
- (undocumented)- Returns:
- (undocumented)
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transform
public <U> DStream<U> transform(scala.Function2<RDD<T>, Time, RDD<U>> transformFunc, scala.reflect.ClassTag<U> evidence$6) Return a new DStream in which each RDD is generated by applying a function on each RDD of 'this' DStream.- Parameters:
transformFunc
- (undocumented)evidence$6
- (undocumented)- Returns:
- (undocumented)
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transformWith
public <U,V> DStream<V> transformWith(DStream<U> other, scala.Function2<RDD<T>, RDD<U>, RDD<V>> transformFunc, scala.reflect.ClassTag<U> evidence$7, scala.reflect.ClassTag<V> evidence$8) Return a new DStream in which each RDD is generated by applying a function on each RDD of 'this' DStream and 'other' DStream.- Parameters:
other
- (undocumented)transformFunc
- (undocumented)evidence$7
- (undocumented)evidence$8
- (undocumented)- Returns:
- (undocumented)
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transformWith
public <U,V> DStream<V> transformWith(DStream<U> other, scala.Function3<RDD<T>, RDD<U>, Time, RDD<V>> transformFunc, scala.reflect.ClassTag<U> evidence$9, scala.reflect.ClassTag<V> evidence$10) Return a new DStream in which each RDD is generated by applying a function on each RDD of 'this' DStream and 'other' DStream.- Parameters:
other
- (undocumented)transformFunc
- (undocumented)evidence$9
- (undocumented)evidence$10
- (undocumented)- Returns:
- (undocumented)
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print
public void print()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. -
print
public void print(int num) 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.- Parameters:
num
- (undocumented)
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window
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.- Parameters:
windowDuration
- width of the window; must be a multiple of this DStream's interval.- Returns:
- (undocumented)
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window
Return a new DStream in which each RDD contains all the elements in seen in a sliding window of time over this DStream.- Parameters:
windowDuration
- width of the window; must be a multiple of this DStream's batching intervalslideDuration
- 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- Returns:
- (undocumented)
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reduceByWindow
public DStream<T> reduceByWindow(scala.Function2<T, T, T> reduceFunc, Duration windowDuration, Duration slideDuration) Return a new DStream in which each RDD has a single element generated by reducing all elements in a sliding window over this DStream.- Parameters:
reduceFunc
- associative and commutative reduce functionwindowDuration
- width of the window; must be a multiple of this DStream's batching intervalslideDuration
- 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- Returns:
- (undocumented)
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reduceByWindow
public DStream<T> reduceByWindow(scala.Function2<T, T, T> reduceFunc, scala.Function2<T, T, T> invReduceFunc, Duration windowDuration, Duration slideDuration) 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".- Parameters:
reduceFunc
- associative and commutative reduce functioninvReduceFunc
- 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 intervalslideDuration
- 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- Returns:
- (undocumented)
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countByWindow
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.- Parameters:
windowDuration
- width of the window; must be a multiple of this DStream's batching intervalslideDuration
- 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- Returns:
- (undocumented)
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countByValueAndWindow
public DStream<scala.Tuple2<T,Object>> countByValueAndWindow(Duration windowDuration, Duration slideDuration, int numPartitions, scala.math.Ordering<T> ord) 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 withnumPartitions
partitions (Spark's default number of partitions ifnumPartitions
not specified).- Parameters:
windowDuration
- width of the window; must be a multiple of this DStream's batching intervalslideDuration
- 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 intervalnumPartitions
- number of partitions of each RDD in the new DStream.ord
- (undocumented)- Returns:
- (undocumented)
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union
Return a new DStream by unifying data of another DStream with this DStream.- Parameters:
that
- Another DStream having the same slideDuration as this DStream.- Returns:
- (undocumented)
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slice
Return all the RDDs defined by the Interval object (both end times included)- Parameters:
interval
- (undocumented)- Returns:
- (undocumented)
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slice
Return all the RDDs between 'fromTime' to 'toTime' (both included)- Parameters:
fromTime
- (undocumented)toTime
- (undocumented)- Returns:
- (undocumented)
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saveAsObjectFiles
Save each RDD in this DStream as a Sequence file of serialized objects. The file name at each batch interval is generated based onprefix
andsuffix
: "prefix-TIME_IN_MS.suffix".- Parameters:
prefix
- (undocumented)suffix
- (undocumented)
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saveAsTextFiles
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 onprefix
andsuffix
: "prefix-TIME_IN_MS.suffix".- Parameters:
prefix
- (undocumented)suffix
- (undocumented)
-