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Object org.apache.spark.streaming.dstream.DStream<T>
public abstract class DStream<T>
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, Flume,
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.
DStreams 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
Constructor Summary | |
---|---|
DStream(StreamingContext ssc,
scala.reflect.ClassTag<T> evidence$1)
|
Method Summary | ||
---|---|---|
DStream<T> |
cache()
Persist RDDs of this DStream with the default storage level (MEMORY_ONLY_SER) |
|
DStream<T> |
checkpoint(Duration interval)
Enable periodic checkpointing of RDDs of this DStream |
|
org.apache.spark.streaming.dstream.DStreamCheckpointData<T> |
checkpointData()
|
|
Duration |
checkpointDuration()
|
|
abstract scala.Option<RDD<T>> |
compute(Time validTime)
Method that generates a RDD for the given time |
|
StreamingContext |
context()
Return the StreamingContext associated with this DStream |
|
DStream<Object> |
count()
Return a new DStream in which each RDD has a single element generated by counting each RDD of this DStream. |
|
DStream<scala.Tuple2<T,Object>> |
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. |
|
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. |
|
DStream<Object> |
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. |
|
org.apache.spark.util.CallSite |
creationSite()
|
|
abstract scala.collection.immutable.List<DStream<?>> |
dependencies()
List of parent DStreams on which this DStream depends on |
|
DStream<T> |
filter(scala.Function1<T,Object> filterFunc)
Return a new DStream containing only the elements that satisfy a predicate. |
|
|
flatMap(scala.Function1<T,scala.collection.Traversable<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 |
|
void |
foreach(scala.Function1<RDD<T>,scala.runtime.BoxedUnit> foreachFunc)
Deprecated. As of 0.9.0, replaced by foreachRDD . |
|
void |
foreach(scala.Function2<RDD<T>,Time,scala.runtime.BoxedUnit> foreachFunc)
Deprecated. As of 0.9.0, replaced by foreachRDD . |
|
void |
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. |
|
scala.collection.mutable.HashMap<Time,RDD<T>> |
generatedRDDs()
|
|
DStream<Object> |
glom()
Return a new DStream in which each RDD is generated by applying glom() to each RDD of this DStream. |
|
org.apache.spark.streaming.DStreamGraph |
graph()
|
|
|
map(scala.Function1<T,U> mapFunc,
scala.reflect.ClassTag<U> evidence$2)
Return a new DStream by applying a function to all elements of this DStream. |
|
|
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. |
|
boolean |
mustCheckpoint()
|
|
DStream<T> |
persist()
Persist RDDs of this DStream with the default storage level (MEMORY_ONLY_SER) |
|
DStream<T> |
persist(StorageLevel level)
Persist the RDDs of this DStream with the given storage level |
|
void |
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. |
|
DStream<T> |
reduce(scala.Function2<T,T,T> reduceFunc)
Return a new DStream in which each RDD has a single element generated by reducing each RDD of this DStream. |
|
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. |
|
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. |
|
Duration |
rememberDuration()
|
|
DStream<T> |
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. |
|
scala.collection.Seq<RDD<T>> |
slice(org.apache.spark.streaming.Interval interval)
Return all the RDDs defined by the Interval object (both end times included) |
|
scala.collection.Seq<RDD<T>> |
slice(Time fromTime,
Time toTime)
Return all the RDDs between 'fromTime' to 'toTime' (both included) |
|
abstract Duration |
slideDuration()
Time interval after which the DStream generates a RDD |
|
StreamingContext |
ssc()
|
|
StorageLevel |
storageLevel()
|
|
static
|
toPairDStreamFunctions(DStream<scala.Tuple2<K,V>> stream,
scala.reflect.ClassTag<K> kt,
scala.reflect.ClassTag<V> vt,
scala.math.Ordering<K> ord)
|
|
|
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. |
|
|
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. |
|
|
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. |
|
|
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. |
|
DStream<T> |
union(DStream<T> that)
Return a new DStream by unifying data of another DStream with this DStream. |
|
DStream<T> |
window(Duration windowDuration)
Return a new DStream in which each RDD contains all the elements in seen in a sliding window of time over this DStream. |
|
DStream<T> |
window(Duration windowDuration,
Duration slideDuration)
Return a new DStream in which each RDD contains all the elements in seen in a sliding window of time over this DStream. |
|
Time |
zeroTime()
|
Methods inherited from class Object |
---|
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
Methods inherited from interface org.apache.spark.Logging |
---|
initializeIfNecessary, initializeLogging, isTraceEnabled, log_, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning |
Constructor Detail |
---|
public DStream(StreamingContext ssc, scala.reflect.ClassTag<T> evidence$1)
Method Detail |
---|
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)
public StreamingContext ssc()
public abstract Duration slideDuration()
public abstract scala.collection.immutable.List<DStream<?>> dependencies()
public abstract scala.Option<RDD<T>> compute(Time validTime)
public scala.collection.mutable.HashMap<Time,RDD<T>> generatedRDDs()
public Time zeroTime()
public Duration rememberDuration()
public StorageLevel storageLevel()
public boolean mustCheckpoint()
public Duration checkpointDuration()
public org.apache.spark.streaming.dstream.DStreamCheckpointData<T> checkpointData()
public org.apache.spark.streaming.DStreamGraph graph()
public StreamingContext context()
public org.apache.spark.util.CallSite creationSite()
public DStream<T> persist(StorageLevel level)
public DStream<T> persist()
public DStream<T> cache()
public DStream<T> checkpoint(Duration interval)
interval
- Time interval after which generated RDD will be checkpointed
public <U> DStream<U> map(scala.Function1<T,U> mapFunc, scala.reflect.ClassTag<U> evidence$2)
public <U> DStream<U> flatMap(scala.Function1<T,scala.collection.Traversable<U>> flatMapFunc, scala.reflect.ClassTag<U> evidence$3)
flatMapFunc
- (undocumented)evidence$3
- (undocumented)
public DStream<T> filter(scala.Function1<T,Object> filterFunc)
public DStream<Object> glom()
public DStream<T> repartition(int numPartitions)
numPartitions
- (undocumented)
public <U> DStream<U> mapPartitions(scala.Function1<scala.collection.Iterator<T>,scala.collection.Iterator<U>> mapPartFunc, boolean preservePartitioning, scala.reflect.ClassTag<U> evidence$4)
mapPartFunc
- (undocumented)preservePartitioning
- (undocumented)evidence$4
- (undocumented)
public DStream<T> reduce(scala.Function2<T,T,T> reduceFunc)
reduceFunc
- (undocumented)
public DStream<Object> count()
public DStream<scala.Tuple2<T,Object>> countByValue(int numPartitions, scala.math.Ordering<T> ord)
numPartitions
partitions (Spark's default number of partitions if
numPartitions
not specified).
numPartitions
- (undocumented)ord
- (undocumented)
public void foreach(scala.Function1<RDD<T>,scala.runtime.BoxedUnit> foreachFunc)
foreachRDD
.
foreachFunc
- (undocumented)public void foreach(scala.Function2<RDD<T>,Time,scala.runtime.BoxedUnit> foreachFunc)
foreachRDD
.
foreachFunc
- (undocumented)public void foreachRDD(scala.Function1<RDD<T>,scala.runtime.BoxedUnit> foreachFunc)
foreachFunc
- (undocumented)public void foreachRDD(scala.Function2<RDD<T>,Time,scala.runtime.BoxedUnit> foreachFunc)
foreachFunc
- (undocumented)public <U> DStream<U> transform(scala.Function1<RDD<T>,RDD<U>> transformFunc, scala.reflect.ClassTag<U> evidence$5)
transformFunc
- (undocumented)evidence$5
- (undocumented)
public <U> DStream<U> transform(scala.Function2<RDD<T>,Time,RDD<U>> transformFunc, scala.reflect.ClassTag<U> evidence$6)
transformFunc
- (undocumented)evidence$6
- (undocumented)
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)
other
- (undocumented)transformFunc
- (undocumented)evidence$7
- (undocumented)evidence$8
- (undocumented)
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)
other
- (undocumented)transformFunc
- (undocumented)evidence$9
- (undocumented)evidence$10
- (undocumented)
public void print()
public void print(int num)
num
- (undocumented)public DStream<T> window(Duration windowDuration)
windowDuration
- width of the window; must be a multiple of this DStream's interval.
public DStream<T> window(Duration windowDuration, Duration slideDuration)
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
public DStream<T> reduceByWindow(scala.Function2<T,T,T> reduceFunc, Duration windowDuration, Duration slideDuration)
reduceFunc
- associative 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
public DStream<T> reduceByWindow(scala.Function2<T,T,T> reduceFunc, scala.Function2<T,T,T> invReduceFunc, Duration windowDuration, Duration slideDuration)
reduceFunc
- associative reduce functioninvReduceFunc
- inverse 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
public DStream<Object> countByWindow(Duration windowDuration, Duration slideDuration)
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
public DStream<scala.Tuple2<T,Object>> countByValueAndWindow(Duration windowDuration, Duration slideDuration, int numPartitions, scala.math.Ordering<T> ord)
numPartitions
partitions (Spark's default number of partitions if
numPartitions
not specified).
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)
public DStream<T> union(DStream<T> that)
that
- Another DStream having the same slideDuration as this DStream.
public scala.collection.Seq<RDD<T>> slice(org.apache.spark.streaming.Interval interval)
interval
- (undocumented)
public scala.collection.Seq<RDD<T>> slice(Time fromTime, Time toTime)
fromTime
- (undocumented)toTime
- (undocumented)
public void saveAsObjectFiles(String prefix, String suffix)
prefix
and
suffix
: "prefix-TIME_IN_MS.suffix".
prefix
- (undocumented)suffix
- (undocumented)public void saveAsTextFiles(String prefix, String suffix)
prefix
and suffix
: "prefix-TIME_IN_MS.suffix".
prefix
- (undocumented)suffix
- (undocumented)
|
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