org.apache.spark.streaming.dstream

Class PairDStreamFunctions<K,V>

Object

org.apache.spark.streaming.dstream.PairDStreamFunctions<K,V>

All Implemented Interfaces:

java.io.Serializable

public class PairDStreamFunctions<K,V>
extends Object
implements scala.Serializable

Extra functions available on DStream of (key, value) pairs through an implicit conversion. Import org.apache.spark.streaming.StreamingContext._ at the top of your program to use these functions.

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
PairDStreamFunctions(DStream<scala.Tuple2<K,V>> self, scala.reflect.ClassTag<K> kt, scala.reflect.ClassTag<V> vt, scala.math.Ordering<K> ord)

Method Summary

Methods

Modifier and Type	Method and Description
<W> DStream<scala.Tuple2<K,scala.Tuple2<scala.collection.Iterable<V>,scala.collection.Iterable<W>>>>	cogroup(DStream<scala.Tuple2<K,W>> other, scala.reflect.ClassTag<W> evidence$8) Return a new DStream by applying 'cogroup' between RDDs of this DStream and other DStream.
<W> DStream<scala.Tuple2<K,scala.Tuple2<scala.collection.Iterable<V>,scala.collection.Iterable<W>>>>	cogroup(DStream<scala.Tuple2<K,W>> other, int numPartitions, scala.reflect.ClassTag<W> evidence$9) Return a new DStream by applying 'cogroup' between RDDs of this DStream and other DStream.
<W> DStream<scala.Tuple2<K,scala.Tuple2<scala.collection.Iterable<V>,scala.collection.Iterable<W>>>>	cogroup(DStream<scala.Tuple2<K,W>> other, Partitioner partitioner, scala.reflect.ClassTag<W> evidence$10) Return a new DStream by applying 'cogroup' between RDDs of this DStream and other DStream.
<C> DStream<scala.Tuple2<K,C>>	combineByKey(scala.Function1<V,C> createCombiner, scala.Function2<C,V,C> mergeValue, scala.Function2<C,C,C> mergeCombiner, Partitioner partitioner, boolean mapSideCombine, scala.reflect.ClassTag<C> evidence$1) Combine elements of each key in DStream's RDDs using custom functions.
<U> DStream<scala.Tuple2<K,U>>	flatMapValues(scala.Function1<V,scala.collection.TraversableOnce<U>> flatMapValuesFunc, scala.reflect.ClassTag<U> evidence$7) Return a new DStream by applying a flatmap function to the value of each key-value pairs in 'this' DStream without changing the key.
DStream<scala.Tuple2<K,scala.collection.Iterable<V>>>	groupByKey() Return a new DStream by applying groupByKey to each RDD.
DStream<scala.Tuple2<K,scala.collection.Iterable<V>>>	groupByKey(int numPartitions) Return a new DStream by applying groupByKey to each RDD.
DStream<scala.Tuple2<K,scala.collection.Iterable<V>>>	groupByKey(Partitioner partitioner) Return a new DStream by applying groupByKey on each RDD.
DStream<scala.Tuple2<K,scala.collection.Iterable<V>>>	groupByKeyAndWindow(Duration windowDuration) Return a new DStream by applying groupByKey over a sliding window.
DStream<scala.Tuple2<K,scala.collection.Iterable<V>>>	groupByKeyAndWindow(Duration windowDuration, Duration slideDuration) Return a new DStream by applying groupByKey over a sliding window.
DStream<scala.Tuple2<K,scala.collection.Iterable<V>>>	groupByKeyAndWindow(Duration windowDuration, Duration slideDuration, int numPartitions) Return a new DStream by applying groupByKey over a sliding window on this DStream.
DStream<scala.Tuple2<K,scala.collection.Iterable<V>>>	groupByKeyAndWindow(Duration windowDuration, Duration slideDuration, Partitioner partitioner) Create a new DStream by applying groupByKey over a sliding window on this DStream.
<W> DStream<scala.Tuple2<K,scala.Tuple2<V,W>>>	join(DStream<scala.Tuple2<K,W>> other, scala.reflect.ClassTag<W> evidence$11) Return a new DStream by applying 'join' between RDDs of this DStream and other DStream.
<W> DStream<scala.Tuple2<K,scala.Tuple2<V,W>>>	join(DStream<scala.Tuple2<K,W>> other, int numPartitions, scala.reflect.ClassTag<W> evidence$12) Return a new DStream by applying 'join' between RDDs of this DStream and other DStream.
<W> DStream<scala.Tuple2<K,scala.Tuple2<V,W>>>	join(DStream<scala.Tuple2<K,W>> other, Partitioner partitioner, scala.reflect.ClassTag<W> evidence$13) Return a new DStream by applying 'join' between RDDs of this DStream and other DStream.
<W> DStream<scala.Tuple2<K,scala.Tuple2<V,scala.Option<W>>>>	leftOuterJoin(DStream<scala.Tuple2<K,W>> other, scala.reflect.ClassTag<W> evidence$14) Return a new DStream by applying 'left outer join' between RDDs of this DStream and other DStream.
<W> DStream<scala.Tuple2<K,scala.Tuple2<V,scala.Option<W>>>>	leftOuterJoin(DStream<scala.Tuple2<K,W>> other, int numPartitions, scala.reflect.ClassTag<W> evidence$15) Return a new DStream by applying 'left outer join' between RDDs of this DStream and other DStream.
<W> DStream<scala.Tuple2<K,scala.Tuple2<V,scala.Option<W>>>>	leftOuterJoin(DStream<scala.Tuple2<K,W>> other, Partitioner partitioner, scala.reflect.ClassTag<W> evidence$16) Return a new DStream by applying 'left outer join' between RDDs of this DStream and other DStream.
<U> DStream<scala.Tuple2<K,U>>	mapValues(scala.Function1<V,U> mapValuesFunc, scala.reflect.ClassTag<U> evidence$6) Return a new DStream by applying a map function to the value of each key-value pairs in 'this' DStream without changing the key.
DStream<scala.Tuple2<K,V>>	reduceByKey(scala.Function2<V,V,V> reduceFunc) Return a new DStream by applying reduceByKey to each RDD.
DStream<scala.Tuple2<K,V>>	reduceByKey(scala.Function2<V,V,V> reduceFunc, int numPartitions) Return a new DStream by applying reduceByKey to each RDD.
DStream<scala.Tuple2<K,V>>	reduceByKey(scala.Function2<V,V,V> reduceFunc, Partitioner partitioner) Return a new DStream by applying reduceByKey to each RDD.
DStream<scala.Tuple2<K,V>>	reduceByKeyAndWindow(scala.Function2<V,V,V> reduceFunc, Duration windowDuration) Return a new DStream by applying reduceByKey over a sliding window on this DStream.
DStream<scala.Tuple2<K,V>>	reduceByKeyAndWindow(scala.Function2<V,V,V> reduceFunc, Duration windowDuration, Duration slideDuration) Return a new DStream by applying reduceByKey over a sliding window.
DStream<scala.Tuple2<K,V>>	reduceByKeyAndWindow(scala.Function2<V,V,V> reduceFunc, Duration windowDuration, Duration slideDuration, int numPartitions) Return a new DStream by applying reduceByKey over a sliding window.
DStream<scala.Tuple2<K,V>>	reduceByKeyAndWindow(scala.Function2<V,V,V> reduceFunc, Duration windowDuration, Duration slideDuration, Partitioner partitioner) Return a new DStream by applying reduceByKey over a sliding window.
DStream<scala.Tuple2<K,V>>	reduceByKeyAndWindow(scala.Function2<V,V,V> reduceFunc, scala.Function2<V,V,V> invReduceFunc, Duration windowDuration, Duration slideDuration, int numPartitions, scala.Function1<scala.Tuple2<K,V>,Object> filterFunc) Return a new DStream by applying incremental reduceByKey over a sliding window.
DStream<scala.Tuple2<K,V>>	reduceByKeyAndWindow(scala.Function2<V,V,V> reduceFunc, scala.Function2<V,V,V> invReduceFunc, Duration windowDuration, Duration slideDuration, Partitioner partitioner, scala.Function1<scala.Tuple2<K,V>,Object> filterFunc) Return a new DStream by applying incremental reduceByKey over a sliding window.
<W> DStream<scala.Tuple2<K,scala.Tuple2<scala.Option<V>,W>>>	rightOuterJoin(DStream<scala.Tuple2<K,W>> other, scala.reflect.ClassTag<W> evidence$17) Return a new DStream by applying 'right outer join' between RDDs of this DStream and other DStream.
<W> DStream<scala.Tuple2<K,scala.Tuple2<scala.Option<V>,W>>>	rightOuterJoin(DStream<scala.Tuple2<K,W>> other, int numPartitions, scala.reflect.ClassTag<W> evidence$18) Return a new DStream by applying 'right outer join' between RDDs of this DStream and other DStream.
<W> DStream<scala.Tuple2<K,scala.Tuple2<scala.Option<V>,W>>>	rightOuterJoin(DStream<scala.Tuple2<K,W>> other, Partitioner partitioner, scala.reflect.ClassTag<W> evidence$19) Return a new DStream by applying 'right outer join' between RDDs of this DStream and other DStream.
void	saveAsHadoopFiles(String prefix, String suffix, Class<?> keyClass, Class<?> valueClass, Class<? extends org.apache.hadoop.mapred.OutputFormat<?,?>> outputFormatClass, org.apache.hadoop.mapred.JobConf conf) Save each RDD in this DStream as a Hadoop file.
<F extends org.apache.hadoop.mapred.OutputFormat<K,V>> void	saveAsHadoopFiles(String prefix, String suffix, scala.reflect.ClassTag<F> fm) Save each RDD in this DStream as a Hadoop file.
void	saveAsNewAPIHadoopFiles(String prefix, String suffix, Class<?> keyClass, Class<?> valueClass, Class<? extends org.apache.hadoop.mapreduce.OutputFormat<?,?>> outputFormatClass, org.apache.hadoop.conf.Configuration conf) Save each RDD in this DStream as a Hadoop file.
<F extends org.apache.hadoop.mapreduce.OutputFormat<K,V>> void	saveAsNewAPIHadoopFiles(String prefix, String suffix, scala.reflect.ClassTag<F> fm) Save each RDD in this DStream as a Hadoop file.
<S> DStream<scala.Tuple2<K,S>>	updateStateByKey(scala.Function1<scala.collection.Iterator<scala.Tuple3<K,scala.collection.Seq<V>,scala.Option<S>>>,scala.collection.Iterator<scala.Tuple2<K,S>>> updateFunc, Partitioner partitioner, boolean rememberPartitioner, scala.reflect.ClassTag<S> evidence$5) 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.
<S> DStream<scala.Tuple2<K,S>>	updateStateByKey(scala.Function2<scala.collection.Seq<V>,scala.Option<S>,scala.Option<S>> updateFunc, scala.reflect.ClassTag<S> evidence$2) 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.
<S> DStream<scala.Tuple2<K,S>>	updateStateByKey(scala.Function2<scala.collection.Seq<V>,scala.Option<S>,scala.Option<S>> updateFunc, int numPartitions, scala.reflect.ClassTag<S> evidence$3) 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.
<S> DStream<scala.Tuple2<K,S>>	updateStateByKey(scala.Function2<scala.collection.Seq<V>,scala.Option<S>,scala.Option<S>> updateFunc, Partitioner partitioner, scala.reflect.ClassTag<S> evidence$4) 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.

Methods inherited from class Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

PairDStreamFunctions

public PairDStreamFunctions(DStream<scala.Tuple2<K,V>> self,
                    scala.reflect.ClassTag<K> kt,
                    scala.reflect.ClassTag<V> vt,
                    scala.math.Ordering<K> ord)

Method Detail

groupByKey

public DStream<scala.Tuple2<K,scala.collection.Iterable<V>>> groupByKey()

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.

groupByKey

public DStream<scala.Tuple2<K,scala.collection.Iterable<V>>> groupByKey(int numPartitions)

Return a new DStream by applying groupByKey to each RDD. Hash partitioning is used to generate the RDDs with numPartitions partitions.

groupByKey

public DStream<scala.Tuple2<K,scala.collection.Iterable<V>>> groupByKey(Partitioner partitioner)

Return a new DStream by applying groupByKey on each RDD. The supplied org.apache.spark.Partitioner is used to control the partitioning of each RDD.

reduceByKey

public DStream<scala.Tuple2<K,V>> reduceByKey(scala.Function2<V,V,V> reduceFunc)

Return a new DStream by applying reduceByKey to each RDD. The values for each key are merged using the associative reduce function. Hash partitioning is used to generate the RDDs with Spark's default number of partitions.

reduceByKey

public DStream<scala.Tuple2<K,V>> reduceByKey(scala.Function2<V,V,V> reduceFunc,
                                     int numPartitions)

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.

reduceByKey

public DStream<scala.Tuple2<K,V>> reduceByKey(scala.Function2<V,V,V> reduceFunc,
                                     Partitioner partitioner)

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.

combineByKey

public <C> DStream<scala.Tuple2<K,C>> combineByKey(scala.Function1<V,C> createCombiner,
                                          scala.Function2<C,V,C> mergeValue,
                                          scala.Function2<C,C,C> mergeCombiner,
                                          Partitioner partitioner,
                                          boolean mapSideCombine,
                                          scala.reflect.ClassTag<C> evidence$1)

Combine elements of each key in DStream's RDDs using custom functions. This is similar to the combineByKey for RDDs. Please refer to combineByKey in org.apache.spark.rdd.PairRDDFunctions in the Spark core documentation for more information.

groupByKeyAndWindow

public DStream<scala.Tuple2<K,scala.collection.Iterable<V>>> groupByKeyAndWindow(Duration windowDuration)

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.

Parameters:

windowDuration - width of the window; must be a multiple of this DStream's batching interval

groupByKeyAndWindow

public DStream<scala.Tuple2<K,scala.collection.Iterable<V>>> groupByKeyAndWindow(Duration windowDuration,
                                                                        Duration slideDuration)

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.

Parameters:

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

groupByKeyAndWindow

public DStream<scala.Tuple2<K,scala.collection.Iterable<V>>> groupByKeyAndWindow(Duration windowDuration,
                                                                        Duration slideDuration,
                                                                        int numPartitions)

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.

Parameters:

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; if not specified then Spark's default number of partitions will be used

groupByKeyAndWindow

public DStream<scala.Tuple2<K,scala.collection.Iterable<V>>> groupByKeyAndWindow(Duration windowDuration,
                                                                        Duration slideDuration,
                                                                        Partitioner partitioner)

Create a new DStream by applying groupByKey over a sliding window on this DStream. Similar to DStream.groupByKey(), but applies it over a sliding window.

Parameters:

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.

reduceByKeyAndWindow

public DStream<scala.Tuple2<K,V>> reduceByKeyAndWindow(scala.Function2<V,V,V> reduceFunc,
                                              Duration windowDuration)

Return 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.

Parameters:

reduceFunc - associative reduce function

windowDuration - width of the window; must be a multiple of this DStream's batching interval

reduceByKeyAndWindow

public DStream<scala.Tuple2<K,V>> reduceByKeyAndWindow(scala.Function2<V,V,V> reduceFunc,
                                              Duration windowDuration,
                                              Duration slideDuration)

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.

Parameters:

reduceFunc - associative 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

reduceByKeyAndWindow

public DStream<scala.Tuple2<K,V>> reduceByKeyAndWindow(scala.Function2<V,V,V> reduceFunc,
                                              Duration windowDuration,
                                              Duration slideDuration,
                                              int numPartitions)

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.

Parameters:

reduceFunc - associative 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.

reduceByKeyAndWindow

public DStream<scala.Tuple2<K,V>> reduceByKeyAndWindow(scala.Function2<V,V,V> reduceFunc,
                                              Duration windowDuration,
                                              Duration slideDuration,
                                              Partitioner partitioner)

Return a new DStream by applying reduceByKey over a sliding window. Similar to DStream.reduceByKey(), but applies it over a sliding window.

Parameters:

reduceFunc - associative 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

partitioner - partitioner for controlling the partitioning of each RDD in the new DStream.

reduceByKeyAndWindow

public DStream<scala.Tuple2<K,V>> reduceByKeyAndWindow(scala.Function2<V,V,V> reduceFunc,
                                              scala.Function2<V,V,V> invReduceFunc,
                                              Duration windowDuration,
                                              Duration slideDuration,
                                              int numPartitions,
                                              scala.Function1<scala.Tuple2<K,V>,Object> filterFunc)

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 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 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.

Parameters:

reduceFunc - associative reduce function

invReduceFunc - inverse 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

filterFunc - Optional function to filter expired key-value pairs; only pairs that satisfy the function are retained

reduceByKeyAndWindow

public DStream<scala.Tuple2<K,V>> reduceByKeyAndWindow(scala.Function2<V,V,V> reduceFunc,
                                              scala.Function2<V,V,V> invReduceFunc,
                                              Duration windowDuration,
                                              Duration slideDuration,
                                              Partitioner partitioner,
                                              scala.Function1<scala.Tuple2<K,V>,Object> filterFunc)

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 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 reduceByKeyAndWindow without "inverse reduce" function. However, it is applicable to only "invertible reduce functions".

Parameters:

reduceFunc - associative reduce function

invReduceFunc - inverse 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

partitioner - partitioner for controlling the partitioning of each RDD in the new DStream.

filterFunc - Optional function to filter expired key-value pairs; only pairs that satisfy the function are retained

updateStateByKey

public <S> DStream<scala.Tuple2<K,S>> updateStateByKey(scala.Function2<scala.collection.Seq<V>,scala.Option<S>,scala.Option<S>> updateFunc,
                                              scala.reflect.ClassTag<S> evidence$2)

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.

Parameters:

updateFunc - State update function. If this function returns None, then corresponding state key-value pair will be eliminated.

updateStateByKey

public <S> DStream<scala.Tuple2<K,S>> updateStateByKey(scala.Function2<scala.collection.Seq<V>,scala.Option<S>,scala.Option<S>> updateFunc,
                                              int numPartitions,
                                              scala.reflect.ClassTag<S> evidence$3)

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.

Parameters:

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.

updateStateByKey

public <S> DStream<scala.Tuple2<K,S>> updateStateByKey(scala.Function2<scala.collection.Seq<V>,scala.Option<S>,scala.Option<S>> updateFunc,
                                              Partitioner partitioner,
                                              scala.reflect.ClassTag<S> evidence$4)

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.

Parameters:

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.

updateStateByKey

public <S> DStream<scala.Tuple2<K,S>> updateStateByKey(scala.Function1<scala.collection.Iterator<scala.Tuple3<K,scala.collection.Seq<V>,scala.Option<S>>>,scala.collection.Iterator<scala.Tuple2<K,S>>> updateFunc,
                                              Partitioner partitioner,
                                              boolean rememberPartitioner,
                                              scala.reflect.ClassTag<S> evidence$5)

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. org.apache.spark.Partitioner is used to control the partitioning of each RDD.

Parameters:

updateFunc - State update function. If this function returns None, then corresponding state key-value pair will be eliminated. Note, that this function may generate a different a tuple with a different key than the input key. It is up to the developer to decide whether to remember the partitioner despite the key being changed.

partitioner - Partitioner for controlling the partitioning of each RDD in the new DStream

rememberPartitioner - Whether to remember the paritioner object in the generated RDDs.

mapValues

public <U> DStream<scala.Tuple2<K,U>> mapValues(scala.Function1<V,U> mapValuesFunc,
                                       scala.reflect.ClassTag<U> evidence$6)

Return a new DStream by applying a map function to the value of each key-value pairs in 'this' DStream without changing the key.

flatMapValues

public <U> DStream<scala.Tuple2<K,U>> flatMapValues(scala.Function1<V,scala.collection.TraversableOnce<U>> flatMapValuesFunc,
                                           scala.reflect.ClassTag<U> evidence$7)

Return a new DStream by applying a flatmap function to the value of each key-value pairs in 'this' DStream without changing the key.

cogroup

public <W> DStream<scala.Tuple2<K,scala.Tuple2<scala.collection.Iterable<V>,scala.collection.Iterable<W>>>> cogroup(DStream<scala.Tuple2<K,W>> other,
                                                                                                           scala.reflect.ClassTag<W> evidence$8)

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.

cogroup

public <W> DStream<scala.Tuple2<K,scala.Tuple2<scala.collection.Iterable<V>,scala.collection.Iterable<W>>>> cogroup(DStream<scala.Tuple2<K,W>> other,
                                                                                                           int numPartitions,
                                                                                                           scala.reflect.ClassTag<W> evidence$9)

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.

cogroup

public <W> DStream<scala.Tuple2<K,scala.Tuple2<scala.collection.Iterable<V>,scala.collection.Iterable<W>>>> cogroup(DStream<scala.Tuple2<K,W>> other,
                                                                                                           Partitioner partitioner,
                                                                                                           scala.reflect.ClassTag<W> evidence$10)

Return a new DStream by applying 'cogroup' between RDDs of this DStream and other DStream. The supplied org.apache.spark.Partitioner is used to partition the generated RDDs.

join

public <W> DStream<scala.Tuple2<K,scala.Tuple2<V,W>>> join(DStream<scala.Tuple2<K,W>> other,
                                                  scala.reflect.ClassTag<W> evidence$11)

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.

join

public <W> DStream<scala.Tuple2<K,scala.Tuple2<V,W>>> join(DStream<scala.Tuple2<K,W>> other,
                                                  int numPartitions,
                                                  scala.reflect.ClassTag<W> evidence$12)

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.

join

public <W> DStream<scala.Tuple2<K,scala.Tuple2<V,W>>> join(DStream<scala.Tuple2<K,W>> other,
                                                  Partitioner partitioner,
                                                  scala.reflect.ClassTag<W> evidence$13)

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.

leftOuterJoin

public <W> DStream<scala.Tuple2<K,scala.Tuple2<V,scala.Option<W>>>> leftOuterJoin(DStream<scala.Tuple2<K,W>> other,
                                                                         scala.reflect.ClassTag<W> evidence$14)

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.

leftOuterJoin

public <W> DStream<scala.Tuple2<K,scala.Tuple2<V,scala.Option<W>>>> leftOuterJoin(DStream<scala.Tuple2<K,W>> other,
                                                                         int numPartitions,
                                                                         scala.reflect.ClassTag<W> evidence$15)

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.

leftOuterJoin

public <W> DStream<scala.Tuple2<K,scala.Tuple2<V,scala.Option<W>>>> leftOuterJoin(DStream<scala.Tuple2<K,W>> other,
                                                                         Partitioner partitioner,
                                                                         scala.reflect.ClassTag<W> evidence$16)

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.

rightOuterJoin

public <W> DStream<scala.Tuple2<K,scala.Tuple2<scala.Option<V>,W>>> rightOuterJoin(DStream<scala.Tuple2<K,W>> other,
                                                                          scala.reflect.ClassTag<W> evidence$17)

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.

rightOuterJoin

public <W> DStream<scala.Tuple2<K,scala.Tuple2<scala.Option<V>,W>>> rightOuterJoin(DStream<scala.Tuple2<K,W>> other,
                                                                          int numPartitions,
                                                                          scala.reflect.ClassTag<W> evidence$18)

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.

rightOuterJoin

public <W> DStream<scala.Tuple2<K,scala.Tuple2<scala.Option<V>,W>>> rightOuterJoin(DStream<scala.Tuple2<K,W>> other,
                                                                          Partitioner partitioner,
                                                                          scala.reflect.ClassTag<W> evidence$19)

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.

saveAsHadoopFiles

public <F extends org.apache.hadoop.mapred.OutputFormat<K,V>> void saveAsHadoopFiles(String prefix,
                                                                            String suffix,
                                                                            scala.reflect.ClassTag<F> fm)

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"

saveAsHadoopFiles

public void saveAsHadoopFiles(String prefix,
                     String suffix,
                     Class<?> keyClass,
                     Class<?> valueClass,
                     Class<? extends org.apache.hadoop.mapred.OutputFormat<?,?>> outputFormatClass,
                     org.apache.hadoop.mapred.JobConf conf)

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"

saveAsNewAPIHadoopFiles

public <F extends org.apache.hadoop.mapreduce.OutputFormat<K,V>> void saveAsNewAPIHadoopFiles(String prefix,
                                                                                     String suffix,
                                                                                     scala.reflect.ClassTag<F> fm)

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".

saveAsNewAPIHadoopFiles

public void saveAsNewAPIHadoopFiles(String prefix,
                           String suffix,
                           Class<?> keyClass,
                           Class<?> valueClass,
                           Class<? extends org.apache.hadoop.mapreduce.OutputFormat<?,?>> outputFormatClass,
                           org.apache.hadoop.conf.Configuration conf)

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".