Class PairDStreamFunctions<K,V>
Object
org.apache.spark.streaming.dstream.PairDStreamFunctions<K,V>
- All Implemented Interfaces:
Serializable
,scala.Serializable
Extra functions available on DStream of (key, value) pairs through an implicit conversion.
- See Also:
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Constructor Summary
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Method Summary
Modifier and TypeMethodDescription<W> DStream<scala.Tuple2<K,
scala.Tuple2<scala.collection.Iterable<V>, scala.collection.Iterable<W>>>> Return a new DStream by applying 'cogroup' between RDDs ofthis
DStream andother
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$15) Return a new DStream by applying 'cogroup' between RDDs ofthis
DStream andother
DStream.<W> DStream<scala.Tuple2<K,
scala.Tuple2<scala.collection.Iterable<V>, scala.collection.Iterable<W>>>> Return a new DStream by applying 'cogroup' between RDDs ofthis
DStream andother
DStream.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.flatMapValues
(scala.Function1<V, scala.collection.TraversableOnce<U>> flatMapValuesFunc, scala.reflect.ClassTag<U> evidence$12) Return a new DStream by applying a flatmap function to the value of each key-value pairs in 'this' DStream without changing the key.fullOuterJoin
(DStream<scala.Tuple2<K, W>> other, int numPartitions, scala.reflect.ClassTag<W> evidence$26) Return a new DStream by applying 'full outer join' between RDDs ofthis
DStream andother
DStream.fullOuterJoin
(DStream<scala.Tuple2<K, W>> other, Partitioner partitioner, scala.reflect.ClassTag<W> evidence$27) Return a new DStream by applying 'full outer join' between RDDs ofthis
DStream andother
DStream.fullOuterJoin
(DStream<scala.Tuple2<K, W>> other, scala.reflect.ClassTag<W> evidence$25) Return a new DStream by applying 'full outer join' between RDDs ofthis
DStream andother
DStream.Return a new DStream by applyinggroupByKey
to each RDD.groupByKey
(int numPartitions) Return a new DStream by applyinggroupByKey
to each RDD.groupByKey
(Partitioner partitioner) Return a new DStream by applyinggroupByKey
on each RDD.groupByKeyAndWindow
(Duration windowDuration) Return a new DStream by applyinggroupByKey
over a sliding window.groupByKeyAndWindow
(Duration windowDuration, Duration slideDuration) Return a new DStream by applyinggroupByKey
over a sliding window.groupByKeyAndWindow
(Duration windowDuration, Duration slideDuration, int numPartitions) Return a new DStream by applyinggroupByKey
over a sliding window onthis
DStream.groupByKeyAndWindow
(Duration windowDuration, Duration slideDuration, Partitioner partitioner) Create a new DStream by applyinggroupByKey
over a sliding window onthis
DStream.Return a new DStream by applying 'join' between RDDs ofthis
DStream andother
DStream.join
(DStream<scala.Tuple2<K, W>> other, Partitioner partitioner, scala.reflect.ClassTag<W> evidence$18) Return a new DStream by applying 'join' between RDDs ofthis
DStream andother
DStream.Return a new DStream by applying 'join' between RDDs ofthis
DStream andother
DStream.leftOuterJoin
(DStream<scala.Tuple2<K, W>> other, int numPartitions, scala.reflect.ClassTag<W> evidence$20) Return a new DStream by applying 'left outer join' between RDDs ofthis
DStream andother
DStream.leftOuterJoin
(DStream<scala.Tuple2<K, W>> other, Partitioner partitioner, scala.reflect.ClassTag<W> evidence$21) Return a new DStream by applying 'left outer join' between RDDs ofthis
DStream andother
DStream.leftOuterJoin
(DStream<scala.Tuple2<K, W>> other, scala.reflect.ClassTag<W> evidence$19) Return a new DStream by applying 'left outer join' between RDDs ofthis
DStream andother
DStream.Return a new DStream by applying a map function to the value of each key-value pairs in 'this' DStream without changing the key.<StateType,
MappedType>
MapWithStateDStream<K,V, StateType, MappedType> mapWithState
(StateSpec<K, V, StateType, MappedType> spec, scala.reflect.ClassTag<StateType> evidence$2, scala.reflect.ClassTag<MappedType> evidence$3) Return aMapWithStateDStream
by applying a function to every key-value element ofthis
stream, while maintaining some state data for each unique key.reduceByKey
(scala.Function2<V, V, V> reduceFunc) Return a new DStream by applyingreduceByKey
to each RDD.reduceByKey
(scala.Function2<V, V, V> reduceFunc, int numPartitions) Return a new DStream by applyingreduceByKey
to each RDD.reduceByKey
(scala.Function2<V, V, V> reduceFunc, Partitioner partitioner) Return a new DStream by applyingreduceByKey
to each RDD.reduceByKeyAndWindow
(scala.Function2<V, V, V> reduceFunc, Duration windowDuration) Return a new DStream by applyingreduceByKey
over a sliding window onthis
DStream.reduceByKeyAndWindow
(scala.Function2<V, V, V> reduceFunc, Duration windowDuration, Duration slideDuration) Return a new DStream by applyingreduceByKey
over a sliding window.reduceByKeyAndWindow
(scala.Function2<V, V, V> reduceFunc, Duration windowDuration, Duration slideDuration, int numPartitions) Return a new DStream by applyingreduceByKey
over a sliding window.reduceByKeyAndWindow
(scala.Function2<V, V, V> reduceFunc, Duration windowDuration, Duration slideDuration, Partitioner partitioner) Return a new DStream by applyingreduceByKey
over a sliding window.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 incrementalreduceByKey
over a sliding window.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 incrementalreduceByKey
over a sliding window.rightOuterJoin
(DStream<scala.Tuple2<K, W>> other, int numPartitions, scala.reflect.ClassTag<W> evidence$23) Return a new DStream by applying 'right outer join' between RDDs ofthis
DStream andother
DStream.rightOuterJoin
(DStream<scala.Tuple2<K, W>> other, Partitioner partitioner, scala.reflect.ClassTag<W> evidence$24) Return a new DStream by applying 'right outer join' between RDDs ofthis
DStream andother
DStream.rightOuterJoin
(DStream<scala.Tuple2<K, W>> other, scala.reflect.ClassTag<W> evidence$22) Return a new DStream by applying 'right outer join' between RDDs ofthis
DStream andother
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 inthis
DStream as a Hadoop file.saveAsHadoopFiles
(String prefix, String suffix, scala.reflect.ClassTag<F> fm) Save each RDD inthis
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 inthis
DStream as a Hadoop file.saveAsNewAPIHadoopFiles
(String prefix, String suffix, scala.reflect.ClassTag<F> fm) Save each RDD inthis
DStream as a Hadoop file.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, RDD<scala.Tuple2<K, S>> initialRDD, scala.reflect.ClassTag<S> evidence$9) 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.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$7) 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.updateStateByKey
(scala.Function2<scala.collection.Seq<V>, scala.Option<S>, scala.Option<S>> updateFunc, int numPartitions, 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.updateStateByKey
(scala.Function2<scala.collection.Seq<V>, scala.Option<S>, scala.Option<S>> updateFunc, Partitioner partitioner, RDD<scala.Tuple2<K, S>> initialRDD, scala.reflect.ClassTag<S> evidence$8) 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.updateStateByKey
(scala.Function2<scala.collection.Seq<V>, scala.Option<S>, scala.Option<S>> updateFunc, Partitioner partitioner, scala.reflect.ClassTag<S> evidence$6) 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.updateStateByKey
(scala.Function2<scala.collection.Seq<V>, scala.Option<S>, scala.Option<S>> updateFunc, 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 each key.updateStateByKey
(scala.Function4<Time, K, scala.collection.Seq<V>, scala.Option<S>, scala.Option<S>> updateFunc, Partitioner partitioner, boolean rememberPartitioner, scala.Option<RDD<scala.Tuple2<K, S>>> initialRDD, scala.reflect.ClassTag<S> evidence$10) 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.
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Constructor Details
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PairDStreamFunctions
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Method Details
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cogroup
public <W> DStream<scala.Tuple2<K,scala.Tuple2<scala.collection.Iterable<V>, cogroupscala.collection.Iterable<W>>>> (DStream<scala.Tuple2<K, W>> other, scala.reflect.ClassTag<W> evidence$13) Return a new DStream by applying 'cogroup' between RDDs ofthis
DStream andother
DStream. Hash partitioning is used to generate the RDDs with Spark's default number of partitions.- Parameters:
other
- (undocumented)evidence$13
- (undocumented)- Returns:
- (undocumented)
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cogroup
public <W> DStream<scala.Tuple2<K,scala.Tuple2<scala.collection.Iterable<V>, cogroupscala.collection.Iterable<W>>>> (DStream<scala.Tuple2<K, W>> other, int numPartitions, scala.reflect.ClassTag<W> evidence$14) Return a new DStream by applying 'cogroup' between RDDs ofthis
DStream andother
DStream. Hash partitioning is used to generate the RDDs withnumPartitions
partitions.- Parameters:
other
- (undocumented)numPartitions
- (undocumented)evidence$14
- (undocumented)- Returns:
- (undocumented)
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cogroup
public <W> DStream<scala.Tuple2<K,scala.Tuple2<scala.collection.Iterable<V>, cogroupscala.collection.Iterable<W>>>> (DStream<scala.Tuple2<K, W>> other, Partitioner partitioner, scala.reflect.ClassTag<W> evidence$15) Return a new DStream by applying 'cogroup' between RDDs ofthis
DStream andother
DStream. The supplied org.apache.spark.Partitioner is used to partition the generated RDDs.- Parameters:
other
- (undocumented)partitioner
- (undocumented)evidence$15
- (undocumented)- Returns:
- (undocumented)
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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.- Parameters:
createCombiner
- (undocumented)mergeValue
- (undocumented)mergeCombiner
- (undocumented)partitioner
- (undocumented)mapSideCombine
- (undocumented)evidence$1
- (undocumented)- Returns:
- (undocumented)
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flatMapValues
public <U> DStream<scala.Tuple2<K,U>> flatMapValues(scala.Function1<V, scala.collection.TraversableOnce<U>> flatMapValuesFunc, scala.reflect.ClassTag<U> evidence$12) Return a new DStream by applying a flatmap function to the value of each key-value pairs in 'this' DStream without changing the key.- Parameters:
flatMapValuesFunc
- (undocumented)evidence$12
- (undocumented)- Returns:
- (undocumented)
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fullOuterJoin
public <W> DStream<scala.Tuple2<K,scala.Tuple2<scala.Option<V>, fullOuterJoinscala.Option<W>>>> (DStream<scala.Tuple2<K, W>> other, scala.reflect.ClassTag<W> evidence$25) Return a new DStream by applying 'full outer join' between RDDs ofthis
DStream andother
DStream. Hash partitioning is used to generate the RDDs with Spark's default number of partitions.- Parameters:
other
- (undocumented)evidence$25
- (undocumented)- Returns:
- (undocumented)
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fullOuterJoin
public <W> DStream<scala.Tuple2<K,scala.Tuple2<scala.Option<V>, fullOuterJoinscala.Option<W>>>> (DStream<scala.Tuple2<K, W>> other, int numPartitions, scala.reflect.ClassTag<W> evidence$26) Return a new DStream by applying 'full outer join' between RDDs ofthis
DStream andother
DStream. Hash partitioning is used to generate the RDDs withnumPartitions
partitions.- Parameters:
other
- (undocumented)numPartitions
- (undocumented)evidence$26
- (undocumented)- Returns:
- (undocumented)
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fullOuterJoin
public <W> DStream<scala.Tuple2<K,scala.Tuple2<scala.Option<V>, fullOuterJoinscala.Option<W>>>> (DStream<scala.Tuple2<K, W>> other, Partitioner partitioner, scala.reflect.ClassTag<W> evidence$27) Return a new DStream by applying 'full outer join' between RDDs ofthis
DStream andother
DStream. The supplied org.apache.spark.Partitioner is used to control the partitioning of each RDD.- Parameters:
other
- (undocumented)partitioner
- (undocumented)evidence$27
- (undocumented)- Returns:
- (undocumented)
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groupByKey
Return a new DStream by applyinggroupByKey
to each RDD. Hash partitioning is used to generate the RDDs with Spark's default number of partitions.- Returns:
- (undocumented)
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groupByKey
Return a new DStream by applyinggroupByKey
to each RDD. Hash partitioning is used to generate the RDDs withnumPartitions
partitions.- Parameters:
numPartitions
- (undocumented)- Returns:
- (undocumented)
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groupByKey
Return a new DStream by applyinggroupByKey
on each RDD. The supplied org.apache.spark.Partitioner is used to control the partitioning of each RDD.- Parameters:
partitioner
- (undocumented)- Returns:
- (undocumented)
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groupByKeyAndWindow
public DStream<scala.Tuple2<K,scala.collection.Iterable<V>>> groupByKeyAndWindow(Duration windowDuration) Return a new DStream by applyinggroupByKey
over a sliding window. This is similar toDStream.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- Returns:
- (undocumented)
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groupByKeyAndWindow
public DStream<scala.Tuple2<K,scala.collection.Iterable<V>>> groupByKeyAndWindow(Duration windowDuration, Duration slideDuration) Return a new DStream by applyinggroupByKey
over a sliding window. Similar toDStream.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 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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groupByKeyAndWindow
public DStream<scala.Tuple2<K,scala.collection.Iterable<V>>> groupByKeyAndWindow(Duration windowDuration, Duration slideDuration, int numPartitions) Return a new DStream by applyinggroupByKey
over a sliding window onthis
DStream. Similar toDStream.groupByKey()
, but applies it over a sliding window. Hash partitioning is used to generate the RDDs withnumPartitions
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 intervalnumPartitions
- number of partitions of each RDD in the new DStream; if not specified then Spark's default number of partitions will be used- Returns:
- (undocumented)
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groupByKeyAndWindow
public DStream<scala.Tuple2<K,scala.collection.Iterable<V>>> groupByKeyAndWindow(Duration windowDuration, Duration slideDuration, Partitioner partitioner) Create a new DStream by applyinggroupByKey
over a sliding window onthis
DStream. Similar toDStream.groupByKey()
, but applies it over a sliding window.- 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 intervalpartitioner
- partitioner for controlling the partitioning of each RDD in the new DStream.- Returns:
- (undocumented)
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join
public <W> DStream<scala.Tuple2<K,scala.Tuple2<V, joinW>>> (DStream<scala.Tuple2<K, W>> other, scala.reflect.ClassTag<W> evidence$16) Return a new DStream by applying 'join' between RDDs ofthis
DStream andother
DStream. Hash partitioning is used to generate the RDDs with Spark's default number of partitions.- Parameters:
other
- (undocumented)evidence$16
- (undocumented)- Returns:
- (undocumented)
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join
public <W> DStream<scala.Tuple2<K,scala.Tuple2<V, joinW>>> (DStream<scala.Tuple2<K, W>> other, int numPartitions, scala.reflect.ClassTag<W> evidence$17) Return a new DStream by applying 'join' between RDDs ofthis
DStream andother
DStream. Hash partitioning is used to generate the RDDs withnumPartitions
partitions.- Parameters:
other
- (undocumented)numPartitions
- (undocumented)evidence$17
- (undocumented)- Returns:
- (undocumented)
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join
public <W> DStream<scala.Tuple2<K,scala.Tuple2<V, joinW>>> (DStream<scala.Tuple2<K, W>> other, Partitioner partitioner, scala.reflect.ClassTag<W> evidence$18) Return a new DStream by applying 'join' between RDDs ofthis
DStream andother
DStream. The supplied org.apache.spark.Partitioner is used to control the partitioning of each RDD.- Parameters:
other
- (undocumented)partitioner
- (undocumented)evidence$18
- (undocumented)- Returns:
- (undocumented)
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leftOuterJoin
public <W> DStream<scala.Tuple2<K,scala.Tuple2<V, leftOuterJoinscala.Option<W>>>> (DStream<scala.Tuple2<K, W>> other, scala.reflect.ClassTag<W> evidence$19) Return a new DStream by applying 'left outer join' between RDDs ofthis
DStream andother
DStream. Hash partitioning is used to generate the RDDs with Spark's default number of partitions.- Parameters:
other
- (undocumented)evidence$19
- (undocumented)- Returns:
- (undocumented)
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leftOuterJoin
public <W> DStream<scala.Tuple2<K,scala.Tuple2<V, leftOuterJoinscala.Option<W>>>> (DStream<scala.Tuple2<K, W>> other, int numPartitions, scala.reflect.ClassTag<W> evidence$20) Return a new DStream by applying 'left outer join' between RDDs ofthis
DStream andother
DStream. Hash partitioning is used to generate the RDDs withnumPartitions
partitions.- Parameters:
other
- (undocumented)numPartitions
- (undocumented)evidence$20
- (undocumented)- Returns:
- (undocumented)
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leftOuterJoin
public <W> DStream<scala.Tuple2<K,scala.Tuple2<V, leftOuterJoinscala.Option<W>>>> (DStream<scala.Tuple2<K, W>> other, Partitioner partitioner, scala.reflect.ClassTag<W> evidence$21) Return a new DStream by applying 'left outer join' between RDDs ofthis
DStream andother
DStream. The supplied org.apache.spark.Partitioner is used to control the partitioning of each RDD.- Parameters:
other
- (undocumented)partitioner
- (undocumented)evidence$21
- (undocumented)- Returns:
- (undocumented)
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mapValues
public <U> DStream<scala.Tuple2<K,U>> mapValues(scala.Function1<V, U> mapValuesFunc, scala.reflect.ClassTag<U> evidence$11) Return a new DStream by applying a map function to the value of each key-value pairs in 'this' DStream without changing the key.- Parameters:
mapValuesFunc
- (undocumented)evidence$11
- (undocumented)- Returns:
- (undocumented)
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mapWithState
public <StateType,MappedType> MapWithStateDStream<K,V, mapWithStateStateType, MappedType> (StateSpec<K, V, StateType, MappedType> spec, scala.reflect.ClassTag<StateType> evidence$2, scala.reflect.ClassTag<MappedType> evidence$3) Return aMapWithStateDStream
by applying a function to every key-value element ofthis
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 usingStateSpec
class. The state data is accessible in as a parameter of typeState
in the mapping function.Example of using
mapWithState
:// A mapping function that maintains an integer state and return a String def mappingFunction(key: String, value: Option[Int], state: State[Int]): Option[String] = { // Use state.exists(), state.get(), state.update() and state.remove() // to manage state, and return the necessary string } val spec = StateSpec.function(mappingFunction).numPartitions(10) val mapWithStateDStream = keyValueDStream.mapWithState[StateType, MappedType](spec)
- Parameters:
spec
- Specification of this transformationevidence$2
- (undocumented)evidence$3
- (undocumented)- Returns:
- (undocumented)
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reduceByKey
Return a new DStream by applyingreduceByKey
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.- Parameters:
reduceFunc
- (undocumented)- Returns:
- (undocumented)
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reduceByKey
Return a new DStream by applyingreduceByKey
to each RDD. The values for each key are merged using the supplied reduce function. Hash partitioning is used to generate the RDDs withnumPartitions
partitions.- Parameters:
reduceFunc
- (undocumented)numPartitions
- (undocumented)- Returns:
- (undocumented)
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reduceByKey
public DStream<scala.Tuple2<K,V>> reduceByKey(scala.Function2<V, V, V> reduceFunc, Partitioner partitioner) Return a new DStream by applyingreduceByKey
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.- Parameters:
reduceFunc
- (undocumented)partitioner
- (undocumented)- Returns:
- (undocumented)
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reduceByKeyAndWindow
public DStream<scala.Tuple2<K,V>> reduceByKeyAndWindow(scala.Function2<V, V, V> reduceFunc, Duration windowDuration) Return a new DStream by applyingreduceByKey
over a sliding window onthis
DStream. Similar toDStream.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 and commutative reduce functionwindowDuration
- width of the window; must be a multiple of this DStream's batching interval- Returns:
- (undocumented)
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reduceByKeyAndWindow
public DStream<scala.Tuple2<K,V>> reduceByKeyAndWindow(scala.Function2<V, V, V> reduceFunc, Duration windowDuration, Duration slideDuration) Return a new DStream by applyingreduceByKey
over a sliding window. This is similar toDStream.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 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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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 applyingreduceByKey
over a sliding window. This is similar toDStream.reduceByKey()
but applies it over a sliding window. Hash partitioning is used to generate the RDDs withnumPartitions
partitions.- 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 intervalnumPartitions
- number of partitions of each RDD in the new DStream.- Returns:
- (undocumented)
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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 applyingreduceByKey
over a sliding window. Similar toDStream.reduceByKey()
, but applies it over a sliding window.- 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 intervalpartitioner
- partitioner for controlling the partitioning of each RDD in the new DStream.- Returns:
- (undocumented)
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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 incrementalreduceByKey
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 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 intervalfilterFunc
- Optional function to filter expired key-value pairs; only pairs that satisfy the function are retainednumPartitions
- (undocumented)- Returns:
- (undocumented)
-
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 incrementalreduceByKey
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 and commutative 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 intervalpartitioner
- 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- Returns:
- (undocumented)
-
rightOuterJoin
public <W> DStream<scala.Tuple2<K,scala.Tuple2<scala.Option<V>, rightOuterJoinW>>> (DStream<scala.Tuple2<K, W>> other, scala.reflect.ClassTag<W> evidence$22) Return a new DStream by applying 'right outer join' between RDDs ofthis
DStream andother
DStream. Hash partitioning is used to generate the RDDs with Spark's default number of partitions.- Parameters:
other
- (undocumented)evidence$22
- (undocumented)- Returns:
- (undocumented)
-
rightOuterJoin
public <W> DStream<scala.Tuple2<K,scala.Tuple2<scala.Option<V>, rightOuterJoinW>>> (DStream<scala.Tuple2<K, W>> other, int numPartitions, scala.reflect.ClassTag<W> evidence$23) Return a new DStream by applying 'right outer join' between RDDs ofthis
DStream andother
DStream. Hash partitioning is used to generate the RDDs withnumPartitions
partitions.- Parameters:
other
- (undocumented)numPartitions
- (undocumented)evidence$23
- (undocumented)- Returns:
- (undocumented)
-
rightOuterJoin
public <W> DStream<scala.Tuple2<K,scala.Tuple2<scala.Option<V>, rightOuterJoinW>>> (DStream<scala.Tuple2<K, W>> other, Partitioner partitioner, scala.reflect.ClassTag<W> evidence$24) Return a new DStream by applying 'right outer join' between RDDs ofthis
DStream andother
DStream. The supplied org.apache.spark.Partitioner is used to control the partitioning of each RDD.- Parameters:
other
- (undocumented)partitioner
- (undocumented)evidence$24
- (undocumented)- Returns:
- (undocumented)
-
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 inthis
DStream as a Hadoop file. The file name at each batch interval is generated based onprefix
andsuffix
: "prefix-TIME_IN_MS.suffix"- Parameters:
prefix
- (undocumented)suffix
- (undocumented)fm
- (undocumented)
-
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 inthis
DStream as a Hadoop file. The file name at each batch interval is generated based onprefix
andsuffix
: "prefix-TIME_IN_MS.suffix"- Parameters:
prefix
- (undocumented)suffix
- (undocumented)keyClass
- (undocumented)valueClass
- (undocumented)outputFormatClass
- (undocumented)conf
- (undocumented)
-
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 inthis
DStream as a Hadoop file. The file name at each batch interval is generated based onprefix
andsuffix
: "prefix-TIME_IN_MS.suffix".- Parameters:
prefix
- (undocumented)suffix
- (undocumented)fm
- (undocumented)
-
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 inthis
DStream as a Hadoop file. The file name at each batch interval is generated based onprefix
andsuffix
: "prefix-TIME_IN_MS.suffix".- Parameters:
prefix
- (undocumented)suffix
- (undocumented)keyClass
- (undocumented)valueClass
- (undocumented)outputFormatClass
- (undocumented)conf
- (undocumented)
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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$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 each key. In every batch the updateFunc will be called for each state even if there are no new values. Hash partitioning is used to generate the RDDs with Spark's default number of partitions.- Parameters:
updateFunc
- State update function. Ifthis
function returns None, then corresponding state key-value pair will be eliminated.evidence$4
- (undocumented)- Returns:
- (undocumented)
-
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$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. In every batch the updateFunc will be called for each state even if there are no new values. Hash partitioning is used to generate the RDDs withnumPartitions
partitions.- Parameters:
updateFunc
- State update function. Ifthis
function returns None, then corresponding state key-value pair will be eliminated.numPartitions
- Number of partitions of each RDD in the new DStream.evidence$5
- (undocumented)- Returns:
- (undocumented)
-
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$6) 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. In every batch the updateFunc will be called for each state even if there are no new values.Partitioner
is used to control the partitioning of each RDD.- Parameters:
updateFunc
- State update function. Ifthis
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.evidence$6
- (undocumented)- Returns:
- (undocumented)
-
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$7) 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. In every batch the updateFunc will be called for each state even if there are no new values.Partitioner
is used to control the partitioning of each RDD.- Parameters:
updateFunc
- State update function. Note, that this function may generate a different tuple with a different key than the input key. Therefore keys may be removed or added in this way. 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 DStreamrememberPartitioner
- Whether to remember the partitioner object in the generated RDDs.evidence$7
- (undocumented)- Returns:
- (undocumented)
-
updateStateByKey
public <S> DStream<scala.Tuple2<K,S>> updateStateByKey(scala.Function2<scala.collection.Seq<V>, scala.Option<S>, scala.Option<S>> updateFunc, Partitioner partitioner, RDD<scala.Tuple2<K, S>> initialRDD, scala.reflect.ClassTag<S> evidence$8) 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. In every batch the updateFunc will be called for each state even if there are no new values. org.apache.spark.Partitioner is used to control the partitioning of each RDD.- Parameters:
updateFunc
- State update function. Ifthis
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.evidence$8
- (undocumented)- Returns:
- (undocumented)
-
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, RDD<scala.Tuple2<K, S>> initialRDD, scala.reflect.ClassTag<S> evidence$9) 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. In every batch the updateFunc will be called for each state even if there are no new values. org.apache.spark.Partitioner is used to control the partitioning of each RDD.- Parameters:
updateFunc
- State update function. Note, that this function may generate a different tuple with a different key than the input key. Therefore keys may be removed or added in this way. 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 DStreamrememberPartitioner
- Whether to remember the partitioner object in the generated RDDs.initialRDD
- initial state value of each key.evidence$9
- (undocumented)- Returns:
- (undocumented)
-
updateStateByKey
public <S> DStream<scala.Tuple2<K,S>> updateStateByKey(scala.Function4<Time, K, scala.collection.Seq<V>, scala.Option<S>, scala.Option<S>> updateFunc, Partitioner partitioner, boolean rememberPartitioner, scala.Option<RDD<scala.Tuple2<K, S>>> initialRDD, scala.reflect.ClassTag<S> evidence$10) 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. In every batch the updateFunc will be called for each state even if there are no new values. org.apache.spark.Partitioner is used to control the partitioning of each RDD.- Parameters:
updateFunc
- State update function. Ifthis
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.rememberPartitioner
- (undocumented)initialRDD
- (undocumented)evidence$10
- (undocumented)- Returns:
- (undocumented)
-