Persist RDDs of this DStream with the default storage level (MEMORY_ONLY_SER)
Enable periodic checkpointing of RDDs of this DStream
Enable periodic checkpointing of RDDs of this DStream
Time interval after which generated RDD will be checkpointed
Generate an RDD for the given duration
Return the StreamingContext associated with this DStream
Return the StreamingContext associated with this DStream
Return a new DStream in which each RDD has a single element generated by counting each RDD of this DStream.
Return a new DStream in which each RDD has a single element generated by counting each RDD of this DStream.
Return a new DStream in which each RDD contains the counts of each distinct value in each RDD of this DStream.
Return a new DStream in which each RDD contains the counts of each distinct value in
each RDD of this DStream. Hash partitioning is used to generate the RDDs with numPartitions
partitions.
number of partitions of each RDD in the new DStream.
Return a new DStream in which each RDD contains the counts of each distinct value in each RDD of this DStream.
Return a new DStream in which each RDD contains the counts of each distinct value in each RDD of this DStream. Hash partitioning is used to generate the RDDs with Spark's default number of partitions.
Return a new DStream in which each RDD contains the count of distinct elements in RDDs in a sliding window over this DStream.
Return a new DStream in which each RDD contains the count of distinct elements in
RDDs in a sliding window over this DStream. Hash partitioning is used to generate the RDDs with numPartitions
partitions.
width of the window; must be a multiple of this DStream's batching interval
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
number of partitions of each RDD in the new DStream.
Return a new DStream in which each RDD contains the count of distinct elements in RDDs in a sliding window over this DStream.
Return a new DStream in which each RDD contains the count of distinct elements in RDDs in a sliding window over this DStream. Hash partitioning is used to generate the RDDs with Spark's default number of partitions.
width of the window; must be a multiple of this DStream's batching interval
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
Return a new DStream in which each RDD has a single element generated by counting the number of elements in a window over this DStream.
Return a new DStream in which each RDD has a single element generated by counting the number of elements in a window over this DStream. windowDuration and slideDuration are as defined in the window() operation. This is equivalent to window(windowDuration, slideDuration).count()
Return a new DStream containing only the elements that satisfy a predicate.
Return a new DStream by applying a function to all elements of this DStream, and then flattening the results
Return a new DStream by applying a function to all elements of this DStream, and then flattening the results
Return a new DStream by applying a function to all elements of this DStream, and then flattening the results
Return a new DStream by applying a function to all elements of this DStream, and then flattening the results
Apply a function to each RDD in this DStream.
Apply a function to each RDD in this DStream. This is an output operator, so this DStream will be registered as an output stream and therefore materialized.
Apply a function to each RDD in this DStream.
Apply a function to each RDD in this DStream. This is an output operator, so this DStream will be registered as an output stream and therefore materialized.
Return a new DStream in which each RDD is generated by applying glom() to each RDD of this DStream.
Return a new DStream in which each RDD is generated by applying glom() to each RDD of this DStream. Applying glom() to an RDD coalesces all elements within each partition into an array.
Return a new DStream by applying a function to all elements of this DStream.
Return a new DStream by applying a function to all elements of this DStream.
Return a new DStream by applying a function to all elements of this DStream.
Return a new DStream by applying a function to all elements of this DStream.
Return a new DStream in which each RDD is generated by applying mapPartitions() to each RDDs of this DStream.
Return a new DStream in which each RDD is generated by applying mapPartitions() to each RDDs of this DStream. Applying mapPartitions() to an RDD applies a function to each partition of the RDD.
Return a new DStream in which each RDD is generated by applying mapPartitions() to each RDDs of this DStream.
Return a new DStream in which each RDD is generated by applying mapPartitions() to each RDDs of this DStream. Applying mapPartitions() to an RDD applies a function to each partition of the RDD.
Persist the RDDs of this DStream with the given storage level
Persist RDDs of this DStream with the default storage level (MEMORY_ONLY_SER)
Print the first ten elements of each RDD generated in this DStream.
Print the first ten elements of each RDD generated in this DStream. This is an output operator, so this DStream will be registered as an output stream and there materialized.
Return a new DStream in which each RDD has a single element generated by reducing each RDD of this DStream.
Return a new DStream in which each RDD has a single element generated by reducing each RDD of this DStream.
Return a new DStream in which each RDD has a single element generated by reducing all elements in a sliding window over this DStream.
Return a new DStream in which each RDD has a single element generated by reducing all elements in a sliding window over this DStream. However, the reduction is done incrementally using the old window's reduced value :
associative reduce function
inverse reduce function
width of the window; must be a multiple of this DStream's batching interval
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
Return a new DStream in which each RDD has a single element generated by reducing all elements in a sliding window over this DStream.
Return a new DStream in which each RDD has a single element generated by reducing all elements in a sliding window over this DStream.
associative reduce function
width of the window; must be a multiple of this DStream's batching interval
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
Return all the RDDs between 'fromDuration' to 'toDuration' (both included)
Return all the RDDs between 'fromDuration' to 'toDuration' (both included)
Return a new DStream in which each RDD is generated by applying a function on each RDD of this DStream.
Return a new DStream in which each RDD is generated by applying a function on each RDD of this DStream.
Return a new DStream in which each RDD is generated by applying a function on each RDD of this DStream.
Return a new DStream in which each RDD is generated by applying a function on each RDD of this DStream.
Return a new DStream in which each RDD is generated by applying a function on each RDD of this DStream.
Return a new DStream in which each RDD is generated by applying a function on each RDD of this DStream.
Return a new DStream in which each RDD is generated by applying a function on each RDD of this DStream.
Return a new DStream in which each RDD is generated by applying a function on each RDD of this DStream.
Return a new DStream by unifying data of another DStream with this DStream.
Return a new DStream by unifying data of another DStream with this DStream.
Another DStream having the same interval (i.e., slideDuration) as 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.
width of the window; must be a multiple of this DStream's batching interval
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
Return a new DStream in which each RDD contains all the elements in seen in a sliding window of time over this DStream.
Return a new DStream in which each RDD contains all the elements in seen in a sliding window of time over this DStream. The new DStream generates RDDs with the same interval as this DStream.
width of the window; must be a multiple of this DStream's interval.
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 spark.RDD for more details on RDDs). DStreams can either be created from live data (such as, data from HDFS, Kafka or Flume) or it can be generated by transformation existing DStreams using operations such as
map
,window
andreduceByKeyAndWindow
. 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
andwindow
. In addition, JavaPairDStream contains operations available only on DStreams of key-value pairs, such asgroupByKeyAndWindow
andjoin
.DStreams internally is characterized by a few basic properties: