org.apache.spark.streaming.dstream
DStream
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object DStream
| package dstream
The base scope associated with the operation that created this DStream.
The base scope associated with the operation that created this DStream.
This is the medium through which we pass the DStream operation name (e.g. updatedStateByKey)
to the RDDs created by this DStream. Note that we never use this scope directly in RDDs.
Instead, we instantiate a new scope during each call to compute based on this one.
This is not defined if the DStream is created outside of one of the public DStream operations.
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
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 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 (Spark's default number of partitions if
numPartitions not specified).
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 (Spark's default number of partitions if
numPartitions not specified).
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 has a single element generated by counting the number of elements in a sliding 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 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
Wrap a body of code such that the call site and operation scope information are passed to the RDDs created in this body properly.
Wrap a body of code such that the call site and operation scope information are passed to the RDDs created in this body properly.
Current batch time that should be embedded in the scope names
Whether the detailed callsites and scopes of the inner RDDs generated
by body will be displayed in the UI; only the scope and callsite
of the DStream operation that generated this will be displayed.
RDD creation code to execute with certain local properties.
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
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 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 RDDs of this DStream with the default storage level (MEMORY_ONLY_SER)
Persist the RDDs of this DStream with the given storage level
Print the first num elements of each RDD generated in this DStream.
Print the first num elements of each RDD generated in this DStream. This is an output operator, so this DStream will be registered as an output stream and there materialized.
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 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 and commutative reduce function
inverse reduce function; such that for all y, invertible x:
invReduceFunc(reduceFunc(x, y), x) = y
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 and commutative 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 with an increased or decreased level of parallelism.
Return a new DStream with an increased or decreased level of parallelism. Each RDD in the returned DStream has exactly numPartitions partitions.
Save each RDD in this DStream as a Sequence file of serialized objects.
Save each RDD in this DStream as a Sequence file of serialized objects.
The file name at each batch interval is generated based on prefix and
suffix: "prefix-TIME_IN_MS.suffix".
Save each RDD in this DStream as at text file, using string representation of elements.
Save each RDD in this DStream as at text file, using string representation
of elements. The file name at each batch interval is generated based on
prefix and suffix: "prefix-TIME_IN_MS.suffix".
Return all the RDDs between 'fromTime' to 'toTime' (both included)
Return all the RDDs defined by the Interval object (both end times 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 and 'other' DStream.
Return a new DStream in which each RDD is generated by applying a function on each RDD of 'this' DStream and 'other' DStream.
Return a new DStream by unifying data of another DStream with this DStream.
Return a new DStream by unifying data of another DStream with this DStream.
Another DStream having the same 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 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 org.apache.spark.streaming.StreamingContext or it can be generated by transforming existing DStreams using operations such as
map,windowandreduceByKeyAndWindow. 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,filterandwindow. In addition, org.apache.spark.streaming.dstream.PairDStreamFunctions contains operations available only on DStreams of key-value pairs, such asgroupByKeyAndWindowandjoin. These operations are automatically available on any DStream of pairs (e.g., DStream[(Int, Int)] through implicit conversions.A DStream internally is characterized by a few basic properties: