org.apache.spark.streaming.dstream
Streaming context that will execute this input stream
Method that generates a RDD for the given time
Method that generates a RDD for the given time
Method called to start receiving data.
Method called to start receiving data. Subclasses must implement this method.
Method called to stop receiving data.
Method called to stop receiving data. Subclasses must implement this method.
Persist RDDs of this DStream with the default storage level (MEMORY_ONLY_SER)
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 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 (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
List of parent DStreams on which this DStream depends on
List of parent DStreams on which this DStream depends on
Return a new DStream containing only the elements that satisfy a predicate.
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
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 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 RDDs of this DStream with the default storage level (MEMORY_ONLY_SER)
Persist the RDDs of this DStream with the given storage level
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 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 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 between 'fromTime' to 'toTime' (both included)
Return all the RDDs defined by the Interval object (both end times included)
Return all the RDDs defined by the Interval object (both end times included)
Time interval after which the DStream generates a RDD
Time interval after which the DStream generates a RDD
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 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 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.
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.
(Since version 0.9.0) use foreachRDD
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.
(Since version 0.9.0) use foreachRDD
This is the abstract base class for all input streams. This class provides methods start() and stop() which is called by Spark Streaming system to start and stop receiving data. Input streams that can generate RDDs from new data by running a service/thread only on the driver node (that is, without running a receiver on worker nodes), can be implemented by directly inheriting this InputDStream. For example, FileInputDStream, a subclass of InputDStream, monitors a HDFS directory from the driver for new files and generates RDDs with the new files. For implementing input streams that requires running a receiver on the worker nodes, use org.apache.spark.streaming.dstream.ReceiverInputDStream as the parent class.