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
Cogroup this DStream with other DStream.
Cogroup this DStream with other DStream. For each key k in corresponding RDDs of this
or other DStreams, the generated RDD will contains a tuple with the list of values for that
key in both RDDs. Partitioner is used to partition each generated RDD.
Cogroup this DStream with other DStream.
Cogroup this DStream with other DStream. For each key k in corresponding RDDs of this
or other DStreams, the generated RDD will contains a tuple with the list of values for that
key in both RDDs. HashPartitioner is used to partition each generated RDD into default number
of partitions.
Combine elements of each key in DStream's RDDs using custom function.
Combine elements of each key in DStream's RDDs using custom function. This is similar to the combineByKey for RDDs. Please refer to combineByKey in spark.PairRDDFunctions for more information.
Method that generates a 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 groupByKey on each RDD of this DStream.
Return a new DStream by applying groupByKey on each RDD of this DStream.
Therefore, the values for each key in this DStream's RDDs are grouped into a
single sequence to generate the RDDs of the new DStream. spark.Partitioner
is used to control the partitioning of each RDD.
Return a new DStream by applying groupByKey to each RDD.
Return a new DStream by applying groupByKey to each RDD. Hash partitioning is used to
generate the RDDs with numPartitions partitions.
Return a new DStream by applying groupByKey to each RDD.
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.
Return a new DStream by applying groupByKey over a sliding window on this DStream.
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.
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
Partitioner for controlling the partitioning of each RDD in the new DStream.
Return a new DStream by applying groupByKey over a sliding window on this DStream.
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.
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 by applying groupByKey over a sliding window.
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.
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 by applying groupByKey over a sliding window.
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.
width of the window; must be a multiple of this DStream's batching interval
Join this DStream with other DStream, that is, each RDD of the new DStream will
be generated by joining RDDs from this and other DStream.
Join this DStream with other DStream, that is, each RDD of the new DStream will
be generated by joining RDDs from this and other DStream. Uses the given
Partitioner to partition each generated RDD.
Join this DStream with other DStream.
Join this DStream with other DStream. HashPartitioner is used
to partition each generated RDD into default number of partitions.
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 by applying reduceByKey to each RDD.
Return a new DStream by applying reduceByKey to each RDD. The values for each key are
merged using the supplied reduce function. spark.Partitioner is used to control the
partitioning of each RDD.
Return a new DStream by applying reduceByKey to each RDD.
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.
Return a new DStream by applying reduceByKey to each RDD.
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.
Return a new DStream by applying incremental reduceByKey over a sliding window.
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 reduce value :
associative reduce function
inverse 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
Partitioner for controlling the partitioning of each RDD in the new DStream.
function to filter expired key-value pairs; only pairs that satisfy the function are retained set this to null if you do not want to filter
Return a new DStream by applying incremental reduceByKey over a sliding window.
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 reduce value :
numPartitions partitions.associative reduce function
inverse 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
number of partitions of each RDD in the new DStream.
function to filter expired key-value pairs; only pairs that satisfy the function are retained set this to null if you do not want to filter
Return a new DStream by reducing over a using incremental computation.
Return a new DStream by reducing over a using incremental computation. The reduced value of over a new window is calculated using the old window's reduce value :
associative reduce function
inverse 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 by applying reduceByKey over a sliding window.
Return a new DStream by applying reduceByKey over a sliding window. Similar to
DStream.reduceByKey(), but applies it over a sliding window.
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
Partitioner for controlling the partitioning of each RDD in the new DStream.
Return a new DStream by applying reduceByKey over a sliding window.
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.
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
Number of partitions of each RDD in the new DStream.
Return a new DStream by applying reduceByKey over a sliding window.
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.
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
Create a new DStream by applying reduceByKey over a sliding window on this DStream.
Create 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.
associative reduce function
width of the window; 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. 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
Save each RDD in this DStream as a Hadoop file.
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".
Save each RDD in this DStream as a Hadoop file.
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".
Save each RDD in this DStream as a Hadoop file.
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".
Save each RDD in this DStream as a Hadoop file.
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".
Save each RDD in this DStream as a Hadoop file.
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".
Save each RDD in this DStream as a Hadoop file.
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".
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.
Create 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.
Create 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. spark.Partitioner is used to control the partitioning of each RDD.
State type
State update function. If this function returns None, then
corresponding state key-value pair will be eliminated.
Partitioner for controlling the partitioning of each RDD in the new DStream.
Create 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.
Create 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.
State type
State update function. If this function returns None, then
corresponding state key-value pair will be eliminated.
Number of partitions of each RDD in the new DStream.
Create 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.
Create 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.
State type
State update function. If this function returns None, then
corresponding state key-value pair will be eliminated.
Return a new DStream which is computed based on windowed batches of this DStream.
Return a new DStream which is computed based on windowed batches of this DStream.
duration (i.e., width) of the window; must be a multiple of this DStream's 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 interval
Return a new DStream which is computed based on windowed batches of this DStream.
Return a new DStream which is computed based on windowed batches of 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. @return