Cogroup this
DStream with other
DStream using a partitioner.
Cogroup this
DStream with other
DStream using a partitioner. 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 functions.
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 spark.PairRDDFunctions for more information.
Return a new DStream by applying groupByKey
on each RDD.
Return a new DStream by applying groupByKey
on each RDD. The supplied 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.
Create a new DStream by applying groupByKey
over a sliding window on this
DStream.
Create 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; if not specified then Spark's default number of partitions will be used
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 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 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
partitioner for controlling the partitioning of each RDD in the new DStream.
Optional function to filter expired key-value pairs; only pairs that satisfy the function are retained
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 reduced value :
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.
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
Optional function to filter expired key-value pairs; only pairs that satisfy the function are retained
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
Return a new DStream by applying reduceByKey
over a sliding window on this
DStream.
Return 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
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 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.
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. spark.Paxrtitioner 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. Note, that
this function may generate a different a tuple with a different key
than the input key. It is up to the developer to decide whether to
remember the partitioner despite the key being changed.
Partitioner for controlling the partitioning of each RDD in the new DStream.
Whether to remember the paritioner object in the generated RDDs.
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.
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.
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.
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.
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.
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. 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.