Github user davies commented on a diff in the pull request:
https://github.com/apache/spark/pull/2538#discussion_r18202907
--- Diff: python/pyspark/streaming/dstream.py ---
@@ -0,0 +1,633 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements. See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from itertools import chain, ifilter, imap
+import operator
+import time
+from datetime import datetime
+
+from pyspark import RDD
+from pyspark.storagelevel import StorageLevel
+from pyspark.streaming.util import rddToFileName, RDDFunction
+from pyspark.rdd import portable_hash
+from pyspark.resultiterable import ResultIterable
+
+__all__ = ["DStream"]
+
+
+class DStream(object):
+ def __init__(self, jdstream, ssc, jrdd_deserializer):
+ self._jdstream = jdstream
+ self._ssc = ssc
+ self.ctx = ssc._sc
+ self._jrdd_deserializer = jrdd_deserializer
+ self.is_cached = False
+ self.is_checkpointed = False
+
+ def context(self):
+ """
+ Return the StreamingContext associated with this DStream
+ """
+ return self._ssc
+
+ def count(self):
+ """
+ Return a new DStream which contains the number of elements in this
DStream.
+ """
+ return self.mapPartitions(lambda i: [sum(1 for _ in i)]).sum()
+
+ def sum(self):
+ """
+ Add up the elements in this DStream.
+ """
+ return self.mapPartitions(lambda x: [sum(x)]).reduce(operator.add)
+
+ def filter(self, f):
+ """
+ Return a new DStream containing only the elements that satisfy
predicate.
+ """
+ def func(iterator):
+ return ifilter(f, iterator)
+ return self.mapPartitions(func, True)
+
+ def flatMap(self, f, preservesPartitioning=False):
+ """
+ Pass each value in the key-value pair DStream through flatMap
function
+ without changing the keys: this also retains the original RDD's
partition.
+ """
+ def func(s, iterator):
+ return chain.from_iterable(imap(f, iterator))
+ return self.mapPartitionsWithIndex(func, preservesPartitioning)
+
+ def map(self, f, preservesPartitioning=False):
+ """
+ Return a new DStream by applying a function to each element of
DStream.
+ """
+ def func(iterator):
+ return imap(f, iterator)
+ return self.mapPartitions(func, preservesPartitioning)
+
+ def mapPartitions(self, f, preservesPartitioning=False):
+ """
+ Return a new DStream by applying a function to each partition of
this DStream.
+ """
+ def func(s, iterator):
+ return f(iterator)
+ return self.mapPartitionsWithIndex(func, preservesPartitioning)
+
+ def mapPartitionsWithIndex(self, f, preservesPartitioning=False):
+ """
+ Return a new DStream by applying a function to each partition of
this DStream,
+ while tracking the index of the original partition.
+ """
+ return self.transform(lambda rdd: rdd.mapPartitionsWithIndex(f,
preservesPartitioning))
+
+ def reduce(self, func):
+ """
+ Return a new DStream by reduceing the elements of this RDD using
the specified
+ commutative and associative binary operator.
+ """
+ return self.map(lambda x: (None, x)).reduceByKey(func,
1).map(lambda x: x[1])
+
+ def reduceByKey(self, func, numPartitions=None):
+ """
+ Merge the value for each key using an associative reduce function.
+
+ This will also perform the merging locally on each mapper before
+ sending results to reducer, similarly to a "combiner" in MapReduce.
+
+ Output will be hash-partitioned with C{numPartitions} partitions,
or
+ the default parallelism level if C{numPartitions} is not specified.
+ """
+ return self.combineByKey(lambda x: x, func, func, numPartitions)
+
+ def combineByKey(self, createCombiner, mergeValue, mergeCombiners,
+ numPartitions=None):
+ """
+ Count the number of elements for each key, and return the result
to the
+ master as a dictionary
+ """
+ def func(rdd):
+ return rdd.combineByKey(createCombiner, mergeValue,
mergeCombiners, numPartitions)
+ return self.transform(func)
+
+ def partitionBy(self, numPartitions, partitionFunc=portable_hash):
+ """
+ Return a copy of the DStream partitioned using the specified
partitioner.
+ """
+ return self.transform(lambda rdd: rdd.partitionBy(numPartitions,
partitionFunc))
+
+ def foreach(self, func):
+ return self.foreachRDD(lambda _, rdd: rdd.foreach(func))
+
+ def foreachRDD(self, func):
+ """
+ Apply userdefined function to all RDD in a DStream.
+ This python implementation could be expensive because it uses
callback server
+ in order to apply function to RDD in DStream.
+ This is an output operator, so this DStream will be registered as
an output
+ stream and there materialized.
+ """
+ jfunc = RDDFunction(self.ctx, func, self._jrdd_deserializer)
+ api = self._ssc._jvm.PythonDStream
+ api.callForeachRDD(self._jdstream, jfunc)
+
+ def pprint(self):
+ """
+ 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.
+ """
+ def takeAndPrint(timestamp, rdd):
+ taken = rdd.take(11)
+ print "-------------------------------------------"
+ print "Time: %s" % datetime.fromtimestamp(timestamp / 1000.0)
+ print "-------------------------------------------"
+ for record in taken[:10]:
+ print record
+ if len(taken) > 10:
+ print "..."
+ print
+
+ self.foreachRDD(takeAndPrint)
+
+ def _first(self):
+ """
+ Return the first RDD in the stream.
+ """
+ return self._take(1)[0]
+
+ def _take(self, n):
+ """
+ Return the first `n` RDDs in the stream (will start and stop).
+ """
+ results = []
+
+ def take(_, rdd):
+ if rdd and len(results) < n:
+ results.extend(rdd.take(n - len(results)))
+
+ self.foreachRDD(take)
+
+ self._ssc.start()
+ while len(results) < n:
+ time.sleep(0.01)
+ self._ssc.stop(False, True)
+ return results
+
+ def _collect(self):
+ """
+ Collect each RDDs into the returned list.
+
+ :return: list, which will have the collected items.
+ """
+ result = []
+
+ def get_output(_, rdd):
+ r = rdd.collect()
+ result.append(r)
+ self.foreachRDD(get_output)
+ return result
+
+ def mapValues(self, f):
+ """
+ Pass each value in the key-value pair RDD through a map function
+ without changing the keys; this also retains the original RDD's
+ partitioning.
+ """
+ map_values_fn = lambda (k, v): (k, f(v))
+ return self.map(map_values_fn, preservesPartitioning=True)
+
+ def flatMapValues(self, f):
+ """
+ Pass each value in the key-value pair RDD through a flatMap
function
+ without changing the keys; this also retains the original RDD's
+ partitioning.
+ """
+ flat_map_fn = lambda (k, v): ((k, x) for x in f(v))
+ return self.flatMap(flat_map_fn, preservesPartitioning=True)
+
+ def glom(self):
+ """
+ Return a new DStream in which RDD is generated by applying glom()
+ to RDD of this DStream. Applying glom() to an RDD coalesces all
+ elements within each partition into an list.
+ """
+ def func(iterator):
+ yield list(iterator)
+ return self.mapPartitions(func)
+
+ def cache(self):
+ """
+ Persist this DStream with the default storage level
(C{MEMORY_ONLY_SER}).
+ """
+ self.is_cached = True
+ self.persist(StorageLevel.MEMORY_ONLY_SER)
+ return self
+
+ def persist(self, storageLevel):
+ """
+ Set this DStream's storage level to persist its values across
operations
+ after the first time it is computed. This can only be used to
assign
+ a new storage level if the DStream does not have a storage level
set yet.
+ """
+ self.is_cached = True
+ javaStorageLevel = self.ctx._getJavaStorageLevel(storageLevel)
+ self._jdstream.persist(javaStorageLevel)
+ return self
+
+ def checkpoint(self, interval):
+ """
+ Mark this DStream for checkpointing. It will be saved to a file
inside the
+ checkpoint directory set with L{SparkContext.setCheckpointDir()}
+
+ @param interval: time in seconds, after which generated RDD will
+ be checkpointed
+ """
+ self.is_checkpointed = True
+ self._jdstream.checkpoint(self._ssc._jduration(interval))
+ return self
+
+ def groupByKey(self, numPartitions=None):
+ """
+ Return a new DStream which contains group the values for each key
in the
+ DStream into a single sequence.
+ Hash-partitions the resulting RDD with into numPartitions
partitions in
+ the DStream.
+
+ Note: If you are grouping in order to perform an aggregation (such
as a
+ sum or average) over each key, using reduceByKey will provide much
+ better performance.
+ """
+ return self.transform(lambda rdd: rdd.groupByKey(numPartitions))
+
+ def countByValue(self):
+ """
+ Return new DStream which contains the count of each unique value
in this
+ DStreeam as a (value, count) pairs.
+ """
+ return self.map(lambda x: (x, None)).reduceByKey(lambda x, y:
None).count()
+
+ def saveAsTextFiles(self, prefix, suffix=None):
+ """
+ Save this DStream as a text file, using string representations of
elements.
+ """
+
+ def saveAsTextFile(time, rdd):
+ """
+ Closure to save element in RDD in DStream as Pickled data in
file.
+ This closure is called by py4j callback server.
+ """
+ path = rddToFileName(prefix, suffix, time)
+ rdd.saveAsTextFile(path)
+
+ return self.foreachRDD(saveAsTextFile)
+
+ def saveAsPickleFiles(self, prefix, suffix=None):
+ """
+ Save this DStream as a SequenceFile of serialized objects. The
serializer
+ used is L{pyspark.serializers.PickleSerializer}, default batch size
+ is 10.
+ """
+
+ def saveAsPickleFile(time, rdd):
+ """
+ Closure to save element in RDD in the DStream as Pickled data
in file.
+ This closure is called by py4j callback server.
+ """
+ path = rddToFileName(prefix, suffix, time)
+ rdd.saveAsPickleFile(path)
+
+ return self.foreachRDD(saveAsPickleFile)
+
+ def transform(self, func):
+ """
+ Return a new DStream in which each RDD is generated by applying a
function
+ on each RDD of 'this' DStream.
+ """
+ return TransformedDStream(self, lambda t, a: func(a), True)
+
+ def transformWithTime(self, func):
+ """
+ Return a new DStream in which each RDD is generated by applying a
function
+ on each RDD of 'this' DStream.
+ """
+ return TransformedDStream(self, func, False)
+
+ def transformWith(self, func, other, keepSerializer=False):
+ """
+ Return a new DStream in which each RDD is generated by applying a
function
+ on each RDD of 'this' DStream and 'other' DStream.
+ """
+ jfunc = RDDFunction(self.ctx, lambda t, a, b: func(a, b),
self._jrdd_deserializer)
+ dstream =
self.ctx._jvm.PythonTransformed2DStream(self._jdstream.dstream(),
+
other._jdstream.dstream(), jfunc)
+ jrdd_serializer = self._jrdd_deserializer if keepSerializer else
self.ctx.serializer
+ return DStream(dstream.asJavaDStream(), self._ssc, jrdd_serializer)
+
+ def repartitions(self, numPartitions):
+ """
+ Return a new DStream with an increased or decreased level of
parallelism. Each RDD in the
+ returned DStream has exactly numPartitions partitions.
+ """
+ return self.transform(lambda rdd: rdd.repartition(numPartitions))
+
+ @property
+ def _slideDuration(self):
+ """
+ Return the slideDuration in seconds of this DStream
+ """
+ return self._jdstream.dstream().slideDuration().milliseconds() /
1000.0
+
+ def union(self, other):
+ """
+ Return a new DStream by unifying data of another DStream with this
DStream.
+ @param other Another DStream having the same interval (i.e.,
slideDuration) as this DStream.
+ """
+ if self._slideDuration != other._slideDuration:
+ raise ValueError("the two DStream should have same slide
duration")
+ return self.transformWith(lambda a, b: a.union(b), other, True)
+
+ def cogroup(self, other, numPartitions=None):
+ """
+ Return a new DStream by applying 'cogroup' between RDDs of `this`
+ DStream and `other` DStream.
+
+ Hash partitioning is used to generate the RDDs with
`numPartitions` partitions.
+ """
+ return self.transformWith(lambda a, b: a.cogroup(b,
numPartitions), other)
+
+ def join(self, other, numPartitions=None):
+ """
+ Return a new DStream by applying 'join' between RDDs of `this`
DStream and
+ `other` DStream.
+
+ Hash partitioning is used to generate the RDDs with `numPartitions`
+ partitions.
+ """
+ return self.transformWith(lambda a, b: a.join(b, numPartitions),
other)
+
+ def leftOuterJoin(self, other, numPartitions=None):
+ """
+ Return a new DStream by applying 'left outer join' between RDDs
of `this` DStream and
+ `other` DStream.
+
+ Hash partitioning is used to generate the RDDs with `numPartitions`
+ partitions.
+ """
+ return self.transformWith(lambda a, b: a.leftOuterJoin(b,
numPartitions), other)
+
+ def rightOuterJoin(self, other, numPartitions=None):
+ """
+ Return a new DStream by applying 'right outer join' between RDDs
of `this` DStream and
+ `other` DStream.
+
+ Hash partitioning is used to generate the RDDs with `numPartitions`
+ partitions.
+ """
+ return self.transformWith(lambda a, b: a.rightOuterJoin(b,
numPartitions), other)
+
+ def fullOuterJoin(self, other, numPartitions=None):
+ """
+ Return a new DStream by applying 'full outer join' between RDDs
of `this` DStream and
+ `other` DStream.
+
+ Hash partitioning is used to generate the RDDs with `numPartitions`
+ partitions.
+ """
+ return self.transformWith(lambda a, b: a.fullOuterJoin(b,
numPartitions), other)
+
+ def _jtime(self, timestamp):
+ """ Convert datetime or unix_timestamp into Time
+ """
+ if isinstance(timestamp, datetime):
+ timestamp = time.mktime(timestamp.timetuple())
+ return self.ctx._jvm.Time(long(timestamp * 1000))
+
+ def slice(self, begin, end):
+ """
+ Return all the RDDs between 'begin' to 'end' (both included)
+
+ `begin`, `end` could be datetime.datetime() or unix_timestamp
+ """
+ jrdds = self._jdstream.slice(self._jtime(begin), self._jtime(end))
+ return [RDD(jrdd, self.ctx, self._jrdd_deserializer) for jrdd in
jrdds]
+
+ def _check_window(self, window, slide):
+ duration = self._jdstream.dstream().slideDuration().milliseconds()
+ if int(window * 1000) % duration != 0:
+ raise ValueError("windowDuration must be multiple of the slide
duration (%d ms)"
+ % duration)
+ if slide and int(slide * 1000) % duration != 0:
+ raise ValueError("slideDuration must be multiple of the slide
duration (%d ms)"
+ % duration)
+
+ def window(self, windowDuration, slideDuration=None):
+ """
+ Return a new DStream in which each RDD contains all the elements
in seen in a
+ sliding window of time over this DStream.
+
+ @param windowDuration width of the window; must be a multiple of
this DStream's
+ batching interval
+ @param slideDuration 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
+ """
+ self._check_window(windowDuration, slideDuration)
+ d = self._ssc._jduration(windowDuration)
+ if slideDuration is None:
+ return DStream(self._jdstream.window(d), self._ssc,
self._jrdd_deserializer)
+ s = self._ssc._jduration(slideDuration)
+ return DStream(self._jdstream.window(d, s), self._ssc,
self._jrdd_deserializer)
+
+ def reduceByWindow(self, reduceFunc, invReduceFunc, windowDuration,
slideDuration):
+ """
+ Return a new DStream in which each RDD has a single element
generated by reducing all
+ elements in a sliding window over this DStream.
+
+ if `invReduceFunc` is not None, the reduction is done incrementally
+ using the old window's reduced value :
+ 1. reduce the new values that entered the window (e.g., adding
new counts)
+ 2. "inverse reduce" the old values that left the window (e.g.,
subtracting old counts)
+ This is more efficient than `invReduceFunc` is None.
+
+ @param reduceFunc associative reduce function
+ @param invReduceFunc inverse reduce function of `reduceFunc`
+ @param windowDuration width of the window; must be a multiple of
this DStream's
+ batching interval
+ @param slideDuration 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
+ """
+ keyed = self.map(lambda x: (1, x))
+ reduced = keyed.reduceByKeyAndWindow(reduceFunc, invReduceFunc,
+ windowDuration,
slideDuration, 1)
+ return reduced.map(lambda (k, v): v)
+
+ def countByWindow(self, windowDuration, slideDuration):
+ """
+ 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(),
+ but will be more efficient if window is large.
+ """
+ return self.map(lambda x: 1).reduceByWindow(operator.add,
operator.sub,
+ windowDuration,
slideDuration)
+
+ def countByValueAndWindow(self, windowDuration, slideDuration,
numPartitions=None):
+ """
+ Return a new DStream in which each RDD contains the count of
distinct elements in
+ RDDs in a sliding window over this DStream.
+
+ @param windowDuration width of the window; must be a multiple of
this DStream's
+ batching interval
+ @param slideDuration 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
+ @param numPartitions number of partitions of each RDD in the new
DStream.
+ """
+ keyed = self.map(lambda x: (x, 1))
+ counted = keyed.reduceByKeyAndWindow(operator.add, operator.sub,
+ windowDuration,
slideDuration, numPartitions)
+ return counted.filter(lambda (k, v): v > 0).count()
+
+ def groupByKeyAndWindow(self, windowDuration, slideDuration,
numPartitions=None):
+ """
+ Return a new DStream by applying `groupByKey` over a sliding
window.
+ Similar to `DStream.groupByKey()`, but applies it over a sliding
window.
+
+ @param windowDuration width of the window; must be a multiple of
this DStream's
+ batching interval
+ @param slideDuration 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
+ @param numPartitions Number of partitions of each RDD in the new
DStream.
+ """
+ ls = self.mapValues(lambda x: [x])
+ grouped = ls.reduceByKeyAndWindow(lambda a, b: a.extend(b) or a,
lambda a, b: a[len(b):],
+ windowDuration, slideDuration,
numPartitions)
+ return grouped.mapValues(ResultIterable)
+
+ def reduceByKeyAndWindow(self, func, invFunc, windowDuration,
slideDuration=None,
+ numPartitions=None, filterFunc=None):
+ """
+ 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 :
+ 1. reduce the new values that entered the window (e.g., adding
new counts)
+ 2. "inverse reduce" the old values that left the window (e.g.,
subtracting old counts)
+
+ `invFunc` can be None, then it will reduce all the RDDs in window,
could be slower
+ than having `invFunc`.
+
+ @param reduceFunc associative reduce function
+ @param invReduceFunc inverse function of `reduceFunc`
+ @param windowDuration width of the window; must be a multiple of
this DStream's
+ batching interval
+ @param slideDuration 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
+ @param numPartitions number of partitions of each RDD in the new
DStream.
+ @param filterFunc 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
+ """
+ self._check_window(windowDuration, slideDuration)
+ reduced = self.reduceByKey(func)
+
+ def reduceFunc(t, a, b):
+ b = b.reduceByKey(func, numPartitions)
+ r = a.union(b).reduceByKey(func, numPartitions) if a else b
+ if filterFunc:
+ r = r.filter(filterFunc)
+ return r
+
+ def invReduceFunc(t, a, b):
+ b = b.reduceByKey(func, numPartitions)
+ joined = a.leftOuterJoin(b, numPartitions)
+ return joined.mapValues(lambda (v1, v2): invFunc(v1, v2) if v2
is not None else v1)
+
+ jreduceFunc = RDDFunction(self.ctx, reduceFunc,
reduced._jrdd_deserializer)
+ if invReduceFunc:
+ jinvReduceFunc = RDDFunction(self.ctx, invReduceFunc,
reduced._jrdd_deserializer)
+ else:
+ jinvReduceFunc = None
+ if slideDuration is None:
+ slideDuration = self._slideDuration
+ dstream =
self.ctx._jvm.PythonReducedWindowedDStream(reduced._jdstream.dstream(),
+ jreduceFunc,
jinvReduceFunc,
+
self._ssc._jduration(windowDuration),
+
self._ssc._jduration(slideDuration))
+ return DStream(dstream.asJavaDStream(), self._ssc,
self.ctx.serializer)
+
+ def updateStateByKey(self, updateFunc, numPartitions=None):
+ """
+ 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 the key.
+
+ @param updateFunc State update function ([(k, vs, s)] -> [(k, s)]).
+ If `s` is None, then `k` will be eliminated.
+ """
+ def reduceFunc(t, a, b):
+ if a is None:
+ g = b.groupByKey(numPartitions).map(lambda (k, vs): (k,
list(vs), None))
+ else:
+ g = a.cogroup(b, numPartitions)
+ g = g.map(lambda (k, (va, vb)): (k, list(vb), list(va)[0]
if len(va) else None))
+ state = g.mapPartitions(lambda x: updateFunc(x))
+ return state.filter(lambda (k, v): v is not None)
+
+ jreduceFunc = RDDFunction(self.ctx, reduceFunc,
+ self.ctx.serializer,
self._jrdd_deserializer)
+ dstream =
self.ctx._jvm.PythonStateDStream(self._jdstream.dstream(), jreduceFunc)
+ return DStream(dstream.asJavaDStream(), self._ssc,
self.ctx.serializer)
+
+
+class TransformedDStream(DStream):
--- End diff --
done
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