Github user tdas commented on a diff in the pull request:
https://github.com/apache/spark/pull/2538#discussion_r18378148
--- Diff: python/pyspark/streaming/dstream.py ---
@@ -0,0 +1,624 @@
+#
+# 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 py4j.protocol import Py4JJavaError
+
+from pyspark import RDD
+from pyspark.storagelevel import StorageLevel
+from pyspark.streaming.util import rddToFileName, TransformFunction
+from pyspark.rdd import portable_hash
+from pyspark.resultiterable import ResultIterable
+
+__all__ = ["DStream"]
+
+
+class DStream(object):
+ """
+ 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 L{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 L{StreamingContext} or it can be
+ generated by transforming existing DStreams using operations such as
+ `map`, `window` and `reduceByKeyAndWindow`. 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.
+
+ DStreams internally is characterized by a few basic properties:
+ - A list of other DStreams that the DStream depends on
+ - A time interval at which the DStream generates an RDD
+ - A function that is used to generate an RDD after each time interval
+ """
+ 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 in which each RDD has a single element
+ generated by counting each RDD of 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):
+ """
+ Return a new DStream by applying a function to all elements of
+ this DStream, and then flattening the results
+ """
+ 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 in which each RDD is generated by applying
+ mapPartitions() to each RDDs 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 in which each RDD is generated by applying
+ mapPartitionsWithIndex() to each RDDs of this DStream.
+ """
+ return self.transform(lambda rdd: rdd.mapPartitionsWithIndex(f,
preservesPartitioning))
+
+ def reduce(self, func):
+ """
+ Return a new DStream in which each RDD has a single element
+ generated by reducing each RDD of this DStream.
+ """
+ return self.map(lambda x: (None, x)).reduceByKey(func,
1).map(lambda x: x[1])
+
+ def reduceByKey(self, func, numPartitions=None):
+ """
+ Return a new DStream by applying reduceByKey to each RDD.
+ """
+ if numPartitions is None:
+ numPartitions = self.ctx.defaultParallelism
+ return self.combineByKey(lambda x: x, func, func, numPartitions)
+
+ def combineByKey(self, createCombiner, mergeValue, mergeCombiners,
+ numPartitions=None):
+ """
+ Return a new DStream by applying combineByKey to each RDD.
+ """
+ if numPartitions is None:
+ numPartitions = self.ctx.defaultParallelism
+
+ 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 in which each RDD are partitioned
+ using the specified partitioner.
+ """
+ return self.transform(lambda rdd: rdd.partitionBy(numPartitions,
partitionFunc))
+
+ def foreachRDD(self, func):
+ """
+ Apply a function to each RDD in this DStream.
+ """
+ jfunc = TransformFunction(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.
+ """
+ def takeAndPrint(time, rdd):
+ taken = rdd.take(11)
+ print "-------------------------------------------"
+ print "Time: %s" % time
+ print "-------------------------------------------"
+ for record in taken[:10]:
+ print record
+ if len(taken) > 10:
+ print "..."
+ print
+
+ self.foreachRDD(takeAndPrint)
+
+ def mapValues(self, f):
+ """
+ Return a new DStream by applying a map function to the value of
+ each key-value pairs in 'this' DStream without changing the key.
+ """
+ map_values_fn = lambda (k, v): (k, f(v))
+ return self.map(map_values_fn, preservesPartitioning=True)
+
+ def flatMapValues(self, f):
+ """
+ Return a new DStream by applying a flatmap function to the value
+ of each key-value pairs in 'this' DStream without changing the key.
+ """
+ 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.
+ """
+ def func(iterator):
+ yield list(iterator)
+ return self.mapPartitions(func)
+
+ def cache(self):
+ """
+ Persist the RDDs of 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):
+ """
+ Persist the RDDs of this DStream with the given storage level
+ """
+ self.is_cached = True
+ javaStorageLevel = self.ctx._getJavaStorageLevel(storageLevel)
+ self._jdstream.persist(javaStorageLevel)
+ return self
+
+ def checkpoint(self, interval):
+ """
+ Enable periodic checkpointing of RDDs of this DStream
+
+ @param interval: time in seconds, after each period of that,
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 by applying groupByKey on each RDD.
+ """
+ if numPartitions is None:
+ numPartitions = self.ctx.defaultParallelism
+ return self.transform(lambda rdd: rdd.groupByKey(numPartitions))
+
+ def countByValue(self):
+ """
+ Return a new DStream in which each RDD contains the counts of each
+ distinct value in each RDD of this DStream.
+ """
+ return self.map(lambda x: (x, None)).reduceByKey(lambda x, y:
None).count()
+
+ def saveAsTextFiles(self, prefix, suffix=None):
+ """
+ Save each RDD in this DStream as at text file, using string
+ representation of elements.
+ """
+ def saveAsTextFile(t, rdd):
+ path = rddToFileName(prefix, suffix, t)
+ try:
+ rdd.saveAsTextFile(path)
+ except Py4JJavaError as e:
+ # after recovered from checkpointing, the foreachRDD may
+ # be called twice
+ if 'FileAlreadyExistsException' not in str(e):
+ raise
+ return self.foreachRDD(saveAsTextFile)
+
+ def _saveAsPickleFiles(self, prefix, suffix=None):
--- End diff --
This is mirror the RDD api of saveAsPickFile. I am not entirely sure
whether to expose this, but not point removing the implementation, so just
having it as private should be fine for now.
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