[SPARK-2377] Python API for Streaming

This patch brings Python API for Streaming.

This patch is based on work from @giwa

Author: giwa <ugw.gi.world@gmail.com>
Author: Ken Takagiwa <ken@Kens-MacBook-Pro.local>
Author: Davies Liu <davies.liu@gmail.com>
Author: Ken Takagiwa <ken@kens-mbp.gateway.sonic.net>
Author: Tathagata Das <tathagata.das1565@gmail.com>
Author: Ken <ugw.gi.world@gmail.com>
Author: Ken Takagiwa <ugw.gi.world@gmail.com>
Author: Matthew Farrellee <matt@redhat.com>

Closes #2538 from davies/streaming and squashes the following commits:

64561e4 [Davies Liu] fix tests
331ecce [Davies Liu] fix example
3e2492b [Davies Liu] change updateStateByKey() to easy API
182be73 [Davies Liu] Merge branch 'master' of github.com:apache/spark into streaming
02d0575 [Davies Liu] add wrapper for foreachRDD()
bebeb4a [Davies Liu] address all comments
6db00da [Davies Liu] Merge branch 'master' of github.com:apache/spark into streaming
8380064 [Davies Liu] Merge branch 'master' of github.com:apache/spark into streaming
52c535b [Davies Liu] remove fix for sum()
e108ec1 [Davies Liu]  address comments
37fe06f [Davies Liu] use random port for callback server
d05871e [Davies Liu] remove reuse of PythonRDD
be5e5ff [Davies Liu] merge branch of env, make tests stable.
8071541 [Davies Liu] Merge branch 'env' into streaming
c7bbbce [Davies Liu] fix sphinx docs
6bb9d91 [Davies Liu] Merge branch 'master' of github.com:apache/spark into streaming
4d0ea8b [Davies Liu] clear reference of SparkEnv after stop
54bd92b [Davies Liu] improve tests
c2b31cb [Davies Liu] Merge branch 'master' of github.com:apache/spark into streaming
7a88f9f [Davies Liu] rollback RDD.setContext(), use textFileStream() to test checkpointing
bd8a4c2 [Davies Liu] fix scala style
7797c70 [Davies Liu] refactor
ff88bec [Davies Liu] rename RDDFunction to TransformFunction
d328aca [Davies Liu] fix serializer in queueStream
6f0da2f [Davies Liu] recover from checkpoint
fa7261b [Davies Liu] refactor
a13ff34 [Davies Liu] address comments
8466916 [Davies Liu] support checkpoint
9a16bd1 [Davies Liu] change number of partitions during tests
b98d63f [Davies Liu] change private[spark] to private[python]
eed6e2a [Davies Liu] rollback not needed changes
e00136b [Davies Liu] address comments
069a94c [Davies Liu] fix the number of partitions during window()
338580a [Davies Liu] change _first(), _take(), _collect() as private API
19797f9 [Davies Liu] clean up
6ebceca [Davies Liu] add more tests
c40c52d [Davies Liu] change first(), take(n) to has the same behavior as RDD
98ac6c2 [Davies Liu] support ssc.transform()
b983f0f [Davies Liu] address comments
847f9b9 [Davies Liu] add more docs, add first(), take()
e059ca2 [Davies Liu] move check of window into Python
fce0ef5 [Davies Liu] rafactor of foreachRDD()
7001b51 [Davies Liu] refactor of queueStream()
26ea396 [Davies Liu] refactor
74df565 [Davies Liu] fix print and docs
b32774c [Davies Liu] move java_import into streaming
604323f [Davies Liu] enable streaming tests
c499ba0 [Davies Liu] remove Time and Duration
3f0fb4b [Davies Liu] refactor fix tests
c28f520 [Davies Liu] support updateStateByKey
d357b70 [Davies Liu] support windowed dstream
bd13026 [Davies Liu] fix examples
eec401e [Davies Liu] refactor, combine TransformedRDD, fix reuse PythonRDD, fix union
9a57685 [Davies Liu] fix python style
bd27874 [Davies Liu] fix scala style
7339be0 [Davies Liu] delete tests
7f53086 [Davies Liu] support transform(), refactor and cleanup
df098fc [Davies Liu] Merge branch 'master' into giwa
550dfd9 [giwa] WIP fixing 1.1 merge
5cdb6fa [giwa] changed for SCCallSiteSync
e685853 [giwa] meged with rebased 1.1 branch
2d32a74 [giwa] added some StreamingContextTestSuite
4a59e1e [giwa] WIP:added more test for StreamingContext
8ffdbf1 [giwa] added atexit to handle callback server
d5f5fcb [giwa] added comment for StreamingContext.sparkContext
63c881a [giwa] added StreamingContext.sparkContext
d39f102 [giwa] added StreamingContext.remember
d542743 [giwa] clean up code
2fdf0de [Matthew Farrellee] Fix scalastyle errors
c0a06bc [giwa] delete not implemented functions
f385976 [giwa] delete inproper comments
b0f2015 [giwa] added comment in dstream._test_output
bebb3f3 [giwa] remove the last brank line
fbed8da [giwa] revert pom.xml
8ed93af [giwa] fixed explanaiton
066ba90 [giwa] revert pom.xml
fa4af88 [giwa] remove duplicated import
6ae3caa [giwa] revert pom.xml
7dc7391 [giwa] fixed typo
62dc7a3 [giwa] clean up exmples
f04882c [giwa] clen up examples
b171ec3 [giwa] fixed pep8 violation
f198d14 [giwa] clean up code
3166d31 [giwa] clean up
c00e091 [giwa] change test case not to use awaitTermination
e80647e [giwa] adopted the latest compression way of python command
58e41ff [giwa] merge with master
455e5af [giwa] removed wasted print in DStream
af336b7 [giwa] add comments
ddd4ee1 [giwa] added TODO coments
99ce042 [giwa] added saveAsTextFiles and saveAsPickledFiles
2a06cdb [giwa] remove waste duplicated code
c5ecfc1 [giwa] basic function test cases are passed
8dcda84 [giwa] all tests are passed if numSlice is 2 and the numver of each input is over 4
795b2cd [giwa] broke something
1e126bf [giwa] WIP: solved partitioned and None is not recognized
f67cf57 [giwa] added mapValues and flatMapVaules WIP for glom and mapPartitions test
953deb0 [giwa] edited the comment to add more precise description
af610d3 [giwa] removed unnesessary changes
c1d546e [giwa] fixed PEP-008 violation
99410be [giwa] delete waste file
b3b0362 [giwa] added basic operation test cases
9cde7c9 [giwa] WIP added test case
bd3ba53 [giwa] WIP
5c04a5f [giwa] WIP: added PythonTestInputStream
019ef38 [giwa] WIP
1934726 [giwa] update comment
376e3ac [giwa] WIP
932372a [giwa] clean up dstream.py
0b09cff [giwa] added stop in StreamingContext
92e333e [giwa] implemented reduce and count function in Dstream
1b83354 [giwa] Removed the waste line
88f7506 [Ken Takagiwa] Kill py4j callback server properly
54b5358 [Ken Takagiwa] tried to restart callback server
4f07163 [Tathagata Das] Implemented DStream.foreachRDD in the Python API using Py4J callback server.
fe02547 [Ken Takagiwa] remove waste file
2ad7bd3 [Ken Takagiwa] clean up codes
6197a11 [Ken Takagiwa] clean up code
eb4bf48 [Ken Takagiwa] fix map function
98c2a00 [Ken Takagiwa] added count operation but this implementation need double check
58591d2 [Ken Takagiwa] reduceByKey is working
0df7111 [Ken Takagiwa] delete old file
f485b1d [Ken Takagiwa] fied input of socketTextDStream
dd6de81 [Ken Takagiwa] initial commit for socketTextStream
247fd74 [Ken Takagiwa] modified the code base on comment in https://github.com/tdas/spark/pull/10
4bcb318 [Ken Takagiwa] implementing transform function in Python
38adf95 [Ken Takagiwa] added reducedByKey not working yet
66fcfff [Ken Takagiwa] modify dstream.py to fix indent error
41886c2 [Ken Takagiwa] comment PythonDStream.PairwiseDStream
0b99bec [Ken] initial commit for pySparkStreaming
c214199 [giwa] added testcase for combineByKey
5625bdc [giwa] added gorupByKey testcase
10ab87b [giwa] added sparkContext as input parameter in StreamingContext
10b5b04 [giwa] removed wasted print in DStream
e54f986 [giwa] add comments
16aa64f [giwa] added TODO coments
74535d4 [giwa] added saveAsTextFiles and saveAsPickledFiles
f76c182 [giwa] remove waste duplicated code
18c8723 [giwa] modified streaming test case to add coment
13fb44c [giwa] basic function test cases are passed
3000b2b [giwa] all tests are passed if numSlice is 2 and the numver of each input is over 4
ff14070 [giwa] broke something
bcdec33 [giwa] WIP: solved partitioned and None is not recognized
270a9e1 [giwa] added mapValues and flatMapVaules WIP for glom and mapPartitions test
bb10956 [giwa] edited the comment to add more precise description
253a863 [giwa] removed unnesessary changes
3d37822 [giwa] fixed PEP-008 violation
f21cab3 [giwa] delete waste file
878bad7 [giwa] added basic operation test cases
ce2acd2 [giwa] WIP added test case
9ad6855 [giwa] WIP
1df77f5 [giwa] WIP: added PythonTestInputStream
1523b66 [giwa] WIP
8a0fbbc [giwa] update comment
fe648e3 [giwa] WIP
29c2bc5 [giwa] initial commit for testcase
4d40d63 [giwa] clean up dstream.py
c462bb3 [giwa] added stop in StreamingContext
d2c01ba [giwa] clean up examples
3c45cd2 [giwa] implemented reduce and count function in Dstream
b349649 [giwa] Removed the waste line
3b498e1 [Ken Takagiwa] Kill py4j callback server properly
84a9668 [Ken Takagiwa] tried to restart callback server
9ab8952 [Tathagata Das] Added extra line.
05e991b [Tathagata Das] Added missing file
b1d2a30 [Tathagata Das] Implemented DStream.foreachRDD in the Python API using Py4J callback server.
678e854 [Ken Takagiwa] remove waste file
0a8bbbb [Ken Takagiwa] clean up codes
bab31c1 [Ken Takagiwa] clean up code
72b9738 [Ken Takagiwa] fix map function
d3ee86a [Ken Takagiwa] added count operation but this implementation need double check
15feea9 [Ken Takagiwa] edit python sparkstreaming example
6f98e50 [Ken Takagiwa] reduceByKey is working
c455c8d [Ken Takagiwa] added reducedByKey not working yet
dc6995d [Ken Takagiwa] delete old file
b31446a [Ken Takagiwa] fixed typo of network_workdcount.py
ccfd214 [Ken Takagiwa] added doctest for pyspark.streaming.duration
0d1b954 [Ken Takagiwa] fied input of socketTextDStream
f746109 [Ken Takagiwa] initial commit for socketTextStream
bb7ccf3 [Ken Takagiwa] remove unused import in python
224fc5e [Ken Takagiwa] add empty line
d2099d8 [Ken Takagiwa] sorted the import following Spark coding convention
5bac7ec [Ken Takagiwa] revert streaming/pom.xml
e1df940 [Ken Takagiwa] revert pom.xml
494cae5 [Ken Takagiwa] remove not implemented DStream functions in python
17a74c6 [Ken Takagiwa] modified the code base on comment in https://github.com/tdas/spark/pull/10
1a0f065 [Ken Takagiwa] implementing transform function in Python
d7b4d6f [Ken Takagiwa] added reducedByKey not working yet
87438e2 [Ken Takagiwa] modify dstream.py to fix indent error
b406252 [Ken Takagiwa] comment PythonDStream.PairwiseDStream
454981d [Ken] initial commit for pySparkStreaming
150b94c [giwa] added some StreamingContextTestSuite
f7bc8f9 [giwa] WIP:added more test for StreamingContext
ee50c5a [giwa] added atexit to handle callback server
fdc9125 [giwa] added comment for StreamingContext.sparkContext
f5bfb70 [giwa] added StreamingContext.sparkContext
da09768 [giwa] added StreamingContext.remember
d68b568 [giwa] clean up code
4afa390 [giwa] clean up code
1fd6bc7 [Ken Takagiwa] Merge pull request #2 from mattf/giwa-master
d9d59fe [Matthew Farrellee] Fix scalastyle errors
67473a9 [giwa] delete not implemented functions
c97377c [giwa] delete inproper comments
2ea769e [giwa] added comment in dstream._test_output
3b27bd4 [giwa] remove the last brank line
acfcaeb [giwa] revert pom.xml
93f7637 [giwa] fixed explanaiton
50fd6f9 [giwa] revert pom.xml
4f82c89 [giwa] remove duplicated import
9d1de23 [giwa] revert pom.xml
7339df2 [giwa] fixed typo
9c85e48 [giwa] clean up exmples
24f95db [giwa] clen up examples
0d30109 [giwa] fixed pep8 violation
b7dab85 [giwa] improve test case
583e66d [giwa] move tests for streaming inside streaming directory
1d84142 [giwa] remove unimplement test
f0ea311 [giwa] clean up code
171edeb [giwa] clean up
4dedd2d [giwa] change test case not to use awaitTermination
268a6a5 [giwa] Changed awaitTermination not to call awaitTermincation in Scala. Just use time.sleep instread
09a28bf [giwa] improve testcases
58150f5 [giwa] Changed the test case to focus the test operation
199e37f [giwa] adopted the latest compression way of python command
185fdbf [giwa] merge with master
f1798c4 [giwa] merge with master
e70f706 [giwa] added testcase for combineByKey
e162822 [giwa] added gorupByKey testcase
97742fe [giwa] added sparkContext as input parameter in StreamingContext
14d4c0e [giwa] removed wasted print in DStream
6d8190a [giwa] add comments
4aa99e4 [giwa] added TODO coments
e9fab72 [giwa] added saveAsTextFiles and saveAsPickledFiles
94f2b65 [giwa] remove waste duplicated code
580fbc2 [giwa] modified streaming test case to add coment
99e4bb3 [giwa] basic function test cases are passed
7051a84 [giwa] all tests are passed if numSlice is 2 and the numver of each input is over 4
35933e1 [giwa] broke something
9767712 [giwa] WIP: solved partitioned and None is not recognized
4f2d7e6 [giwa] added mapValues and flatMapVaules WIP for glom and mapPartitions test
33c0f94d [giwa] edited the comment to add more precise description
774f18d [giwa] removed unnesessary changes
3a671cc [giwa] remove export PYSPARK_PYTHON in spark submit
8efa266 [giwa] fixed PEP-008 violation
fa75d71 [giwa] delete waste file
7f96294 [giwa] added basic operation test cases
3dda31a [giwa] WIP added test case
1f68b78 [giwa] WIP
c05922c [giwa] WIP: added PythonTestInputStream
1fd12ae [giwa] WIP
c880a33 [giwa] update comment
5d22c92 [giwa] WIP
ea4b06b [giwa] initial commit for testcase
5a9b525 [giwa] clean up dstream.py
79c5809 [giwa] added stop in StreamingContext
189dcea [giwa] clean up examples
b8d7d24 [giwa] implemented reduce and count function in Dstream
b6468e6 [giwa] Removed the waste line
b47b5fd [Ken Takagiwa] Kill py4j callback server properly
19ddcdd [Ken Takagiwa] tried to restart callback server
c9fc124 [Tathagata Das] Added extra line.
4caae3f [Tathagata Das] Added missing file
4eff053 [Tathagata Das] Implemented DStream.foreachRDD in the Python API using Py4J callback server.
5e822d4 [Ken Takagiwa] remove waste file
aeaf8a5 [Ken Takagiwa] clean up codes
9fa249b [Ken Takagiwa] clean up code
05459c6 [Ken Takagiwa] fix map function
a9f4ecb [Ken Takagiwa] added count operation but this implementation need double check
d1ee6ca [Ken Takagiwa] edit python sparkstreaming example
0b8b7d0 [Ken Takagiwa] reduceByKey is working
d25d5cf [Ken Takagiwa] added reducedByKey not working yet
7f7c5d1 [Ken Takagiwa] delete old file
967dc26 [Ken Takagiwa] fixed typo of network_workdcount.py
57fb740 [Ken Takagiwa] added doctest for pyspark.streaming.duration
4b69fb1 [Ken Takagiwa] fied input of socketTextDStream
02f618a [Ken Takagiwa] initial commit for socketTextStream
4ce4058 [Ken Takagiwa] remove unused import in python
856d98e [Ken Takagiwa] add empty line
490e338 [Ken Takagiwa] sorted the import following Spark coding convention
5594bd4 [Ken Takagiwa] revert pom.xml
2adca84 [Ken Takagiwa] remove not implemented DStream functions in python
e551e13 [Ken Takagiwa] add coment for hack why PYSPARK_PYTHON is needed in spark-submit
3758175 [Ken Takagiwa] add coment for hack why PYSPARK_PYTHON is needed in spark-submit
c5518b4 [Ken Takagiwa] modified the code base on comment in https://github.com/tdas/spark/pull/10
dcf243f [Ken Takagiwa] implementing transform function in Python
9af03f4 [Ken Takagiwa] added reducedByKey not working yet
6e0d9c7 [Ken Takagiwa] modify dstream.py to fix indent error
e497b9b [Ken Takagiwa] comment PythonDStream.PairwiseDStream
5c3a683 [Ken] initial commit for pySparkStreaming
665bfdb [giwa] added testcase for combineByKey
a3d2379 [giwa] added gorupByKey testcase
636090a [giwa] added sparkContext as input parameter in StreamingContext
e7ebb08 [giwa] removed wasted print in DStream
d8b593b [giwa] add comments
ea9c873 [giwa] added TODO coments
89ae38a [giwa] added saveAsTextFiles and saveAsPickledFiles
e3033fc [giwa] remove waste duplicated code
a14c7e1 [giwa] modified streaming test case to add coment
536def4 [giwa] basic function test cases are passed
2112638 [giwa] all tests are passed if numSlice is 2 and the numver of each input is over 4
080541a [giwa] broke something
0704b86 [giwa] WIP: solved partitioned and None is not recognized
90a6484 [giwa] added mapValues and flatMapVaules WIP for glom and mapPartitions test
a65f302 [giwa] edited the comment to add more precise description
bdde697 [giwa] removed unnesessary changes
e8c7bfc [giwa] remove export PYSPARK_PYTHON in spark submit
3334169 [giwa] fixed PEP-008 violation
db0a303 [giwa] delete waste file
2cfd3a0 [giwa] added basic operation test cases
90ae568 [giwa] WIP added test case
a120d07 [giwa] WIP
f671cdb [giwa] WIP: added PythonTestInputStream
56fae45 [giwa] WIP
e35e101 [giwa] Merge branch 'master' into testcase
ba5112d [giwa] update comment
28aa56d [giwa] WIP
fb08559 [giwa] initial commit for testcase
a613b85 [giwa] clean up dstream.py
c40c0ef [giwa] added stop in StreamingContext
31e4260 [giwa] clean up examples
d2127d6 [giwa] implemented reduce and count function in Dstream
48f7746 [giwa] Removed the waste line
0f83eaa [Ken Takagiwa] delete py4j 0.8.1
1679808 [Ken Takagiwa] Kill py4j callback server properly
f96cd4e [Ken Takagiwa] tried to restart callback server
fe86198 [Ken Takagiwa] add py4j 0.8.2.1 but server is not launched
1064fe0 [Ken Takagiwa] Merge branch 'master' of https://github.com/giwa/spark
28c6620 [Ken Takagiwa] Implemented DStream.foreachRDD in the Python API using Py4J callback server
85b0fe1 [Ken Takagiwa] Merge pull request #1 from tdas/python-foreach
54e2e8c [Tathagata Das] Added extra line.
e185338 [Tathagata Das] Added missing file
a778d4b [Tathagata Das] Implemented DStream.foreachRDD in the Python API using Py4J callback server.
cc2092b [Ken Takagiwa] remove waste file
d042ac6 [Ken Takagiwa] clean up codes
84a021f [Ken Takagiwa] clean up code
bd20e17 [Ken Takagiwa] fix map function
d01a125 [Ken Takagiwa] added count operation but this implementation need double check
7d05109 [Ken Takagiwa] merge with remote branch
ae464e0 [Ken Takagiwa] edit python sparkstreaming example
04af046 [Ken Takagiwa] reduceByKey is working
3b6d7b0 [Ken Takagiwa] implementing transform function in Python
571d52d [Ken Takagiwa] added reducedByKey not working yet
5720979 [Ken Takagiwa] delete old file
e604fcb [Ken Takagiwa] fixed typo of network_workdcount.py
4b7c08b [Ken Takagiwa] Merge branch 'master' of https://github.com/giwa/spark
ce7d426 [Ken Takagiwa] added doctest for pyspark.streaming.duration
a8c9fd5 [Ken Takagiwa] fixed for socketTextStream
a61fa9e [Ken Takagiwa] fied input of socketTextDStream
1e84f41 [Ken Takagiwa] initial commit for socketTextStream
6d012f7 [Ken Takagiwa] remove unused import in python
25d30d5 [Ken Takagiwa] add empty line
6e0a64a [Ken Takagiwa] sorted the import following Spark coding convention
fa4a7fc [Ken Takagiwa] revert streaming/pom.xml
8f8202b [Ken Takagiwa] revert streaming pom.xml
c9d79dd [Ken Takagiwa] revert pom.xml
57e3e52 [Ken Takagiwa] remove not implemented DStream functions in python
0a516f5 [Ken Takagiwa] add coment for hack why PYSPARK_PYTHON is needed in spark-submit
a7a0b5c [Ken Takagiwa] add coment for hack why PYSPARK_PYTHON is needed in spark-submit
72bfc66 [Ken Takagiwa] modified the code base on comment in https://github.com/tdas/spark/pull/10
69e9cd3 [Ken Takagiwa] implementing transform function in Python
94a0787 [Ken Takagiwa] added reducedByKey not working yet
88068cf [Ken Takagiwa] modify dstream.py to fix indent error
1367be5 [Ken Takagiwa] comment PythonDStream.PairwiseDStream
eb2b3ba [Ken] Merge remote-tracking branch 'upstream/master'
d8e51f9 [Ken] initial commit for pySparkStreaming

1 files changed, 621 insertions, 0 deletions

diff --git a/python/pyspark/streaming/dstream.py b/python/pyspark/streaming/dstream.py
new file mode 100644
index 0000000000..5ae5cf07f0
--- /dev/null
+++ b/python/pyspark/streaming/dstream.py
@@ -0,0 +1,621 @@
+#
+# 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._sc = 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)]).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._sc.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._sc.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.
+        """
+        if func.func_code.co_argcount == 1:
+            old_func = func
+            func = lambda t, rdd: old_func(rdd)
+        jfunc = TransformFunction(self._sc, 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._sc._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._sc.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)
+
+    # TODO: uncomment this until we have ssc.pickleFileStream()
+    # def saveAsPickleFiles(self, prefix, suffix=None):
+    #     """
+    #     Save each RDD in this DStream as at binary file, the elements are
+    #     serialized by pickle.
+    #     """
+    #     def saveAsPickleFile(t, rdd):
+    #         path = rddToFileName(prefix, suffix, t)
+    #         try:
+    #             rdd.saveAsPickleFile(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(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.
+
+        `func` can have one argument of `rdd`, or have two arguments of
+        (`time`, `rdd`)
+        """
+        if func.func_code.co_argcount == 1:
+            oldfunc = func
+            func = lambda t, rdd: oldfunc(rdd)
+        assert func.func_code.co_argcount == 2, "func should take one or two arguments"
+        return TransformedDStream(self, func)
+
+    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.
+
+        `func` can have two arguments of (`rdd_a`, `rdd_b`) or have three
+        arguments of (`time`, `rdd_a`, `rdd_b`)
+        """
+        if func.func_code.co_argcount == 2:
+            oldfunc = func
+            func = lambda t, a, b: oldfunc(a, b)
+        assert func.func_code.co_argcount == 3, "func should take two or three arguments"
+        jfunc = TransformFunction(self._sc, func, self._jrdd_deserializer, other._jrdd_deserializer)
+        dstream = self._sc._jvm.PythonTransformed2DStream(self._jdstream.dstream(),
+                                                          other._jdstream.dstream(), jfunc)
+        jrdd_serializer = self._jrdd_deserializer if keepSerializer else self._sc.serializer
+        return DStream(dstream.asJavaDStream(), self._ssc, jrdd_serializer)
+
+    def repartition(self, numPartitions):
+        """
+        Return a new DStream with an increased or decreased level of parallelism.
+        """
+        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.
+        """
+        if numPartitions is None:
+            numPartitions = self._sc.defaultParallelism
+        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.
+        """
+        if numPartitions is None:
+            numPartitions = self._sc.defaultParallelism
+        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.
+        """
+        if numPartitions is None:
+            numPartitions = self._sc.defaultParallelism
+        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.
+        """
+        if numPartitions is None:
+            numPartitions = self._sc.defaultParallelism
+        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.
+        """
+        if numPartitions is None:
+            numPartitions = self._sc.defaultParallelism
+        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._sc._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._sc, self._jrdd_deserializer) for jrdd in jrdds]
+
+    def _validate_window_param(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._validate_window_param(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._validate_window_param(windowDuration, slideDuration)
+        if numPartitions is None:
+            numPartitions = self._sc.defaultParallelism
+
+        reduced = self.reduceByKey(func, numPartitions)
+
+        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 = TransformFunction(self._sc, reduceFunc, reduced._jrdd_deserializer)
+        if invReduceFunc:
+            jinvReduceFunc = TransformFunction(self._sc, invReduceFunc, reduced._jrdd_deserializer)
+        else:
+            jinvReduceFunc = None
+        if slideDuration is None:
+            slideDuration = self._slideDuration
+        dstream = self._sc._jvm.PythonReducedWindowedDStream(reduced._jdstream.dstream(),
+                                                             jreduceFunc, jinvReduceFunc,
+                                                             self._ssc._jduration(windowDuration),
+                                                             self._ssc._jduration(slideDuration))
+        return DStream(dstream.asJavaDStream(), self._ssc, self._sc.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. If this function returns None, then
+                           corresponding state key-value pair will be eliminated.
+        """
+        if numPartitions is None:
+            numPartitions = self._sc.defaultParallelism
+
+        def reduceFunc(t, a, b):
+            if a is None:
+                g = b.groupByKey(numPartitions).mapValues(lambda vs: (list(vs), None))
+            else:
+                g = a.cogroup(b, numPartitions)
+                g = g.mapValues(lambda (va, vb): (list(vb), list(va)[0] if len(va) else None))
+            state = g.mapValues(lambda (vs, s): updateFunc(vs, s))
+            return state.filter(lambda (k, v): v is not None)
+
+        jreduceFunc = TransformFunction(self._sc, reduceFunc,
+                                        self._sc.serializer, self._jrdd_deserializer)
+        dstream = self._sc._jvm.PythonStateDStream(self._jdstream.dstream(), jreduceFunc)
+        return DStream(dstream.asJavaDStream(), self._ssc, self._sc.serializer)
+
+
+class TransformedDStream(DStream):
+    """
+    TransformedDStream is an DStream generated by an Python function
+    transforming each RDD of an DStream to another RDDs.
+
+    Multiple continuous transformations of DStream can be combined into
+    one transformation.
+    """
+    def __init__(self, prev, func):
+        self._ssc = prev._ssc
+        self._sc = self._ssc._sc
+        self._jrdd_deserializer = self._sc.serializer
+        self.is_cached = False
+        self.is_checkpointed = False
+        self._jdstream_val = None
+
+        if (isinstance(prev, TransformedDStream) and
+                not prev.is_cached and not prev.is_checkpointed):
+            prev_func = prev.func
+            self.func = lambda t, rdd: func(t, prev_func(t, rdd))
+            self.prev = prev.prev
+        else:
+            self.prev = prev
+            self.func = func
+
+    @property
+    def _jdstream(self):
+        if self._jdstream_val is not None:
+            return self._jdstream_val
+
+        jfunc = TransformFunction(self._sc, self.func, self.prev._jrdd_deserializer)
+        dstream = self._sc._jvm.PythonTransformedDStream(self.prev._jdstream.dstream(), jfunc)
+        self._jdstream_val = dstream.asJavaDStream()
+        return self._jdstream_val