Github user JoshRosen commented on a diff in the pull request:
https://github.com/apache/spark/pull/2538#discussion_r18373682
--- 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
--- End diff --
We have a `context` method that returns the StreamingContext and this
public `ctx` field that returns the underlying SparkContext. This seems like
it could be confusing, so maybe we should make the `ctx` field private. On the
other hand, RDD has a `ctx` field that returns the SparkContext, so this is
consistent with that.
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