hanv89
diff --git a/‎pubsub/streaming-analytics/PubSubToGCS.py
Lines changed: 29 additions & 28 deletions b/‎pubsub/streaming-analytics/PubSubToGCS.py
Lines changed: 29 additions & 28 deletions
@@ -1,3 +1,18 @@
+# Copyright 2019 Google Inc.
+#
+# Licensed 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.
+
+# [START pubsub_to_gcs]
 import argparse
 import datetime
 import json
@@ -9,8 +24,9 @@
 
 
 class GroupWindowsIntoBatches(beam.PTransform):
-    """A composite transform that enriches Pub/Sub messages by publish
-    timestamps and then groups them based on their window information.
+    """A composite transform that groups Pub/Sub messages based on publish
+    time and outputs a list of dictionaries, where each contains one message
+    and its publish timestamp.
     """
 
     def __init__(self, window_size):
@@ -19,32 +35,23 @@ def __init__(self, window_size):
 
     def expand(self, pcoll):
         return (pcoll
-                # Assigns window info to each element in the PCollection.
+                # Assigns window info to each Pub/Sub message based on its
+                # publish timestamp.
                 | beam.WindowInto(window.FixedWindows(self.window_size))
-                # Transform each element by adding publish timestamp info.
                 | 'Process Pub/Sub Message' >> (beam.ParDo(AddTimestamps()))
-                # Use a dummy key to group all the elements in this window.
+                # Use a dummy key to group the elements in the same window.
                 | 'Add Dummy Key' >> beam.Map(lambda elem: (None, elem))
                 | 'Groupby' >> beam.GroupByKey()
                 | 'Abandon Dummy Key' >> beam.MapTuple(lambda _, val: val))
 
 
 class AddTimestamps(beam.DoFn):
-    """A distributed processing function that acts on the elements in
-    the PCollection.
-    """
 
     def process(self, element, publish_time=beam.DoFn.TimestampParam):
-        """Enrich each Pub/Sub message with its publish timestamp.
-
-        Args:
-            element (bytes): A Pub/Sub message.
-            publish_time: Default to the publish timestamp returned by the
-            Pub/Sub server that's been bound to the message by Apache Beam
-            at runtime.
-
-        Yields:
-            dict of str: Message body and publish timestamp.
+        """Processes each incoming windowed element by extracting the Pub/Sub
+        message body and its publish timestamp into a dictionary.
+        `publish_time` defaults to the publish timestamp returned by the
+        Pub/Sub server and is accessible by Beam at runtime.
         """
 
         yield {
@@ -60,13 +67,7 @@ def __init__(self, output_path):
         self.output_path = output_path
 
     def process(self, batch, window=beam.DoFn.WindowParam):
-        """Write one batch per file to a Google Cloud Storage bucket.
-
-        Args:
-            batch (list of dict): Each dictionary contains one message and its
-            publish timestamp.
-            window: Window inforamtion bound to the batch.
-        """
+        """Write one batch per file to a Google Cloud Storage bucket. """
 
         ts_format = '%H:%M'
         window_start = window.start.to_utc_datetime().strftime(ts_format)
@@ -95,9 +96,8 @@ def run(argv=None):
 
     # `save_main_session` is set to true because one or more DoFn's rely on
     # globally imported modules.
-    pipeline_options = PipelineOptions(pipeline_args,
-                                       streaming=True,
-                                       save_main_session=True)
+    pipeline_options = PipelineOptions(
+        pipeline_args, streaming=True, save_main_session=True)
 
     with beam.Pipeline(options=pipeline_options) as pipeline:
         (pipeline
@@ -111,3 +111,4 @@ def run(argv=None):
 if __name__ == '__main__': # noqa
     logging.getLogger().setLevel(logging.INFO)
     run()
+# [END pubsub_to_gcs]