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https://issues.apache.org/jira/browse/BEAM-8335?focusedWorklogId=342131&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-342131
]
ASF GitHub Bot logged work on BEAM-8335:
----------------------------------------
Author: ASF GitHub Bot
Created on: 12/Nov/19 20:39
Start Date: 12/Nov/19 20:39
Worklog Time Spent: 10m
Work Description: rohdesamuel commented on pull request #9720:
[BEAM-8335] Add initial modules for interactive streaming support
URL: https://github.com/apache/beam/pull/9720#discussion_r345432351
##########
File path:
sdks/python/apache_beam/runners/interactive/caching/streaming_cache.py
##########
@@ -0,0 +1,179 @@
+#
+# 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 __future__ import absolute_import
+
+from apache_beam.portability.api.beam_interactive_api_pb2 import
InteractiveStreamHeader
+from apache_beam.portability.api.beam_interactive_api_pb2 import
InteractiveStreamRecord
+from apache_beam.portability.api.beam_runner_api_pb2 import TestStreamPayload
+from apache_beam.utils import timestamp
+from apache_beam.utils.timestamp import Timestamp
+
+
+class StreamingCache(object):
+ """Abstraction that holds the logic for reading and writing to cache.
+ """
+ def __init__(self, readers):
+ self._readers = readers
+
+ class Reader(object):
+ """Abstraction that reads from PCollection readers.
+
+ This class is an Abstraction layer over multiple PCollection readers to be
+ used for supplying the Interactive Service with TestStream events.
+
+ This class is also responsible for holding the state of the clock,
injecting
+ clock advancement events, and watermark advancement events.
+ """
+ def __init__(self, readers):
+ # This timestamp is used as the monotonic clock to order events in the
+ # replay.
+ self._monotonic_clock = timestamp.Timestamp.of(0)
+
+ # The maximum timestamp read.
+ self._target_timestamp = timestamp.Timestamp.of(0)
+
+ # The PCollection cache readers.
+ self._readers = {}
+
+ # The file headers that are metadata for that particular PCollection.
+ self._headers = {}
+
+ # The header allows for metadata about an entire stream, so that the data
+ # isn't copied per record.
+ readers = [r.read() for r in readers]
+ for r in readers:
+ header = InteractiveStreamHeader()
+ header.ParseFromString(next(r))
+
+ # Main PCollections in Beam have a tag as None. Deserializing a Proto
+ # with an empty tag becomes an empty string. Here we normalize to what
+ # Beam expects.
+ self._headers[header.tag if header.tag else None] = header
+ self._readers[header.tag if header.tag else None] = r
+
+ # The watermarks per tag. Useful for introspection in the stream.
+ self._watermarks = {tag: timestamp.MIN_TIMESTAMP for tag in
self._headers}
+
+ # The most recently read timestamp per tag.
+ self._stream_times = {tag: timestamp.MIN_TIMESTAMP
+ for tag in self._headers}
+
+ def _read_next(self):
+ """Reads the next iteration of elements from each stream.
+ """
+ records = []
+ for tag, r in self._readers.items():
+ # The target_timestamp is the maximum timestamp that was read from the
+ # stream. Some readers may have elements that are less than this. Thus,
+ # we skip all readers that already have elements that are at this
+ # timestamp so that we don't read everything into memory.
+ if self._stream_times[tag] >= self._target_timestamp:
+ continue
+ try:
+ record = InteractiveStreamRecord()
+ record.ParseFromString(next(r))
+ records.append((tag, record))
+ self._stream_times[tag] = Timestamp.from_proto(
+ record.processing_time)
+ except StopIteration:
+ pass
+ return records
+
+ def read(self):
+ """Reads records from PCollection readers.
+ """
+ # We use a generator here because the underlying readers may have to much
+ # data to read into memory.
+
+ events = []
+ while True:
+ # Read the next set of events. The read events will most likely be
+ # out of order if there are multiple readers. Here we sort them into
+ # a more manageable state.
+ events = events + self._read_next()
+ events = sorted(events,
+ key=lambda x: Timestamp.from_proto(
+ x[1].processing_time),
+ reverse=True)
+ if not events:
+ break
+
+ # Retrieves the minimum timestamp from the read events.
+ min_timestamp = (
+ lambda: Timestamp.from_proto(events[-1][1].processing_time)
+ if events else timestamp.MAX_TIMESTAMP)
+
+ # Get the next largest timestamp in the stream. This is used as the
+ # timestamp for readers to "catch-up" to. This will only read from
+ # readers with a timestamp less than this.
+ self._target_timestamp = min_timestamp()
+
+ # Loop through the elements with the correct timestamp.
+ while events and min_timestamp() <= self._target_timestamp:
+ tag, r = events.pop()
+
+ # First advance the clock to match the time of the stream. This has
+ # a side-effect of also advancing this cache's clock.
+ curr_timestamp = Timestamp.from_proto(r.processing_time)
+ if curr_timestamp > self._monotonic_clock:
+ yield self._advance_processing_time(curr_timestamp)
+
+ # Then, send either a new element or watermark.
+ if r.HasField('element'):
+ yield self._add_element(r.element, tag)
+ elif r.HasField('watermark'):
+ yield self._advance_watermark(r.watermark, tag)
+ self._target_timestamp = min_timestamp()
+
+ def _add_element(self, element, tag):
+ """Constructs an AddElement event for the specified element and tag.
+ """
+ return TestStreamPayload.Event(
+ element_event=TestStreamPayload.Event.AddElements(
+ elements=[element], tag=tag))
+
+ def _advance_processing_time(self, new_timestamp):
+ """Advances the internal clock and returns an AdvanceProcessingTime
event.
+ """
+ advancy_by = new_timestamp.micros - self._monotonic_clock.micros
+ e = TestStreamPayload.Event(
+ processing_time_event=TestStreamPayload.Event.AdvanceProcessingTime(
+ advance_duration=advancy_by))
+ self._monotonic_clock = new_timestamp
+ return e
+
+ def _advance_watermark(self, watermark, tag):
+ """Advances the watermark for tag and returns AdvanceWatermark event.
+ """
+ self._watermarks[tag] = Timestamp.from_proto(watermark)
+ e = TestStreamPayload.Event(
+ watermark_event=TestStreamPayload.Event.AdvanceWatermark(
+ new_watermark=self._watermarks[tag].micros, tag=tag))
+ return e
+
+ def stream_time(self):
+ return self._monotonic_clock
+
+ def watermarks(self):
+ return self._watermarks
Review comment:
Removed
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Issue Time Tracking
-------------------
Worklog Id: (was: 342131)
Time Spent: 21h 40m (was: 21.5h)
> Add streaming support to Interactive Beam
> -----------------------------------------
>
> Key: BEAM-8335
> URL: https://issues.apache.org/jira/browse/BEAM-8335
> Project: Beam
> Issue Type: Improvement
> Components: runner-py-interactive
> Reporter: Sam Rohde
> Assignee: Sam Rohde
> Priority: Major
> Time Spent: 21h 40m
> Remaining Estimate: 0h
>
> This issue tracks the work items to introduce streaming support to the
> Interactive Beam experience. This will allow users to:
> * Write and run a streaming job in IPython
> * Automatically cache records from unbounded sources
> * Add a replay experience that replays all cached records to simulate the
> original pipeline execution
> * Add controls to play/pause/stop/step individual elements from the cached
> records
> * Add ability to inspect/visualize unbounded PCollections
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