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https://issues.apache.org/jira/browse/BEAM-10603?focusedWorklogId=478118&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-478118
]
ASF GitHub Bot logged work on BEAM-10603:
-----------------------------------------
Author: ASF GitHub Bot
Created on: 02/Sep/20 20:41
Start Date: 02/Sep/20 20:41
Worklog Time Spent: 10m
Work Description: davidyan74 commented on a change in pull request #12703:
URL: https://github.com/apache/beam/pull/12703#discussion_r482420061
##########
File path: sdks/python/apache_beam/runners/interactive/recording_manager_test.py
##########
@@ -288,18 +347,122 @@ def test_basic_wordcount(self):
# Create the recording objects. By calling `record` a new PipelineFragment
# is started to compute the given PCollections and cache to disk.
rm = RecordingManager(p)
- recording = rm.record([elems], max_n=3, max_duration_secs=500)
- stream = recording.stream(elems)
- recording.wait_until_finish()
+ numbers_recording = rm.record([numbers], max_n=3, max_duration_secs=500)
+ numbers_stream = numbers_recording.stream(numbers)
+ numbers_recording.wait_until_finish()
# Once the pipeline fragment completes, we can read from the stream and
know
# that all elements were written to cache.
- elems = list(stream.read())
+ elems = list(numbers_stream.read())
expected_elems = [
WindowedValue(i, MIN_TIMESTAMP, [GlobalWindow()]) for i in range(3)
]
self.assertListEqual(elems, expected_elems)
+ # Make an extra recording and test the description.
+ letters_recording = rm.record([letters], max_n=3, max_duration_secs=500)
+ letters_recording.wait_until_finish()
+
+ self.assertEqual(
+ rm.describe()['size'],
+ numbers_recording.describe()['size'] +
+ letters_recording.describe()['size'])
+
+ rm.cancel()
+
+ @unittest.skipIf(
+ sys.version_info < (3, 6, 0),
+ 'This test requires at least Python 3.6 to work.')
+ def test_cancel_stops_recording(self):
+ # Add the TestStream so that it can be cached.
+ ib.options.capturable_sources.add(TestStream)
+
+ p = beam.Pipeline(
+ InteractiveRunner(), options=PipelineOptions(streaming=True))
+ elems = (
+ p
+ | TestStream().advance_watermark_to(0).advance_processing_time(
+ 1).add_elements(list(range(10))).advance_processing_time(1))
+ squares = elems | beam.Map(lambda x: x**2)
+
+ # Watch the local scope for Interactive Beam so that referenced
PCollections
+ # will be cached.
+ ib.watch(locals())
+
+ # This is normally done in the interactive_utils when a transform is
+ # applied but needs an IPython environment. So we manually run this here.
+ ie.current_env().track_user_pipelines()
+
+ # Get the recording then the BackgroundCachingJob.
Review comment:
Are we still calling it BackgroundCachingJob?
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Issue Time Tracking
-------------------
Worklog Id: (was: 478118)
Time Spent: 20h 40m (was: 20.5h)
> Large Source Recording for Interarctive Runner
> ----------------------------------------------
>
> Key: BEAM-10603
> URL: https://issues.apache.org/jira/browse/BEAM-10603
> Project: Beam
> Issue Type: Improvement
> Components: runner-py-interactive
> Reporter: Sam Rohde
> Assignee: Sam Rohde
> Priority: P1
> Time Spent: 20h 40m
> Remaining Estimate: 0h
>
> This changes the Interactive Runner to create a long-running background
> caching job that is decoupled from the user pipeline. When a user invokes a
> collect() or show(), it will read from the cache to compute the requested
> PCollections. Previously, the user would have to wait for the cache to be
> fully written to. This allows for the user to start experimenting immediately.
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