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https://issues.apache.org/jira/browse/BEAM-10603?focusedWorklogId=480124&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-480124
]
ASF GitHub Bot logged work on BEAM-10603:
-----------------------------------------
Author: ASF GitHub Bot
Created on: 08/Sep/20 13:23
Start Date: 08/Sep/20 13:23
Worklog Time Spent: 10m
Work Description: pabloem commented on a change in pull request #12703:
URL: https://github.com/apache/beam/pull/12703#discussion_r482544524
##########
File path: sdks/python/apache_beam/runners/interactive/recording_manager.py
##########
@@ -63,6 +63,12 @@ def var(self):
"""Returns the variable named that defined this PCollection."""
return self._var
+ def cache_key(self):
Review comment:
Generally doing this is not necessary. You can name the attribute
`self.cache_key`, and users can access the attribute directly, unless there's a
strong reason not to.
##########
File path: sdks/python/apache_beam/runners/interactive/recording_manager.py
##########
@@ -258,16 +278,44 @@ def _watch(self, pcolls):
ie.current_env().watch(
{'anonymous_pcollection_{}'.format(id(pcoll)): pcoll})
- def clear(self, pcolls):
+ def _clear(self, pipeline_instrument):
# type: (List[beam.pvalue.PCollection]) -> None
- """Clears the cache of the given PCollections."""
+ """Clears the recording of all non-source PCollections."""
cache_manager = ie.current_env().get_cache_manager(self.user_pipeline)
- for pc in pcolls:
- cache_key = self._pipeline_instrument.cache_key(pc)
+
+ # Only clear the PCollections that aren't being populated from the
+ # BackgroundCachingJob.
+ all_cached = set(
+ str(c.to_key()) for c in pipeline_instrument.cacheables.values())
Review comment:
It's a little unsafe to use string representation as keys, no? Should
CacheKey implement a hash method instead?
##########
File path: sdks/python/apache_beam/runners/interactive/recording_manager.py
##########
@@ -258,16 +278,44 @@ def _watch(self, pcolls):
ie.current_env().watch(
{'anonymous_pcollection_{}'.format(id(pcoll)): pcoll})
- def clear(self, pcolls):
+ def _clear(self, pipeline_instrument):
# type: (List[beam.pvalue.PCollection]) -> None
- """Clears the cache of the given PCollections."""
+ """Clears the recording of all non-source PCollections."""
cache_manager = ie.current_env().get_cache_manager(self.user_pipeline)
- for pc in pcolls:
- cache_key = self._pipeline_instrument.cache_key(pc)
+
+ # Only clear the PCollections that aren't being populated from the
+ # BackgroundCachingJob.
+ all_cached = set(
+ str(c.to_key()) for c in pipeline_instrument.cacheables.values())
Review comment:
It seems that we're using the string representation everywhere, so it
may represent a large change - and I see it;s a carefully written repr method,
so it's not necessary for this PR.
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Issue Time Tracking
-------------------
Worklog Id: (was: 480124)
Time Spent: 23h 20m (was: 23h 10m)
> 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: 23h 20m
> 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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