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https://issues.apache.org/jira/browse/BEAM-10603?focusedWorklogId=477384&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-477384
]
ASF GitHub Bot logged work on BEAM-10603:
-----------------------------------------
Author: ASF GitHub Bot
Created on: 01/Sep/20 18:14
Start Date: 01/Sep/20 18:14
Worklog Time Spent: 10m
Work Description: rohdesamuel commented on a change in pull request
#12701:
URL: https://github.com/apache/beam/pull/12701#discussion_r481335695
##########
File path: sdks/python/apache_beam/runners/direct/test_stream_impl.py
##########
@@ -276,11 +276,20 @@ def _stream_events_from_rpc(endpoint, output_tags, coder,
channel, is_alive):
output_ids=[str(tag) for tag in output_tags])
event_stream = stub.Events(event_request)
- for e in event_stream:
- channel.put(_TestStream.test_stream_payload_to_events(e, coder))
- if not is_alive():
+ try:
+ for e in event_stream:
+ channel.put(_TestStream.test_stream_payload_to_events(e, coder))
+ if not is_alive():
+ return
+ except grpc.RpcError as e:
+ # This happens when the Python interpreter shuts down or whn in a
+ # notebook environment when the kernel is interrupted.
+ if e.code() == grpc.StatusCode.UNAVAILABLE:
Review comment:
So far I've only seen UNAVAILABLE as a non-error code. But looking at
https://developers.google.com/maps-booking/reference/grpc-api/status_codes I
think we can also add CANCELLED to not forward the exception.
At L290, we do have a finally clause which will run regardless, so the
endofstream will always be written to the channel.
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Issue Time Tracking
-------------------
Worklog Id: (was: 477384)
Time Spent: 16h 20m (was: 16h 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: 16h 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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