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https://issues.apache.org/jira/browse/BEAM-10698?focusedWorklogId=470145&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-470145
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ASF GitHub Bot logged work on BEAM-10698:
-----------------------------------------
Author: ASF GitHub Bot
Created on: 13/Aug/20 08:49
Start Date: 13/Aug/20 08:49
Worklog Time Spent: 10m
Work Description: angoenka commented on pull request #12561:
URL: https://github.com/apache/beam/pull/12561#issuecomment-673350523
I see, My understanding is that streaming appliance and streaming engine
should not have any pipeline graph difference.
The idea was to to make streaming engine vs streaming appliance choice at
the service so that we can control it.
Secondly, as streaming engine has a billing implication, we don't want to
change the default behavior from streaming appliance to streaming engine
without announcement.
Was their a specific test which was failing. In that case, we should set the
enable_streaming_engine flag in the test.
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Issue Time Tracking
-------------------
Worklog Id: (was: 470145)
Time Spent: 1h (was: 50m)
> X-lang Kafka is broken for DataflowRunner due to timestamps being out of bound
> ------------------------------------------------------------------------------
>
> Key: BEAM-10698
> URL: https://issues.apache.org/jira/browse/BEAM-10698
> Project: Beam
> Issue Type: Bug
> Components: cross-language, io-py-kafka
> Reporter: Chamikara Madhusanka Jayalath
> Assignee: Boyuan Zhang
> Priority: P1
> Fix For: 2.24.0
>
> Time Spent: 1h
> Remaining Estimate: 0h
>
> Seems like this is a regression introduced by
> [https://github.com/apache/beam/pull/11749]
> I have a short term fix here: [https://github.com/apache/beam/pull/12557]
> We should either.
> (1) Include [https://github.com/apache/beam/pull/12557] in the release branch
> (2) Find the underlying issue that produces wrong timestamps and do that fix
> (3) Revert [https://github.com/apache/beam/pull/11749] from the 2.24.0
> release branch.
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