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https://issues.apache.org/jira/browse/BEAM-9822?focusedWorklogId=429671&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-429671
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ASF GitHub Bot logged work on BEAM-9822:
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Author: ASF GitHub Bot
Created on: 01/May/20 21:43
Start Date: 01/May/20 21:43
Worklog Time Spent: 10m
Work Description: allenpradeep commented on pull request #11570:
URL: https://github.com/apache/beam/pull/11570#issuecomment-622580239
This is great niel. With these changes, there are 3 modes of using
SpannerIO write.
a) Use the conventional way(as it was till now) with a grouping factor where
data is grouped, sorted, batched and written as per parameters
b) Batching without grouping - Set grouping factor as 1 with a larger
batched bytes or cells. This will just ensure data is just batched without sort.
c) No Batching - Set any of the max rows or max mutations or batch bytes to
0 or 1.
Questions:
1) What mode should our import pipeline use? Should it use option b as data
in AVRO seems already sorted?
2) Where should we document these modes of operation so that some customer
can use these?
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Issue Time Tracking
-------------------
Worklog Id: (was: 429671)
Time Spent: 50m (was: 40m)
> SpannerIO: Reduce memory usage - especially when streaming
> ----------------------------------------------------------
>
> Key: BEAM-9822
> URL: https://issues.apache.org/jira/browse/BEAM-9822
> Project: Beam
> Issue Type: Bug
> Components: io-java-gcp
> Affects Versions: 2.20.0
> Reporter: Niel Markwick
> Priority: Major
> Labels: google-cloud-spanner
> Time Spent: 50m
> Remaining Estimate: 0h
>
> SpannerIO uses a lot of memory.
> In Streaming Dataflow, it uses many times as much (because dataflow creates
> many worker threads)
> Lower the memory use, and change default parameters during streaming to use
> smaller batches and disable grouping.
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