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https://issues.apache.org/jira/browse/BEAM-9822?focusedWorklogId=435158&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-435158
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ASF GitHub Bot logged work on BEAM-9822:
----------------------------------------
Author: ASF GitHub Bot
Created on: 19/May/20 20:02
Start Date: 19/May/20 20:02
Worklog Time Spent: 10m
Work Description: TheNeuralBit commented on a change in pull request
#11532:
URL: https://github.com/apache/beam/pull/11532#discussion_r427566080
##########
File path:
sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/spanner/SpannerIO.java
##########
@@ -1066,7 +1079,12 @@ public SpannerWriteResult
expand(PCollection<MutationGroup> input) {
spec.getBatchSizeBytes(),
spec.getMaxNumMutations(),
spec.getMaxNumRows(),
- spec.getGroupingFactor(),
+ // Do not group on streaming unless explicitly
set.
+ spec.getGroupingFactor()
+ .orElse(
+ input.isBounded() == IsBounded.BOUNDED
Review comment:
> They already can set groupingFactorb to 1 if they want...
Ha yeah sorry that was unclear. At the time I thought that groupingFactor =
1 enabled the optimization in https://github.com/apache/beam/pull/11529, so I
was wondering if this was really necessary since users could just enable them
by setting grouping factor manually. But I see now that grouping is separate
from batching. And its disabling batching that enables your other PR.
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Issue Time Tracking
-------------------
Worklog Id: (was: 435158)
Time Spent: 6h 20m (was: 6h 10m)
> 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, 2.21.0
> Reporter: Niel Markwick
> Assignee: Niel Markwick
> Priority: P2
> Labels: google-cloud-spanner
> Fix For: 2.22.0
>
> Time Spent: 6h 20m
> 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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