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Posted to issues@beam.apache.org by "ASF GitHub Bot (Jira)" <ji...@apache.org> on 2019/11/27 15:42:00 UTC
[jira] [Work logged] (BEAM-8825) OOM when writing large numbers of
'narrow' rows
[ https://issues.apache.org/jira/browse/BEAM-8825?focusedWorklogId=350486&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-350486 ]
ASF GitHub Bot logged work on BEAM-8825:
----------------------------------------
Author: ASF GitHub Bot
Created on: 27/Nov/19 15:41
Start Date: 27/Nov/19 15:41
Worklog Time Spent: 10m
Work Description: nielm commented on pull request #10232: [BEAM-8825] Add limit on number of mutated rows to batching/sorting stages.
URL: https://github.com/apache/beam/pull/10232
Fixes a potential OOM when very narrow row mutations are added (ie
<5 cells, <100 bytes).
The overhead of storing a mutation is significant, so with the default
grouping of 5000*1000 cells, potentially many millions of mutated rows
can be stored in memory during the grouping phase, leading to an OOM.
This change adds a 3rd limit to the grouping by number of mutated rows per batch - default 500 (which corresponds to 500K rows in the grouping stage).
@chamikaramj
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Issue Time Tracking
-------------------
Worklog Id: (was: 350486)
Remaining Estimate: 0h
Time Spent: 10m
> OOM when writing large numbers of 'narrow' rows
> -----------------------------------------------
>
> Key: BEAM-8825
> URL: https://issues.apache.org/jira/browse/BEAM-8825
> Project: Beam
> Issue Type: Bug
> Components: io-java-gcp
> Affects Versions: 2.9.0, 2.10.0, 2.11.0, 2.12.0, 2.13.0, 2.14.0, 2.15.0, 2.16.0, 2.17.0
> Reporter: Niel Markwick
> Assignee: Niel Markwick
> Priority: Major
> Fix For: 2.18.0
>
> Time Spent: 10m
> Remaining Estimate: 0h
>
> SpannerIO can OOM when writing large numbers of 'narrow' rows.
>
> SpannerIO puts input mutation elements into batches for efficient writing.
> These batches are limited by number of cells mutated, and size of data written (5000 cells, 1MB data). SpannerIO groups enough mutations to build 1000 of these groups (5M cells, 1GB data), then sorts and batches them.
> When the number of cells and size of data is very small (<5 cells, <100 bytes), the memory overhead of storing millions of mutations for batching is significant, and can lead to OOMs.
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