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https://issues.apache.org/jira/browse/CARBONDATA-1373?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
xuchuanyin resolved CARBONDATA-1373.
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Resolution: Fixed
> Enhance update performance in carbondata
> ----------------------------------------
>
> Key: CARBONDATA-1373
> URL:
https://issues.apache.org/jira/browse/CARBONDATA-1373> Project: CarbonData
> Issue Type: Improvement
> Components: data-load
> Reporter: xuchuanyin
> Assignee: xuchuanyin
> Fix For: 1.2.0
>
> Time Spent: 4h 10m
> Remaining Estimate: 0h
>
> # Scenario
> Recently I have tested the update feature provided in Carbondata and found its poor performance.
> I had a table containing about 14 million records with about 370 columns(no dictionary columns) and the data files are about 3.8 GB in total. All the data files were in one segment.
> I performed an update SQL which update a column for all the records and the SQL looked like `UPDATE myTable SET (col1)=(col1+1000) WHERE TRUE`. In my environment, the update job failed with 'executor lost errors'. And I found 'spill data' related messages in the container logs.
> # Analyze
> I've read about the implementation of update-delete in Carbondata in ISSUE#440. The update consists a delete and an insert operation. And the error occurred during the insert operation.
> After studying the code, I have found that while doing inserting, the updated records are grouped by the `segmentId`, which means all the recoreds in one segment will be processed in only one task, thus will cause task failure when the amount of input data is quite large.
> # Solution
> We should improve the parallelism when doing update for a segment.
> I append a random key to the `segmentId` to increase the partition number before doing the insertion stage and then remove the suffix when doing the real insertion.
> I have tested in my example and the job finished in about 13 minutes successfully. The records were updated as expected.
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