Major Description
- 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.
