Ruslan Dautkhanov created SQOOP-2920:
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Summary: sqoop performance deteriorates significantly on wide
datasets; sqoop 100% on cpu
Key: SQOOP-2920
URL: https://issues.apache.org/jira/browse/SQOOP-2920
Project: Sqoop
Issue Type: Bug
Components: connectors/oracle, hive-integration, metastore
Affects Versions: 1.4.5
Environment: - sqoop export on a very wide dataset (over 700 columns)
- sqoop export to oracle
- subset of columns is exported (using --columns argument)
- parquet files
- --table --hcatalog-database --hcatalog-table options are used
Reporter: Ruslan Dautkhanov
Priority: Critical
We sqoop export from datalake to Oracle quite often.
Every time we sqoop "narrow" datasets, Oracle always have scalability issues
(3-node all-flash Oracle RAC) normally can't keep up with more than 45-55 sqoop
mappers. Map-reduce framework shows sqoop mappers are not so loaded.
On wide datasets, this picture is quite opposite. Oracle shows 95% of sessions
are bored and waiting for new INSERTs. Even when we go over hundred of mappers.
Sqoop has serious scalability issues on very wide datasets. (Our company
normally has very wide datasets)
For example, on the last sqoop export:
Started ~2.5 hours ago and 95 mappers already accumulated
CPU time spent (ms) 1,065,858,760
(looking at this metric through map-reduce framework stats)
1 million seconds of CPU time.
Or 11219.57 per mapper. Which is roughly 3.11 hours of CPU time per mapper.
So they are 100% on cpu.
Will also attach jstack files.
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