Hi Ravindra:
I re-test my test cases mentioned above with Spark 2.3.2 + CarbonData
master branch, the query performance of carbondata are almost the same as
the parquet:
*Test result:**
SQL1: Parquet: 4.6s 4s 3.8s
CarbonData: 4.7s 3.6s 3.5s
SQL2: Parquet: 9s 8s 8s
CarbonData: 9s 8s 8s
The query performance of CarbonData has improved a lot (SQL1: 12s to 4s,
SQL2: 18 to 8s) while the query performance of parquet has also improved
(SQL2: 10s to 8s). That's great.
But I saw the test result you mentioned in
'http://apache-carbondata-dev-mailing-list-archive.1130556.n5.nabble.com/CarbonData-Performance-Optimization-td62950.html',
the query performance of carbondata were almost better than the parquet. I
want to know how you tested those cases? And are there other optimizations
that have not been merged yet?
Regards,
Zhichao.
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