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From Jinfeng Ni <jinfengn...@gmail.com>
Subject Re: Improving Performance of SELECT * FROM hive.table LIMIT 0
Date Fri, 25 Sep 2015 05:01:22 GMT
"FragmentExecutor took 1,070,926 ms to create RecordBatch tree."

1,070,926 ms ~ 17.x  minutes. In other words, the majority of 18
minutes of execution in hive case is spent on the initialization of
Hive readers. If we want to improve "limit n", we probably should make
"lazy" initialization of Hive reader; only when Drill has to read rows
from reader, we do the initialization. Otherwise, to initialize all
the readers before reading any single row means long setup time for
limit "n" query, when n is relative small.

For the second case, the 94 seconds query time seems to be too long as
well. I guess most of the time is spent on parquet reader
initialization (?)



On Thu, Sep 24, 2015 at 9:32 PM, Sudheesh Katkam <skatkam@maprtech.com> wrote:
> Hey y'all,
>
> ### Short Question:
>
> How do we improve performance of SELECT * FROM plugin.table LIMIT 0?
>
> ### Extended Question:
>
> While investigating DRILL-3623 <https://issues.apache.org/jira/browse/DRILL-3623>,
I did an analysis to see where we spend time for SELECT * FROM hive.table LIMIT 0 query.
>
> ## Setup:
> Copy the drill/sample-data/region.parquet (x 20000) into a DFS (MapR-FS in my case) directory
named region. Create a Hive external table pointing to region. Run Drill with default configuration.
>
> ## Now there are two ways to query this table:
>
>> SELECT * FROM hive.region LIMIT 0;
> +--------------+---------+------------+
> | r_regionkey  | r_name  | r_comment  |
> +--------------+---------+------------+
> +--------------+---------+------------+
> No rows selected (1203.179 seconds)
> ...
>
>> SELECT * FROM dfs.test.region LIMIT 0;
> +--------------+---------+------------+
> | r_regionkey  | r_name  | r_comment  |
> +--------------+---------+------------+
> +--------------+---------+------------+
> No rows selected (94.396 seconds)
>
> Currently, we use HiveRecordReader for the first case and ParquetRecordReader in the
second case. With DRILL-3209 <https://issues.apache.org/jira/browse/DRILL-3209>, both
queries will use ParquetRecordReader. However, for formats that are non-native to Drill or
other storage plugins, we still face this problem. Summarizing the query profile,
> +-------+-----------+---------------+----------------+
> | Query | Fragments | Planning time | Execution time |
> +-------+-----------+---------------+----------------+
> | hive  | 1         | ~2 min        | ~18 min        |
> | dfs   | 1         | ~1 min        | ~33 sec        |
> +-------+-----------+---------------+----------------+
>
> ## The time hogs:
>
> # Planning time in both cases needs to improve. How?
>
> # With respect to execution, in the first case ImplCreator.getExec(…) call in the FragmentExecutor
took 1,070,926 ms to create RecordBatch tree. There are 20,000 readers being initialized in
HiveScanBatchCreator. How do we avoid this? What are the implications of chained impersonation
(opening readers in ctor() rather than in setup())?
>
> ### Extending further:
>
> This can be generalized to any "LIMIT n" query with n is a small number. For n > 0,
we parallelize scanning. So LIMIT 1 query runs faster than LIMIT 0. However there is a sweet
"n" after which parallelization hurts.
>
> ###
>
> Thank you,
> Sudheesh
>

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