Limit zero shouldn't use any readers if we know the schema. Look at the upstream constant reduction rule. We should be able to go straight from calcite algebra to result without hitting any execution code. Think direct response same as explain. On Sep 24, 2015 10:46 PM, "Jinfeng Ni" <jinfengn...@gmail.com> wrote:
> The query itself is quite simple; it normally should not take 60 > seconds for planning. I guess most of the planning time is spent on > reading parquet metadata. The metadata caching that Steven worked > should help in this case. > > > On Thu, Sep 24, 2015 at 10:42 PM, Sudheesh Katkam <skat...@maprtech.com> > wrote: > > For the table below, 33 seconds for execution (includes parquet reader > initialization) and 60 seconds for planning. > > > >> On Sep 24, 2015, at 10:01 PM, Jinfeng Ni <jinfengn...@gmail.com> wrote: > >> > >> "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 <skat...@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 > >>> > > >
