Re: Erratically behaving query needs optimization

Em 21/08/2019 04:30, Barbu Paul - Gheorghe escreveu:
> I wonder how I missed that... probabily because of the "WHERE" clause
> in what I already had.
>
> I indexed by scheduler_task_executions.device_id and the new plan is
> as follows: https://explain.depesz.com/s/cQRq
>
> Can it be further improved?
>
> Limit  (cost=138511.45..138519.36 rows=2 width=54) (actual
> time=598.703..618.524 rows=2 loops=1)
>    Buffers: shared hit=242389 read=44098
>    ->  Unique  (cost=138511.45..138527.26 rows=4 width=54) (actual
> time=598.701..598.878 rows=2 loops=1)
>          Buffers: shared hit=242389 read=44098
>          ->  Sort  (cost=138511.45..138519.36 rows=3162 width=54)
> (actual time=598.699..598.767 rows=2052 loops=1)
>                Sort Key: results.attribute_id, results."timestamp" DESC
>                Sort Method: quicksort  Memory: 641kB
>                Buffers: shared hit=242389 read=44098
>                ->  Gather  (cost=44082.11..138327.64 rows=3162
> width=54) (actual time=117.548..616.456 rows=4102 loops=1)
>                      Workers Planned: 2
>                      Workers Launched: 2
>                      Buffers: shared hit=242389 read=44098
>                      ->  Nested Loop  (cost=43082.11..137011.44
> rows=1318 width=54) (actual time=47.436..525.664 rows=1367 loops=3)
>                            Buffers: shared hit=242389 read=44098
>                            ->  Parallel Hash Join
> (cost=43081.68..124545.34 rows=24085 width=4) (actual
> time=33.099..469.958 rows=19756 loops=3)
>                                  Hash Cond:
> (scheduler_operation_executions.task_execution_id =
> scheduler_task_executions.id)
>                                  Buffers: shared hit=32518 read=44098
>                                  ->  Parallel Seq Scan on
> scheduler_operation_executions  (cost=0.00..74945.82 rows=2482982
> width=8) (actual time=8.493..245.190 rows=1986887 loops=3)
>                                        Buffers: shared hit=6018 read=44098
>                                  ->  Parallel Hash
> (cost=42881.33..42881.33 rows=16028 width=4) (actual
> time=23.272..23.272 rows=13558 loops=3)
>                                        Buckets: 65536  Batches: 1
> Memory Usage: 2112kB
>                                        Buffers: shared hit=26416
>                                        ->  Parallel Bitmap Heap Scan on
> scheduler_task_executions  (cost=722.55..42881.33 rows=16028 width=4)
> (actual time=27.290..61.563 rows=40675 loops=1)
>                                              Recheck Cond: (device_id = 97)
>                                              Heap Blocks: exact=26302
>                                              Buffers: shared hit=26416
>                                              ->  Bitmap Index Scan on
> scheduler_task_executions_device_id_idx  (cost=0.00..712.93 rows=38467
> width=0) (actual time=17.087..17.087 rows=40675 loops=1)
>                                                    Index Cond: (device_id = 97)
>                                                    Buffers: shared hit=114
>                            ->  Index Scan using
> index_operation_execution_id_asc on results  (cost=0.43..0.51 rows=1
> width=58) (actual time=0.003..0.003 rows=0 loops=59269)
>                                  Index Cond: (operation_execution_id =
> scheduler_operation_executions.id)
>                                  Filter: ((data IS NOT NULL) AND (data
> <> '<NullData/>'::text) AND (attribute_id = ANY ('{4,5}'::integer[]))
> AND (object_id = 1955) AND (data_access_result = 'SUCCESS'::text))
>                                  Rows Removed by Filter: 0
>                                  Buffers: shared hit=209871
> Planning Time: 2.327 ms
> Execution Time: 618.935 ms
>
> On Tue, Aug 20, 2019 at 5:54 PM Luís Roberto Weck
> <luisroberto@siscobra.com.br> wrote:
>> Em 20/08/2019 10:54, Barbu Paul - Gheorghe escreveu:
>>> Hello,
>>> I'm running "PostgreSQL 11.2, compiled by Visual C++ build 1914,
>>> 64-bit" and I have a query that runs several times per user action
>>> (9-10 times).
>>> The query takes a long time to execute, specially at first, due to
>>> cold caches I think, but the performance varies greatly during a run
>>> of the application (while applying the said action by the user several
>>> times).
>>>
>>> My tables are only getting bigger with time, not much DELETEs and even
>>> less UPDATEs as far as I can tell.
>>>
>>> Problematic query:
>>>
>>> EXPLAIN (ANALYZE,BUFFERS)
>>> SELECT DISTINCT ON (results.attribute_id) results.timestamp,
>>> results.data FROM results
>>>       JOIN scheduler_operation_executions ON
>>> scheduler_operation_executions.id = results.operation_execution_id
>>>       JOIN scheduler_task_executions ON scheduler_task_executions.id =
>>> scheduler_operation_executions.task_execution_id
>>> WHERE scheduler_task_executions.device_id = 97
>>>       AND results.data <> '<NullData/>'
>>>       AND results.data IS NOT NULL
>>>       AND results.object_id = 1955
>>>       AND results.attribute_id IN (4, 5) -- possibly a longer list here
>>>       AND results.data_access_result = 'SUCCESS'
>>> ORDER BY results.attribute_id, results.timestamp DESC
>>> LIMIT 2 -- limit by the length of the attributes list
>>>
>>> In words: I want the latest (ORDER BY results.timestamp DESC) results
>>> of a device (scheduler_task_executions.device_id = 97 - hence the
>>> joins results -> scheduler_operation_executions ->
>>> scheduler_task_executions)
>>> for a given object and attributes with some additional constraints on
>>> the data column. But I only want the latest attributes for which we
>>> have results, hence the DISTINCT ON (results.attribute_id) and LIMIT.
>>>
>>> First run: https://explain.depesz.com/s/qh4C
>>> Limit  (cost=157282.39..157290.29 rows=2 width=54) (actual
>>> time=44068.166..44086.970 rows=2 loops=1)
>>>     Buffers: shared hit=215928 read=85139
>>>     ->  Unique  (cost=157282.39..157298.20 rows=4 width=54) (actual
>>> time=44068.164..44069.301 rows=2 loops=1)
>>>           Buffers: shared hit=215928 read=85139
>>>           ->  Sort  (cost=157282.39..157290.29 rows=3162 width=54)
>>> (actual time=44068.161..44068.464 rows=2052 loops=1)
>>>                 Sort Key: results.attribute_id, results."timestamp" DESC
>>>                 Sort Method: quicksort  Memory: 641kB
>>>                 Buffers: shared hit=215928 read=85139
>>>                 ->  Gather  (cost=62853.04..157098.57 rows=3162
>>> width=54) (actual time=23518.745..44076.385 rows=4102 loops=1)
>>>                       Workers Planned: 2
>>>                       Workers Launched: 2
>>>                       Buffers: shared hit=215928 read=85139
>>>                       ->  Nested Loop  (cost=61853.04..155782.37
>>> rows=1318 width=54) (actual time=23290.514..43832.223 rows=1367
>>> loops=3)
>>>                             Buffers: shared hit=215928 read=85139
>>>                             ->  Parallel Hash Join
>>> (cost=61852.61..143316.27 rows=24085 width=4) (actual
>>> time=23271.275..40018.451 rows=19756 loops=3)
>>>                                   Hash Cond:
>>> (scheduler_operation_executions.task_execution_id =
>>> scheduler_task_executions.id)
>>>                                   Buffers: shared hit=6057 read=85139
>>>                                   ->  Parallel Seq Scan on
>>> scheduler_operation_executions  (cost=0.00..74945.82 rows=2482982
>>> width=8) (actual time=7.575..15694.435 rows=1986887 loops=3)
>>>                                         Buffers: shared hit=2996 read=47120
>>>                                   ->  Parallel Hash
>>> (cost=61652.25..61652.25 rows=16029 width=4) (actual
>>> time=23253.337..23253.337 rows=13558 loops=3)
>>>                                         Buckets: 65536  Batches: 1
>>> Memory Usage: 2144kB
>>>                                         Buffers: shared hit=2977 read=38019
>>>                                         ->  Parallel Seq Scan on
>>> scheduler_task_executions  (cost=0.00..61652.25 rows=16029 width=4)
>>> (actual time=25.939..23222.174 rows=13558 loops=3)
>>>                                               Filter: (device_id = 97)
>>>                                               Rows Removed by Filter: 1308337
>>>                                               Buffers: shared hit=2977 read=38019
>>>                             ->  Index Scan using
>>> index_operation_execution_id_asc on results  (cost=0.43..0.51 rows=1
>>> width=58) (actual time=0.191..0.191 rows=0 loops=59269)
>>>                                   Index Cond: (operation_execution_id =
>>> scheduler_operation_executions.id)
>>>                                   Filter: ((data IS NOT NULL) AND (data
>>> <> '<NullData/>'::text) AND (attribute_id = ANY ('{4,5}'::integer[]))
>>> AND (object_id = 1955) AND (data_access_result = 'SUCCESS'::text))
>>>                                   Rows Removed by Filter: 0
>>>                                   Buffers: shared hit=209871
>>> Planning Time: 29.295 ms
>>> Execution Time: 44087.365 ms
>>>
>>>
>>> Second run: https://explain.depesz.com/s/uy9f
>>> Limit  (cost=157282.39..157290.29 rows=2 width=54) (actual
>>> time=789.363..810.440 rows=2 loops=1)
>>>     Buffers: shared hit=216312 read=84755
>>>     ->  Unique  (cost=157282.39..157298.20 rows=4 width=54) (actual
>>> time=789.361..789.535 rows=2 loops=1)
>>>           Buffers: shared hit=216312 read=84755
>>>           ->  Sort  (cost=157282.39..157290.29 rows=3162 width=54)
>>> (actual time=789.361..789.418 rows=2052 loops=1)
>>>                 Sort Key: results.attribute_id, results."timestamp" DESC
>>>                 Sort Method: quicksort  Memory: 641kB
>>>                 Buffers: shared hit=216312 read=84755
>>>                 ->  Gather  (cost=62853.04..157098.57 rows=3162
>>> width=54) (actual time=290.356..808.454 rows=4102 loops=1)
>>>                       Workers Planned: 2
>>>                       Workers Launched: 2
>>>                       Buffers: shared hit=216312 read=84755
>>>                       ->  Nested Loop  (cost=61853.04..155782.37
>>> rows=1318 width=54) (actual time=238.313..735.472 rows=1367 loops=3)
>>>                             Buffers: shared hit=216312 read=84755
>>>                             ->  Parallel Hash Join
>>> (cost=61852.61..143316.27 rows=24085 width=4) (actual
>>> time=237.966..677.975 rows=19756 loops=3)
>>>                                   Hash Cond:
>>> (scheduler_operation_executions.task_execution_id =
>>> scheduler_task_executions.id)
>>>                                   Buffers: shared hit=6441 read=84755
>>>                                   ->  Parallel Seq Scan on
>>> scheduler_operation_executions  (cost=0.00..74945.82 rows=2482982
>>> width=8) (actual time=0.117..234.279 rows=1986887 loops=3)
>>>                                         Buffers: shared hit=3188 read=46928
>>>                                   ->  Parallel Hash
>>> (cost=61652.25..61652.25 rows=16029 width=4) (actual
>>> time=236.631..236.631 rows=13558 loops=3)
>>>                                         Buckets: 65536  Batches: 1
>>> Memory Usage: 2144kB
>>>                                         Buffers: shared hit=3169 read=37827
>>>                                         ->  Parallel Seq Scan on
>>> scheduler_task_executions  (cost=0.00..61652.25 rows=16029 width=4)
>>> (actual time=0.132..232.758 rows=13558 loops=3)
>>>                                               Filter: (device_id = 97)
>>>                                               Rows Removed by Filter: 1308337
>>>                                               Buffers: shared hit=3169 read=37827
>>>                             ->  Index Scan using
>>> index_operation_execution_id_asc on results  (cost=0.43..0.51 rows=1
>>> width=58) (actual time=0.003..0.003 rows=0 loops=59269)
>>>                                   Index Cond: (operation_execution_id =
>>> scheduler_operation_executions.id)
>>>                                   Filter: ((data IS NOT NULL) AND (data
>>> <> '<NullData/>'::text) AND (attribute_id = ANY ('{4,5}'::integer[]))
>>> AND (object_id = 1955) AND (data_access_result = 'SUCCESS'::text))
>>>                                   Rows Removed by Filter: 0
>>>                                   Buffers: shared hit=209871
>>> Planning Time: 1.787 ms
>>> Execution Time: 810.634 ms
>>>
>>> You can see that the second run takes less than one second to run...
>>> which is 43 seconds better than the first try, just by re-running the
>>> query.
>>> Other runs take maybe 1s, 3s, still a long time.
>>>
>>> How can I improve it to be consistently fast (is it possible to get to
>>> several milliseconds?)?
>>> What I don't really understand is why the nested loop has 3 loops
>>> (three joined tables)?
>>> And why does the first index scan indicate ~60k loops? And does it
>>> really work? It doesn't seem to filter out any rows.
>>>
>>> Should I add an index only on (attribute_id, object_id)? And maybe
>>> data_access_result?
>>> Does it make sens to add it on a text column (results.data)?
>>>
>>> My tables:
>>>
https://gist.githubusercontent.com/paulbarbu/0d36271d710349d8fb6102d9a466bb54/raw/7a6946ba7c2adec5b87ed90f343f1aff37432d21/gistfile1.txt
>>>
>>> As you can see from the gist the foreign keys are indexed. Other
>>> indices were added to speed up other queries.
>>> Other relevant information (my tables have 3+ millions of rows, not
>>> very big I think?), additional info with regards to size also included
>>> below.
>>> This query has poor performance on two PCs (both running off of HDDs)
>>> so I think it has more to do with my indices and query than Postgres
>>> config & hardware, will post those if necessary.
>>>
>>>
>>> Size info:
>>> SELECT relname, relpages, reltuples, relallvisible, relkind, relnatts,
>>> relhassubclass, reloptions, pg_table_size(oid) FROM pg_class WHERE
>>> relname IN ('results', 'scheduler_operation_executions',
>>> 'scheduler_task_executions');
>>> -[ RECORD 1 ]--+-------------------------------
>>> relname        | results
>>> relpages       | 65922
>>> reltuples      | 3.17104e+06
>>> relallvisible  | 65922
>>> relkind        | r
>>> relnatts       | 9
>>> relhassubclass | f
>>> reloptions     |
>>> pg_table_size  | 588791808
>>> -[ RECORD 2 ]--+-------------------------------
>>> relname        | scheduler_operation_executions
>>> relpages       | 50116
>>> reltuples      | 5.95916e+06
>>> relallvisible  | 50116
>>> relkind        | r
>>> relnatts       | 8
>>> relhassubclass | f
>>> reloptions     |
>>> pg_table_size  | 410697728
>>> -[ RECORD 3 ]--+-------------------------------
>>> relname        | scheduler_task_executions
>>> relpages       | 40996
>>> reltuples      | 3.966e+06
>>> relallvisible  | 40996
>>> relkind        | r
>>> relnatts       | 12
>>> relhassubclass | f
>>> reloptions     |
>>> pg_table_size  | 335970304
>>>
>>> Thanks for your time!
>>>
>>> --
>>> Barbu Paul - Gheorghe
>>>
>> Can you create an index on scheduler_task_executions.device_id and run
>> it again?
Can you try this query, please?  Although I'm not really sure it'll give 
you the same results.

  SELECT DISTINCT ON (results.attribute_id)
         results.timestamp,
         results.data
    FROM results
   WHERE results.data <> '<NullData/>'
     AND results.data IS NOT NULL
     AND results.object_id = 1955
     AND results.attribute_id IN (4, 5) -- possibly a longer list here
     AND results.data_access_result = 'SUCCESS'
     AND EXISTS (SELECT 1
                   FROM scheduler_operation_executions
                        JOIN scheduler_task_executions ON 
scheduler_task_executions.id = 
scheduler_operation_executions.task_execution_id
                  WHERE scheduler_operation_executions.id = 
results.operation_execution_id
                    AND scheduler_task_executions.device_id = 97)
   ORDER BY results.attribute_id, results.timestamp DESC
   LIMIT 2 -- limit by the length of the attributes list