Thread: Query plan getting less efficient over time with frequent updates and deletes..
Query plan getting less efficient over time with frequent updates and deletes..
From
Wells Oliver
Date:
Hi all: we have a table which receives frequent daily updates and deletes on the order of 100-600k. The overall row length is approximately 80m. This table has 50 indexes and 303 columns and is quite frequently queried by humans and applications.
I've been in the habit of using pg_repack maybe once a month on this table because I can't quite figure out why querying gets bogged down. The vacuum and analyze thresholds are set such that the table is both auto vacuumed and analyzed every night.
My questions/concerns:
- this gets vacuumed and analyzed due to auto thresholds every night, so it's surprising that the planner's choices would change and choose a worse plan right?
- I look at pg_stat_user_tables to asses n_dead_tup along with n_tup_del and n_tup_upd to try and assess if there is table bloat and this seems to be under control
- I look at pgstattuple to check free_space and try and guess how much is being given back to the server for new rows, it's roughly 20-28% and it seems OK?
- what other factors are at play here? index bloat? disk IO? etc? something I can eyeball better in pg_stat_user_tables, or pgstattuple?
How can I make this table behave properly, with all of its updates, and without restoring to frequent pg_repack?
The much faster version of the query plan does a bitmap heap scan with a bitmap index scan. The slower version just does an index scan on the same index.
Here are the EXPLAIN ANALYZE VERBOSE results of a routine SELECT query with a few columns in a WHERE clause. I've abbreviated these otherwise it would just be too much text, glad to share more. After a fresh pg_repack, the former is performed for a while (like two weeks) then it reverts to the latter and everyone is unhappy.
Fast, better!
Sort (cost=20279.85..20281.77 rows=768 width=380) (actual time=218.563..219.431 rows=7892 loops=1)
Output: p.season, p.game_date, ... p.pitch_seq
Sort Key: p.game_date DESC, p.game_bam_id, ((p.at_bat_index + 1)), p.pitch_seq
Sort Method: quicksort Memory: 4215kB
-> Bitmap Heap Scan on stats.pitches p (cost=58.49..20243.04 rows=768 width=380) (actual time=11.319..167.570 rows=7892 loops=1)
Output: p.season, p.game_date, ... p.pitch_seq
Recheck Cond: (p.batter_common_key = 2181560)
Filter: ((p.pitch_type IS NOT NULL) AND p.is_pitch AND (COALESCE(p.pitch_code, p.pitch_result) IS NOT NULL) AND (p.season = ANY ('{2024,2023,2022}'::integer[])) AND (p.pitch_type <> ALL ('{UN,XX,PO,IB,AB}'::text[])) AND (p.game_type = ANY ('{R,F,D,L,W,C,S}'::text[])))
Rows Removed by Filter: 2824
Heap Blocks: exact=10327
-> Bitmap Index Scan on pitches_batter_common_key_idx (cost=0.00..58.26 rows=5026 width=0) (actual time=6.625..6.625 rows=10716 loops=1)
Index Cond: (p.batter_common_key = 2181560)
Query Identifier: -9080545043453138058
Planning Time: 3.358 ms
Execution Time: 224.082 ms
Output: p.season, p.game_date, ... p.pitch_seq
Sort Key: p.game_date DESC, p.game_bam_id, ((p.at_bat_index + 1)), p.pitch_seq
Sort Method: quicksort Memory: 4215kB
-> Bitmap Heap Scan on stats.pitches p (cost=58.49..20243.04 rows=768 width=380) (actual time=11.319..167.570 rows=7892 loops=1)
Output: p.season, p.game_date, ... p.pitch_seq
Recheck Cond: (p.batter_common_key = 2181560)
Filter: ((p.pitch_type IS NOT NULL) AND p.is_pitch AND (COALESCE(p.pitch_code, p.pitch_result) IS NOT NULL) AND (p.season = ANY ('{2024,2023,2022}'::integer[])) AND (p.pitch_type <> ALL ('{UN,XX,PO,IB,AB}'::text[])) AND (p.game_type = ANY ('{R,F,D,L,W,C,S}'::text[])))
Rows Removed by Filter: 2824
Heap Blocks: exact=10327
-> Bitmap Index Scan on pitches_batter_common_key_idx (cost=0.00..58.26 rows=5026 width=0) (actual time=6.625..6.625 rows=10716 loops=1)
Index Cond: (p.batter_common_key = 2181560)
Query Identifier: -9080545043453138058
Planning Time: 3.358 ms
Execution Time: 224.082 ms
Slow, bad!
Sort (cost=20381.86..20383.74 rows=753 width=381) (actual time=6344.784..6345.568 rows=7892 loops=1)
Output: p.season, p.game_date, ... p.pitch_seq
Sort Key: p.game_date DESC, p.game_bam_id, ((p.at_bat_index + 1)), p.pitch_seq
Sort Method: quicksort Memory: 4215kB
-> Index Scan using pitches_batter_common_key_idx on stats.pitches p (cost=0.60..20345.88 rows=753 width=381) (actual time=6.765..6294.745 rows=7892 loops=1)
Output: p.season, p.game_date, ... p.pitch_seq
Index Cond: (p.batter_common_key = 2181560)
Filter: ((p.pitch_type IS NOT NULL) AND p.is_pitch AND (COALESCE(p.pitch_code, p.pitch_result) IS NOT NULL) AND (p.season = ANY ('{2024,2023,2022}'::integer[])) AND (p.pitch_type <> ALL ('{UN,XX,PO,IB,AB}'::text[])) AND (p.game_type = ANY ('{R,F,D,L,W,C,S}'::text[])))
Rows Removed by Filter: 2824
Query Identifier: -9080545043453138058
Planning Time: 3.197 ms
Execution Time: 6349.608 ms
Output: p.season, p.game_date, ... p.pitch_seq
Sort Key: p.game_date DESC, p.game_bam_id, ((p.at_bat_index + 1)), p.pitch_seq
Sort Method: quicksort Memory: 4215kB
-> Index Scan using pitches_batter_common_key_idx on stats.pitches p (cost=0.60..20345.88 rows=753 width=381) (actual time=6.765..6294.745 rows=7892 loops=1)
Output: p.season, p.game_date, ... p.pitch_seq
Index Cond: (p.batter_common_key = 2181560)
Filter: ((p.pitch_type IS NOT NULL) AND p.is_pitch AND (COALESCE(p.pitch_code, p.pitch_result) IS NOT NULL) AND (p.season = ANY ('{2024,2023,2022}'::integer[])) AND (p.pitch_type <> ALL ('{UN,XX,PO,IB,AB}'::text[])) AND (p.game_type = ANY ('{R,F,D,L,W,C,S}'::text[])))
Rows Removed by Filter: 2824
Query Identifier: -9080545043453138058
Planning Time: 3.197 ms
Execution Time: 6349.608 ms
Wells Oliver
wells.oliver@gmail.com
wells.oliver@gmail.com
Re: Query plan getting less efficient over time with frequent updates and deletes..
From
Ron Johnson
Date:
On Thu, Sep 12, 2024 at 6:52 PM Wells Oliver <wells.oliver@gmail.com> wrote:
Hi all: we have a table which receives frequent daily updates and deletes on the order of 100-600k. The overall row length is approximately 80m. This table has 50 indexes and 303 columns and is quite frequently queried by humans and applications.I've been in the habit of using pg_repack maybe once a month on this table because I can't quite figure out why querying gets bogged down. The vacuum and analyze thresholds are set such that the table is both auto vacuumed and analyzed every night.
1. You're absolutely positive that the VACUUM and ANALYZE complete every night?
2. Nightly may not be often enough.
Re: Query plan getting less efficient over time with frequent updates and deletes..
From
Wells Oliver
Date:
Yes, I regularly look at pg_stat_user_tables and in particular last_autovacuum and last_autoanalyze and these are always the current date (or within two days) after our nightly processes soon finish.
I wondered if the similar low planning time but the dissimilar longer execution time might indicate rows are spread out over disk, thereby negating a bitmap heap scan and the slower query taking longer due to having to read a lot more disk? Is that a possibility?
On Thu, Sep 12, 2024 at 4:47 PM Ron Johnson <ronljohnsonjr@gmail.com> wrote:
On Thu, Sep 12, 2024 at 6:52 PM Wells Oliver <wells.oliver@gmail.com> wrote:Hi all: we have a table which receives frequent daily updates and deletes on the order of 100-600k. The overall row length is approximately 80m. This table has 50 indexes and 303 columns and is quite frequently queried by humans and applications.I've been in the habit of using pg_repack maybe once a month on this table because I can't quite figure out why querying gets bogged down. The vacuum and analyze thresholds are set such that the table is both auto vacuumed and analyzed every night.1. You're absolutely positive that the VACUUM and ANALYZE complete every night?2. Nightly may not be often enough.
Wells Oliver
wells.oliver@gmail.com
wells.oliver@gmail.com
Re: Query plan getting less efficient over time with frequent updates and deletes..
From
Ron Johnson
Date:
On Thu, Sep 12, 2024 at 7:56 PM Wells Oliver <wells.oliver@gmail.com> wrote:
Yes, I regularly look at pg_stat_user_tables and in particular last_autovacuum and last_autoanalyze and these are always the current date (or within two days) after our nightly processes soon finish.
"Or within two days". I used to think that was adequate, but now I vacuum and analyze some tables multiple times a day.
1.5% autovacuum_X_scale_factor and 200 autovacuum_X_threshold is required on some tables.
Because there's sooo many indices on that table, you might have to manually vacuum it with a pretty high PARALLEL value.
I wondered if the similar low planning time but the dissimilar longer execution time might indicate rows are spread out over disk, thereby negating a bitmap heap scan and the slower query taking longer due to having to read a lot more disk? Is that a possibility?
It was 30 years ago. Modern (like ext2 and newer) filesystems purposefully spread files across devices.
On Thu, Sep 12, 2024 at 4:47 PM Ron Johnson <ronljohnsonjr@gmail.com> wrote:On Thu, Sep 12, 2024 at 6:52 PM Wells Oliver <wells.oliver@gmail.com> wrote:Hi all: we have a table which receives frequent daily updates and deletes on the order of 100-600k. The overall row length is approximately 80m. This table has 50 indexes and 303 columns and is quite frequently queried by humans and applications.I've been in the habit of using pg_repack maybe once a month on this table because I can't quite figure out why querying gets bogged down. The vacuum and analyze thresholds are set such that the table is both auto vacuumed and analyzed every night.1. You're absolutely positive that the VACUUM and ANALYZE complete every night?2. Nightly may not be often enough.--Wells Oliver
wells.oliver@gmail.com
Death to America, and butter sauce.
Iraq lobster!
Re: Query plan getting less efficient over time with frequent updates and deletes..
From
Wells Oliver
Date:
Well, hmm, does it stand to reason that if it's just a stale plan where vacuum and/or analyze is needed, I could execute a VACUUM ANALYZE rather than doing a full pg_repack and it should solve the issue, right? I could try that next time and see if the query plan reverts to the faster plan.
On Thu, Sep 12, 2024 at 7:42 PM Ron Johnson <ronljohnsonjr@gmail.com> wrote:
On Thu, Sep 12, 2024 at 7:56 PM Wells Oliver <wells.oliver@gmail.com> wrote:Yes, I regularly look at pg_stat_user_tables and in particular last_autovacuum and last_autoanalyze and these are always the current date (or within two days) after our nightly processes soon finish."Or within two days". I used to think that was adequate, but now I vacuum and analyze some tables multiple times a day.1.5% autovacuum_X_scale_factor and 200 autovacuum_X_threshold is required on some tables.Because there's sooo many indices on that table, you might have to manually vacuum it with a pretty high PARALLEL value.I wondered if the similar low planning time but the dissimilar longer execution time might indicate rows are spread out over disk, thereby negating a bitmap heap scan and the slower query taking longer due to having to read a lot more disk? Is that a possibility?It was 30 years ago. Modern (like ext2 and newer) filesystems purposefully spread files across devices.On Thu, Sep 12, 2024 at 4:47 PM Ron Johnson <ronljohnsonjr@gmail.com> wrote:On Thu, Sep 12, 2024 at 6:52 PM Wells Oliver <wells.oliver@gmail.com> wrote:Hi all: we have a table which receives frequent daily updates and deletes on the order of 100-600k. The overall row length is approximately 80m. This table has 50 indexes and 303 columns and is quite frequently queried by humans and applications.I've been in the habit of using pg_repack maybe once a month on this table because I can't quite figure out why querying gets bogged down. The vacuum and analyze thresholds are set such that the table is both auto vacuumed and analyzed every night.1. You're absolutely positive that the VACUUM and ANALYZE complete every night?2. Nightly may not be often enough.--Wells Oliver
wells.oliver@gmail.com--Death to America, and butter sauce.Iraq lobster!
Wells Oliver
wells.oliver@gmail.com
wells.oliver@gmail.com