Covering index to improve performance of a SELECT query? - sql

Question

I have following Postgres query that runs over a large table and whenever it does, I observe a high CPU load that reaches 100% :
SELECT id, user_id, type
FROM table ss
WHERE end_timestamp > CURRENT_TIMESTAMP
AND (notification_channel1 = true OR notification_channel2 = true)
AND last_notification_timestamp < CURRENT_TIMESTAMP - interval '1 days'
ORDER BY user_id, type, title
Table schema:
CREATE TABLE saved_search (
id BIGINT PRIMARY KEY,
user_id varchar(40) NOT NULL,
title varchar(500) NOT NULL,
start_timestamp timestamp with time zone NOT NULL,
end_timestamp timestamp with time zone NOT NULL,
last_notification_timestamp timestamp with time zone NOT NULL,
notification_channel1 boolean NOT NULL DEFAULT TRUE,
notification_channel2 boolean NOT NULL DEFAULT TRUE,
);
I am thinking of using a covering index to speed up this query and hopefully avoid the cpu spike.
First question: would that be a valid path to follow?
The index I'm thinking of would be something like:
CREATE INDEX IX_COVERING
ON table (end_date, notification_channel1, notification_channel2, last_notification_timestamp)
INCLUDE (id, user_id, type)
Would that be useful ? Would the INCLUDE be needed actually?
Should I change the order of the columns in the index?
Are there other / better approaches?
Gather Merge (cost=387647.26..737625.73 rows=2999606 width=40) (actual time=36085.232..47805.119 rows=3634052 loops=1)
Workers Planned: 2
Workers Launched: 2
-> Sort (cost=386647.24..390396.74 rows=1499803 width=40) (actual time=35820.825..40497.683 rows=1211351 loops=3)
" Sort Key: user_id, type, title"
Sort Method: external merge Disk: 63640kB
Worker 0: Sort Method: external merge Disk: 63944kB
Worker 1: Sort Method: external merge Disk: 57896kB
-> Parallel Seq Scan on table (cost=0.00..150768.88 rows=1499803 width=40) (actual time=0.200..4176.269 rows=1211351 loops=3)
Filter: ((notification_channel1 OR notification_channel2) AND (end_timestamp > CURRENT_TIMESTAMP) AND (last_notification_timestamp < CURRENT_TIMESTAMP - interval '1 days'))
Rows Removed by Filter: 136960
Planning Time: 3.632 ms
Execution Time: 48292.788 ms
With SET work_mem = '100MB';, here is the output I'm getting:
`Gather Merge (cost=305621.26..655599.73 rows=2999606 width=40) (actual time=48856.376..55606.264 rows=3634097 loops=1)
Workers Planned: 2
Workers Launched: 2
-> Sort (cost=304621.24..308370.74 rows=1499803 width=40) (actual time=46436.173..47563.732 rows=1211366 loops=3)
" Sort Key: user_id, type, title"
Sort Method: external merge Disk: 72232kB
Worker 0: Sort Method: external merge Disk: 55288kB
Worker 1: Sort Method: external merge Disk: 57816kB
-> Parallel Seq Scan on table (cost=0.00..150768.88 rows=1499803 width=40) (actual time=0.911..4643.228 rows=1211366 loops=3)
Filter: ((notification_channel1 OR notification_channel2) AND (end_timestamp > CURRENT_TIMESTAMP) AND ((ast_notification_timestamp < CURRENT_TIMESTAMP - interval '1 days'))
Rows Removed by Filter: 136960
Planning Time: 0.450 ms
Execution Time: 56035.995 ms

Answer 1

You have "like 4 million rows" in the table and the query selects rows=3634052. That's 90 % of all rows.
Long story short: an index is not going to help (much). Probably not worth the added storage and maintenance cost.
More work_mem would help performance - as indicated by:
Sort Method: external merge Disk: ...
See:
Configuration parameter work_mem in PostgreSQL on Linux
You may not need more work_mem if you can optimize the ORDER BY.
user_id is something like : '95068145 , 401229705 , 407722349`
That column should be integer or bigint, most likely. Much more efficient for storage and sorting.
The big column title varchar(500) in your ORDER BY is probably expensive. Maybe something like left(title, 15) is good enough?
If you can bring down the size of ordering columns, an index (or partial index) on just those ordering columns may help some.
Either way, an upgrade to Postgres 15 will instantly improve performance. The release notes:
Performance improvements, particularly for in-memory and on-disk sorting.

Answer 2

Your sort is incredibly slow. If I simulate data with those date types and the same row count and data volume as yours, my parallel seq scan is twice as slow but my sort is 10 times faster (although you don't list the "type" column in your CREATE, but do use it in the query, so I had to improvise). So maybe you are using a very slow collation, or there is something pretty sad about your hardware. Another thought, how long does it take if you do EXPLAIN (ANALYZE, TIMING OFF)? Maybe your system has slow access to the clock, and doing all those clock calls slows things down.
If you can't do anything about your sort being so slow, then you would want an index only scan which can avoid the sort. Which means the index must start with the columns being sorted on. The columns other than those ones can be in the INCLUDE, but there is generally no point in putting them there rather than the body. The non-sort columns can be listed in any order.
(user_id, type, title, end_timestamp, notification_channel1, notification_channel2, last_notification_timestamp, id)
If your supplied params of the channels never change, then a partial index might be slightly better:
(user_id, type, title, end_timestamp, last_notification_timestamp, id) WHERE notification_channel1 = true OR notification_channel2 = true
You didn't increase your work_mem by enough to do the sorts in memory. The on-disk format is more compact than the in-memory format, so a sort that took 72,232kB of disk might take take ~250MB of memory to do it entirely in memory. Whether that comes with a financial cost we can't say, as we don't know how much RAM you currently have, or how much of that RAM typically goes unused, or how many concurrent sessions you expect to have. Increasing work_mem but not by enough to get rid of the external merge sort altogether can make the sorting slower (not dramatically, but noticeably) I think because it makes poorer use of the layers of caching above registers but below main memory (L1, L2, L3, etc.)