We have a Postgres 11 server. There is table of machines and history table which contains the changes within machines table.
There is insert rule on machines table:
CREATE RULE machine_insert AS ON INSERT TO machines
DO ALSO
INSERT INTO history(machine_name, stamp, ...)
VALUES(new.machine_name, now(), ...);
The insert into history works perfectly if the statement looks like this
INSERT INTO machines(machine_name, ....)
SELECT machine_name from new_machines; -- No condition
But if there is a JOIN and WHERE condition in the select statement, the rule does not fire at all. e.g.
INSERT INTO machines(machine_name, ....)
SELECT machine_name
FROM new_machines N
LEFT OUTER JOIN machines D on D.machine_name = N.machine_name
WHERE D.machine_name is null -- "Anti Join"
As I understand the documentation, the select statement in my case above should really have no impact on the rule(s). Remark: simple WHERE condition (WHERE machine_type is not null) does not disable the rule. What am I doing wrong?
The EXPLAIN ANALYZE revealed the cause of the issue. Here is the output:
QUERY PLAN
-----------------------------------------------------------------------------------------------------------------------------------------------------------
Insert on machines (cost=17.88..14522.89 rows=335609 width=227) (actual time=12586.221..12586.223 rows=0 loops=1)
-> Hash Anti Join (cost=17.88..14522.89 rows=335609 width=227) (actual time=0.029..347.021 rows=335959 loops=1)
Hash Cond: (n.machine_name = d.machine_name)
-> Seq Scan on new_machines n (cost=0.00..8588.59 rows=335959 width=105) (actual time=0.013..59.920 rows=335959 loops=1)
-> Hash (cost=13.50..13.50 rows=350 width=4) (actual time=0.002..0.003 rows=0 loops=1)
Buckets: 1024 Batches: 1 Memory Usage: 8kB
-> Seq Scan on machines d (cost=0.00..13.50 rows=350 width=4) (actual time=0.002..0.002 rows=0 loops=1)
Planning Time: 1.497 ms
Execution Time: 12586.289 ms
Insert on history (cost=0.84..30581.85 rows=121724 width=1493) (actual time=212.911..212.912 rows=0 loops=1)
-> Merge Anti Join (cost=0.84..30581.85 rows=121724 width=1493) (actual time=212.909..212.909 rows=0 loops=1)
Merge Cond: (n.machine_name = d.machine_name)
-> Index Scan using new_machines_idx on new_machines n (cost=0.42..12138.50 rows=335959 width=15) (actual time=0.005..72.153
-> Index Only Scan using machines_id_key on machines d (cost=0.42..11274.51 rows=214235 width=4) (actual time=0.005..81.540 rows=335959 loops=1)
Heap Fetches: 335959
Planning Time: 0.264 ms
Execution Time: 212.975 ms
The rule does fire (gets executed) and it does work as designed.
The query resulting from the RULE (see Insert on history above) really tries to add records to history, but under the WHERE condition that the record must not exist in machines table which is now FALSE for all records as INSERT into machines already went through, note Scan using machines_id_key ... rows=335959.
Related
I have been trying to debug an issue with postgres where it decides to not use an index when LIMIT is above a specific value.
For example I have a table of 150k rows and when searching with LIMIT of 286 it uses the index while with LIMIT above 286 it does not.
LIMIT 286 uses index
db=# explain (analyze, buffers) SELECT * FROM tempz.tempx AS r INNER JOIN tempz.tempy AS z ON (r.id_tempy=z.id) WHERE z.int_col=2000 AND z.string_col='temp_string' ORDER BY r.name ASC, r.type ASC, r.id ASC LIMIT 286;
QUERY PLAN
---------------------------------------------------------------------------------------------------------------------------------------------------------------
Limit (cost=0.56..5024.12 rows=286 width=810) (actual time=0.030..0.992 rows=286 loops=1)
Buffers: shared hit=921
-> Nested Loop (cost=0.56..16968.23 rows=966 width=810) (actual time=0.030..0.977 rows=286 loops=1)
Join Filter: (r.id_tempy = z.id)
Rows Removed by Join Filter: 624
Buffers: shared hit=921
-> Index Scan using tempz_tempx_name_type_id_idx on tempx r (cost=0.42..14357.69 rows=173878 width=373) (actual time=0.016..0.742 rows=910 loops=1)
Buffers: shared hit=919
-> Materialize (cost=0.14..2.37 rows=1 width=409) (actual time=0.000..0.000 rows=1 loops=910)
Buffers: shared hit=2
-> Index Scan using tempy_string_col_idx on tempy z (cost=0.14..2.37 rows=1 width=409) (actual time=0.007..0.008 rows=1 loops=1)
Index Cond: (string_col = 'temp_string'::text)
Filter: (int_col = 2000)
Buffers: shared hit=2
Planning Time: 0.161 ms
Execution Time: 1.032 ms
(16 rows)
vs.
LIMIT 287 doing sort
db=# explain (analyze, buffers) SELECT * FROM tempz.tempx AS r INNER JOIN tempz.tempy AS z ON (r.id_tempy=z.id) WHERE z.int_col=2000 AND z.string_col='temp_string' ORDER BY r.name ASC, r.type ASC, r.id ASC LIMIT 287;
QUERY PLAN
-------------------------------------------------------------------------------------------------------------------------------------------------------------
Limit (cost=4976.86..4977.58 rows=287 width=810) (actual time=49.802..49.828 rows=287 loops=1)
Buffers: shared hit=37154
-> Sort (cost=4976.86..4979.27 rows=966 width=810) (actual time=49.801..49.813 rows=287 loops=1)
Sort Key: r.name, r.type, r.id
Sort Method: top-N heapsort Memory: 506kB
Buffers: shared hit=37154
-> Nested Loop (cost=0.42..4932.59 rows=966 width=810) (actual time=0.020..27.973 rows=51914 loops=1)
Buffers: shared hit=37154
-> Seq Scan on tempy z (cost=0.00..12.70 rows=1 width=409) (actual time=0.006..0.008 rows=1 loops=1)
Filter: ((int_col = 2000) AND (string_col = 'temp_string'::text))
Rows Removed by Filter: 2
Buffers: shared hit=1
-> Index Scan using tempx_id_tempy_idx on tempx r (cost=0.42..4340.30 rows=57959 width=373) (actual time=0.012..17.075 rows=51914 loops=1)
Index Cond: (id_tempy = z.id)
Buffers: shared hit=37153
Planning Time: 0.258 ms
Execution Time: 49.907 ms
(17 rows)
Update:
This is Postgres 11 and VACUUM ANALYZE is run daily. Also, I have already tried to use CTE to remove the filter but the problem is the sorting specifically
-> Sort (cost=4976.86..4979.27 rows=966 width=810) (actual time=49.801..49.813 rows=287 loops=1)
Sort Key: r.name, r.type, r.id
Sort Method: top-N heapsort Memory: 506kB
Buffers: shared hit=37154
Update 2:
After running VACUUM ANALYZE the database starts using the index for some hours and then it goes back to not using it.
Turns out that I can force Postgres to avoid doing any sort if I run SET enable_sort TO OFF;. This raises the cost of sorting very high which causes the Postgres planner to do index scan instead.
I am not really sure why Postgres thinks that index scan is so costly cost=0.42..14357.69 and thinks sorting is cheaper and ends up choosing that. It is also very weird that immediately after a VACUUM ANALYZE it analyzes the costs correctly but after some hours it goes back to sorting.
With sort off plan is still not optimized as it does materialize and loads stuff into memory but it is still faster than sorting.
When selecting MIN on a column in PostgreSQL (11, 12, 13) after a GROUP BY operation on multiple columns, any index created on the grouped columns is not used: https://dbfiddle.uk/?rdbms=postgres_13&fiddle=30e0f341940f4c1fa6013677643a0baf
CREATE TABLE tags (id serial, series int, index int, page int);
CREATE INDEX ON tags (page, series, index);
INSERT INTO tags (series, index, page)
SELECT
ceil(random() * 10),
ceil(random() * 100),
ceil(random() * 1000)
FROM generate_series(1, 100000);
EXPLAIN ANALYZE
SELECT tags.page, tags.series, MIN(tags.index)
FROM tags GROUP BY tags.page, tags.series;
HashAggregate (cost=2291.00..2391.00 rows=10000 width=12) (actual time=108.968..133.153 rows=9999 loops=1)
Group Key: page, series
Batches: 1 Memory Usage: 1425kB
-> Seq Scan on tags (cost=0.00..1541.00 rows=100000 width=12) (actual time=0.015..55.240 rows=100000 loops=1)
Planning Time: 0.257 ms
Execution Time: 133.771 ms
Theoretically, the index should allow the database to seek in steps of (tags.page, tags.series) instead of performing a full scan. This would result in 10,000 processed rows for above dataset instead of 100,000. This link describes the method with no grouped columns.
This answer (as well as this one) suggests using DISTINCT ON with an ordering instead of GROUP BY but that produces this query plan:
Unique (cost=0.42..5680.42 rows=10000 width=12) (actual time=0.066..268.038 rows=9999 loops=1)
-> Index Only Scan using tags_page_series_index_idx on tags (cost=0.42..5180.42 rows=100000 width=12) (actual time=0.064..227.219 rows=100000 loops=1)
Heap Fetches: 100000
Planning Time: 0.426 ms
Execution Time: 268.712 ms
While the index is now being used, it still appears to be scanning the full set of rows. When using SET enable_seqscan=OFF, the GROUP BY query degrades to the same behaviour.
How can I encourage PostgreSQL to use the multi-column index?
If you can pull the set of distinct page,series from another table then you can hack it with a lateral join:
CREATE TABLE pageseries AS SELECT DISTINCT page,series FROM tags ORDER BY page,series;
EXPLAIN ANALYZE SELECT p.*, minindex FROM pageseries p CROSS JOIN LATERAL (SELECT index minindex FROM tags t WHERE t.page=p.page AND t.series=p.series ORDER BY page,series,index LIMIT 1) x;
Nested Loop (cost=0.42..8720.00 rows=10000 width=12) (actual time=0.039..56.013 rows=10000 loops=1)
-> Seq Scan on pageseries p (cost=0.00..145.00 rows=10000 width=8) (actual time=0.012..1.872 rows=10000 loops=1)
-> Limit (cost=0.42..0.84 rows=1 width=12) (actual time=0.005..0.005 rows=1 loops=10000)
-> Index Only Scan using tags_page_series_index_idx on tags t (cost=0.42..4.62 rows=10 width=12) (actual time=0.004..0.004 rows=1 loops=10000)
Index Cond: ((page = p.page) AND (series = p.series))
Heap Fetches: 0
Planning Time: 0.168 ms
Execution Time: 57.077 ms
...but it is not necessarily faster:
EXPLAIN ANALYZE SELECT tags.page, tags.series, MIN(tags.index)
FROM tags GROUP BY tags.page, tags.series;
HashAggregate (cost=2291.00..2391.00 rows=10000 width=12) (actual time=56.177..58.923 rows=10000 loops=1)
Group Key: page, series
Batches: 1 Memory Usage: 1425kB
-> Seq Scan on tags (cost=0.00..1541.00 rows=100000 width=12) (actual time=0.010..12.845 rows=100000 loops=1)
Planning Time: 0.129 ms
Execution Time: 59.644 ms
It would be massively faster IF the number of iterations in the nested loop was small, in other words if there was a low number of distinct (page,series). I'll try with series alone, since that has only 10 distinct values:
CREATE TABLE series AS SELECT DISTINCT series FROM tags;
EXPLAIN ANALYZE SELECT p.*, minindex FROM series p CROSS JOIN LATERAL (SELECT index minindex FROM tags t WHERE t.series=p.series ORDER BY series,index LIMIT 1) x;
Nested Loop (cost=0.29..886.18 rows=2550 width=8) (actual time=0.081..0.264 rows=10 loops=1)
-> Seq Scan on series p (cost=0.00..35.50 rows=2550 width=4) (actual time=0.007..0.010 rows=10 loops=1)
-> Limit (cost=0.29..0.31 rows=1 width=8) (actual time=0.024..0.024 rows=1 loops=10)
-> Index Only Scan using tags_series_index_idx on tags t (cost=0.29..211.29 rows=10000 width=8) (actual time=0.023..0.023 rows=1 loops=10)
Index Cond: (series = p.series)
Heap Fetches: 0
Planning Time: 0.198 ms
Execution Time: 0.292 ms
In this case, definitely worth it, because the query hits only 10/100000 rows. The other queries hit 10000/100000 rows, or 10% of the table, which is above the threshold where an index would really help.
Note putting the column with lower cardinality first will result in a smaller index:
CREATE INDEX ON tags (series, page, index);
select pg_relation_size( 'tags_page_series_index_idx' );
4284416
select pg_relation_size( 'tags_series_page_index_idx' );
3104768
...but it doesn't make the query any faster.
If this type of stuff is really critical, perhaps try clickhouse or dolphindb.
To support that kind of thing PostgreSQL would have to have something like an index skip scan, and it is only efficient to use that if there are few groups.
If the speed of that query is essential, you could consider using a materialized view.
I have this function, and it works, it gives the most recent b record.
create or replace function most_recent_b(the_a a) returns b as $$
select distinct on (c.a_id) b.*
from c
join b on b.c_id = c.id
where c.a_id = the_a.id
order by c.a_id, b.date desc
$$ language sql stable;
This runs ~5000ms with real data. V.S. the following which runs in 500ms
create or replace function most_recent_b(the_a a) returns b as $$
select distinct on (c.a_id) b.*
from c
join b on b.c_id = c.id
where c.a_id = 1347
order by c.a_id, b.date desc
$$ language sql stable;
The only difference being that I've hard coded a.id with a value 1347 instead of using its param value.
Also running this query without a function also gives me speeds around 500ms
I'm running PostgreSQL 9.6, so the query planner failing in functions results I see suggested elsewhere shouldn't apply to me right?
I'm sure its not the query itself that is the issue, as this is my third iteration at it, different techniques to get this result all result in the same slow down when inside a function.
As requested by #laurenz-albe
Result of EXPLAIN (ANALYZE, BUFFERS) with constant
Unique (cost=60.88..60.89 rows=3 width=463) (actual time=520.117..520.122 rows=1 loops=1)
Buffers: shared hit=14555
-> Sort (cost=60.88..60.89 rows=3 width=463) (actual time=520.116..520.120 rows=9 loops=1)
Sort Key: b.date DESC
Sort Method: quicksort Memory: 28kB
Buffers: shared hit=14555
-> Hash Join (cost=13.71..60.86 rows=3 width=463) (actual time=386.848..520.083 rows=9 loops=1)
Hash Cond: (b.c_id = c.id)
Buffers: shared hit=14555
-> Seq Scan on b (cost=0.00..46.38 rows=54 width=459) (actual time=25.362..519.140 rows=51 loops=1)
Filter: b_can_view(b.*)
Rows Removed by Filter: 112
Buffers: shared hit=14530
-> Hash (cost=13.67..13.67 rows=3 width=8) (actual time=0.880..0.880 rows=10 loops=1)
Buckets: 1024 Batches: 1 Memory Usage: 9kB
Buffers: shared hit=25
-> Subquery Scan on c (cost=4.21..13.67 rows=3 width=8) (actual time=0.222..0.872 rows=10 loops=1)
Buffers: shared hit=25
-> Bitmap Heap Scan on c c_1 (cost=4.21..13.64 rows=3 width=2276) (actual time=0.221..0.863 rows=10 loops=1)
Recheck Cond: (a_id = 1347)
Filter: c_can_view(c_1.*)
Heap Blocks: exact=4
Buffers: shared hit=25
-> Bitmap Index Scan on c_a_id_c_number_idx (cost=0.00..4.20 rows=8 width=0) (actual time=0.007..0.007 rows=10 loops=1)
Index Cond: (a_id = 1347)
Buffers: shared hit=1
Execution time: 520.256 ms
And this is the result after running six times with the parameter being passed ( it was exactly six times as you predicted :) )
Slow query;
Unique (cost=57.07..57.07 rows=1 width=463) (actual time=5040.237..5040.243 rows=1 loops=1)
Buffers: shared hit=145325
-> Sort (cost=57.07..57.07 rows=1 width=463) (actual time=5040.237..5040.240 rows=9 loops=1)
Sort Key: b.date DESC
Sort Method: quicksort Memory: 28kB
Buffers: shared hit=145325
-> Nested Loop (cost=0.14..57.06 rows=1 width=463) (actual time=912.354..5040.195 rows=9 loops=1)
Join Filter: (c.id = b.c_id)
Rows Removed by Join Filter: 501
Buffers: shared hit=145325
-> Index Scan using c_a_id_idx on c (cost=0.14..9.45 rows=1 width=2276) (actual time=0.378..1.171 rows=10 loops=1)
Index Cond: (a_id = $1)
Filter: c_can_view(c.*)
Buffers: shared hit=25
-> Seq Scan on b (cost=0.00..46.38 rows=54 width=459) (actual time=24.842..503.854 rows=51 loops=10)
Filter: b_can_view(b.*)
Rows Removed by Filter: 112
Buffers: shared hit=145300
Execution time: 5040.375 ms
Its worth noting that I have some strict row level security involved, and I suspect this is why these queries are both slow, however, one is 10 times slower than the other.
I've changed my original table names hopefully my search and replace was good here.
The expensive part of your query execution is the filter b_can_view(b.*), which must come from your row level security definition.
The fast execution:
Seq Scan on b (cost=0.00..46.38 rows=54 width=459)
(actual time=25.362..519.140 rows=51 loops=1)
Filter: b_can_view(b.*)
Rows Removed by Filter: 112
Buffers: shared hit=14530
The slow execution:
Seq Scan on b (cost=0.00..46.38 rows=54 width=459)
(actual time=24.842..503.854 rows=51 loops=10)
Filter: b_can_view(b.*)
Rows Removed by Filter: 112
Buffers: shared hit=145300
The difference is that the scan is executed 10 times in the slow case (loops=10) and touches 10 times as many data blocks.
When using the generic plan, PostgreSQL underestimates how many rows in c will satisfy the condition c.a_id = $1, because it doesn't know that the actual value is 1347, which is more frequent than average.
Since PostgreSQL thinks there will be at most one row from c, it chooses a nested loop join with a sequential scan of b on the inner side.
Now two problems combine:
Calling function b_can_view takes over 3 milliseconds per row (which PostgreSQL doesn't know), which accounts for the half second that a sequential scan of the 163 rows takes.
There are actually 10 rows found in c instead of the predicted 1, so table b is scanned 10 times, and you end up with a query duration of 5 seconds.
So what can you do?
Tell PostgreSQL how expensive the b_can_view is. Use ALTER TABLE to set the COST for that function to 1000 or 10000 to reflect reality. That alone will not be enough to get a faster plan, since PostgreSQL thinks that it has to execute a single sequential scan anyway, but it is a good thing to give the optimizer correct data.
Create an index on b(c_id). That will enable PostgreSQL to avoid a sequential scan of b, which it will try to do once it is aware how expensive the function is.
Also, try to make the function b_can_view cheaper. That will make your experience so much better.
I'm importing a non circular graph and flattening the ancestors to an array per code. This works fine (for a bit): ~45s for 400k codes over ~900k edges.
However, after the first successful execution Postgres decides to stop using the Nested Loop and the update query performance drops drastically: ~2s per code.
I can force the issue by putting a vacuum right before the update but I am curious why the unoptimization is happening.
DROP TABLE IF EXISTS tmp_anc;
DROP TABLE IF EXISTS tmp_rel;
DROP TABLE IF EXISTS tmp_edges;
DROP TABLE IF EXISTS tmp_codes;
CREATE TABLE tmp_rel (
from_id BIGINT,
to_id BIGINT,
);
COPY tmp_rel FROM 'rel.txt' WITH DELIMITER E'\t' CSV HEADER;
CREATE TABLE tmp_edges(
start_node BIGINT,
end_node BIGINT
);
INSERT INTO tmp_edges(start_node, end_node)
SELECT from_id AS start_node, to_id AS end_node
FROM tmp_rel;
CREATE INDEX tmp_edges_end ON tmp_edges (end_node);
CREATE TABLE tmp_codes (
id BIGINT,
active SMALLINT,
);
COPY tmp_codes FROM 'codes.txt' WITH DELIMITER E'\t' CSV HEADER;
CREATE TABLE tmp_anc(
code BIGINT,
ancestors BIGINT[]
);
INSERT INTO tmp_anc
SELECT DISTINCT(id)
FROM tmp_codes
WHERE active = 1;
CREATE INDEX tmp_anc_codes ON tmp_anc_codes (code);
VACUUM; -- Need this for the update to execute optimally
UPDATE tmp_anc sa SET ancestors = (
WITH RECURSIVE ancestors(code) AS (
SELECT start_node FROM tmp_edges WHERE end_node = sa.code
UNION
SELECT se.start_node
FROM tmp_edges se, ancestors a
WHERE se.end_node = a.code
)
SELECT array_agg(code) FROM ancestors
);
Table stats:
tmp_rel 507 MB 0 bytes
tmp_edges 74 MB 37 MB
tmp_codes 32 MB 0 bytes
tmp_anc 22 MB 8544 kB
Explains:
Without VACUUM before UPDATE:
Update on tmp_anc sa (cost=10000000000.00..11081583053.74 rows=10 width=46) (actual time=38294.005..38294.005 rows=0 loops=1)
-> Seq Scan on tmp_anc sa (cost=10000000000.00..11081583053.74 rows=10 width=46) (actual time=3300.974..38292.613 rows=10 loops=1)
SubPlan 2
-> Aggregate (cost=108158305.25..108158305.26 rows=1 width=32) (actual time=3829.253..3829.253 rows=1 loops=10)
CTE ancestors
-> Recursive Union (cost=81.97..66015893.05 rows=1872996098 width=8) (actual time=0.037..3827.917 rows=45 loops=10)
-> Bitmap Heap Scan on tmp_edges (cost=81.97..4913.18 rows=4328 width=8) (actual time=0.022..0.022 rows=2 loops=10)
Recheck Cond: (end_node = sa.code)
Heap Blocks: exact=12
-> Bitmap Index Scan on tmp_edges_end (cost=0.00..80.89 rows=4328 width=0) (actual time=0.014..0.014 rows=2 loops=10)
Index Cond: (end_node = sa.code)
-> Merge Join (cost=4198.89..2855105.79 rows=187299177 width=8) (actual time=163.746..425.295 rows=10 loops=90)
Merge Cond: (a.code = se.end_node)
-> Sort (cost=4198.47..4306.67 rows=43280 width=8) (actual time=0.012..0.016 rows=5 loops=90)
Sort Key: a.code
Sort Method: quicksort Memory: 25kB
-> WorkTable Scan on ancestors a (cost=0.00..865.60 rows=43280 width=8) (actual time=0.000..0.001 rows=5 loops=90)
-> Materialize (cost=0.42..43367.08 rows=865523 width=16) (actual time=0.010..337.592 rows=537171 loops=90)
-> Index Scan using tmp_edges_end on edges se (cost=0.42..41203.27 rows=865523 width=16) (actual time=0.009..247.547 rows=537171 loops=90)
-> CTE Scan on ancestors (cost=0.00..37459921.96 rows=1872996098 width=8) (actual time=1.227..3829.159 rows=45 loops=10)
With VACUUM before UPDATE:
Update on tmp_anc sa (cost=0.00..2949980136.43 rows=387059 width=14) (actual time=74701.329..74701.329 rows=0 loops=1)
-> Seq Scan on tmp_anc sa (cost=0.00..2949980136.43 rows=387059 width=14) (actual time=0.336..70324.848 rows=387059 loops=1)
SubPlan 2
-> Aggregate (cost=7621.50..7621.51 rows=1 width=8) (actual time=0.180..0.180 rows=1 loops=387059)
CTE ancestors
-> Recursive Union (cost=0.42..7583.83 rows=1674 width=8) (actual time=0.005..0.162 rows=32 loops=387059)
-> Index Scan using tmp_edges_end on tmp_edges (cost=0.42..18.93 rows=4 width=8) (actual time=0.004..0.005 rows=2 loops=387059)
Index Cond: (end_node = sa.code)
-> Nested Loop (cost=0.42..753.14 rows=167 width=8) (actual time=0.003..0.019 rows=10 loops=2700448)
-> WorkTable Scan on ancestors a (cost=0.00..0.80 rows=40 width=8) (actual time=0.000..0.001 rows=5 loops=2700448)
-> Index Scan using tmp_edges_end on tmp_edges se (cost=0.42..18.77 rows=4 width=16) (actual time=0.003..0.003 rows=2 loops=12559395)
Index Cond: (end_node = a.code)
-> CTE Scan on ancestors (cost=0.00..33.48 rows=1674 width=8) (actual time=0.007..0.173 rows=32 loops=387059)
The first execution plan has really bad estimates (Bitmap Index Scan on tmp_edges_end estimates 4328 instead of 2 rows), while the second execution has good estimates and thus chooses a good plan.
So something between the two executions you quote above must have changed the estimates.
Moreover, you say that the first execution of the UPDATE (for which we have no EXPLAIN (ANALYZE) output) was fast.
The only good explanation for the initial performance drop is that it takes the autovacuum daemon some time to collect statistics for the new tables. This normally improves query performance, but of course it can also work the other way around.
Also, a VACUUM usually doesn't fix performance issues. Could it be that you used VACUUM (ANALYZE)?
It would be interesting to know how things are when you collect statistics before your initial UPDATE:
ANALYZE tmp_edges;
When I read your query more closely, however, I wonder why you use a correlated subquery for that. Maybe it would be faster to do something like:
UPDATE tmp_anc sa
SET ancestors = a.codes
FROM (WITH RECURSIVE ancestors(code, start_node) AS
(SELECT tmp_anc.code, tmp_edges.start_node
FROM tmp_edges
JOIN tmp_anc ON tmp_edges.end_node = tmp_anc.code
UNION
SELECT a.code, se.start_node
FROM tmp_edges se
JOIN ancestors a ON se.end_node = a.code
)
SELECT code,
array_agg(start_node) AS codes
FROM ancestors
GROUP BY (code)
) a
WHERE sa.code = a.code;
(This is untested, so there may be mistakes.)
I have the following query:
SELECT "person_dimensions"."dimension"
FROM "person_dimensions"
join users
on users.id = person_dimensions.user_id
where users.team_id = 2
The following is the result of EXPLAIN ANALYZE:
Nested Loop (cost=0.43..93033.84 rows=452 width=11) (actual time=1245.321..42915.426 rows=827 loops=1)
-> Seq Scan on person_dimensions (cost=0.00..254.72 rows=13772 width=15) (actual time=0.022..9.907 rows=13772 loops=1)
-> Index Scan using users_pkey on users (cost=0.43..6.73 rows=1 width=4) (actual time=2.978..3.114 rows=0 loops=13772)
Index Cond: (id = person_dimensions.user_id)
Filter: (team_id = 2)
Rows Removed by Filter: 1
Planning time: 0.396 ms
Execution time: 42915.678 ms
Indexes exist on person_dimensions.user_id and users.team_id, so it is unclear as to why this seemingly simple query would be taking so long.
Maybe it has something to do with team_id being unable to be used in the join condition? Ideas how to speed this up?
EDIT:
I tried this query:
SELECT "person_dimensions"."dimension"
FROM "person_dimensions"
join users ON users.id = person_dimensions.user_id
WHERE users.id IN (2337,2654,3501,56,4373,1060,3170,97,4629,41,3175,4541,2827)
which contains the id's returned by the subquery:
SELECT id FROM users WHERE team_id = 2
The result was 380ms versus 42s as above. I could use this as a workaround, but I am really curious as to what is going on here...
I rebooted my DB server yesterday, and when it came back up this same query was performing as expected with a completely different query plan that used expected indices:
QUERY PLAN
Hash Join (cost=1135.63..1443.45 rows=84 width=11) (actual time=0.354..6.312 rows=835 loops=1)
Hash Cond: (person_dimensions.user_id = users.id)
-> Seq Scan on person_dimensions (cost=0.00..255.17 rows=13817 width=15) (actual time=0.002..2.764 rows=13902 loops=1)
-> Hash (cost=1132.96..1132.96 rows=214 width=4) (actual time=0.175..0.175 rows=60 loops=1)
Buckets: 1024 Batches: 1 Memory Usage: 11kB
-> Bitmap Heap Scan on users (cost=286.07..1132.96 rows=214 width=4) (actual time=0.032..0.157 rows=60 loops=1)
Recheck Cond: (team_id = 2)
Heap Blocks: exact=68
-> Bitmap Index Scan on index_users_on_team_id (cost=0.00..286.02 rows=214 width=0) (actual time=0.021..0.021 rows=82 loops=1)
Index Cond: (team_id = 2)
Planning time: 0.215 ms
Execution time: 6.474 ms
Anyone have any ideas why it required a reboot to be aware of all of this? Could it be that manual vacuums were required that hadn't been done in a while, or something like this? Recall I did do an analyze on the relevant tables before the reboot and it didn't change anything.