I have one table that includes about 100K rows and their growth and growth.
My query response has a bad response time and it affects my front-end user experience.
I want to ask for your help to improve my response time from the DB.
Today the PostgreSQL runs on my local machine, Macbook pro 13 2019 16 RAM and I5 Core.
I the future I will load this DB on a docker and run it on a Better server.
What can I do to improve it for now?
Table Structure:
CREATE TABLE dots
(
dot_id INT,
site_id INT,
latitude float ( 6 ),
longitude float ( 6 ),
rsrp float ( 6 ),
dist INT,
project_id INT,
dist_from_site INT,
geom geometry,
dist_from_ref INT,
file_name VARCHAR
);
The dot_id resets after inserting the bulk of data and each for "file_name" column.
Table Dots:
The queries:
Query #1:
await db.query(
`select MAX(rsrp) FROM dots where site_id=$1 and ${table}=$2 and project_id = $3 and file_name ilike $4`,
[site_id, dist, project_id, filename]
);
Time for response: 200ms
QUERY PLAN
--------------------------------------------------------------------------------------------------------------------------------------------
Finalize Aggregate (cost=37159.88..37159.89 rows=1 width=4) (actual time=198.416..201.762 rows=1 loops=1)
Buffers: shared hit=16165 read=16031
-> Gather (cost=37159.66..37159.87 rows=2 width=4) (actual time=198.299..201.752 rows=3 loops=1)
Workers Planned: 2
Workers Launched: 2
Buffers: shared hit=16165 read=16031
-> Partial Aggregate (cost=36159.66..36159.67 rows=1 width=4) (actual time=179.009..179.010 rows=1 loops=3)
Buffers: shared hit=16165 read=16031
-> Parallel Seq Scan on dots (cost=0.00..36150.01 rows=3861 width=4) (actual time=122.889..178.817 rows=1088 loops=3)
Filter: (((file_name)::text ~~* 'BigFile'::text) AND (site_id = 42047) AND (dist_from_ref = 500) AND (project_id = 1))
Rows Removed by Filter: 157073
Buffers: shared hit=16165 read=16031
Planning Time: 0.290 ms
Execution Time: 201.879 ms
(14 rows)
Query #2:
await db.query(
`SELECT DISTINCT (${table}) FROM dots where site_id=$1 and project_id = $2 and file_name ilike $3 order by ${table}`,
[site_id, project_id, filename]
);
Time for response: 1100ms
QUERY PLAN
---------------------------------------------------------------------------------------------------------------------------
Sort (cost=41322.12..41322.31 rows=77 width=4) (actual time=1176.071..1176.077 rows=66 loops=1)
Sort Key: dist_from_ref
Sort Method: quicksort Memory: 28kB
Buffers: shared hit=16175 read=16021
-> HashAggregate (cost=41318.94..41319.71 rows=77 width=4) (actual time=1176.024..1176.042 rows=66 loops=1)
Group Key: dist_from_ref
Batches: 1 Memory Usage: 24kB
Buffers: shared hit=16175 read=16021
-> Seq Scan on dots (cost=0.00..40499.42 rows=327807 width=4) (actual time=0.423..1066.316 rows=326668 loops=1)
Filter: (((file_name)::text ~~* 'BigFile'::text) AND (site_id = 42047) AND (project_id = 1))
Rows Removed by Filter: 147813
Buffers: shared hit=16175 read=16021
Planning:
Buffers: shared hit=5 dirtied=1
Planning Time: 0.242 ms
Execution Time: 1176.125 ms
(16 rows)
Query #3:
await db.query(
`SELECT count(*) FROM dots WHERE site_id = $1 AND ${table} = $2 and project_id = $3 and file_name ilike $4`,
[site_id, dist, project_id, filename]
);
Time for response: 200ms
QUERY PLAN
--------------------------------------------------------------------------------------------------------------------------------------------
Finalize Aggregate (cost=37159.88..37159.89 rows=1 width=8) (actual time=198.725..202.335 rows=1 loops=1)
Buffers: shared hit=16160 read=16036
-> Gather (cost=37159.66..37159.87 rows=2 width=8) (actual time=198.613..202.328 rows=3 loops=1)
Workers Planned: 2
Workers Launched: 2
Buffers: shared hit=16160 read=16036
-> Partial Aggregate (cost=36159.66..36159.67 rows=1 width=8) (actual time=179.182..179.183 rows=1 loops=3)
Buffers: shared hit=16160 read=16036
-> Parallel Seq Scan on dots (cost=0.00..36150.01 rows=3861 width=0) (actual time=119.340..179.020 rows=1088 loops=3)
Filter: (((file_name)::text ~~* 'BigFile'::text) AND (site_id = 42047) AND (dist_from_ref = 500) AND (project_id = 1))
Rows Removed by Filter: 157073
Buffers: shared hit=16160 read=16036
Planning Time: 0.109 ms
Execution Time: 202.377 ms
(14 rows)
Tables do no have any indexes.
I added an index and it helped a bit... create index idx1 on dots (site_id, project_id, file_name, dist_from_site,dist_from_ref)
OK, that's a bit too much.
An index on columns (a,b) is useful for "where a=..." and also for "where a=... and b=..." but it is not really useful for "where b=...". Creating an index with many columns uses more disk space and is slower than with fewer columns, which is a waste if the extra columns in the index don't make your queries faster. Both dist_ columns in the index will probably not be used.
Indices are a compromise : if your table has small rows, like two integer columns, and you create an index on these two columns, then it will be as big as the table, so you better be sure you need it. But in your case, your rows are pretty large at around 5kB and the number of rows is small, so adding an index or several on small int columns costs very little overhead.
So, since you very often use WHERE conditions on both site_id and project_id you can create an index on site_id,project_id. This will also work for a WHERE condition on site_id alone. If you sometimes use project_id alone, you can swap the order of the columns so it appears first, or even create another index.
You say the cardinality of these columns is about 30, so selecting on one value of site_id or project_id should hit 1/30 or 3.3% of the table, and selecting on both columns should hit 0.1% of the table, if they are uncorrelated and evenly distributed. This should already result in a substantial speedup.
You could also add an index on dist_from_site, and another one on dist_on_ref, if they have good selectivity (ie, high cardinality in those columns). Postgres can combine indices with bitmap index scan. But, if say 50% of the rows in the table have the same value for dist_from_site, then an index will be useless for this value, due to not having enough selectivity.
You could also replace the previous 2-column index with 2 indices on site_id,project_id,dist_from_site and site_id,project_id,dist_from_ref. You can try it, see if it is worth the extra resources.
Also there's the filename column and ILIKE. ILIKE can't use an index, which means it's slow. One solution is to use an expression index
CREATE INDEX dots_filename ON dots( lower(file_name) );
and replace your where condition with:
lower(file_name) like lower($4)
This will use the index unless the parameter $4 starts with a "%". And if you never use the LIKE '%' wildcards, and you're just using ILIKE for case-insensitive comparison, then you can replace LIKE with the = operator. Basically lower(a) = lower(b) is a case-insensitive comparison.
Likewise you could replace the previous 2-column index with an index on site_id,project_id,lower(filename) if you often use these three together in the WHERE condition. But as said above, it won't optimize searches on filename alone.
Since your rows are huge, even adding 1 kB of index per row will only add 20% overhead to your table, so you can overdo it without too much trouble. So go ahead and experiment, you'll see what works best.
I have table (over 100 millions records) on PostgreSQL 13.1
CREATE TABLE report
(
id serial primary key,
license_plate_id integer,
datetime timestamp
);
Indexes (for test I create both of them):
create index report_lp_datetime_index on report (license_plate_id, datetime);
create index report_lp_datetime_desc_index on report (license_plate_id desc, datetime desc);
So, my question is why query like
select * from report r
where r.license_plate_id in (1,2,4,5,6,7,8,10,15,22,34,75)
order by datetime desc
limit 100
Is very slow (~10sec). But query without order statement is fast (milliseconds).
Explain:
explain (analyze, buffers, format text) select * from report r
where r.license_plate_id in (1,2,4,5,6,7,8,10,15,22,34, 75,374,57123)
limit 100
Limit (cost=0.57..400.38 rows=100 width=316) (actual time=0.037..0.216 rows=100 loops=1)
Buffers: shared hit=103
-> Index Scan using report_lp_id_idx on report r (cost=0.57..44986.97 rows=11252 width=316) (actual time=0.035..0.202 rows=100 loops=1)
Index Cond: (license_plate_id = ANY ('{1,2,4,5,6,7,8,10,15,22,34,75,374,57123}'::integer[]))
Buffers: shared hit=103
Planning Time: 0.228 ms
Execution Time: 0.251 ms
explain (analyze, buffers, format text) select * from report r
where r.license_plate_id in (1,2,4,5,6,7,8,10,15,22,34,75,374,57123)
order by datetime desc
limit 100
Limit (cost=44193.63..44193.88 rows=100 width=316) (actual time=4921.030..4921.047 rows=100 loops=1)
Buffers: shared hit=11455 read=671
-> Sort (cost=44193.63..44221.76 rows=11252 width=316) (actual time=4921.028..4921.035 rows=100 loops=1)
Sort Key: datetime DESC
Sort Method: top-N heapsort Memory: 128kB
Buffers: shared hit=11455 read=671
-> Bitmap Heap Scan on report r (cost=151.18..43763.59 rows=11252 width=316) (actual time=54.422..4911.927 rows=12148 loops=1)
Recheck Cond: (license_plate_id = ANY ('{1,2,4,5,6,7,8,10,15,22,34,75,374,57123}'::integer[]))
Heap Blocks: exact=12063
Buffers: shared hit=11455 read=671
-> Bitmap Index Scan on report_lp_id_idx (cost=0.00..148.37 rows=11252 width=0) (actual time=52.631..52.632 rows=12148 loops=1)
Index Cond: (license_plate_id = ANY ('{1,2,4,5,6,7,8,10,15,22,34,75,374,57123}'::integer[]))
Buffers: shared hit=59 read=4
Planning Time: 0.427 ms
Execution Time: 4921.128 ms
You seem to have rather slow storage, if reading 671 8kB-blocks from disk takes a couple of seconds.
The way to speed this up is to reorder the table in the same way as the index, so that you can find the required rows in the same or adjacent table blocks:
CLUSTER report_lp_id_idx USING report_lp_id_idx;
Be warned that rewriting the table in this way causes downtime – the table will not be available while it is being rewritten. Moreover, PostgreSQL does not maintain the table order, so subsequent data modifications will cause performance to gradually deteriorate, so that after a while you will have to run CLUSTER again.
But if you need this query to be fast no matter what, CLUSTER is the way to go.
Your two indices do exactly the same thing, so you can remove the second one, it's useless.
To optimize your query, the order of the fields inside the index must be reversed:
create index report_lp_datetime_index on report (datetime,license_plate_id);
BEGIN;
CREATE TABLE foo (d INTEGER, i INTEGER);
INSERT INTO foo SELECT random()*100000, random()*1000 FROM generate_series(1,1000000) s;
CREATE INDEX foo_d_i ON foo(d DESC,i);
COMMIT;
VACUUM ANALYZE foo;
EXPLAIN ANALYZE SELECT * FROM foo WHERE i IN (1,2,4,5,6,7,8,10,15,22,34,75) ORDER BY d DESC LIMIT 100;
Limit (cost=0.42..343.92 rows=100 width=8) (actual time=0.076..9.359 rows=100 loops=1)
-> Index Only Scan Backward using foo_d_i on foo (cost=0.42..40976.43 rows=11929 width=8) (actual time=0.075..9.339 rows=100 loops=1)
Filter: (i = ANY ('{1,2,4,5,6,7,8,10,15,22,34,75}'::integer[]))
Rows Removed by Filter: 9016
Heap Fetches: 0
Planning Time: 0.339 ms
Execution Time: 9.387 ms
Note the index is not used to optimize the WHERE clause. It is used here as a compact and fast way to store references to the rows ordered by date DESC, so the ORDER BY can do an index-only scan and avoid sorting. By adding column id to the index, an index-only scan can be performed to test the condition on id, without hitting the table for every row. Since there is a low LIMIT value it does not need to scan the whole index, it only scans it in date DESC order until it finds enough rows satisfying the WHERE condition to return the result.
It will be faster if you create the index in date DESC order, this could be useful if you use ORDER BY date DESC + LIMIT in other queries too.
You forget that OP's table has a third column, and he is using SELECT *. So that wouldn't be an index-only scan.
Easy to work around. The optimum way to do this query would be an index-only scan to filter on WHERE conditions, then LIMIT, then hit the table to get the rows. For some reason if "select *" is used postgres takes the id column from the table instead of taking it from the index, which results in lots of unnecessary heap fetches for rows whose id is rejected by the WHERE condition.
Easy to work around, by doing it manually. I've also added another bogus column to make sure the SELECT * hits the table.
EXPLAIN (ANALYZE,buffers) SELECT * FROM foo
JOIN (SELECT d,i FROM foo WHERE i IN (1,2,4,5,6,7,8,10,15,22,34,75) ORDER BY d DESC LIMIT 100) f USING (d,i)
ORDER BY d DESC LIMIT 100;
Limit (cost=0.85..1281.94 rows=1 width=17) (actual time=0.052..3.618 rows=100 loops=1)
Buffers: shared hit=453
-> Nested Loop (cost=0.85..1281.94 rows=1 width=17) (actual time=0.050..3.594 rows=100 loops=1)
Buffers: shared hit=453
-> Limit (cost=0.42..435.44 rows=100 width=8) (actual time=0.037..2.953 rows=100 loops=1)
Buffers: shared hit=53
-> Index Only Scan using foo_d_i on foo foo_1 (cost=0.42..51936.43 rows=11939 width=8) (actual time=0.037..2.935 rows=100 loops=1)
Filter: (i = ANY ('{1,2,4,5,6,7,8,10,15,22,34,75}'::integer[]))
Rows Removed by Filter: 9010
Heap Fetches: 0
Buffers: shared hit=53
-> Index Scan using foo_d_i on foo (cost=0.42..8.45 rows=1 width=17) (actual time=0.005..0.005 rows=1 loops=100)
Index Cond: ((d = foo_1.d) AND (i = foo_1.i))
Buffers: shared hit=400
Execution Time: 3.663 ms
Another option is to just add the primary key to the date,license_plate index.
SELECT * FROM foo JOIN (SELECT id FROM foo WHERE i IN (1,2,4,5,6,7,8,10,15,22,34,75) ORDER BY d DESC LIMIT 100) f USING (id) ORDER BY d DESC LIMIT 100;
Limit (cost=1357.98..1358.23 rows=100 width=17) (actual time=3.920..3.947 rows=100 loops=1)
Buffers: shared hit=473
-> Sort (cost=1357.98..1358.23 rows=100 width=17) (actual time=3.919..3.931 rows=100 loops=1)
Sort Key: foo.d DESC
Sort Method: quicksort Memory: 32kB
Buffers: shared hit=473
-> Nested Loop (cost=0.85..1354.66 rows=100 width=17) (actual time=0.055..3.858 rows=100 loops=1)
Buffers: shared hit=473
-> Limit (cost=0.42..509.41 rows=100 width=8) (actual time=0.039..3.116 rows=100 loops=1)
Buffers: shared hit=73
-> Index Only Scan using foo_d_i_id on foo foo_1 (cost=0.42..60768.43 rows=11939 width=8) (actual time=0.039..3.093 rows=100 loops=1)
Filter: (i = ANY ('{1,2,4,5,6,7,8,10,15,22,34,75}'::integer[]))
Rows Removed by Filter: 9010
Heap Fetches: 0
Buffers: shared hit=73
-> Index Scan using foo_pkey on foo (cost=0.42..8.44 rows=1 width=17) (actual time=0.006..0.006 rows=1 loops=100)
Index Cond: (id = foo_1.id)
Buffers: shared hit=400
Execution Time: 3.972 ms
Edit
After thinking about it... since the LIMIT restricts the output to 100 rows ordered by date desc, wouldn't it be nice if we could get the 100 most recent rows for each license_plate_id, put all that into a top-n sort, and only keep the best 100 for all license_plate_ids? That would avoid reading and throwing away a lot of rows from the index. Even if that's much faster than hitting the table, it will still load up these index pages in RAM and clog up your buffers with stuff you don't actually need to keep in cache. Let's use LATERAL JOIN:
EXPLAIN (ANALYZE,BUFFERS)
SELECT * FROM foo
JOIN (SELECT d,i FROM
(VALUES (1),(2),(4),(5),(6),(7),(8),(10),(15),(22),(34),(75)) idlist
CROSS JOIN LATERAL
(SELECT d,i FROM foo WHERE i=idlist.column1 ORDER BY d DESC LIMIT 100) f2
ORDER BY d DESC LIMIT 100
) f3 USING (d,i)
ORDER BY d DESC LIMIT 100;
It's even faster: 2ms, and it uses the index on (license_plate_id,date) instead of the other way around. Also, and this is important, since each subquery in the lateral hits only the index pages that contain rows that will actually be selected, while the previous queries hit much more index pages. So you save on RAM buffers.
If you don't need the index on (date,license_plate_id) and don't want to keep a useless index, that could be interesting since this query doesn't use it. On the other hand, if you need the index on (date,license_plate_id) for something else and want to keep it, then... maybe not.
Please post results for the winning query 🔥
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.
Postgres is using a much heavier Seq Scan on table tracking when an index is available. The first query was the original attempt, which uses a Seq Scan and therefore has a slow query. I attempted to force an Index Scan with an Inner Select, but postgres converted it back to effectively the same query with nearly the same runtime. I finally copied the list from the Inner Select of query two to make the third query. Finally postgres used the Index Scan, which dramatically decreased the runtime. The third query is not viable in a production environment. What will cause postgres to use the last query plan?
(vacuum was used on both tables)
Tables
tracking (worker_id, localdatetime) total records: 118664105
project_worker (id, project_id) total records: 12935
INDEX
CREATE INDEX tracking_worker_id_localdatetime_idx ON public.tracking USING btree (worker_id, localdatetime)
Queries
SELECT worker_id, localdatetime FROM tracking t JOIN project_worker pw ON t.worker_id = pw.id WHERE project_id = 68475018
Hash Join (cost=29185.80..2638162.26 rows=19294218 width=16) (actual time=16.912..18376.032 rows=177681 loops=1)
Hash Cond: (t.worker_id = pw.id)
-> Seq Scan on tracking t (cost=0.00..2297293.86 rows=118716186 width=16) (actual time=0.004..8242.891 rows=118674660 loops=1)
-> Hash (cost=29134.80..29134.80 rows=4080 width=8) (actual time=16.855..16.855 rows=2102 loops=1)
Buckets: 4096 Batches: 1 Memory Usage: 115kB
-> Seq Scan on project_worker pw (cost=0.00..29134.80 rows=4080 width=8) (actual time=0.004..16.596 rows=2102 loops=1)
Filter: (project_id = 68475018)
Rows Removed by Filter: 10833
Planning Time: 0.192 ms
Execution Time: 18382.698 ms
SELECT worker_id, localdatetime FROM tracking t WHERE worker_id IN (SELECT id FROM project_worker WHERE project_id = 68475018 LIMIT 500)
Hash Semi Join (cost=6905.32..2923969.14 rows=27733254 width=24) (actual time=19.715..20191.517 rows=20530 loops=1)
Hash Cond: (t.worker_id = project_worker.id)
-> Seq Scan on tracking t (cost=0.00..2296948.27 rows=118698327 width=24) (actual time=0.005..9184.676 rows=118657026 loops=1)
-> Hash (cost=6899.07..6899.07 rows=500 width=8) (actual time=1.103..1.103 rows=500 loops=1)
Buckets: 1024 Batches: 1 Memory Usage: 28kB
-> Limit (cost=0.00..6894.07 rows=500 width=8) (actual time=0.006..1.011 rows=500 loops=1)
-> Seq Scan on project_worker (cost=0.00..28982.65 rows=2102 width=8) (actual time=0.005..0.968 rows=500 loops=1)
Filter: (project_id = 68475018)
Rows Removed by Filter: 4493
Planning Time: 0.224 ms
Execution Time: 20192.421 ms
SELECT worker_id, localdatetime FROM tracking t WHERE worker_id IN (322016383,316007840,...,285702579)
Index Scan using tracking_worker_id_localdatetime_idx on tracking t (cost=0.57..4766798.31 rows=21877360 width=24) (actual time=0.079..29.756 rows=22112 loops=1)
" Index Cond: (worker_id = ANY ('{322016383,316007840,...,285702579}'::bigint[]))"
Planning Time: 1.162 ms
Execution Time: 30.884 ms
... is in place of the 500 id entries used in the query
Same query ran on another set of 500 id's
Index Scan using tracking_worker_id_localdatetime_idx on tracking t (cost=0.57..4776714.91 rows=21900980 width=24) (actual time=0.105..5528.109 rows=117838 loops=1)
" Index Cond: (worker_id = ANY ('{286237712,286237844,...,216724213}'::bigint[]))"
Planning Time: 2.105 ms
Execution Time: 5534.948 ms
The distribution of "worker_id" within "tracking" seems very skewed. For one thing, the number of rows in one of your instances of query 3 returns over 5 times as many rows as the other instance of it. For another, the estimated number of rows is 100 to 1000 times higher than the actual number. This can certainly lead to bad plans (although it is unlikely to be the complete picture).
What is the actual number of distinct values for worker_id within tracking: select count(distinct worker_id) from tracking? What does the planner think this value is: select n_distinct from pg_stats where tablename='tracking' and attname='worker_id'? If those values are far apart and you force the planner to use a more reasonable value with alter table tracking alter column worker_id set (n_distinct = <real value>); analyze tracking; does that change the plans?
If you want to nudge PostgreSQL towards a nested loop join, try the following:
Create an index on tracking that can be used for an index-only scan:
CREATE INDEX ON tracking (worker_id) INCLUDE (localdatetime);
Make sure that tracking is VACUUMed often, so that an index-only scan is effective.
Reduce random_page_cost and increase effective_cache_size so that the optimizer prices index scans lower (but don't use insane values).
Make sure that you have good estimates on project_worker:
ALTER TABLE project_worker ALTER project_id SET STATISTICS 1000;
ANALYZE project_worker;