Subject: Unfortunate Nested Loop + Missing Autovacuum

From: Lincoln Swaine-Moore <[email-masked]>
Date: Fri, 21 Feb 2025 15:46:15 -0800

Hi all--

Writing in with a two-parter performance issue.

I've got two tables roughly shaped like this:

                                              Table "public.rawdata"
        Column         |            Type             | Collation | Nullable
|                     Default
-----------------------+-----------------------------+-----------+----------+-------------------------------------------------
 id                    | integer                     |           | not null
| nextval('rawdata_id_seq'::regclass)
 timestamp             | timestamp without time zone |           | not null
|
 sn                    | character varying(36)       |           | not null
|
 raw_value             | double precision            |           |
 |
 Indexes:
    "rawdata_pkey" PRIMARY KEY, btree (id)
    "rawdata_multicol_sn_and_timestamp_desc_idx" btree (sn, "timestamp"
DESC)
Foreign-key constraints:
    "rawdata_sn_fkey" FOREIGN KEY (sn) REFERENCES devices(sn) ON UPDATE
CASCADE ON DELETE CASCADE
Referenced by:
    TABLE "cleandata" CONSTRAINT "cleandata_raw_table_id_fkey" FOREIGN KEY
(raw_table_id) REFERENCES rawdata(id)

                                            Table "public.cleandata"
     Column      |            Type             | Collation | Nullable |
                 Default
-----------------+-----------------------------+-----------+----------+---------------------------------------------------
 id              | integer                     |           | not null |
nextval('cleandata_id_seq'::regclass)
 timestamp       | timestamp without time zone |           | not null |
 sn              | character varying(36)       |           | not null |
 raw_table_id    | integer                     |           |          |
 clean_value     | double precision            |           |          |
Indexes:
    "cleandata_pkey" PRIMARY KEY, btree (id)
    "cleandata_multicol_sn_and_timestamp_desc_idx" btree (sn, "timestamp"
DESC)
Foreign-key constraints:
    "cleandata_raw_table_id_fkey" FOREIGN KEY (raw_table_id) REFERENCES
rawdata(id)
    "cleandata_sn_fkey" FOREIGN KEY (sn) REFERENCES devices(sn) ON UPDATE
CASCADE ON DELETE CASCADE

Rows are inserted into the raw table, then slightly later inserted into the
cleandata table, at a rate of ~1-5 million
rows a day. The tables are on the order of hundreds of millions of rows.
They are almost never updated or deleted,
but are selected regularly. Technically, not every row in rawdata will have
a corresponding row in cleandata,
but in practice the vast majority do.

Thanks to the combined index on sn / timestamp, selecting data from one or
the other of these tables is
fairly performant. Where things get hairy is when I try to join between
them. I can write a performant query that joins
from clean to raw, but the opposite direction (which is not logically
equivalent) gets hairy because raw_table_id is not indexed on cleandata.
I think in the medium term, it would be good to build that index, but in
the short term, I can help speed things up
by limiting the data by doubly filtering like:

explain analyze SELECT count(*)
FROM (
    SELECT *
    FROM
        rawdata
    WHERE
       timestamp >= NOW() - interval '2 weeks'
        AND sn ='FOO'
    ) r
JOIN (
    SELECT *
    FROM
        cleandata
    WHERE
        timestamp >= NOW() - interval '2 weeks'
         AND sn ='FOO'
) c
ON
    r.id = c.raw_table_id;

which produces:
Hash Join  (cost=42.80..83.52 rows=1 width=44) (actual
time=852.341..1338.713 rows=20141 loops=1)
  Hash Cond: (cleandata.raw_table_id = rawdata.id)
  ->  Index Scan using cleandata_multicol_sn_and_timestamp_desc_idx on
cleandata  (cost=0.70..41.30 rows=47 width=12) (actual time=0.051..476.287
rows=20141 loops=1)
"        Index Cond: (((sn)::text = 'FOO'::text) AND (""timestamp"" >=
(now() - '14 days'::interval)))"
  ->  Hash  (cost=41.51..41.51 rows=47 width=40) (actual
time=852.273..852.275 rows=20141 loops=1)
        Buckets: 32768 (originally 1024)  Batches: 1 (originally 1)  Memory
Usage: 1751kB
        ->  Index Scan using raw_multicol_sn_and_timestamp_desc_idx on
rawdata  (cost=0.70..41.51 rows=47 width=40) (actual time=0.050..844.555
rows=20141 loops=1)
"              Index Cond: (((sn)::text = 'FOO'::text) AND (""timestamp""
>= (now() - '14 days'::interval)))"
Planning Time: 2.211 ms
Execution Time: 1339.680 ms

The estimates are totally wrong, but the plan is good and the query is fast.
However, when I change the query slightly, and make it `timestamp >= NOW()
- interval '2 weeks' and timestamp < NOW()`,
which actually covers the same period (or really any other time period),
the plan changes dramatically:
Nested Loop  (cost=1.42..5.47 rows=1 width=572) (actual
time=0.100..185218.340 rows=20141 loops=1)
  Join Filter: (rawdata.id = cleandata.raw_table_id)
  Rows Removed by Join Filter: 405639740
  ->  Index Scan using raw_multicol_sn_and_timestamp_desc_idx on rawdata
 (cost=0.71..2.73 rows=1 width=424) (actual time=0.056..100.014 rows=20141
loops=1)
"        Index Cond: (((sn)::text = 'FOO'::text) AND (""timestamp"" >=
(now() - '14 days'::interval)) AND (""timestamp"" < now()))"
  ->  Index Scan using cleandata_multicol_sn_and_timestamp_desc_idx on
cleandata  (cost=0.71..2.73 rows=1 width=148) (actual time=0.045..7.584
rows=20141 loops=20141)
"        Index Cond: (((sn)::text = 'FOO'::text) AND (""timestamp"" >=
(now() - '14 days'::interval)) AND (""timestamp"" < now()))"
Planning Time: 0.450 ms
Execution Time: 185225.028 ms

The row estimate for each table gets even worse, and I guess this is enough
to encourage postgres to do this as a Nested Loop, with disastrous
consequences.

So, obviously there's a statistics problem, which led me to realize that
actually these tables have *never* been autovacuumed/analyzed according to
pg_stat_user_tables.
I'm using a managed database which makes it a little tricky to debug, but
all my settings
(autovacuum/autovacuum_vacuum_threshold/autovacuum_analyze_threshold/autovacuum_vacuum_insert_threshold)
are default,
and I can see that other tables have been vacuumed recently.

I assume this has something to do with the fact that these tables don't
accumulate dead tuples since they're basically append-only?
But I still think INSERTs should trigger autovacuum/analyze eventually (at
least when the table grows by 10%, because of
autovacuum_analyze_scale_factor),
and I'm confused why that doesn't seem to have happened. Seems like this is
probably hurting my queries' performance elsewhere.

So, my two big questions are:
- Is there a better way to write my query to hint away from the awful
nested loop join?
- Can anyone think of why autovacuum is declining to vacuum/analyze these
tables?

Thanks for reading, and for any help with this!

-- 
Lincoln Swaine-Moore

---

From: Andrei Lepikhov <[email-masked]>
Date: Sat, 22 Feb 2025 09:37:40 +0100

On 22/2/2025 00:46, Lincoln Swaine-Moore wrote:
> actually these tables have *never* been autovacuumed/analyzed according 
> to pg_stat_user_tables.
> I'm using a managed database which makes it a little tricky to debug, 
> but all my settings
> (autovacuum/autovacuum_vacuum_threshold/autovacuum_analyze_threshold/ 
> autovacuum_vacuum_insert_threshold) are default,
I know a couple of reports related to this kind of behaviour and 
different solutions to resolve it. But first, if you execute the ANALYZE 
command on these problematic tables, does it fix your issue? May you 
live with manual vacuum analysis each time after batch insertion?
If not, may you provide a synthetic reproduction of the case?

-- 
regards, Andrei Lepikhov

---

From: Lincoln Swaine-Moore <[email-masked]>
Date: Sun, 23 Feb 2025 14:49:06 -0800

Thanks for the reply! I tried the analysis on our much shorter staging
table and it did change the plan. I haven’t tried it on the production ones
because my understanding is that the autovacuum process is gentler with
resource consumption and I didn’t want to gum things up in the meantime.
But that may be false or avoidable.

Unfortunately the insertions are not batched—they arrive in a fairly steady
stream. Given that, and the fact that we’re talking about the behavior of
the daemon, I’m not sure how to provide a reproduction (though would be
game if you had pointers). I could in theory make a job analyze these
tables on a regular cadence, though I guess that feels like I’d be stepping
on autovacuum’s toes.

Would be happy to read over similar reports if you could steer me toward
them.

Thanks again for your help.

Lincoln Swaine-Moore

---

From: Jeff Janes <[email-masked]>
Date: Sun, 23 Feb 2025 21:08:47 -0500

> But that may be false or avoidable.

The default setting of vacuum_cost_delay is zero, while the default setting
of autovacuum_vacuum_cost_delay is 2ms (or 20ms, depending on the software
version).  So you can give a manual run the same resource consumption as an
autorun just by changing vacuum_cost_delay in that session so that it has
the same value as autovacuum_vacuum_cost_delay.  Also, if you just do
ANALYZE (rather than VACUUM ANALYZE) the resource usage should be
rather modest anyway.

Another difference is that autovac will cancel itself if it detects it is
blocking something else, while a manual vac/analyze operation will not
do that.  Normal operations (DML) don't block against vacuum anyway, only
things like index creation or partition maintenance do that.  But if those
types of operation are frequent, then doing a manual VACUUM or ANALYZE
could indeed gum things up.  Also, if those operations are frequent, it
could explain the missing autovac.  If every auto attempt gets cancelled
before it completes, then it will never complete.  pg_stat_user_tables
doesn't reflect cancelled vacuum or analyze so those will go missing.  (You
should see mentions of cancelled autovac operations in the log file though.)

Database restarts will also interrupt vacuums.  So if your database is
shutdown and restarted regularly (for cold back-ups, or just out of some
misguided belief that restarting occasionally is a good practise) and the
period between restarts is shorter than how long it would take autovac to
run, this could also explain the lack of completed autovacs.  Also, if a
table qualifies for both auto vacuum and auto analyze, the vacuum is done
first.  So even if auto analyze would be fast by itself, it still won't
complete if auto vacuum is slow and never gets to finish.

It is possible to override your vacuum settings on a per-table basis.  So
another possible explanation for the missing autovacs is that those two
tables have been specifically configured to disable autovacuum on them and
only them.  If you use psql, \dt+ (but not regular \dt) will show such
customizations.  I'm sure other tools also have ways to detect this, but I
don't know what those ways are off the top of my head.

Cheers,

Jeff

---

From: Andrei Lepikhov <[email-masked]>
Date: Mon, 24 Feb 2025 15:01:16 +0100

On 23/2/2025 23:49, Lincoln Swaine-Moore wrote:
> table and it did change the plan. I haven’t tried it on the production 
> ones because my understanding is that the autovacuum process is gentler 
> with resource consumption and I didn’t want to gum things up in the 
> meantime. But that may be false or avoidable.
> 
> Unfortunately the insertions are not batched—they arrive in a fairly 
> steady stream. Given that, and the fact that we’re talking about the 
> behavior of the daemon, I’m not sure how to provide a reproduction 
> (though would be game if you had pointers). I could in theory make a job 
> analyze these tables on a regular cadence, though I guess that feels 
> like I’d be stepping on autovacuum’s toes.
> 
I know about at least two problematic use cases:
1. Batch insertions of tuples with steadily growing value of a column 
(timestamp as an example). Selecting "just arrived" tuples, we can't 
estimate cardinalities correctly because these values are out of 
histogram and MCV statistics until the next analyze.
2. The table is overcrowded by queries, and autovacuum does not have a 
chance to pass the table.

For case No.1, I attempted to extend the 'probe indexes' technique [1]. 
However, it is applicable only in specific conditions (analytic load, 
for example) and should be designed as an extension. Unfortunately, we 
still do not have selectivity hooks to let it go.

To resolve case No.2 (and not only), I have invented the 'plan freezing' 
extension [2,3]. Using it, you may find the optimal plan once and freeze 
it in the instance's plan cache, picking constants that have to be 
treated as parameters. Any changes of the table content, statistics, or 
even GUCs will not impact the query plan until "unfreezing".

Also, you may use the pg_hint_plan extension [4] to provide Postgres 
optimiser with hints that can help it generate the plan you need.

[1] https://danolivo.substack.com/p/probing-indexes-to-survive-data-skew
[2] https://danolivo.substack.com/p/designing-a-prototype-postgres-plan
[3] https://postgrespro.com/docs/enterprise/16/sr-plan
[4] https://github.com/ossc-db/pg_hint_plan

-- 
regards, Andrei Lepikhov

---

From: Lincoln Swaine-Moore <[email-masked]>
Date: Mon, 24 Feb 2025 18:17:08 -0800

Thanks Jeff for the response--I did end up just analyzing the tables
manually, as a stopgap. Resource consumption was a non-issue as you
predicted (and plan was corrected, though estimates were still slightly
awkward).

With respect to the blocking of the autovacuum/analyze: no it shouldn't be
the case that those are running frequently in our case. Ditto re: database
restarts--this is out of my control because it would be DigitalOcean's
doing, but I don't see any evidence of it. Nor anything amiss in \dt+,
unfortunately.

I'll try to figure out if I can get access to the logs to search for
cancellations. Do you happen to know what that would look like if I'm
grep-ing for it? And do you have any other guesses about possible
explanations?

Lincoln

On Sun, Feb 23, 2025 at 6:09 PM Jeff Janes <[email-masked]> wrote:

> On Sun, Feb 23, 2025 at 5:49 PM Lincoln Swaine-Moore <
> [email-masked]> wrote:
>
> The default setting of vacuum_cost_delay is zero, while the default
> setting of autovacuum_vacuum_cost_delay is 2ms (or 20ms, depending on the
> software version).  So you can give a manual run the same resource
> consumption as an autorun just by changing vacuum_cost_delay in that
> session so that it has the same value as autovacuum_vacuum_cost_delay.
> Also, if you just do ANALYZE (rather than VACUUM ANALYZE) the resource
> usage should be rather modest anyway.
>

-- 
Lincoln Swaine-Moore