Hi,
I haven’t caught the issue yet with this debug etc. in place, but auto_explain (and some pg_stat_statements poking about) has helped me find something interesting that might(?) be related.
My data ingest is in 2 functions, depending on the type of data: - RADIUS data with usage info - RADIUS data without usage info
The functions are the largely same, except the one with usage info has to go and work with a table with lots of partitions (444, right at the moment).
The function that works with the usage info is *significantly* slower. One thing I have specifically noticed is that the run time for the total function doesn’t add up to the run time of each of the of the nested statements. Not even close. It’s around 16ms on average (both in pg_stat_statements and in the auto_explain), but the nested statements add up to around 1-2ms or so - which I think means the planner is the culprit here.
I have been stepping through the various statements which are different between the two functions, and note that when I do math on a timestamp in a SELECT statement (i.e. _event_timestamp - INTERVAL ‘1 hour’), the planner takes 50ms or so - note that the result of the timestamp is used to search the partition key. If I declare a function which does the math in advance, stores it in a variable and then runs the SELECT, the planner takes less than 1ms.
Does this mean it’s calculating the timestamp for each partition, or something like that?
I have updated the function in my production database to do this math in advance, and the mean time is down to around 6ms, from 16ms. This is still longer than the actual statement execution times in the auto_explain output - which add up to around 1-2ms - but it’s better!
I’ve also turned on pg_stat_statements.track_planning and will see what that looks like after some time.
I see Postgres 14 release notes has information about performance improvements in the planner for updates on tables with "many partitions”. Is 444 partitions “many”? My updates are all impacting a single partition only.
On 11/07/2022, at 4:05 PM, Justin Pryzby <pryzby@xxxxxxxxxxxxx> wrote:
On Mon, Jul 11, 2022 at 03:21:38PM +1200, Nathan Ward wrote:
Note that postgres doesn't automatically analyze parent tables, so you should
maybe do that whenever the data changes enough for it to matter.
Hmm. This raises some stuff I’m not familiar with - does analysing a parent table do anything?
Yes
You could check if you have stats now (maybe due to a global ANALYZE or
analyzedb) and how the query plans change if you analyze.
The transaction may be overly conservative.
SELECT COUNT(1) FROM pg_stats WHERE tablename=PARENT;
SELECT last_analyze, last_autoanalyze, relname FROM pg_stat_all_tables WHERE relname=PARENT;
begin;
SET default_statistics_target=10;
ANALYZE;
explain SELECT [...];
rollback;
I have a development database which gets a mirror of about 50% of the data coming in, and ran a global ANALYZE earlier on - and note that the disk IO is actually a lot higher since which is interesting and not desirable obviously, so I have some more fiddling to do..The behaviour during the ANALYZE was very similar to what happens on my production database when things go funny though, so, this feels like it’s getting me close.The above is going to be a bit tricky to do I think - the ingest process runs a stored procedure, and behaviour varies quite a bit if I stick in synthetic values.I think probably my approach for now will be to turn on auto explain with some sampling, and see what happens.Side note, in the auto_explain docs, there is a note in a callout box saying that log_analyze has a high impact even if the query isn’t logged - if I use sampling, is this still the case - i.e. all queries are impacted - or is it only the sampled queries?I got the impression that analysing the parent was just shorthand for analysing all of the attached partitions.
Could you let us know if the documentation left that impression ?
See here (this was updated recently).
https://www.postgresql.org/docs/13/sql-analyze.html#id-1.9.3.46.8
For partitioned tables, ANALYZE gathers statistics by sampling rows from all partitions; in addition, it will recurse into each partition and update its statistics. Each leaf partition is analyzed only once, even with multi-level partitioning. No statistics are collected for only the parent table (without data from its partitions), because with partitioning it's guaranteed to be empty.
By contrast, if the table being analyzed has inheritance children, ANALYZE gathers two sets of statistics: one on the rows of the parent table only, and a second including rows of both the parent table and all of its children. This second set of statistics is needed when planning queries that process the inheritance tree as a whole. The child tables themselves are not individually analyzed in this case.
The autovacuum daemon does not process partitioned tables, nor does it process inheritance parents if only the children are ever modified. It is usually necessary to periodically run a manual ANALYZE to keep the statistics of the table hierarchy up to date.
It was this part:“””No statistics are collected for *only* the parent table (without data from its partitions), because with partitioning it's guaranteed to be empty.“””Emphasis around “only” is mine - I think my brain skipped that word, but, it’s obviously critical.I also note this:“””It is usually necessary to periodically run a manual ANALYZE to keep the statistics of the table hierarchy up to date.“””This seems really important and is something I was entirely unaware of - maybe this should be in one of those callout boxes.Perhaps because I attach a table with data, the parent sometimes decides it needs to run analyse on a bunch of things?
No, that doesn't happen.
Ack.Or, maybe it uses the most recently attached partition, with bad statistics, to plan queries that only touch other partitions?
This is closer to what I was talking about.
To be clear, you are using relkind=p partitions (added in v10), and not
inheritance parents, right ?
Yes, relkind=p.--Nathan Ward
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