Re: to_jsonb performance on array aggregated correlated subqueries

I knew I forgot something: We are currently on 13.6. When was this issue 
fixed?

Am 12.08.2022 um 20:56 schrieb Justin Pryzby:
> What version of postgres ?
>
> I wonder if you're hitting the known memory leak involving jit.
> Try with jit=off or jit_inline_above_cost=-1.

>     Good day,
>
>
>
>     consider the following query:
>
>
>
>     WITH aggregation(
>
>          SELECT
>
>                 a.*,
>
>                (SELECT array_agg(b.*) FROM b WHERE b.a_id = a.id) as "bs",
>
>                (SELECT array_agg(c.*) FROM c WHERE c.a_id = a.id) as "cs",
>
>                (SELECT array_agg(d.*) FROM d WHERE d.a_id = a.id) as "ds",
>
>                (SELECT array_agg(e.*) FROM d WHERE e.a_id = a.id) as "es"
>
>          FROM a WHERE a.id IN (<some big list, ranging from 20-180 entries)
>
>     )
>
>     SELECT to_jsonb(aggregation.*) as "value" FROM aggregation;
>
>
>
>     Imagine that for each "a" there exists between 5-100 "b", "c", "d" and
>     "e" which makes the result of this pretty big (worst case: around 300kb
>     when saved to a text file).
>
>     I noticed that adding the "to_jsonb" increases the query time by 100%,
>     from 9-10ms to 17-23ms on average.
>
>     This may not seem slow at all but this query has another issue: on an
>     AWS Aurora Serverless V2 instance we are running into a RAM usage of
>     around 30-50 GB compared to < 10 GB when using a simple LEFT JOINed
>     query when under high load (> 1000 queries / sec). Furthermore the CPU
>     usage is quite high.
>
>
>
>     Is there anything I could improve? I am open for other solutions but I
>     am wondering if I ran into an edge case of "to_jsonb" for "anonymous
>     records" (these are just rows without a defined UDT) - this is just a
>     wild guess though.
>
>     I am mostly looking to decrease the load (CPU and memory) on Postgres
>     itself. Furthermore I would like to know why the memory usage is so
>     significant. Any tips on how to analyze this issue are appreciated as
>     well -  my knowledge is limited to being average at interpreting EXPLAIN
>     ANALYZE results.
>
>
>
>     Here's a succinct list of the why's, what I have found out so far and
>     solution I already tried/ don't want to consider:
>
>
>
>     - LEFT JOINing potentially creates a huge resultset because of the
>     cartesian product, thats a nono
>
>     - not using "to_jsonb" is sadly also not possible as Postgres' array +
>     record syntax is very unfriendly and hard to parse (it's barely
>     documented if at all and the quoting rules are cumbersome, furthermore I
>     lack column names in the array which would make the parsing sensitive to
>     future table changes and thus cumbersome to maintain) in my application
>
>     - I know I could solve this with a separate query for a,b,c,d and e
>     while "joinining" the result in my application, but I am looking for
>     another way to do this (bear with me, treat this as an academic question :))
>
>     - I am using "to_jsonb" to simply map the result to my data model via a
>     json mapper
>
>     - EXPLAIN ANALYZE is not showing anything special when using "to_jsonb"
>     vs. not using it, the outermost (hash) join just takes more time - is
>     there a more granular EXPLAIN that shows me the runtime of functions
>     like "to_jsonb"?
>
>     - I tried an approach where b,c,d,e where array columns of UDTs: UDTs
>     are not well supported by my application stack (JDBC) and are generally
>     undesireable for me (because of a lack of migration possibilities)
>
>     - I don't want to duplicate my data into another table (e.g. that has
>     jsonb columns)
>
>     - MATERIALIZED VIEWS are also undesirable as the manual update, its
>     update is non-incremental which would make a refresh on a big data set
>     take a long time
>
>     - split the query into chunks to reduce the IN()-statement list size
>     makes no measurable difference
>
>     - I don't want to use JSONB columns for b,c,d and e because future
>     changes of b,c,d or e's structure (e.g. new fields, changing a datatype)
>     are harder to achieve with JSONB and it lacks constraint checks on
>     insert (e.g. not null on column b.xy)
>
>
>
>     Kind regards and thank you for your time,
>
>     Nico Heller
>
>
>
>     P.S: Sorry for the long list of "I don't want to do this", some of them
>     are not possible because of other requirements
>
>
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