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
