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
