On Mon, Feb 2, 2026 at 7:04 PM Ron Johnson <[email protected]> wrote:
> On Mon, Feb 2, 2026 at 6:39 AM yudhi s <[email protected]> > wrote: > >> >> >> On Mon, Feb 2, 2026 at 3:17 AM Peter J. Holzer <[email protected]> wrote: >> >>> >>> If you do have that many simultaneous accesses to the landing page, and >>> you can't speed up the query significantly (I take it you've seen the >>> suggestion to check whether there's an index on >>> APP_schema.txn_tbl.tran_date), then maybe you don't need to perform it >>> for every user? I don't know what the query is supposed to do, but >>> unless the "ent_id" is really a user id, it doesn't seem to be specific >>> to the user. So maybe you can cache the result for a minute or an hour >>> and show the same result to everybody who logs in during that time. >>> >>> >>> >> >> There was no index on column tran_date , I created one and it's making >> the query finish in ~200ms, a lot faster than in the past. Below is the >> portion of the query and its plan which actually consumes most of the >> resource and time post the new index creation. >> >> https://gist.github.com/databasetech0073/344df46c328e02b98961fab0cd221492 >> >> 1) Now the part which takes time is the "nested loop" join on the >> "ent_id" column. Can we do anything to make it much better/faster? >> >> 2) Also another question I had was, with this new index the table scan >> of txn_tbl is now fully eliminated by the "Index Scan Backward" even i have >> other columns from that table projected in the query, so how its getting >> all those column values without visiting table but just that index scan >> backward operation? >> > > Reading through EXPLAIN output isn't always a mystery. > > Search for "actual time" and you'll find row 53, which is the "deepest" > (most nested) row with the highest actual time. > > That tells you where the time is now spent, and what it's doing. > > > My apologies if i misunderstand the plan, But If I see, it's spending ~140ms(140ms-6ms) i.e. almost all the time now, in performing the below nested loop join. So my question was , is there any possibility to reduce the resource consumption or response time further here? Hope my understanding is correct here. -> Nested Loop (cost=266.53..1548099.38 rows=411215 width=20) (actual time= *6.009..147.695* rows=1049 loops=1) Join Filter: ((df.ent_id)::numeric = m.ent_id) Rows Removed by Join Filter: 513436 Buffers: shared hit=1939
