Re: Estimations are as good as it can be, what else can be done here?

It's not totally trivial working out the timing of activity in a parallel plan, although a good starting point is to follow the ordering given by the TQ details for each individual DFO tree. And the "Start Active / Time Active" is an interesting problem - presumably it's likely to cover the first slave start to the last slave ending time, which in unbalanced cases could lead to some confusion.

Looking at your plan here, though, the tablescan at operation 86 starts at +9 and takes 4 seconds to complete. I'm having a little difficulty working out exactly how this table scan passed through lagers of slave to get to the sort group by at operation 6 that starts at +12 and runs for 5 seconds - though that 5 seconds is probably about receiving data from the tablescan and join, and not really about sorting.

Given that it's a parallel query, it's not surprising that there are two sort group by operations - this is Oracle attempting to reduce the data size each PX slave has picked up from its hash join before distributing by range for a final sort group by. The sort group by at operation 3 starts at +16 and appears to take 24 seconds, but again I think very little time is spent in the actual sort, and most of the active time is related to passing the data to the client; the "PX SEND QC" is probably the indicator that the sort completed at +17.

Given that you seem to have 2M rows moving up the pipeline it's possible that the two-pass sorting could be eliminated. If you check the Outline Data you probably had a gby_pushdown(_at_qbname) in it. If you change this to no_gby_pushdown(_at_qbname) you should find that the preliminary sort group by disappears - this might make a couple of seconds difference. (I've had a draft note on that hint for 9 years but haven't got around to finishing it - there is a mention of it in
https://jonathanlewis.wordpress.com/2022/04/13/adaptive-joins/ )

As far as a small amount of data making a big difference - that's always a possibility in any query. Given the complete cascade of hash joins, though, I don't think you're likely to see that happen. I have to say, though, that you do seem to have a skew in your data that might be adding a couple of seconds to your query. In the PX details you can see that slave p005 uses far more CPU time than the rest in slave set 1, and slave p00a does far more I/O to temp that the rest in slave set 2. Snapshots of PX session stats may give you some clues about those two anomalies.

Your general question about timing for 10 table joins with 2M rows - you've got more joins than that, and I can see 4, 5, and 8m rows reported, so plenty of work to do. 40 seconds doesn't seem unreasonable, until you say you only want to look at the first few. Possibly you could find a way to create a materialized view (perhaps of only a subset of the tables) that reduces that enough to make it look a lot better.

Regards
Jonathan Lewis

On Thu, 9 Nov 2023 at 10:51, yudhi s <learnerdatabase99_at_gmail.com> wrote:

> I ran the query manually with parallel(6) and below is the sql monitor. It
> does finish in ~40 seconds overall. But yes need to execute it from the UI
> by changing the view definition with parallel hint , to see howmuch extent
> its helping the real production run which runs with a first_rows kind of
> setup.
>
> https://gist.github.com/databasetech0073/62b51587b3f590914e9a92218609672a
>
> But again, if volume increase a bit the response time may again exceed and
> goes beyond ~10 second reponse which is not good experience for any UI
> user. So my doubt is, if we should really not expect these type of queries
> (e.g. with 10 table joins +~2million matching rows +sorting on top) to
> finish in <10 seconds and thus some different design approach should be
> followed to cater such requirement? Like persisting the
> transformed/aggregated results before hand in a materialized view or
> anything else?
>
>>
>>>>>>

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