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

Thank you Jonathan. The blog you pointed is really awesome. It helps in getting an idea of how to approach the problem without throwing everything to the optimizer.

So basically its a two pass thing. The key strategy should be to get the sorting of the large record sets(Millions or billions) happen with minimal resources and that can only be achieved if all the column which are getting read(which should mostly be PK column or rowid) and the columns those are part of filter/join criteria + the sorting criteria ,are part of an index, so that it can be sorted as efficiently as possible. And then the related handful of rows can be fetched efficiently using the selected PK or Rowids and passed to the UI.

It may be challenging when the query is having Joins from multiple tables or the columns in select query or the sorting criteria are from different tables. But then flattening the data before or de-normalizing the results set such that they would be available in one single table(materialized view), will make the things easier in such situation.

But yes sorting has to happen during run time only , we cant avoid the run time sorting by storing the results in sorted way before hand and just show it to the UI user upfront. Correct me if my understanding is wrong.

On Wed, 15 Nov, 2023, 8:17 pm Jonathan Lewis, <jlewisoracle_at_gmail.com> wrote:

> For examples where you have to select a large volume of data, sort it, and
> then return only the first few sorted results (pagination queries), you
> will find that sometimes the only thing to do is a complex rewrite of the
> query that tries to find the smallest amount of data that needs to be
> selected to allow the correct sorting followed by a "join back" by rowid or
> primary key to the tables that supply other non-sorted data.
>
> There are some indications of technique at Manual Optimisation | Oracle
> Scratchpad (wordpress.com)
> <https://jonathanlewis.wordpress.com/2008/04/27/manual-optimisation/> with
> a 2nd and 3rd linked at the end of the article.
>
> Regards
> Jonathan Lewis
>
>
> On Wed, 15 Nov 2023 at 05:18, Pap <oracle.developer35_at_gmail.com> wrote:
>
>> Jonathan/Andy,
>>
>> The OP's query here is really doing a lot of work like passing ~2million
>> rows all the way across 10+ table joins and also sorting all of them before
>> publishing the first_n_rows to the client. But a common case could be like,
>> a scenario in which the UI user has to see latest ~100 transactions details
>> of certain type for a user within a date range(say one week/month). And in
>> cases where the daily transaction can go up to 100's of millions and even a
>> two table join criteria with an order by clause(for finding the latest
>> transaction details based on date column) can take lot of time. So how
>> should one handle the design of such a requirement?
>>
>> Even one can have the de-normalized/flatten table or materialized view to
>> persist the two table JOIN data before hand but the order is not
>> guaranteed while we fetch the data, so we have to have the "order by
>> date_column" clause added to the query on top of the materialized view
>> scan(which might be on 100's of millions rows) , and that can well be the
>> bottleneck then. How should one design to cater such user needs? Can
>> Parallel processing help here?
>>
>> And it's also true that UI users don't understand about the detailed
>> work it had to go through before publishing those handful of rows in the UI
>> screen but they want it in a few seconds.
>>
>> On Fri, Nov 10, 2023 at 6:27 PM Jonathan Lewis <jlewisoracle_at_gmail.com>
>> wrote:
>>
>>>
>>> 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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