Re: Parallel Query Performance Issues

Thanks Andy and Jonathan.

I did change _parallel_broadcast_enabled to TRUE to have "PX BROADCAST in the plan. But still it doesn't improve the response time of the SQL. Can you please help me to identify why the step 38 actual rows shows 495M whereas Oracle estimates it to be 1 row. Below are the gist details.

https://gist.github.com/aryangoti/ec2804a7b832a7fe606ec0bf6a0681b7

Thanks,

Goti

On Thu, Jun 9, 2022 at 8:15 PM Andy Sayer <andysayer_at_gmail.com> wrote:

> Just quick thoughts - replace the distincts with group by, this might
> allow group by placement to happen for you.
>
> The inner distinct doesn’t seem to be executed as a distinct, there might
> be clues in the outline if it’s decided that it only need wants to do a
> sort.
>
> I’ll have a closer look when I can
>
> Thanks,
> Andy
>
> On Thu, 9 Jun 2022 at 15:39, Goti <aryan.goti_at_gmail.com> wrote:
>
>> Thanks Jonathan for the quick response!
>>
>> I tried for the first 2 workarounds and that didn't work as expected. I
>> will work on the 3rd and 4th action plan and update here.
>>
>> Thanks,
>>
>> Goti
>>
>>
>> On Thu, Jun 9, 2022 at 5:41 PM Jonathan Lewis <jlewisoracle_at_gmail.com>
>> wrote:
>>
>>>
>>> The two queries may return the same size result, but the 2019 report
>>> generates and aggregates roughly 12 times as much data as the 2018 report.
>>> Check the "Actual Rows" figures - the 2018 report hits 3M rows (and 3M
>>> execs of the subsequent table probes) while the 2919 report hits 39M
>>> rows/execs - and that's where a lot of time goes on CPU.
>>>
>>> Strangely (almost) all the data is passed to one PX server (at operation
>>> 13/14, I think) that blows it up through segement NL joins to get most of
>>> the 39M rows that have to be "buffer sorted" (i.e. buffered, but not
>>> actually sorted) which is where the temp space and I/O time goes.
>>>
>>> Possible workarounds
>>> - MAYBE if you tried parallel 7 rather than 8 the hash disrtibution at
>>> operation MIGHT be better balanced;
>>> - MAYBE if you set "_gby_hash_aggregation_enabled" to false and got a
>>> SORT UNIQUE instead of a hash unique the distribution would work better.
>>> - if you get the outline information for the plan you should be able to
>>> find the pq_distribute hint controls the distribution at operation 14 and
>>> change it from a hash distribution to a round-robin - this will probably
>>> introduce a 2nd layer of aggregation/uniqueness, but two small, shared
>>> stages may well do better than one very large operation.
>>> - can you rewrite the query to eliminate duplication earlier. This may
>>> require you to include inline non-mergeable views: ideally you want to
>>> avoid generating 39M rows at any point and then executing 39M join steps as
>>> that will still account for a lot of your time.
>>>
>>>
>>> Regards
>>> Jonathan Lewis
>>>
>>>
>>>
>>>
>>> On Thu, 9 Jun 2022 at 12:15, Goti <aryan.goti_at_gmail.com> wrote:
>>>
>>>> Environment : 11.2.0.4 database running on Linux.
>>>>
>>>> Need help to understand parallel query performance issues. Below are
>>>> the query details and its associated plans. The 2018_query does execute in
>>>> 24 seconds and returns about 2.5K rows. The 2019_query is also expected to
>>>> process almost the same number of rows however it consumes a lot of TEMP
>>>> space and finally fails. The 2019_query without parallel completes in 45
>>>> minutes (Just by removing the parallel hint). The only difference between
>>>> both the queries is related to the predicate "opclf.year_number =
>>>> to_number('YYYY')". The stats are up to date for the tables are partitions.
>>>>
>>>>
>>>> 2019_query:
>>>> https://gist.github.com/aryangoti/a7704a8075f118f7d942e49acee1900d
>>>>
>>>> 2018_query:
>>>> https://gist.github.com/aryangoti/a7704a8075f118f7d942e49acee1900d
>>>>
>>>> Stats and other details:
>>>> https://gist.github.com/aryangoti/a3797424ce0cb4fd87e194c05ad099b6
>>>>
>>>> Thanks,
>>>>
>>>> Goti
>>>>
>>>

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