Thank you so much Jonathan and Yudhi.
Jonathan, To your point "*Side note: Unless things have changed in recent
versions a 10104 trace will tell you about what's going on with the hash
join, including the number of buckets in the hash table and the
distribution of rows per bucket - that might be quite interesting and
corroborate some of my comments about collisions/false positives etc"*
I was struggling to generate the '10104' trace initially as I was trying to
do it as level 10. But then I just removed the level from there and saw the
HASH join information populated in the trace file, below is the git link.
Not able to interpret much of it , however I do see a few things...like
say, In one case 'number of partitions fit in memory' is 32 VS 8 in
another. The Total number of rows in in-memory partitions is 2548554 in
both cases.
Not sure if it gives any clue which would help here making existing query
better, but in coming days, yes we are planning to make the tab_encrypt
table(which is ~900GB in size) as ~256 HASH partitions so that each
partition would be small enough i.e. ~3.5GB in size. And the same number of
HASH subpartitions for the transaction table, I hope that will work in this
scenario.
Below is the trace with cardinality hint 10M(where the bloom filter was
more effective) vs cardinality hint 50K(where the bloom filter was small).
ALTER SESSION SET EVENTS '10104 trace name context forever';
**** with cardiality hint of 10Million ********
https://gist.github.com/oracle9999/fb0598d04b0c0938bb6de695c20131fb
**** with cardinality hint of 50K ********
https://gist.github.com/oracle9999/a9145310a6158f151ac9e04f2bf3ba31
On Thu, Feb 2, 2023 at 8:51 PM Jonathan Lewis <jlewisoracle@xxxxxxxxx>
wrote:
Yuhdi,
As I pointed out, I don't think that there's likely to be *much* change
in performance by hacking in a different cardinality estimates; but we have
seen that the two different figures produce significant changes in WHERE
the time is spent and some change in the effectiveness of off-loading.
Given that clue (and assuming that there isn't a more important task to
address) I would have spent an hour or two re-running the query with a few
different cardinality hints between 49K and 2M to see if there was a sweet
spot that reduced the CPU required to apply the filter, maximised the
effectiveness of offloading, and minimised the number of rows passed up the
plan.
IIRC none of the plans showed any writes on the hash join, so I wasn't
thinking about overheads of hash joins spilling to disk.
The suggestion for re-engineering the data so that Oracle could iterate
through a partition-wise join was also about offload and CPU efficiency. On
smaller data volumes a hash table could have both a smaller number of
buckets and be more accurate in its distribution, so a Bloom filter could
be more effective and cheaper to use on the offload.
The switch to RAW, of course, is mostly about reducing I/O: the very slow
runs are probably about resource use by other users on the Cell Servers so
a smaler data size means less I/O which means less impact when the hardware
gets busy; it did occur to me to wonder if the CPU cost of hashing a 64
byte raw would be less than the cost of hashing a 128 byte varchar (answer:
probably) which would also reduce run time and the load on the cell server
(and that last one woudl reduce the risk of large volumes of data being
sent unprocessed to the database server).
Regards
Jonathan Lewis
On Tue, 31 Jan 2023 at 19:54, yudhi s <learnerdatabase99@xxxxxxxxx> wrote:
So Jonathan, OP has supplied plans which shows both cases, I. E, with
large estimation the bigger bloom filter is consuming additional CPU cycle
and with smaller estimation the hash join is consuming higher CPU cycle.
But both the cases the total query execution time is closely equal, also op
mentioned both the plans running for ~30minutes+ many times of the day
so...
when you said below I. E favoring large bloom filter option, so I am
wondering if it's because it might help in less temp spill? Or say, do you
mean its better option of hinting the inline view or tran_tab estimation
very high so that a bigger bloom filter will be applied and the lesser
amounts of rows will be passed to the hash join which may also benefit in
case of large data volume as temp spill will be minimal?
*Note that the Offload Returned Bytes was 300GB for the 49K estimate with
the small Bloom filter, and 500GB for the 2M estimate with the large Bloom
filter.*
*It looks like we need to "fake" the system so that the Bloom filter
(estimate) is large enough to eliminate a lot of data while being small
enough to be sent to the cell server so that the 14 concurrently active
cells can do the row elimination. Beyond that I don't think there's a way
to make the query go faster than the (roughly) 650 seconds you've seen so
far*.
On Tue, 31 Jan, 2023, 3:51 am Jonathan Lewis, <jlewisoracle@xxxxxxxxx>
wrote:
Comparing the 5 hash join plans you've posted:
