On 26.11.2024 01:15, Ilia Evdokimov wrote:
On 22.11.2024 09:08, Alexander Korotkov wrote:
On Wed, Nov 20, 2024 at 12:07 AM Michael Paquier<mich...@paquier.xyz> wrote:
On Tue, Nov 19, 2024 at 09:39:21AM -0500, Greg Sabino Mullane wrote:
Oh, and a +1 in general to the patch, OP, although it would also be nice to
start finding the bottlenecks that cause such performance issues.
FWIW, I'm not eager to integrate this proposal without looking at this
exact argument in depth.
One piece of it would be to see how much of such "bottlenecks" we
would be able to get rid of by integrating pg_stat_statements into
the central pgstats with the custom APIs, without pushing the module
into core. This means that we would combine the existing hash of pgss
to shrink to 8 bytes for objid rather than 13 bytes now as the current
code relies on (toplevel, userid, queryid) for the entry lookup (entry
removal is sniped with these three values as well, or dshash seq
scans). The odds of conflicts still still play in our favor even if
we have a few million entries, or even ten times that.
If you run "pgbench -S -M prepared" on a pretty large machine with
high concurrency, then spin lock in pgss_store() could become pretty
much of a bottleneck. And I'm not sure switching all counters to
atomics could somehow improve the situation given we already have
pretty many counters.
I'm generally +1 for the approach taken in this thread. But I would
suggest introducing a threshold value for a query execution time, and
sample just everything below that threshold. The slower query
shouldn't be sampled, because it can't be too frequent, and also it
could be more valuable to be counter individually (while very fast
queries probably only matter "in average").
------
Regards,
Alexander Korotkov
Supabase
I really liked your idea, and I’d like to propose an enhancement that
I believe improves it further.
Yes, if a query’s execution time exceeds the threshold, it should
always be tracked without sampling. However, for queries with
execution times below the threshold, the sampling logic should
prioritize shorter queries over those closer to the threshold. In my
view, the ideal approach is for shorter queries to have the highest
probability of being sampled, while queries closer to the threshold
are less likely to be sampled.
This behavior can be achieved with the following logic:
pg_stat_statements.sample_exectime_threshold * random(0, 1) < total_time
Here’s how it works:
* As a query’s execution time approaches zero, the probability of it
being sampled approaches one.
* Conversely, as a query’s execution time approaches the threshold,
the probability of it being sampled approaches zero.
In other words, the sampling probability decreases linearly from 1 to
0 as the execution time gets closer to the threshold.
I believe this approach offers an ideal user experience. I have
attached a new patch implementing this logic. Please let me know if
you have any feedback regarding the comments in the code, the naming
of variables or documentation. I’m always open to discussion.
--
Best regards,
Ilia Evdokimov,
Tantor Labs LLC.
I’ve been closely reviewing my last (v5-*.patch) patch on implementing
time-based sampling, and I’ve realized that it might not be the best
approach. Let me explain the reasons.
* We can only perform sampling before the 'pgss_planner()' function.
However, at that point, we don’t yet know the query's execution time
since it only becomes available during 'pgss_ExecutorEnd()' or
'pgss_ProcessUtility()';
* If we wait to sample until the execution completes and we have the
actual execution time, this introduces a problem. By that point, we
might have already recorded the query's statistics into shared
memory from the 'pgss_planner()' making it too late to decide
whether to sample the query;
* Delaying sampling until the execution finishes would require waiting
for the execution time, which could introduce performance overhead.
This defeats the purpose of sampling, which aims to reduce the cost
of tracking query.
I believe we should reconsider the approach and revert to sampling based
on v4-*.patch. If I’ve missed anything or there’s an alternative way to
implement time threshold-based sampling efficiently, I’d be grateful to
hear your insights.
--
Best regards,
Ilia Evdokimov,
Tantor Labs LLC.
