On 28/12/2025 8:08 PM, Tomas Vondra wrote:
On 12/25/25 16:39, Konstantin Knizhnik wrote:
On 21/12/2025 7:55 PM, Peter Geoghegan wrote:
On Wed, Dec 10, 2025 at 9:21 PM Peter Geoghegan <[email protected]> wrote:
Attached is v4.
Attached is v5. Changes from v4:
* Simplified and optimized index-only scans, with a particular
emphasis on avoiding regressions with nested loop joins with an inner
index-only scan.
There were quite a number of small problems/dead code related to
index-only scans fixed by this new v5. Overall, I'm quite a bit
happier with the state of index-only scans, which I'd not paid too
much attention to before now.
* Added Valgrind instrumentation to the hash index patch, which was
required to fix some false positives.
The generic indexam_util_batch_unlock routine had Valgrind
instrumentation in earlier versions, just to keep nbtree's buffer
locking checks from generating similar false positives. Some time
later, when I added the hashgetbatch patch, there were new Valgrind
false positives during hash index scans -- which I missed at first.
This new v5 revisions adds similar Valgrind checks to hash itself
(changes that add code that is more or less a direct port of the stuff
added to nbtree by commit 4a70f829), which fixes the false positives,
and is independently useful.
The rule for amgetbatch-based index AMs is that they must have similar
buffer locking instrumentation. That seems like a good thing.
--
Peter Geoghegan
I the previous mail I shared results of my experiments with different
prefetch distance.
I think that we should start prefetching of heap tuples not from the
second batch, but after some number of proceeded tids.
Attached please find a patch which implements this approach.
And below are updated results:
limit\prefetch on off always inc threshold
1 12074 12765 3146 3282 12394
2 5912 6198 2463 2438 6124
4 2919 3047 1334 1964 2910
8 1554 1496 1166 1409 1588
16 815 775 947 940 600
32 424 403 687 695 478
64 223 208 446 453 358
128 115 106 258 270 232
256 68 53 138 149 131
512 43 27 72 78 71
1024 28 13 38 40 38
Last column is result of prefetch with read_stream_threshold=10.
That's great, but it only works for cases that can (and do) benefit from
the prefetching. Try running the benchmark with a data set that fits
into shared buffers (or RAM), which makes prefetching useless.
I tried that with your test, comparing master, v5 and v5 + your
read_stream_threshold patch. See the attached run.sh script, and the PDF
summarizing the results. The last two column groups are comparisons to
master, with green=improvement, red=regression. There are no actual
improvements (1% delta is just noise). But the read_stream_threshold
results have a clear pattern of pretty massive (20-30%) regressions.
The difference between v5 and v5-threshold is pretty clear.
IIRC cases like this are *exactly* why we ended up with the current
heuristics, enabling prefetching only from the second batch. This
removes the risk of expensive read_stream init for very fast queries
that don't benefit anything. Of course, prefetching may be useless for
later batches too (e.g. if all the data is cached), but the query will
be expensive enough for the read_stream init cost to be negligible.
To put this differently, the more aggressive the heuristics is (enabling
prefetching in more case), the more likely it's to cause regressions.
We've chosen to be more defensive, i.e. to sacrifice some possible gains
in order to not regress plausible workloads. I hope we agree queries on
fully cached "hot" data are pretty common / important.
We can probably do better in the future. But we'll never know for sure
if a given scan benefits from prefetching. It's not just about the
number of items in the batch, but also about how many heap pages that
translates to, what I/O pattern (random vs. sequential?), how many are
already cached. For some queries we don't even know how many items we'll
actually need. We can't check all that at the very beginning, because
it's simply prohibitively expensive.
Thank you for looking at my patch.
I agree with you that such overhead in case of presence of data in
shared buffers is certainly not acceptable.
But it just means that we need some better criteria than number of
scanned TIDs - i.e. number of smgr heap reads.
I do not think that it will be too complex or expensive to implement - I
will try.
But in any case - the current heuristics: prefetching only from the
second batch, is IMHO not solving this problem.
First of all, as far as I understand batch = TIDs from one leaf page and
if there are large keys (i.e. URLs), there will be just few items in the
batch.
Also if all pages are present in shared buffers, then even starting
prefetching from the second batch will have negative impact on performance.
May be for long queries (scanning a lot of data) this overhead will be
less noticeable.
But as far as it is proportional to the amount of scanned data, it can
be still desirable to avoid it.
regards
