Re: index prefetching

Tomas Vondra <tomas@vondra.me>

From: Tomas Vondra <tomas@vondra.me>
To: Andres Freund <andres@anarazel.de>
Cc: Peter Geoghegan <pg@bowt.ie>, Thomas Munro <thomas.munro@gmail.com>, Nazir Bilal Yavuz <byavuz81@gmail.com>, Robert Haas <robertmhaas@gmail.com>, Melanie Plageman <melanieplageman@gmail.com>, PostgreSQL Hackers <pgsql-hackers@lists.postgresql.org>, Georgios <gkokolatos@protonmail.com>, Konstantin Knizhnik <knizhnik@garret.ru>, Dilip Kumar <dilipbalaut@gmail.com>
Date: 2025-08-26T15:06:11Z
Lists: pgsql-hackers

Commits

Same data as JSON: GET /api/v1/messages/:b64id/commits the thread's linked commits as JSON, with link sources. API reference →
  1. aio: io_uring: Trigger async processing for large IOs

  2. read stream: Split decision about look ahead for AIO and combining

  3. read_stream: Only increase read-ahead distance when waiting for IO

  4. read_stream: Prevent distance from decaying too quickly

  5. Reduce ExecSeqScan* code size using pg_assume()

  6. Fix rare bug in read_stream.c's split IO handling.

  7. Fix multiranges to behave more like dependent types.

  8. Add EXPLAIN (MEMORY) to report planner memory consumption

  9. Optimize nbtree backward scan boundary cases.

  10. Increment xactCompletionCount during subtransaction abort.

  11. Add nbtree Valgrind buffer lock checks.

  12. Add nbtree high key "continuescan" optimization.

  13. Reduce pinning and buffer content locking for btree scans.

  14. Teach btree to handle ScalarArrayOpExpr quals natively.


On 8/26/25 01:48, Andres Freund wrote:
> Hi,
> 
> On 2025-08-25 15:00:39 +0200, Tomas Vondra wrote:
>> Thanks. Based on the testing so far, the patch seems to be a substantial
>> improvement. What's needed to make this prototype committable?
> 
> Mainly some testing infrastructure that can trigger this kind of stream. The
> logic is too finnicky for me to commit it without that.
> 

So, what would that look like? The "naive" approach to testing is to
simply generate a table/index, producing the right sequence of blocks.
That shouldn't be too hard, it'd be enough to have an index that

- has ~2-3 rows per value, on different heap pages
- the values "overlap", e.g. like this (value,page)

   (A,1), (A,2), (A,3), (B,2), (B,3), (B,4), ...

Another approach would be to test this at C level, sidestepping the
query execution entirely. We'd have a "stream generator" that just
generates a sequence of blocks of our own choosing (could be hard-coded,
some pattern, read from a file ...), and feed it into a read stream.

But how would we measure success for these tests? I don't think we want
to look at query duration, that's very volatile.

> 
>> I assume this is PG19+ improvement, right? It probably affects PG18 too,
>> but it's harder to hit / the impact is not as bad as on PG19.
> 
> Yea. It does apply to 18 too, but I can't come up with realistic scenarios
> where it's a real issue. I can repro a slowdown when using many parallel
> seqscans with debug_io_direct=data - but that's even slower in 17...
> 

Makes sense.

> 
>> On a related note, my test that generates random datasets / queries, and
>> compares index prefetching with different io_method values found a
>> pretty massive difference between worker and io_uring. I wonder if this
>> might be some issue in io_method=worker.
> 
>> while with index prefetching (with the aio prototype patch), it looks
>> like this:
>>
>>                                 QUERY PLAN
>>   ----------------------------------------------------------------------
>>    Index Scan using idx on t (actual rows=9048576.00 loops=1)
>>      Index Cond: ((a >= 16150) AND (a <= 4540437))
>>      Index Searches: 1
>>      Prefetch Distance: 2.032
>>      Prefetch Count: 868165
>>      Prefetch Stalls: 2140228
>>      Prefetch Skips: 6039906
>>      Prefetch Resets: 0
>>      Stream Ungets: 0
>>      Stream Forwarded: 4
>>      Prefetch Histogram: [2,4) => 855753, [4,8) => 12412
>>      Buffers: shared hit=2577599 read=455610
>>    Planning:
>>      Buffers: shared hit=78 read=26 dirtied=1
>>    Planning Time: 1.032 ms
>>    Execution Time: 3150.578 ms
>>   (16 rows)
>>
>> So it's about 2x slower. The prefetch distance collapses, because
>> there's a lot of cache hits (about 50% of requests seem to be hits of
>> already visited blocks). I think that's a problem with how we adjust the
>> distance, but I'll post about that separately.
>>
>> Let's try to simply set io_method=io_uring:
>>
>>                                 QUERY PLAN
>>   ----------------------------------------------------------------------
>>    Index Scan using idx on t  (actual rows=9048576.00 loops=1)
>>      Index Cond: ((a >= 16150) AND (a <= 4540437))
>>      Index Searches: 1
>>      Prefetch Distance: 2.032
>>      Prefetch Count: 868165
>>      Prefetch Stalls: 2140228
>>      Prefetch Skips: 6039906
>>      Prefetch Resets: 0
>>      Stream Ungets: 0
>>      Stream Forwarded: 4
>>      Prefetch Histogram: [2,4) => 855753, [4,8) => 12412
>>      Buffers: shared hit=2577599 read=455610
>>    Planning:
>>      Buffers: shared hit=78 read=26
>>    Planning Time: 2.212 ms
>>    Execution Time: 1837.615 ms
>>   (16 rows)
>>
>> That's much closer to master (and the difference could be mostly noise).
>>
>> I'm not sure what's causing this, but almost all regressions my script
>> is finding look like this - always io_method=worker, with distance close
>> to 2.0. Is this some inherent io_method=worker overhead?
> 
> I think what you might be observing might be the inherent IPC / latency
> overhead of the worker based approach. This is particularly pronounced if the
> workers are idle (and the CPU they get scheduled on is clocked down). The
> latency impact of that is small, but if you never actually get to do much
> readahead it can be visible.
> 

Yeah, that's quite possible. If I understand the mechanics of this, this
can behave in a rather unexpected way - lowering the load (i.e. issuing
fewer I/O requests) can make the workers "more idle" and therefore more
likely to get suspended ...

Is there a good way to measure if this is what's happening, and the
impact? For example, it'd be interesting to know how long it took for a
submitted process to get picked up by a worker. And % of time a worker
spent handling I/O.


regards

-- 
Tomas Vondra