Received: from malur.postgresql.org ([217.196.149.56]) by arkaria.postgresql.org with esmtps (TLS1.3) tls TLS_ECDHE_RSA_WITH_AES_256_GCM_SHA384 (Exim 4.96) (envelope-from ) id 1vwiKm-008H0d-1f for pgsql-hackers@arkaria.postgresql.org; Sun, 01 Mar 2026 15:03:53 +0000 Received: from localhost ([127.0.0.1] helo=malur.postgresql.org) by malur.postgresql.org with esmtp (Exim 4.96) (envelope-from ) id 1vwiKl-00Dkm5-1X for pgsql-hackers@arkaria.postgresql.org; Sun, 01 Mar 2026 15:03:51 +0000 Received: from makus.postgresql.org ([2001:4800:3e1:1::229]) by malur.postgresql.org with esmtps (TLS1.3) tls TLS_ECDHE_RSA_WITH_AES_256_GCM_SHA384 (Exim 4.96) (envelope-from ) id 1vwiKk-00Dklp-0d for pgsql-hackers@lists.postgresql.org; Sun, 01 Mar 2026 15:03:51 +0000 Received: from relay3-d.mail.gandi.net ([217.70.183.195]) by makus.postgresql.org with esmtps (TLS1.3) tls TLS_ECDHE_RSA_WITH_AES_256_GCM_SHA384 (Exim 4.98.2) (envelope-from ) id 1vwiKb-00000001wtp-1uqp for pgsql-hackers@lists.postgresql.org; Sun, 01 Mar 2026 15:03:49 +0000 Received: by mail.gandi.net (Postfix) with ESMTPSA id 4EBF5205F7; Sun, 1 Mar 2026 15:03:37 +0000 (UTC) DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=vondra.me; s=gm1; t=1772377419; h=from:from:reply-to:subject:subject:date:date:message-id:message-id: to:to:cc:cc:mime-version:mime-version:content-type:content-type: in-reply-to:in-reply-to:references:references; bh=HjXBlRX9d1jx5SlYT+q/hJy0PiAAAgNntlJ0KLmrLJ0=; b=T6cvJz8QV/AjZB+paQ1KRGVKhXNIVu0+glPlhchNuvkqbDPxfmhygV7muZGZTA3sgwaFlo LuERlk/WoDreuGLsrl55mFGGGByugts7azFljkQHHutVYb9wOaaZ2p91l/mlhxBniBUNfV B3FR4fQlir/KIw58EZR3EgN5whFCD+xmOF10n0ZYkCkajNIeHMZy0TH7L8LXKiN1vL+JxP Nt9VBBp75gE3SI94rKXXoy5gQ/tCEYygLBerSXO8gp7jOXpPBwmormkO1mdafTO9l/KSpJ 4rWllLCpePK6CEyTXwexfoZPHAmU6BYpjbz2PfYOoGNBcpL8PRKlNvwWqEKngw== Content-Type: multipart/mixed; boundary="------------lCcyRf2ECI2jWUIQn0w2Z02O" Message-ID: Date: Sun, 1 Mar 2026 16:03:36 +0100 MIME-Version: 1.0 User-Agent: Mozilla Thunderbird Subject: Re: index prefetching From: Tomas Vondra To: Alexandre Felipe , Andres Freund Cc: Peter Geoghegan , Thomas Munro , Nazir Bilal Yavuz , Robert Haas , Melanie Plageman , PostgreSQL Hackers , Georgios , Konstantin Knizhnik , Dilip Kumar References: <64a2re223ajj4popowsyu4xekbuvvyfwkrihn5yzyrkwsmsuvp@2lls3tpww5dl> <52512325-b1f2-4fff-819e-f68122b2e427@vondra.me> <64mfcfv7iihc4pmqlxarii4esnmqry52ckz5m7lmwylnfnuxuz@oxh4ioxkjtep> <7e707787-272a-4c52-b5f1-5ac990514ecc@vondra.me> Content-Language: en-US In-Reply-To: <7e707787-272a-4c52-b5f1-5ac990514ecc@vondra.me> X-GND-Sasl: tomas@vondra.me X-GND-Score: -100 X-GND-Cause: gggruggvucftvghtrhhoucdtuddrgeefgedrtddtgddvheehudduucetufdoteggodetrfdotffvucfrrhhofhhilhgvmecuifetpfffkfdpucggtfgfnhhsuhgsshgtrhhisggvnecuuegrihhlohhuthemuceftddunecusecvtfgvtghiphhivghnthhsucdlqddutddtmdenucfjughrpegtkfffgggfuffhvfevfhgjsehmtderredtvdejnecuhfhrohhmpefvohhmrghsucggohhnughrrgcuoehtohhmrghssehvohhnughrrgdrmhgvqeenucggtffrrghtthgvrhhnpeetheegudegieegvdeitdegkeetiedtheelveffgefggfeuvdduueffleeuvdevueenucfkphepkeeirdegledrvdeftddrvddtieenucevlhhushhtvghrufhiiigvpedtnecurfgrrhgrmhepihhnvghtpeekiedrgeelrddvfedtrddvtdeipdhhvghloheplgdutddrudefjedrtddrvdgnpdhmrghilhhfrhhomhepthhomhgrshesvhhonhgurhgrrdhmvgdpqhhiugepgefguefhhedvtdehhfejpdhmohguvgepshhmthhpohhuthdpnhgspghrtghpthhtohepuddupdhrtghpthhtohepohgrlhgvgigrnhgurhgvfhgvlhhiphgvsehgmhgrihhlrdgtohhmpdhrtghpthhtoheprghnughrvghssegrnhgrrhgriigvlhdruggvpdhrtghpthhtohepphhgsegsohifthdrihgvpdhrtghpthhtohepthhhohhmrghsmhhunhhrohesghhmrghilhdrtghomhdprhgtphhtthhopegshigrvhhuiiekudesghhmrghilhdrtghomhdprhgtphhtthhop ehrohgsvghrthhmhhgrrghssehgmhgrihhlrdgtohhm X-GND-State: clean List-Id: List-Help: List-Subscribe: List-Post: List-Owner: List-Archive: Archived-At: Precedence: bulk This is a multi-part message in MIME format. --------------lCcyRf2ECI2jWUIQn0w2Z02O Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 7bit Hi, I've decided to run a couple tests, trying to reproduce some of the behaviors described in your (Felipe's) messages. I'm not trying to redo the tests exactly, because (a) I don't have a M1 machine, and (b) there's not enough details about the hardware and configuration to actually redo it properly. I've focused on quantifying the impact of a couple things mentioned in the previous message: 1) the distance limit 2) the profiling instrumentation 3) concurrency (multiple backends doing I/O) I wrote a couple scripts to run two benchmarks, one focusing on (1) and (2), and the second one focusing on (3). Both were ran on four builds: 1) master 2) patched (index prefetch v11) 3) patched-limit (patched + distance limit) 4) patched-limit-instrument (patched-limit + instrumentation) The scripts initialize an instance, vary a couple important parameters (shared buffers, io_method, direct I/O, ...) and run index scans on a table with either sequential or random data. I'm attaching the full scripts, raw results, and PDFs with a nicer version of the results. single-client test (single-client.tgz) -------------------------------------- The test varies the following parameters: * buffered or direct I/O * io_method = (worker | io_uring) * shared_buffers = (128MB | 8GB) * enable_indexscan_prefetch = (on | off) * indexscan_prefetch_distance = (0, 1, 4, 16, 64, 128) * sequential / random data (1M rows, 550MB, ~15 rows per page) This was done on an old Xeon machine from ~2016, with a WD Ultrastar DC SN640 960GB NVMe SSD. The single-client.pdf shows the timings for different combinations of parameters, branches and distance limit values. There's also a table with timing relative to master (100% means the same as master, green = good, red = bad). There are literally only 4 cases where prefetching does worse than master, and those are for random data with distance limit 1. I claim this is irrelevant, because it literally disables prefetching while still paying the full cost (all 4 are for io_method=worker, where the signal overhead can be high, so it's not a surprise). We ram up the distance exactly for this reason, that's the solution for this overhead problem. I refuse to consider these regressions with limit=1 a problem. It's a bit like buying a race horse, break its leg and then complain it's not running very fast. The overhead of the instrumentation seems relatively small, probably within 5% or so. That's a bit less than I expected, but I still don't understand what this is meant to say us. It's measuring wall-time, and it's no surprise that in an I/O-bound workload most of the time is spent in functions doing (and waiting for) I/O. Like read_stream_next_buffer. But it does not give any indication *why*. multi-client test (multi-client.tgz) ------------------------------------ The test varies the following parameters: * buffered or direct I/O * io_method = (worker | io_uring) * io_workers = (12 | 32) * shared_buffers = (128MB | 8GB) * enable_indexscan_prefetch = (on | off) * indexscan_prefetch_distance = (0, 1, 4, 16, 64, 128) * sequential / random data (1M rows, 550MB, ~15 rows per page) * number of parallel workers (1, 2, 4, 8) This was done on a Ryzen 9 machine from ~2023, with 4x Samsung 990 PRO 1TB drives in RAID0. The test prepares a separate table for each worker, and then runs the index scans concurrently (and "syncs" the workers to start at the same time). It measures the duration, and we can compare it to the timing from master (without prefetching). The multi-client-full.pdf has detailed results for all parameters, but as I said I don't think the distance limit (particularly for limit 1) is interesting. The multi-client-simple.pdf shows only results for limit=0 (i.e. without limit), and is hopefully easier to understand. The first table shows timings for each combination, the second table shows timing relative to master (for the same number of workers etc.). The results are pretty positive. For random data (which is about the worst case for I/O), it's consistently faster than master. Yes, the gains with 8 workers is not as significant as with 1 worker. For example, it may look like this: master prefetch 1 worker: 2960 1898 64% 8 workers: 5585 5361 96% But that's not a huge surprise. The storage has a limited throughput, and at some point it gets saturated. Whether it's by prefetching, or by having multiple workers does not matter. For sequential data (which is what you did in your examples) it's much simpler. For buffered there's not much benefit, because page cache does read-ahead with mostly the same effect, or there's a nice consistent speedup for direct I/O. This all seems perfectly fine to me. The bad behavior would be if the prefetching gets slower than master, because that would be a regression affecting users. But that happens only in 4 cells in the table. My guess is it hits some limit on the number of signals the system can process. The random data set is not great for this, it's worse with more workers, and the 128MB buffers make that even worse. This is a bit of perfect storm, and it's already there - bitmap scans can hit that too, AFAICS. (But I'm speculating, I haven't investigated this in detail yet.) Moreover, io_uring does not have this issue. Which is another indication it's something about the signal overhead. In any case, these results clearly prefetching can be a huge improvement even in environments with concurrent activity, etc. If you see something different on the Mac, you need to investigate why. It could be something in the OS, or maybe it it's hardware specific thing (consumer SSDs can choke on too many requests). Hard to say. I don't even know what kind of M1 machine you have, what SSD etc. regards -- Tomas Vondra --------------lCcyRf2ECI2jWUIQn0w2Z02O Content-Type: application/pdf; name="single-client.pdf" Content-Disposition: attachment; filename="single-client.pdf" Content-Transfer-Encoding: base64 JVBERi0xLjQKJSDi48/TCjMKMApvYmoKPDwKL1R5cGUKL0NhdGFsb2cKL05hbWVzCjw8Cj4+ Ci9QYWdlTGFiZWxzCjw8Ci9OdW1zClsKMAo8PAovUwovRAovU3QKMQo+PgpdCj4+Ci9PdXRs aW5lcwoyCjAKUgovUGFnZXMKMQowClIKPj4KZW5kb2JqCjQKMApvYmoKPDwKL0NyZWF0b3IK KP7/AEcAbwBvAGcAbABlACAAUwBoAGUAZQB0AHMpCi9UaXRsZQoo/v8AcwBpAG4AZwBsAGUA IABjAGwAaQBlAG4AdCkKPj4KZW5kb2JqCjUKMApvYmoKPDwKL1R5cGUKL1BhZ2UKL1BhcmVu dAoxCjAKUgovTWVkaWFCb3gKWwowCjAKNzkyCjYxMgpdCi9Db250ZW50cwo2CjAKUgovUmVz b3VyY2VzCjcKMApSCi9Bbm5vdHMKOQowClIKL0dyb3VwCjw8Ci9TCi9UcmFuc3BhcmVuY3kK L0NTCi9EZXZpY2VSR0IKPj4KPj4KZW5kb2JqCjYKMApvYmoKPDwKL0ZpbHRlcgovRmxhdGVE ZWNvZGUKL0xlbmd0aAo4CjAKUgo+PgpzdHJlYW0KeJyU/cGSJDuzpAfu/3e5h+4OB+BY8im4 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