http://symas.com/mdb/inmem/scaling.html i just looked at this and i have a sneaking suspicion that you may be running into the same problem that i encountered when accidentally opening 10 LMDBs 20 times by forking 30 processes *after* opening the 10 LMDBs... (and also forgetting to close them in the parent). what i found was that when i reduced the number of LMDBs to 3 or below, the loadavg on the multi-core system was absolutely fine [ok, it was around 3 to 4 but that's ok] adding one more LMDB (remember that's 31 extra file handles opened to a shm-mmap'd file) increased the loadavg to 6 or 7. by the time that was up to 10 the loadavg had completely unreasonably jumped to over 30. i could log in over ssh - that was not a problem. editing a file was ok (opening it) but trying to create new files resulted in applications (such as vim) was blocked so badly that i often could not even press ctrl-z in order to background the task, and had to kill the entire ssh session. in each test run the amount of work being done was actually relatively small. basically i suspect a severe bug in the linux kernel which these extreme circumstances (32 or 16 processes accessing the same mmap'd file for example) have never been encountered, so the bug is simply... unknown.

can i make the suggestion that, whilst i am aware that it is generally not recommended for production environments to run more processes than there are cores, you try running 128, 256 and even 512 processes all hitting that 64-core system, and monitor its I/O usage (iostats) and loadavg whilst doing so?

the hypothesis to test is that the performance, which should scale reasonably linearly downwards as a ratio of the number of processes to the number of cores, instead drops like a lead balloon. l.

Luke Kenneth Casson Leighton wrote: > http://symas.com/mdb/inmem/scaling.html > > can i make the suggestion that, whilst i am aware that it is generally > not recommended for production environments to run more processes than > there are cores, you try running 128, 256 and even 512 processes all > hitting that 64-core system, and monitor its I/O usage (iostats) and > loadavg whilst doing so? Sure, I can conduct that test and collect system stats using atop. Will let ya know. By the way, we're using threads here, not processes. But the overall loading behavior should be the same either way. > the hypothesis to test is that the performance, which should scale > reasonably linearly downwards as a ratio of the number of processes to > the number of cores, instead drops like a lead balloon.

On Mon, Oct 20, 2014 at 1:00 PM, Howard Chu <hyc(a)symas.com&gt; wrote: > Howard Chu wrote: >> >> Luke Kenneth Casson Leighton wrote: >>> >>> http://symas.com/mdb/inmem/scaling.html >>> >>> can i make the suggestion that, whilst i am aware that it is generally >>> not recommended for production environments to run more processes than >>> there are cores, you try running 128, 256 and even 512 processes all >>> hitting that 64-core system, and monitor its I/O usage (iostats) and >>> loadavg whilst doing so? >>> the hypothesis to test is that the performance, which should scale >>> reasonably linearly downwards as a ratio of the number of processes to >>> the number of cores, instead drops like a lead balloon. > > Looks to me like the system was reasonably well behaved. and it looks like the writer rate is approximately-halving with each doubling from 64 onwards. ok, so that didn't show anything up... but wait... there's only one writer, right? the scenarios where i am seeing difficulties is when there are multiple writers and readers (actually, multiple writers and readers to multiple envs simultaneously). so to duplicate that scenario, it would either be necessary to modify the benchmark to do multiple writer threads (knowing that they are going to have contention, but that's ok) or, to be closer to the scenario where i have observed difficulties to run the test several times *simultaneously* on the same database. *thinks*.... actually in order to ensure that the reads and writes are approximately balanced, it would likely be necessary to modify the benchmark code to allow multiple writer threads and distribute the workload amongst them whilst at the same time keeping the number of reader threads the same as it was previously. then it would be possible to make a direct comparison (against the figures you just sent), against the e.g. 32-threads case. 32 readers, 2 writers. 32 readers, 4 writers. 32 readers, 8 writers and so on. keeping the number of threads (write plus read) to below or equal the total number of cores avoids any unnecessary context-switching

the hypothesis being tested is that the writers performance overall remains the same, as only one may perform writes at a time.

i know it sounds silly to do that: it sounds so obvious that yeah it really should not make any difference given that no matter how many writers there are they will always do absolutely nothing (except one of them), and the context switching when one finishes should also be negligeable, but i know there's something wrong and i'd like to help find out what it is.

On Mon, Oct 20, 2014 at 1:53 PM, Howard Chu <hyc(a)symas.com&gt; wrote: > My experience from benchmarking OpenLDAP over the years is that mutexes > scale only up to a point. When you have threads grabbing the same mutex from > across socket boundaries, things go into the toilet. There's no fix for > this; that's the nature of inter-socket communication. argh. ok. so... actually.... accidentally, the design where i used a single LMDB (one env) shared amongst (20 to 30) processes using db_open to create (10 or so) databases would mitigate against that... taking a quick look at mdb.c the mutex lock is done on the env not on the database... sooo compared to the previous design there would only be a 20/30-to-1 mutex contention whereas previously there were *10 sets* of 20 or 30 to 1 mutexes all competing... and if mutexes use sockets underneath that would explain why the inter-process communication (which also used sockets) was so dreadful.

huh, how about that. do you happen to have access to a straight 8-core SMP system,

or is it relatively easy to turn off the NUMA architecture?

On Mon, Oct 20, 2014 at 2:34 PM, Howard Chu <hyc(a)symas.com&gt; wrote: > I can probably use taskset or something similar to restrict a process to a > particular set of cores. What exactly do you have in mind? keeping the writers and readers proposed test, but onto the same 8 cores only, running:

On Mon, Oct 20, 2014 at 3:25 PM, Howard Chu <hyc(a)symas.com&gt; wrote: > The timings on the left are for the writer process; the reader process > is on the right. You were right, the number of writers is largely > irrelevant because they're all waiting most of the time. There's a > huge jump in System CPU time from 1 writer to 2 writers, because with > only 1 writer the mutex is uncontended and essentially zero-cost. As > more writer threads are added, the System CPU time will rise. by some 15 to 20% or so, from 2 writers going up to 32 writers. which is not unreasonable - nothing like what i saw (CPU usage dropping to 40%, loadavg jumping to over 8x the number of cores). ok... what's the number of writes being carried out in each transaction? the original scenario in which i saw the issue was when there was one (!!). the design at the time was not optimised for batches of reads followed by batches of writes.

a single write per transaction would mean quite a lot more mutex usage.

On Mon, Oct 20, 2014 at 1:53 PM, Howard Chu <hyc(a)symas.com&gt; wrote: >> then it would be possible to make a direct comparison (against the >> figures you just sent), against the e.g. 32-threads case. 32 readers, >> 2 writers. 32 readers, 4 writers. 32 readers, 8 writers and so on. >> keeping the number of threads (write plus read) to below or equal the >> total number of cores avoids any unnecessary context-switching > > We can do that by running two instances of the benchmark program > concurrently; one doing a read-only job with a fixed number of threads (32) > and one doing a write-only job with the increasing number of threads. ohh, ok - great. saves a job doing some programming at least.