Re: weird performance issue on ceph

[Mark Nelson <mnelson@xxxxxxxxxx>]

Hi Zoltan,


Sadly it looks like some of the debug symbols are messed which makes 
things a little rough to debug from this.  On the write path if you look 
at the bstore_kv_sync thread:


Good state write test:

1.
   + 86.00% FileJournal::_open_file(long, long, bool)
2.
   |+ 86.00% ???
3.
   + 11.50% ???
4.
   |+ 0.20% ???

Bad state write test:

1.
   Thread 2869223 (bstore_kv_sync) - 1000 samples
2.
   + 73.70% FileJournal::_open_file(long, long, bool)
3.
   |+ 73.70% ???
4.
   + 24.90% ???

That's really strange, because FileJournal is part of filestore. There 
also seems to be stuff in this trace regarding BtrfsFileStoreBackend and 
FuseStore::Stop().  Seems like the debug symbols are basically just 
wrong.  Is it possible that some how you ended up with debug symbols for 
the wrong version of ceph or something?


Mark


On 8/12/22 11:13, Zoltan Langi wrote:
> Hi Mark,
>
> I managed to profile one osd before and after the bad state. We have 
> downgraded ceph to 14.2.22
>
> Good state with read test:
>
> https://pastebin.com/etreYzQc
>
> Good state with write test:
>
> https://pastebin.com/qrN5MaY6
>
> Bad state with read test:
>
> https://pastebin.com/S1pRiJDq
>
> Bad state with write test:
>
> https://pastebin.com/dEv05eGV
>
> Do you see anything obvious that could give us a clue what is going on?
>
> Many thanks!
>
> Zoltan
>
> Am 02.08.22 um 19:01 schrieb Mark Nelson:
> > Ah, too bad!  I suppose that was too easy. :)
> >
> >
> > Ok, so my two lines of thought:
> >
> > 1) Something related to the weird performance issues we ran into on 
> > the PM983 after lots of fragmented writes over the drive.  I think 
> > we've worked around that with the fix in quincy, but perhaps you are 
> > hitting a manifestation of it that we haven't. The way to investigate 
> > that is to look at the NVMe block device stats with collectl or 
> > iostat and see if you see higher io service times and longer device 
> > queue lengths in the "bad" case vs the "good" case. If you do, it 
> > means that something is making the drive(s) themselves laggy at 
> > fulfilling requests.  You might have to look at a bunch of drives in 
> > case there's one acting up before the others do, but that's pretty 
> > easy to do with either tool.  For extra bonus point you can use 
> > blktrace/blkparse/iowatcher to see if writes are really being 
> > fragmented (there could be other causes of a drive becoming slow).
> >
> > The other thing that comes to mind is RocksDB...either due to just 
> > having more metadata to deal with, or perhaps as a result of having a 
> > ton more objects, not enough onode cache, and having to issue onode 
> > reads to rocksdb when you have cache misses.  I believe we have hit 
> > rate perf counters for the onode cache, but you can get a hint if you 
> > see a bunch of reads (specifically to the DB partition if you've 
> > configured it to be separate) during writes.  You may also want to 
> > look at the compaction stats in the OSD log just to make sure it's 
> > not super laggy.  You can run this tool against the log to see a 
> > summary and details regarding individual compaction events:
> >
> >
> > https://github.com/ceph/cbt/blob/master/tools/ceph_rocksdb_log_parser.py
> >
> >
> > Those would be the first places I would look.  If neither are 
> > helpful, you could try profiling the OSDs using uwpmp as I mentioned 
> > earlier.
> >
> >
> > Mark
> >
> >
> > On 8/2/22 09:50, Zoltan Langi wrote:
> > > Hey Mark, taking back these options solve the issue. I just ran my 
> > > tests twice again and here are the results:
> > >
> > > https://ibb.co/9vY5xgS
> > > https://ibb.co/71pSCQv
> > >
> > > Back to where it was, performance dropped down today. So seems like the
> > >
> > >     bdev_enable_discard = true
> > >     bdev_async_discard = true
> > >
> > > options didn't make any difference in the end and the problem 
> > > reappeared, just a bit later.
> > >
> > > I have read all the articles you posted, thanks for it, however I am 
> > > still struggling with this. Any other recommendation or idea what to 
> > > check?
> > >
> > > Thanks a lot,
> > > Zoltan
> > >
> > > Am 01.08.22 um 17:53 schrieb Mark Nelson:
> > > > Hi Zoltan,
> > > >
> > > >
> > > > It doesn't look like your pictures showed up for me at least. Very 
> > > > interesting results though!  Are (or were) the drives particularly 
> > > > full when you've run into performance problems that the discard 
> > > > option appears to fix?  There have been some discussions in the 
> > > > past regarding online discard vs periodic discard ala fstrim.  The 
> > > > gist of it is that there are performance implications for online 
> > > > trim, but there are (eventual) performance implications if let the 
> > > > drive get too full before doing an offline trim (that itself can be 
> > > > impactful).  There's been quite a bit of discussion about it on the 
> > > > mailing list and in PRs:
> > > >
> > > >
> > > > https://lists.ceph.io/hyperkitty/list/ceph-users@xxxxxxx/thread/YFQKVCAMHHQ72AMTL2MQAA7QN7YCJ7GA/ 
> > > >
> > > >
> > > > https://github.com/ceph/ceph/pull/14727
> > > >
> > > > Specifically, see this comment regarding how it can affect garbage 
> > > > collection but also burst TRIM command effect on the FTL:
> > > >
> > > > https://github.com/ceph/ceph/pull/14727#issuecomment-342399578
> > > >
> > > > And some performance testing by Igor here:
> > > >
> > > > https://github.com/ceph/ceph/pull/20723#pullrequestreview-104218724
> > > >
> > > >
> > > > It would be very interesting to see if you see a similar 
> > > > performance improvement if we had a fstrim like discard option you 
> > > > could run before the new test.  There's a tracker ticket for it, 
> > > > but afaik no one has actually implemented anything yet:
> > > >
> > > > https://tracker.ceph.com/issues/38494
> > > >
> > > >
> > > > Regarding whether it's safe to have (async) discard enabled... 
> > > > Maybe? :)  We left it disabled by default because we didn't want to 
> > > > deal with having to situationally disable it for drives with buggy 
> > > > firmwares and some of the other associated problems with online 
> > > > discard.  Having said that, in your case it sounds like enabling it 
> > > > is yielding good results with the PM983 and your workload.
> > > >
> > > >
> > > > There's a really good (but slightly old now) article on LWN 
> > > > detailing the discussion the kernel engineers were having regarding 
> > > > all of this at the LSFMM Summit a few years ago:
> > > >
> > > >
> > > > https://lwn.net/Articles/787272/
> > > >
> > > >
> > > > In the comments, Chris Mason mentions the same delete issue we 
> > > > probably need to tackle (see Igor's comment linked above):
> > > >
> > > >
> > > > "The XFS async trim implementation is pretty reasonable, and it can 
> > > > be a big win in some workloads. Basically anything that gets pushed 
> > > > out of the critical section of the transaction commit can have a 
> > > > huge impact on performance. The major thing it's missing is a way 
> > > > to throttle new deletes from creating a never ending stream of 
> > > > discards, but I don't think any of the filesystems are doing that 
> > > > yet."
> > > >
> > > >
> > > > Mark
> > > >
> > > >
> > > > On 8/1/22 08:36, Zoltan Langi wrote:
> > > > > Hey Frank and Mark,
> > > > >
> > > > > Thanks for your response and sorry about coming back a bit late, 
> > > > > but I needed to test something that needs time.
> > > > >
> > > > > How I reproduced this issue: Created 100 volumes with ceph-csi ran 
> > > > > 3 set of tests, let the volumes sit for 48 hours and then deleted 
> > > > > the volumes, recreated them and ran the tests 3x in a row.
> > > > >
> > > > > If you look at the picture:
> > > > >
> > > > > picture1
> > > > >
> > > > > The picture above clearly shows the performance degradation. We 
> > > > > run the first test first read then write at 09:20 finishes at 
> > > > > 09:45, at 11:00 we run the new test, 11:20 finishes and already 
> > > > > struggling with the read iops and the write iops drops a lot, but 
> > > > > it is more like a saw graph in case of the read. 11:40 I reran the 
> > > > > test and now, the write normalised on a bad level, no more saw 
> > > > > pattern and the write sticks to the bad levels.
> > > > >
> > > > > Let's have a look at the bandwidth graph:
> > > > >
> > > > > picture2
> > > > >
> > > > > Compare the 09:40-10:05 part and the 12:00-12:25 part. Those are 
> > > > > the identical tests. Dropped a lot. The only way to recover from 
> > > > > this state is to recreate the bluestore devices from scratch.
> > > > >
> > > > > We have enabled the following options in rook-ceph:
> > > > >
> > > > >     bdev_enable_discard = true
> > > > >     bdev_async_discard = true
> > > > >
> > > > > Now let's have a look at the speed comparsion:
> > > > >
> > > > > Data from last Friday, before the volumes sat for 48 hours:
> > > > >
> > > > > picture3
> > > > >
> > > > > picture4
> > > > >
> > > > > We see 3 tests. Test 1: 16:40-19:00 Test 2: 20:00-21:35 and Test 
> > > > > 3: 21:40-23:30. We see slight write degradation, but it should 
> > > > > stay the same for the rest of the time.
> > > > >
> > > > > Now let's see the test runs from today:
> > > > >
> > > > > picture5
> > > > >
> > > > > picture6
> > > > >
> > > > > We see 3 tests. Test 1: 09:20-11:00 Test 2: 11:05-12:40 Test 3: 
> > > > > 13:10-14:40.
> > > > >
> > > > > As we see, after enabling these options, the system is delivering 
> > > > > constant speeds without degradation and huge performance loss like 
> > > > > before.
> > > > >
> > > > > Has anyone came across with something like this behaviour before? 
> > > > > We haven't seen any mention of these options int he official docs 
> > > > > just in pull requests. Is it safe to use these options in 
> > > > > production at all?
> > > > >
> > > > > Many thanks,
> > > > > Zoltan
> > > > >
> > > > > Am 25.07.22 um 21:42 schrieb Mark Nelson:
> > > > > > I don't think so if this is just plain old RBD.  RBD  shouldn't 
> > > > > > require a bunch of RocksDB iterator seeks in the read/write hot 
> > > > > > path and writes should pretty quickly clear out tombstones as 
> > > > > > part of the memtable flush and compaction process even in the 
> > > > > > slow case. Maybe in some kind of pathologically bad read-only 
> > > > > > corner case with no onode cache but it would be bad for more 
> > > > > > reasons than what's happening in that tracker ticket imho (even 
> > > > > > reading onodes from rocksdb block cache is significantly slower 
> > > > > > than BlueStore's onode cache).
> > > > > >
> > > > > > If RBD mirror (or snapshots) are involved that could be a 
> > > > > > different story though.  I believe to deal with deletes in that 
> > > > > > case we have to go through iteration/deletion loops that have 
> > > > > > same root issue as what's going on in the tracker ticket and it 
> > > > > > can end up impacting client IO. Gabi and Paul and 
> > > > > > testing/reworking how the snapmapper works and I've started a 
> > > > > > sort of a catch-all PR for improving our RocksDB tunings/glue here:
> > > > > >
> > > > > >
> > > > > > https://github.com/ceph/ceph/pull/47221
> > > > > >
> > > > > >
> > > > > > Mark
> > > > > >
> > > > > > On 7/25/22 12:48, Frank Schilder wrote:
> > > > > > > Could it be related to this performance death trap: 
> > > > > > > https://tracker.ceph.com/issues/55324 ?
> > > > > > > =================
> > > > > > > Frank Schilder
> > > > > > > AIT Risø Campus
> > > > > > > Bygning 109, rum S14
> > > > > > >
> > > > > > > ________________________________________
> > > > > > > From: Mark Nelson <mnelson@xxxxxxxxxx>
> > > > > > > Sent: 25 July 2022 18:50
> > > > > > > To: ceph-users@xxxxxxx
> > > > > > > Subject:  Re: weird performance issue on ceph
> > > > > > >
> > > > > > > Hi Zoltan,
> > > > > > >
> > > > > > >
> > > > > > > We have a very similar setup with one of our upstream community
> > > > > > > performance test clusters.  60 4TB PM983 drives spread across 10 
> > > > > > > nodes.
> > > > > > > We get similar numbers to what you are initially seeing (scaled 
> > > > > > > down to
> > > > > > > 60 drives) though with somewhat lower random read IOPS (we tend 
> > > > > > > to max
> > > > > > > out at around 2M with 60 drives on this HW). I haven't seen any 
> > > > > > > issues
> > > > > > > with quincy like what you are describing, but on this cluster 
> > > > > > > most of
> > > > > > > the tests have been on bare metal.  One issue we have noticed 
> > > > > > > with the
> > > > > > > PM983 drives is that they may be more susceptible to non-optimal 
> > > > > > > write
> > > > > > > patterns causing slowdowns vs other NVMe drives in the lab. We 
> > > > > > > actually
> > > > > > > had to issue a last minute PR for quincy to change the disk 
> > > > > > > allocation
> > > > > > > behavior to deal with it.  See:
> > > > > > >
> > > > > > >
> > > > > > > https://github.com/ceph/ceph/pull/45771
> > > > > > >
> > > > > > > https://github.com/ceph/ceph/pull/45884
> > > > > > >
> > > > > > >
> > > > > > > I don't *think* this is the issue you are hitting since the fix in
> > > > > > > #45884 should have taken care of it, but it might be something 
> > > > > > > to keep
> > > > > > > in the back of your mind.  Otherwise, the fact that you are 
> > > > > > > seeing such
> > > > > > > a dramatic difference across both small and large read/write 
> > > > > > > benchmarks
> > > > > > > makes me think there is something else going on.  Is there any 
> > > > > > > chance
> > > > > > > that some other bottleneck is being imposed when the pods and 
> > > > > > > volumes
> > > > > > > are deleted and recreated? Might be worth looking at memory and CPU
> > > > > > > usage of the OSDs in all of the cases and RocksDB 
> > > > > > > flushing/compaction
> > > > > > > stats from the OSD logs.  Also a quick check with 
> > > > > > > collectl/iostat/sar
> > > > > > > during the slow case to make sure none of the drives are showing 
> > > > > > > latency
> > > > > > > and built up IOs in the device queues.
> > > > > > >
> > > > > > > If you want to go deeper down the rabbit hole you can try 
> > > > > > > running my
> > > > > > > wallclock profiler against one of your OSDs in the fast/slow 
> > > > > > > cases, but
> > > > > > > you'll have to make sure it has access to debug symbols:
> > > > > > >
> > > > > > >
> > > > > > > https://github.com/markhpc/uwpmp.git
> > > > > > >
> > > > > > >
> > > > > > > run it like:
> > > > > > >
> > > > > > >
> > > > > > > ./uwpmp -n 10000 -p <pid of ceph-osd> -b libdw > output.txt
> > > > > > >
> > > > > > >
> > > > > > > If the libdw backend is having problems you can use -b libdwarf 
> > > > > > > instead,
> > > > > > > but it's much slower and takes longer to collect as many samples 
> > > > > > > (you
> > > > > > > might want to do -n 1000 instead).
> > > > > > >
> > > > > > >
> > > > > > > Mark
> > > > > > >
> > > > > > >
> > > > > > > On 7/25/22 11:17, Zoltan Langi wrote:
> > > > > > > > Hi people, we got an interesting issue here and I would like to 
> > > > > > > > ask if
> > > > > > > > anyone seen anything like this before.
> > > > > > > >
> > > > > > > >
> > > > > > > > First: our system:
> > > > > > > >
> > > > > > > > The ceph version is 17.2.1 but we also seen the same behaviour 
> > > > > > > > on 16.2.9.
> > > > > > > >
> > > > > > > > Our kernel version is 5.13.0-51 and our NVMe disks are Samsung 
> > > > > > > > PM983.
> > > > > > > >
> > > > > > > > In our deployment we got 12 nodes in total, 72 disks and 2 osd per
> > > > > > > > disk makes 144 osd in total.
> > > > > > > >
> > > > > > > > The depoyment was done by ceph-rook with default values, 6 CPU 
> > > > > > > > cores
> > > > > > > > allocated to the OSD each and 4GB of memory allocated to each OSD.
> > > > > > > >
> > > > > > > >
> > > > > > > > The issue we are experiencing: We create for example 100 
> > > > > > > > volumes via
> > > > > > > > ceph-csi and attach it to kubernetes pods via rbd. We talk 
> > > > > > > > about 100
> > > > > > > > volumes in total, 2GB each. We run fio performance tests (read, 
> > > > > > > > write,
> > > > > > > > mixed) on them so the volumes are being used heavily. Ceph 
> > > > > > > > delivers
> > > > > > > > good performance, no problems as all.
> > > > > > > >
> > > > > > > > Performance we get for example: read iops 3371027 write iops: 
> > > > > > > > 727714
> > > > > > > > read bw: 79.9 GB/s write bw: 31.2 GB/s
> > > > > > > >
> > > > > > > >
> > > > > > > > After the tests are complete, these volumes just sitting there 
> > > > > > > > doing
> > > > > > > > nothing for a longer period of time for example 48 hours. After 
> > > > > > > > that,
> > > > > > > > we clean the pods up, clean the volumes up and delete them.
> > > > > > > >
> > > > > > > > Recreate the volumes and pods once more, same spec (2GB each 
> > > > > > > > 100 pods)
> > > > > > > > then run the same tests once again. We don’t even have half the
> > > > > > > > performance of that we have measured before leaving the pods 
> > > > > > > > sitting
> > > > > > > > there doing notning for 2 days.
> > > > > > > >
> > > > > > > >
> > > > > > > > Performance we get after deleting the volumes and recreating them,
> > > > > > > > rerun the tests: read iops: 1716239 write iops: 370631 read bw: 
> > > > > > > > 37.8
> > > > > > > > GB/s write bw: 7.47 GB/s
> > > > > > > >
> > > > > > > > We can clearly see that it’s a big performance loss.
> > > > > > > >
> > > > > > > >
> > > > > > > > If we clean up the ceph deployment, wipe the disks out 
> > > > > > > > completely and
> > > > > > > > redeploy, the cluster once again delivering great performance.
> > > > > > > >
> > > > > > > >
> > > > > > > > We haven’t seen such a behaviour with ceph version 14.x
> > > > > > > >
> > > > > > > >
> > > > > > > > Has anyone seen such a thing? Thanks in advance!
> > > > > > > >
> > > > > > > > Zoltan
> > > > > > > > _______________________________________________
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> > > > > >
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> > > >
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