yeah, will share the information once I have full understanding what's
happened.
For now I've got quite fragmentary view which is too early to publish.
Thanks,
Igor
On 07/09/2023 22:06, Mark Nelson wrote:
Oh that's very good to know. I'm sure Igor will respond here, but do
you know which PR this was related to? (possibly
https://github.com/ceph/ceph/pull/50321)
If we think there's a regression here we should get it into the
tracker ASAP.
Mark
On 9/7/23 13:45, J-P Methot wrote:
To be quite honest, I will not pretend I have a low level
understanding of what was going on. There is very little
documentation as to what the bluestore allocator actually does and we
had to rely on Igor's help to find the solution, so my understanding
of the situation is limited. What I understand is as follows:
-Our workload requires us to move around, delete, write a fairly high
amount of RBD data around the cluster.
-The AVL allocator doesn't seem to like that and changes added to it
in 16.2.14 made it worse than before.
-It made the OSDs become unresponsive and lag quite a bit whenever
high amounts of data was written or deleted, which is, all the time.
-We basically changed the allocator to bitmap and, as we speak, this
seems to have solved the problem. I understand that this is not ideal
as it's apparently less performant, but here it's the difference
between a cluster that gives me enough I/Os to work properly and a
cluster that murders my performances.
I hope this helps. Feel free to ask us if you need further details
and I'll see what I can do.
On 9/7/23 13:59, Mark Nelson wrote:
Ok, good to know. Please feel free to update us here with what you
are seeing in the allocator. It might also be worth opening a
tracker ticket as well. I did some work in the AVL allocator a
while back where we were repeating the linear search from the same
offset every allocation, getting stuck, and falling back to fast
search over and over leading to significant allocation
fragmentation. That got fixed, but I wouldn't be surprised if we
have some other sub-optimal behaviors we don't know about.
Mark
On 9/7/23 12:28, J-P Methot wrote:
Hi,
By this point, we're 95% sure that, contrary to our previous
beliefs, it's an issue with changes to the bluestore_allocator and
not the compaction process. That said, I will keep this email in
mind as we will want to test optimizations to compaction on our
test environment.
On 9/7/23 12:32, Mark Nelson wrote:
Hello,
There are two things that might help you here. One is to try the
new "rocksdb_cf_compaction_on_deletion" feature that I added in
Reef and we backported to Pacific in 16.2.13. So far this appears
to be a huge win for avoiding tombstone accumulation during
iteration which is often the issue with threadpool timeouts due to
rocksdb. Manual compaction can help, but if you are hitting a case
where there's concurrent iteration and deletions with no writes,
tombstones will accumulate quickly with no compactions taking
place and you'll eventually end up back in the same place. The
default sliding window and trigger settings are fairly
conservative to avoid excessive compaction, so it may require some
tuning to hit the right sweet spot on your cluster. I know of at
least one site that's using this feature with more aggressive
settings than default and had an extremely positive impact on
their cluster.
The other thing that can help improve compaction performance in
general is enabling lz4 compression in RocksDB. I plan to make
this the default behavior in Squid assuming we don't run into any
issues in testing. There are several sites that are using this now
in production and the benefits have been dramatic relative to the
costs. We're seeing significantly faster compactions and about
2.2x lower space requirement for the DB (RGW workload). There may
be a slight CPU cost and read/index listing performance impact,
but even with testing on NVMe clusters this was quite low (maybe a
couple of percent).
Mark
On 9/7/23 10:21, J-P Methot wrote:
Hi,
Since my post, we've been speaking with a member of the Ceph dev
team. He did, at first, believe it was an issue linked to the
common performance degradation after huge deletes operation. So
we did do offline compactions on all our OSDs. It fixed nothing
and we are going through the logs to try and figure this out.
To answer your question, no the OSD doesn't restart after it logs
the timeout. It manages to get back online by itself, at the cost
of sluggish performances for the cluster and high iowait on VMs.
We mostly run RBD workloads.
Deep scrubs or no deep scrubs doesn't appear to change anything.
Deactivating scrubs altogether did not impact performances in any
way.
Furthermore, I'll stress that this is only happening since we
upgraded to the latest Pacific, yesterday.
On 9/7/23 10:49, Stefan Kooman wrote:
On 07-09-2023 09:05, J-P Methot wrote:
Hi,
We're running latest Pacific on our production cluster and
we've been seeing the dreaded 'OSD::osd_op_tp thread
0x7f346aa64700' had timed out after 15.000000954s' error. We
have reasons to believe this happens each time the RocksDB
compaction process is launched on an OSD. My question is, does
the cluster detecting that an OSD has timed out interrupt the
compaction process? This seems to be what's happening, but it's
not immediately obvious. We are currently facing an infinite
loop of random OSDs timing out and if the compaction process is
interrupted without finishing, it may explain that.
Does the OSD also restart after it logged the timeouts?
You might want to perform an offline compaction every
$timeperiod to fix any potential RocksDB degradation. That's
what we do. What kind of workload do you run (i.e. RBD, CephFS,
RGW)?
Do you also see these timeouts occur during deep-scrubs?
Gr. Stefan
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