Re: Why is the performance of my lvmthin snapshot so poor

Demi Marie Obenour <[email protected]> Thu, 16 Jun 2022 03:53:23 -0400

Newsgroups	gmane.linux.lvm.general
Message-ID	<Yqrhmwwbp5KfiEWb@itl-email>

On Wed, Jun 15, 2022 at 03:42:17PM +0800, Zhiyong Ye wrote:
> 
> 
> 在 6/14/22 10:54 PM, Gionatan Danti 写道:
> > Il 2022-06-14 15:29 Zhiyong Ye ha scritto:
> > > The reason for this may be that when the volume creates a snapshot,
> > > each write to an existing block will cause a COW (Copy-on-write), and
> > > the COW is a copy of the entire data block in chunksize, for example,
> > > when the chunksize is 64k, even if only 4k of data is written, the
> > > entire 64k data block will be copied. I'm not sure if I understand
> > > this correctly.
> > 
> > Yes, in your case, the added copies are lowering total available IOPs.
> > But note how the decrease is sub-linear (from 64K to 1M you have a 16x
> > increase in chunk size but "only" a 10x hit in IOPs): this is due to the
> > lowered metadata overhead.
> 
> It seems that the consumption of COW copies when sending 4k requests is much
> greater than the loss from metadata.
> 
> > A last try: if you can, please regenerate your thin volume with 64K
> > chunks and set fio to execute 64K requests. Lets see if LVM is at least
> > smart enough to avoid coping a to-be-completely-overwritten chunks.
> 
> I regenerated the thin volume with the chunksize of 64K and the random write
> performance data tested with fio 64k requests is as follows:
> case                    iops
> thin lv                 9381
> snapshotted thin lv     8307

That seems reasonable.  My conclusion is that dm-thin (which is what LVM
uses) is not a good fit for workloads with a lot of small random writes
and frequent snapshots, due to the 64k minimum chunk size.  This also
explains why dm-thin does not allow smaller blocks: not only would it
only support very small thin pools, it would also have massive metadata
write overhead.  Hopefully dm-thin v2 will improve the situation.

-- 
Sincerely,
Demi Marie Obenour (she/her/hers)
Invisible Things Lab

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