On 01/23/2017 09:57 AM, Amit Kapila wrote:
On Mon, Jan 23, 2017 at 1:18 PM, Tomas Vondra
<tomas.von...@2ndquadrant.com> wrote:
On 01/23/2017 08:30 AM, Amit Kapila wrote:

I think if we can get data for pgbench read-write workload when data
doesn't fit in shared buffers but fit in RAM, that can give us some
indication.  We can try by varying the ratio of shared buffers w.r.t
data.  This should exercise the checksum code both when buffers are
evicted and at next read.  I think it also makes sense to check the
WAL data size for each of those runs.

Yes, I'm thinking that's pretty much the worst case for OLTP-like workload,
because it has to evict buffers from shared buffers, generating a continuous
stream of writes. Doing that on good storage (e.g. PCI-e SSD or possibly
tmpfs) will further limit the storage overhead, making the time spent
computing checksums much more significant. Makes sense?

Yeah, I think that can be helpful with respect to WAL, but for data,
if we are considering the case where everything fits in RAM, then
faster storage might or might not help.

I'm not sure I understand. Why wouldn't faster storage help? It's only a matter of generating enough dirty buffers (that get evicted from shared buffers) to saturate the storage. With some storage you'll hit that at 100 MB/s, with PCI-e it might be more like 1GB/s.

Of course, if the main bottleneck is somewhere else (e.g. hitting 100% CPU utilization before putting any pressure on storage), that's not going to make much difference.

Or perhaps I missed something important?


Tomas Vondra                  http://www.2ndQuadrant.com
PostgreSQL Development, 24x7 Support, Remote DBA, Training & Services

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