On 01/23/2017 09:57 AM, Amit Kapila wrote:
> On Mon, Jan 23, 2017 at 1:18 PM, Tomas Vondra
> <tomas.vondra@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?
regards
--
Tomas Vondra http://www.2ndQuadrant.com
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