On Mon, Jan 23, 2017 at 3:56 PM, Tomas Vondra
<tomas.von...@2ndquadrant.com> wrote:
> 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.

When the page gets evicted from shared buffer, it is just pushed to
kernel; the real write to disk won't happen until the kernel feels
like it.They are written to storage later when a checkpoint occurs.
So, now if we have fast storage subsystem then it can improve the
writes from kernel to disk, but not sure how much that can help in
improving TPS.

With Regards,
Amit Kapila.
EnterpriseDB: http://www.enterprisedb.com

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