On 01/23/2017 08:30 AM, Amit Kapila wrote:
> On Sun, Jan 22, 2017 at 3:43 PM, Tomas Vondra
> <tomas.vondra@2ndquadrant.com> wrote:
>>
>> That being said, I'm ready to do some benchmarking on this, so that we have
>> at least some numbers to argue about. Can we agree on a set of workloads
>> that we want to benchmark in the first round?
>>
>
> 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?
What about other types of workload? I think we should not look just at
write-heavy workloads - I wonder what is the overhead of verifying the
checksums in read-only workloads (again, with data that fits into RAM).
What about large data loads simulating OLAP, and exports (e.g. pg_dump)?
That leaves us with 4 workload types, I guess:
1) read-write OLTP (shared buffers < data < RAM)
2) read-only OLTP (shared buffers < data < RAM)
3) large data loads (COPY)
4) large data exports (pg_dump)
Anything else?
The other question is of course hardware - IIRC there are differences
between CPUs. I do have a new e5-2620v4, but perhaps it'd be good to
also do some testing on a Power machine, or an older Intel CPU.
regards
--
Tomas Vondra http://www.2ndQuadrant.com
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