| From: | Robert Haas <robertmhaas(at)gmail(dot)com> |
|---|---|
| To: | Alvaro Herrera <alvherre(at)2ndquadrant(dot)com> |
| Cc: | Amit Kapila <amit(dot)kapila16(at)gmail(dot)com>, Pavan Deolasee <pavan(dot)deolasee(at)gmail(dot)com>, Bruce Momjian <bruce(at)momjian(dot)us>, Jaime Casanova <jaime(dot)casanova(at)2ndquadrant(dot)com>, Haribabu Kommi <kommi(dot)haribabu(at)gmail(dot)com>, Tomas Vondra <tomas(dot)vondra(at)2ndquadrant(dot)com>, pgsql-hackers <pgsql-hackers(at)postgresql(dot)org> |
| Subject: | Re: Patch: Write Amplification Reduction Method (WARM) |
| Date: | 2017-03-21 14:40:43 |
| Message-ID: | CA+TgmoZwvso+PgexELwbP3xpVCTCH0fkRQLTUQeytocKnHzKsA@mail.gmail.com |
| Views: | Raw Message | Whole Thread | Download mbox | Resend email |
| Thread: | |
| Lists: | pgsql-hackers |
On Tue, Mar 21, 2017 at 10:21 AM, Alvaro Herrera
<alvherre(at)2ndquadrant(dot)com> wrote:
> Robert Haas wrote:
>> On Tue, Mar 21, 2017 at 10:01 AM, Amit Kapila <amit(dot)kapila16(at)gmail(dot)com> wrote:
>
>> > Sure, we can try that. I think we need to try it with
>> > synchronous_commit = off, otherwise, WAL writes completely overshadows
>> > everything.
>>
>> synchronous_commit = off is a much more realistic scenario than fsync = off.
>
> Sure, synchronous_commit=off is a reasonable case. But I say if we lose
> a few % on the case where you update only the first indexed of a large
> number of very wide columns all indexed, and this is only noticeable if
> you don't write WAL and only if you update all the rows in the table,
> then I don't see much reason for concern.
If the WAL writing hides the loss, then I agree that's not a big
concern. But if the loss is still visible even when WAL is written,
then I'm not so sure.
--
Robert Haas
EnterpriseDB: http://www.enterprisedb.com
The Enterprise PostgreSQL Company
| From | Date | Subject | |
|---|---|---|---|
| Next Message | David Steele | 2017-03-21 14:41:56 | Re: increasing the default WAL segment size |
| Previous Message | Robert Haas | 2017-03-21 14:39:04 | Re: Partition-wise join for join between (declaratively) partitioned tables |