When you create an Postgres RDS instance, it's comes with a
"default.postgres9.3" parameter group which contains substitutions based on
the server size. The defaults for the memory related settings are:
effective_cache_size = {DBInstanceClassMemory/16384}
maintenance_work_mem = GREATEST({DBInstanc
On 10.06.2016 16:04, Rowan Seymour wrote:
In our Django app we have messages (currently about 7 million in table
msgs_message) and labels (about 300), and a join table to associate
messages with labels (about 500,000 in msgs_message_labels). Not sure
you'll need them, but here are the relevant
Yves Dorfsman writes:
> On 2016-06-10 08:13, Tom Lane wrote:
>> It looks like everything is fine as long as all the data the query needs
>> is already in PG's shared buffers. As soon as it has to go to disk,
>> you're hurting, because disk reads seem to be taking ~10ms on average.
>
I thought this was a really interesting case, and would love to learn from it,
please bare with me if my questions are naive.
On 2016-06-10 08:13, Tom Lane wrote:
> Rowan Seymour writes:
>> Most of time, this query performs like https://explain.depesz.com/s/ksOC
>> (~15ms). It's no longer using
Rowan Seymour writes:
> Most of time, this query performs like https://explain.depesz.com/s/ksOC
> (~15ms). It's no longer using the using the msgs_inbox index, but it's
> plenty fast. However, sometimes it performs like
> https://explain.depesz.com/s/81c (67000ms)
> And if you run it again, it'll
In our Django app we have messages (currently about 7 million in table
msgs_message) and labels (about 300), and a join table to associate
messages with labels (about 500,000 in msgs_message_labels). Not sure
you'll need them, but here are the relevant table schemas:
CREATE TABLE msgs_message
(
tion, and is there any way to
achive closer to optimal performance using postgresql functionality and
extensibility?
Chavdar Kopoev
-Original Message-
From: Craig Ringer [mailto:[EMAIL PROTECTED]
Sent: 2008-11-26, 19:40:47
To: Chavdar Kopoev [mailto:[EMAIL PROTECTED]
Subject: Re: [PERFORM]
Hello,
I have following common situation:
Category IDs: about 50 000
Document IDs: about 3 000 000
Many to many relationship.
A document id have a relation with 10 up to 1000 category ids
One query, with input set of document ids, resulting set of category ids,
having relation with input ids. (v
Chavdar Kopoev wrote:
> I want to use as a data storage postgresql. Tried several data structures,
> testing btree, gin, gist indecies over them, but best achieved performance
> for a 10 times smaller dataset (10k cat ids, 100k doc ids, 1m relations) is
> slower more than 5 times.
Can you post
Hello,
I have following common situation:
Category IDs: about 50 000
Document IDs: about 3 000 000
Many to many relationship.
A document id have a relation with 10 up to 1000 category ids
One query, with input set of document ids, resulting set of category ids,
having relation with input ids. (v
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