On Sat, Dec 6, 2014 at 12:27 AM, Jim Nasby <
Jim.Nasby@bluetreble.com> wrote:
> On 12/5/14, 9:08 AM, José Luis Tallón wrote:
>>
>>
>> More over, when load goes up, the relative cost of parallel working should go up as well.
>> Something like:
>> p = number of cores
>> l = 1min-load
>>
>> additional_cost = tuple estimate * cpu_tuple_cost * (l+1)/(c-1)
>>
>> (for c>1, of course)
>
>
> ...
>
>> The parallel seq scan nodes are definitively the best approach for "parallel query", since the planner can optimize them based on cost.
>> I'm wondering about the ability to modify the implementation of some methods themselves once at execution time: given a previously planned query, chances are that, at execution time (I'm specifically thinking about prepared statements here), a different implementation of the same "node" might be more suitable and could be used instead while the condition holds.
>
>
> These comments got me wondering... would it be better to decide on parallelism during execution instead of at plan time? That would allow us to dynamically scale parallelism based on system load. If we don't even consider parallelism until we've pulled some number of tuples/pages from a relation,
>
>this would also eliminate all parallel overhead on small relations.
> --
I think we have access to this information in planner (RelOptInfo -> pages),
if we want, we can use that to eliminate the small relations from
parallelism, but question is how big relations do we want to consider
for parallelism, one way is to check via tests which I am planning to
follow, do you think we have any heuristic which we can use to decide
how big relations should be consider for parallelism?
