On Wed, May 17, 2017 at 2:57 PM, Amit Kapila <amit.kapila16@gmail.com> wrote:
> On Tue, May 16, 2017 at 2:14 PM, Ashutosh Bapat
> <ashutosh.bapat@enterprisedb.com> wrote:
>> On Mon, May 15, 2017 at 9:23 PM, Robert Haas <robertmhaas@gmail.com> wrote:
>>
>> Also, looking at the patch, it doesn't look like it take enough care
>> to build execution state of new worker so that it can participate in a
>> running query. I may be wrong, but the execution state initialization
>> routines are written with the assumption that all the workers start
>> simultaneously?
>>
>
> No such assumptions, workers started later can also join the execution
> of the query.
>
If we are talking of run-time allocation of workers I'd like to
propose an idea to safeguard parallelism from selectivity-estimation
errors. Start each query (if it qualifies for the use of parallelism)
with a minimum number of workers (say 2) irrespective of the #planned
workers. Then as query proceeds and we find that there is more work to
do, we allocate more workers.
Let's get to the details a little, we'll have following new variables,
- T_int - a time interval at which we'll periodically check if the
query requires more workers,
- work_remaining - a variable which estimates the work yet to do. This
will use the selectivity estimates to find the total work done and the
remaining work accordingly. Once, the actual number of rows crosses
the estimated number of rows, take maximum possible tuples for that
operator as the new estimate.
Now, we'll check at gather, after each T_int if the work is remaining
and allocate another 2 (say) workers. This way we'll keep on adding
the workers in small chunks and not in one go. Thus, saving resources
in case over-estimation is done.
Some of the things we may improvise upon are,
- check if we want to increase workers or kill some of them. e.g. if
the filtering is not happening at estimated at the node, i.e. #output
tuples is same as #input tuples, then do not add any more workers as
it will increase the work at gather only.
- Instead of just having a number of 2 or 4 workers at the start,
allocate x% of planned workers.
- As the query progresses, we may alter the value of T_int and/or
#workers to allocate. e.g. till a query is done something less than
50%, check at every T_int, after that increase T_int to T_int(1 + .5),
similarly for #workers, because now allocating more workers might not
do much work rather the cost of adding new workers could be more.
This scheme is likely to safeguard parallelism with selectivity
estimation errors in a sense that it is using resources only when
required.
Certainly there are many more things to think about in terms of
implementation and the cases where this can help or regress, will be
glad to know opinion of more people on this.
--
Regards,
Rafia Sabih
EnterpriseDB: http://www.enterprisedb.com/
