On Sun, Jan 11, 2015 at 6:01 AM, Stephen Frost <sfrost@snowman.net> wrote: > So, for my 2c, I've long expected us to parallelize at the relation-file > level for these kinds of operations. This goes back to my other > thoughts on how we should be thinking about parallelizing inbound data > for bulk data loads but it seems appropriate to consider it here also. > One of the issues there is that 1G still feels like an awful lot for a > minimum work size for each worker and it would mean we don't parallelize > for relations less than that size.
Yes, I think that's a killer objection.
One approach that I has worked well for me is to break big jobs into much smaller bite size tasks. Each task is small enough to complete quickly.
We add the tasks to a task queue and spawn a generic worker pool which eats through the task queue items.
This solves a lot of problems.
- Small to medium jobs can be parallelized efficiently.
- No need to split big jobs perfectly.
- We don't get into a situation where we are waiting around for a worker to finish chugging through a huge task while the other workers sit idle.
- Worker memory footprint is tiny so we can afford many of them.
- Worker pool management is a well known problem.
- Worker spawn time disappears as a cost factor.
- The worker pool becomes a shared resource that can be managed and reported on and becomes considerably more predictable.
