On 3/13/09 10:16 AM, "Tom Lane" <tgl@xxxxxxxxxxxxx> wrote:
Robert Haas <robertmhaas@xxxxxxxxx> writes:In an earlier post in this thread I mentioned the three main ways to solve scalability problems with respect to locking:
> I think that changing the locking behavior is attacking the problem at
> the wrong level anyway.
Right. By the time a patch here could have any effect, you've already
lost the game --- having to deschedule and reschedule a process is a
large cost compared to the typical lock hold time for most LWLocks. So
it would be better to look at how to avoid blocking in the first place.
regards, tom lane
Avoid locking (atomics, copy-on-write, etc), finer grained locks (data structure partitioning, etc) and optimizing the locks themselves.
I don’t know which of the above has the greatest opportunity in postgres. My base assumption was that lock avoidance was something that had been worked on significantly already, and that since lock algorithm optimization is rediculously hardware dependant, there was probably low hanging fruit there.
Messing with unfair locks does not have to be the solution to the problem, but it can be a means to an end:
It takes less time and lines of code to change the lock and see what the benefit less locking would cause, than it does to change the code to avoid the locks.
So what we have here, is a tool — not necessarily what you want to use in production, but a handy tool. If you switch to unfair locks, and things speed up, you’re lock bound and avoiding those locks will make things faster. The Dtrace data is also a great tool, that is showing the same thing but without the ability to know how large or small the gain is or being sure what the next bottleneck will be.
