Andrew Morton wrote:
On Tue, 19 Aug 2008 07:01:11 -0400 Ric Wheeler <rwheeler@xxxxxxxxxx> wrote:
It would be great to be able to use this batching technique for faster
devices, but we currently sleep 3-4 times longer waiting to batch for an
array than it takes to complete the transaction.
Obviously, tuning that delay down to the minimum necessary is a good
thing. But doing it based on commit-time seems indirect at best. What
happens on a slower disk when commit times are in the tens of
milliseconds? When someone runs a concurrent `dd if=/dev/zero of=foo'
when commit times go up to seconds?
Perhaps a better scheme would be to tune it based on how many other
processes are joining that transaction. If it's "zero" then decrease
the timeout. But one would need to work out how to increase it, which
perhaps could be done by detecting the case where process A runs an
fsync when a commit is currently in progress, and that commit was
caused by process B's fsync.
But before doing all that I would recommend/ask that the following be
investigated:
- How effective is the present code?
- What happens when it is simply removed?
- Add instrumentation (a counter and a printk) to work out how
many other tasks are joining this task's transaction.
- If the answer is "zero" or "small", work out why.
- See if we can increase its effectiveness.
Because it could be that the code broke. There might be issues with
higher-level locks which are preventing the batching. For example, if
all the files which the test app is syncing are in the same directory,
perhaps all the tasks are piling up on that directory's i_mutex?
One other way to think about this is as a fairly normal queuing problem:
(1) arrival rate is the rate at which we see new tasks coming into
the code
(2) service time is basically the time spent committing the
transaction to storage
and we have the assumption that some number of tasks can join a
transaction more or less for "free."
What the existing code assumes is that all devices have an equal service
time. That worked well as long as we only looked at devices that were
roughly equal (10-20 ms latencies) or used a higher HZ for the kernel
(1000HZ and you don't see this as much as with 100HZ).
The two key issues that Josef's code tried to address are that first
assumption that all devices have a similar service time and the tie
between how long we wait and the HZ. It would seem to be generically
useful to be able to sleep for less than 1 jiffie, not just for file
systems, but maybe also in some other contexts?
ric
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