Hello Loïc,
On Thu, May 4, 2017 at 8:30 AM Loic Dachary <loic@xxxxxxxxxxx> wrote:
Is there a way to calculate the optimum nearfull ratio for a given crushmap ?
This is a question that I was planning to cover in those calculations I was working on for python-crush. I've currently shelved the work for a few weeks but intend to look at it again as time frees up.
5. Finally, pools can occupy different and overlapping sets of OSDs, and hold independent sets of objects.
Thanks to your new CRUSH tools, I think #1 and #4 are solved respectively by the ability to:
- generate a CRUSH map for a precise (and even) distribution of PGs;
- test mappings for every scenario of N failures and find the worst-case scenario (very expensive calculation, but possible).
Issues
#2 and #3 are more tricky. The big picture is that a given amount of
data is placed more evenly the more objects there are, and there should
be a way to use statistics to quantify that. Variance in object size
then brings in more uncertainty, but I think that metric is difficult to
quantify outside of very specific use cases where object size are
known.
Finally, this might all be made redundant by the new
auto-rebalancing feature that Sage is planning for Luminous. If we can
assume even data placement at all times the #4 is the only thing we need
to worry about. For performance-based placement that would be very
different however. And if pools have overlapping OSD sets, that could be fairly tricky too.
Maybe
some other users here already have some rule of thumb or actual
calculations for that. I was planning to get into the statistical
calculations of data placement assuming unique object size as the next
step for the paper I am working on. Would there be a need for such
tools?
Regards,
--
--
Xavier Villaneau
Storage Software Eng. at Concurrent Computer Corp.
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