I created a new cluster under jewel but straw1 still seems to be the default? Greets, Stefan Am 02.05.2017 um 07:43 schrieb Stefan Priebe - Profihost AG: > Hi Loic, > > yes i didn't changed them to straw2 as i didn't saw any difference. I > switched to straw2 now but it didn't change anything at all. > > If i use those weights manuall i've to adjust them on every crush change > on the cluster? That's something i don't really like to do. > > Greets, > Stefan > > Am 02.05.2017 um 01:12 schrieb Loic Dachary: >> It is working, with straw2 (your cluster still is using straw). >> >> For instance for one host it goes from: >> >> ~expected~ ~objects~ ~over/under used %~ ~delta~ ~delta%~ >> ~name~ >> osd.24 149 159 6.65 10.0 6.71 >> osd.29 149 159 6.65 10.0 6.71 >> osd.0 69 77 11.04 8.0 11.59 >> osd.2 69 69 -0.50 0.0 0.00 >> osd.42 149 148 -0.73 -1.0 -0.67 >> osd.1 69 62 -10.59 -7.0 -10.14 >> osd.23 69 62 -10.59 -7.0 -10.14 >> osd.36 149 132 -11.46 -17.0 -11.41 >> >> to >> >> ~expected~ ~objects~ ~over/under used %~ ~delta~ ~delta%~ >> ~name~ >> osd.0 69 69 -0.50 0.0 0.00 >> osd.23 69 69 -0.50 0.0 0.00 >> osd.24 149 149 -0.06 0.0 0.00 >> osd.29 149 149 -0.06 0.0 0.00 >> osd.36 149 149 -0.06 0.0 0.00 >> osd.1 69 68 -1.94 -1.0 -1.45 >> osd.2 69 68 -1.94 -1.0 -1.45 >> osd.42 149 147 -1.40 -2.0 -1.34 >> >> By changing the weights to >> >> [0.6609248140022604, 0.9148542821020436, 0.8174711575190294, 0.8870680217468655, 1.6031393139865695, 1.5871079208467038, 1.8784764188501162, 1.7308530904776616] >> >> And you could set these weights on the crushmap, there would be no need for backporting. >> >> >> On 05/01/2017 08:06 PM, Stefan Priebe - Profihost AG wrote: >>> Am 01.05.2017 um 19:47 schrieb Loic Dachary: >>>> Hi Stefan, >>>> >>>> On 05/01/2017 07:15 PM, Stefan Priebe - Profihost AG wrote: >>>>> That sounds amazing! Is there any chance this will be backported to jewel? >>>> >>>> There should be ways to make that work with kraken and jewel. It may not even require a backport. If you know of a cluster with an uneven distribution, it would be great if you could send the crushmap so that I can test the algorithm. I'm still not sure this is the right solution and it would help confirm that. >>> >>> I've lots of them ;-) >>> >>> Will sent you one via private e-mail in some minutes. >>> >>> Greets, >>> Stefan >>> >>>> Cheers >>>> >>>>> >>>>> Greets, >>>>> Stefan >>>>> >>>>> Am 30.04.2017 um 16:15 schrieb Loic Dachary: >>>>>> Hi, >>>>>> >>>>>> Ideally CRUSH distributes PGs evenly on OSDs so that they all fill in >>>>>> the same proportion. If an OSD is 75% full, it is expected that all >>>>>> other OSDs are also 75% full. >>>>>> >>>>>> In reality the distribution is even only when more than 100,000 PGs >>>>>> are distributed in a pool of size 1 (i.e. no replication). >>>>>> >>>>>> In small clusters there are a few thousands PGs and it is not enough >>>>>> to get an even distribution. Running the following with >>>>>> python-crush[1], shows a 15% difference when distributing 1,000 PGs on >>>>>> 6 devices. Only with 1,000,000 PGs does the difference drop under 1%. >>>>>> >>>>>> for PGs in 1000 10000 100000 1000000 ; do >>>>>> crush analyze --replication-count 1 \ >>>>>> --type device \ >>>>>> --values-count $PGs \ >>>>>> --rule data \ >>>>>> --crushmap tests/sample-crushmap.json >>>>>> done >>>>>> >>>>>> In larger clusters, even though a greater number of PGs are >>>>>> distributed, there are at most a few dozens devices per host and the >>>>>> problem remains. On a machine with 24 OSDs each expected to handle a >>>>>> few hundred PGs, a total of a few thousands PGs are distributed which >>>>>> is not enough to get an even distribution. >>>>>> >>>>>> There is a secondary reason for the distribution to be uneven, when >>>>>> there is more than one replica. The second replica must be on a >>>>>> different device than the first replica. This conditional probability >>>>>> is not taken into account by CRUSH and would create an uneven >>>>>> distribution if more than 10,000 PGs were distributed per OSD[2]. But >>>>>> a given OSD can only handle a few hundred PGs and this conditional >>>>>> probability bias is dominated by the uneven distribution caused by the >>>>>> low number of PGs. >>>>>> >>>>>> The uneven CRUSH distributions are always caused by a low number of >>>>>> samples, even in large clusters. Since this noise (i.e. the difference >>>>>> between the desired distribution and the actual distribution) is >>>>>> random, it cannot be fixed by optimizations methods. The >>>>>> Nedler-Mead[3] simplex converges to a local minimum that is far from >>>>>> the optimal minimum in many cases. Broyden–Fletcher–Goldfarb–Shanno[4] >>>>>> fails to find a gradient that would allow it to converge faster. And >>>>>> even if it did, the local minimum found would be as often wrong as >>>>>> with Nedler-Mead, only it would go faster. A least mean squares >>>>>> filter[5] is equally unable to suppress the noise created by the >>>>>> uneven distribution because no coefficients can model a random noise. >>>>>> >>>>>> With that in mind, I implemented a simple optimization algorithm[6] >>>>>> which was first suggested by Thierry Delamare a few weeks ago. It goes >>>>>> like this: >>>>>> >>>>>> - Distribute the desired number of PGs[7] >>>>>> - Subtract 1% of the weight of the OSD that is the most over used >>>>>> - Add the subtracted weight to the OSD that is the most under used >>>>>> - Repeat until the Kullback–Leibler divergence[8] is small enough >>>>>> >>>>>> Quoting Adam Kupczyk, this works because: >>>>>> >>>>>> "...CRUSH is not random proces at all, it behaves in numerically >>>>>> stable way. Specifically, if we increase weight on one node, we >>>>>> will get more PGs on this node and less on every other node: >>>>>> CRUSH([10.1, 10, 10, 5, 5]) -> [146(+3), 152, 156(-2), 70(-1), 76]" >>>>>> >>>>>> A nice side effect of this optimization algorithm is that it does not >>>>>> change the weight of the bucket containing the items being >>>>>> optimized. It is local to a bucket with no influence on the other >>>>>> parts of the crushmap (modulo the conditional probability bias). >>>>>> >>>>>> In all tests the situation improves at least by an order of >>>>>> magnitude. For instance when there is a 30% difference between two >>>>>> OSDs, it is down to less than 3% after optimization. >>>>>> >>>>>> The tests for the optimization method can be run with >>>>>> >>>>>> git clone -b wip-fix-2 http://libcrush.org/dachary/python-crush.git >>>>>> tox -e py27 -- -s -vv -k test_fix tests/test_analyze.py >>>>>> >>>>>> If anyone think of a reason why this algorithm won't work in some >>>>>> cases, please speak up :-) >>>>>> >>>>>> Cheers >>>>>> >>>>>> [1] python-crush http://crush.readthedocs.io/ >>>>>> [2] crush multipick anomaly http://marc.info/?l=ceph-devel&m=148539995928656&w=2 >>>>>> [3] Nedler-Mead https://en.wikipedia.org/wiki/Nelder%E2%80%93Mead_method >>>>>> [4] L-BFGS-B https://docs.scipy.org/doc/scipy-0.18.1/reference/optimize.minimize-lbfgsb.html#optimize-minimize-lbfgsb >>>>>> [5] Least mean squares filter https://en.wikipedia.org/wiki/Least_mean_squares_filter >>>>>> [6] http://libcrush.org/dachary/python-crush/blob/c6af9bbcbef7123af84ee4d75d63dd1b967213a2/tests/test_analyze.py#L39 >>>>>> [7] Predicting Ceph PG placement http://dachary.org/?p=4020 >>>>>> [8] Kullback–Leibler divergence https://en.wikipedia.org/wiki/Kullback%E2%80%93Leibler_divergence >>>>>> >>>>> -- >>>>> To unsubscribe from this list: send the line "unsubscribe ceph-devel" in >>>>> the body of a message to majordomo@xxxxxxxxxxxxxxx >>>>> More majordomo info at http://vger.kernel.org/majordomo-info.html >>>>> >>>> >>> -- >>> To unsubscribe from this list: send the line "unsubscribe ceph-devel" in >>> the body of a message to majordomo@xxxxxxxxxxxxxxx >>> More majordomo info at http://vger.kernel.org/majordomo-info.html >>> >> -- To unsubscribe from this list: send the line "unsubscribe ceph-devel" in the body of a message to majordomo@xxxxxxxxxxxxxxx More majordomo info at http://vger.kernel.org/majordomo-info.html
