Hello Loic, thanks for clarification. Sounds good so far. It it planned to get packages out of the repo so we do not need to have pip and a compiler installed on the systems? Greets, Stefan Am 15.05.2017 um 22:35 schrieb Loic Dachary: > > > On 05/15/2017 09:08 PM, Stefan Priebe - Profihost AG wrote: >> Hello Loic, >> >> sounds good but my initial question was if this shouldn't be integrated >> in ceph-deploy - so when you add OSDs it also does the correct reweight? > > Ideally it should be fully transparent and we can forget the problem ever existed. I think we'll get there, maybe with a ceph-mgr task running on a regular basis to gradually optimize when it can't be done in real time. It won't be ready for Luminous but it could be for M*. > > Cheers > >> Greets, >> Stefan >> >> Am 14.05.2017 um 19:46 schrieb Loic Dachary: >>> Hi Stefan, >>> >>> A new python-crush[1] subcommand will be available next week that you could use to rebalance your clusters. You give it a crushmap and it optimizes the weights to fix the uneven distribution. It can produce a series of crushmaps, each with a small modification so that you can gradually improve the situation and better control how many PGs are moving. >>> >>> Would that be useful for the clusters you have ? >>> >>> Cheers >>> >>> [1] http://crush.readthedocs.io/ >>> >>> On 05/02/2017 09:32 AM, Loic Dachary wrote: >>>> >>>> >>>> On 05/02/2017 07:43 AM, Stefan Priebe - Profihost AG wrote: >>>>> 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. >>>> >>>> straw vs straw2 is not responsible for the uneven distribution you're seeing. I meant to say the optimization only works on straw2 buckets, it is not implemented for straw buckets. >>>> >>>>> 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. >>>> >>>> This is not practical indeed :-) I'm hoping python-crush can automate that. >>>> >>>> Cheers >>>> >>>>> 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 >> > -- 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
