Hi, Loic
we already notice this problem before and find that it is not easy to
solved. The problem not only occurs in collision case, but also in
rejection when is_out is called:
static int is_out(const struct crush_map *map,
const __u32 *weight, int weight_max,
int item, int x)
{
if (item >= weight_max)
return 1;
if (weight[item] >= 0x10000)
return 0;
if (weight[item] == 0)
return 1;
if ((crush_hash32_2(CRUSH_HASH_RJENKINS1, x, item) & 0xffff)
< weight[item])
return 0;
return 1;
}
That means if we use ceph osd out [osd.id], or osd is marked out
automatically when it is down, or we use reweight-by-utilization to
alter the reweight for an osd.
I think it may use the same way as Sage mentioned to solve this problem.
Regards
Ning Yao
2017-05-08 22:45 GMT+08:00 Loic Dachary <loic@xxxxxxxxxxx>:
>
>
> On 05/08/2017 03:42 PM, Sage Weil wrote:
>> On Mon, 8 May 2017, Loic Dachary wrote:
>>> On 05/08/2017 05:09 AM, Sage Weil wrote:
>>>> On Sun, 7 May 2017, Loic Dachary wrote:
>>>>> Hi Sage,
>>>>>
>>>>> When choosing the second replica, crush_bucket_choose picks an item and
>>>>> crush_choose_{indep,firstn} checks if it has already been chosen for the
>>>>> first replica. If so, it is discarded as a collision[1], r is modified
>>>>> and another attempt is made to get a different item. If no value is
>>>>> found after choose_tries attempt, the mapping is incomplete.
>>>>>
>>>>> Another way to do the same would be that crush_bucket_choose /
>>>>> bucket_straw2_choose[2] ignores the items that have already been chosen.
>>>>> It could be done by looping over the list but that would mean N (number
>>>>> of replicas) lookups for each item.
>>>>>
>>>>> The current implementation is optimized for the cases where collisions
>>>>> are rare. However, when the weights of the items are two order of
>>>>> magnitude appart or more, choose_tries has to be set to a very large
>>>>> value (more than 1000) for the mapping to succeed. In practice that is
>>>>> not a problem as it is unlikely that a host is 100 times bigger than
>>>>> another host ;-)
>>>>>
>>>>> When fixing an uneven distribution[3], CRUSH weights are sometime set to
>>>>> values with that kind of difference (1 against 100) to compensate for
>>>>> the probability bias and/or a small number of samples. For instance when
>>>>> there are 5 hosts with weights 5 1 1 1 1, modifying the weight set
>>>>> fails. It goes as far as 8.9, 0.01, 0.01, 0.01, 0.01 with choose_tries
>>>>> 2000. The value of choose_tries has to be increased a lot for a gain
>>>>> that is smaller and smaller and the CPU usage goes up. In this
>>>>> situation, an alternative implementation of the CRUSH collision seems a
>>>>> good idea.
>>>>>
>>>>> Instead of failing after choose_tries attempts, crush_bucket_choose
>>>>> could be called with the list of already chosen items and loop over them
>>>>> to ensure none of them are candidate. The result will always be correct
>>>>> but the implementation more expensive. The default choose_tries could
>>>>> even be set to a lower value (19 instead of 50 ?) since it is likely
>>>>> more expensive to handle 30 more collisions rather than looping over
>>>>> each item already selected.
>>>>>
>>>>> What do you think ?
>>>>
>>>> I think this direction is promising! The problem is that I think it's not
>>>> quite as simple as you suggest, since you may be choosing over multiple
>>>> levels of a hierarchy. If the weight tree is something like
>>>>
>>>> 4
>>>> / \
>>>> 2 2
>>>> / \ / \
>>>> 1 1 1 1
>>>> a b c d
>>>>
>>>> and you chose a, then yes, if you get back into the left branch you can
>>>> filter it out of the straw2 selections. And num_rep is usually small so
>>>> that won't be expensive. But you also need the first choice at the top
>>>> level of the hierarchy to weight the left *tree* with 1 instead of 2.
>>>
>>> I don't understand why this is necessary ? Here are the scenarios I have in mind:
>>>
>>>
>>> / \
>>> / \
>>> r1 4 r2 4 rack (failure domain)
>>> / \ / \
>>> 2 2 2 2 host
>>> / \ / \ / \ / \
>>> 1 1 1 1 1 1 1 1 device
>>> a b c d e f g h
>>>
>>> Say value 10 ends up in a the first time, it first went through rack
>>> r1 which is the failure domain. If value 10 also ends up in r1 the
>>> second time, straw2 will skip/collide it at that level because r1 is
>>> stored in out while a is stored in out2.
>>>
>>> There only case I can think of that requires collision to be resolved
>>> in a higher hierarchical level is when there is no alternative.
>>>
>>>
>>>
>>> / \
>>> / \
>>> r1 4 r2 4 rack
>>> / / \
>>> h1 2 2 2 host (failure domain)
>>> / / \ / \
>>> 1 1 1 1 1 device
>>> a e f g h
>>>
>>> If 10 ends up in h1 the first time and the second time, it will
>>> collide because there is no alternative. It will then retry_descent,
>>> ftotal increases which goes into r and it gets another chance at landing on a host
>>> that's not h1.
>>
>> The problem isn't when choose[leaf] is specifying the intermediate
>> level (rack or host in your examples); it's when there is an intervening
>> level that a single choose is crossing. In your first tree,
>>
>> root 6
>>> / \
>>> / \
>>> r1 4 r2 4 rack
>>> / \ / \
>>> h1 2 h2 2 h3 2 h4 2 host (failure domain)
>>> / \ / \ / \ / \
>>> 1 1 1 1 1 1 1 1 device
>>> a b c d e f g h
>>
>> let's say the failure domain is the host instead of the rack. If we
>> choose a (h1) the first time, for subsequent descents from the root we
>> still pick r1 and r2 equally (4 vs 4) even though r1 only has 1 usable
>> host (h2). This normally is fine because we reject the entire descent for
>> 50% of the r1 choices, so *effectively* r1's weight is only 2 (it's as if
>> the other attempts never happened). But with the smarter straw2 choose h2
>> 100% for r1 and you'll end up with 2x more tiems for that second position
>> on h2 than you want.
>>
>> Does that make sense?
>
> Absolutely, thanks for explaining. The easier route as far as getting an even distribution is concerned seems to find a way to calculate when a combination of weights (5 1 1 1 1 with 2 replica for instance) cannot be evenly distributed.
>
> Cheers
>
>>
>> sage
>>
>>
>>>
>>> I must be missing a use case :-)
>>>
>>>
>>>>
>>>> I think this could be done by adjusting the hierarchical weights as you go
>>>> (and I think one of Adam's early passes at the multipick problem did
>>>> something similar), but it's a bit more complex.
>>>>
>>>> It seems worth pursuing, though!
>>>>
>>>> And dynamically doing this only after the first N 'normal' attempts fail
>>>> seems like a good way to avoid slowing down the common path (no
>>>> collisions). I suspect the optimal N is probably closer to 5 than 19,
>>>> though?
>>>>
>>>> sage
>>>>
>>>>
>>>>>
>>>>> Cheers
>>>>>
>>>>> P.S. Note that even in this border case modifying the weights to 7.1,
>>>>> 0.5, 0.5, 0.4, 0.4 significantly improves the distribution (twice better
>>>>> instead of ten times better). Only it cannot do better because it hits a
>>>>> limitation of the current CRUSH implementation. But it looks like it is
>>>>> not too difficult to fix.
>>>>>
>>>>>
>>>>> [1] https://github.com/ceph/ceph/blob/master/src/crush/mapper.c#L541
>>>>> [2] https://github.com/ceph/ceph/blob/master/src/crush/mapper.c#L332
>>>>> [3] http://marc.info/?l=ceph-devel&m=149407691823750&w=2
>>>>> --
>>>>> Loïc Dachary, Artisan Logiciel Libre
>>>>>
>>>
>>> --
>>> Loïc Dachary, Artisan Logiciel Libre
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>
> --
> Loïc Dachary, Artisan Logiciel Libre
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