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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