Re: chooseleaf_descend_once

["Jim Schutt" <jaschut@sandia.gov>]

On 11/28/2012 09:11 AM, Caleb Miles wrote:
> Hey Jim,
>
> Running the third test with tunable chooseleaf_descend_once 0 with no
> devices marked out yields the following result
>
> (999.82733333333397, 0.48667056652539997)
>
> so chi squared value is 999 with a corresponding p value of 0.487 so that
> the placement distribution seems to be drawn from the uniform distribution
> as desired.

Great, thanks for doing that extra test.

Plus, I see that Sage has merged it.   Cool.

Thanks -- Jim


>
> Caleb
>
>
> On Tue, Nov 27, 2012 at 1:28 PM, Jim Schutt<jaschut@sandia.gov>  wrote:
>
>> Hi Caleb,
>>
>>
>> On 11/26/2012 07:28 PM, caleb miles wrote:
>>
>>> Hello all,
>>>
>>> Here's what I've done to try and validate the new chooseleaf_descend_once
>>> tunable first described in commit f1a53c5e80a48557e63db9c52b83f3**9391bc69b8
>>> in the wip-crush branch of ceph.git.
>>>
>>> First I set the new tunable to it's legacy value, disabled,
>>>
>>> tunable choose_local_tries 0
>>> tunable choose_local_fallback_tries 0
>>> tunable choose_total_tries 50
>>> tunable chooseleaf_descend_once 0
>>>
>>> The map contains one thousand osd devices contained in one hundred hosts
>>> with the following data rule
>>>
>>> rule data {
>>> ruleset 0
>>> type replicated
>>> min_size 1
>>> max_size 10
>>> step take default
>>> step chooseleaf firstn 0 type host
>>> step emit
>>> }
>>>
>>> I then simulate the creation of one million placement groups using the
>>> crushtool
>>>
>>> $ crushtool -i hundred.map --test --min-x 0 --max-x 999999 --num-rep 3
>>> --output-csv --weight 120 0.0 --weight 121 0.0 --weight 122 0.0 --weight
>>> 123 0.0 --weight 124 0.0 --weight 125 0.0 --weight 125 0.0 --weight 150 0.0
>>> --weight 151 0.0 --weight 152 0.0 --weight 153 0.0 --weight 154 0.0
>>> --weight 155 0.0 --weight 156 0.0 --weight 180 0.0 --weight 181 0.0
>>> --weight 182 0.0 --weight 183 0.0 --weight 184 0.0 --weight 185 0.0
>>> --weight 186 0.0
>>>
>>> with the majority of devices in three hosts marked out. Then in (I)Python
>>>
>>> import scipy.stats as s
>>> import matplotlib.mlab as m
>>>
>>> data = m.csv2rec("data-device_**utilization.csv")
>>> s.chisquare(data['number_of_**objects_stored'], data['number_of_objects_*
>>> *expected'])
>>>
>>> which will output
>>>
>>> (122939.76474477499, 0.0)
>>>
>>> so that the chi squared value is 122939.795 and the p value is, rounded
>>> to, 0.0 and the observed placement distribution statistically differs from
>>> a uniform distribution. Repeating with the new tunable set to
>>>
>>> tunable chooseleaf_descend_once 1
>>>
>>> I obtain the following result
>>>
>>> (998.97643161876761, 0.32151775131589833)
>>>
>>> so that the chi squared value is 998.976 and the p value is 0.32 and the
>>> observed placement distribution is statistically identical to the uniform
>>> distribution at the five and ten percent confidence levels, higher as well
>>> of course. The p value is the probability of obtaining a chi squared value
>>> more extreme than the statistic observed. Basically, from my rudimentary
>>> understanding of probability theory, that if you obtain a p value p<  P
>>> then reject the null hypothesis, in our case that the observed placement
>>> distribution is drawn from the uniform distribution, at the P confidence
>>> level.
>>>
>>>
>> Cool.  Thanks for doing these tests.
>>
>> Is there any point to doing a third test, with
>>
>> tunable chooseleaf_descend_once 0
>>
>> and no devices marked out, but in all other respects
>> the same as the above two tests?
>>
>> I would expect the results for that case and the last
>> case you tested to be essentially identical in the degree
>> of uniformity, but is it worth verifying?
>>
>> -- Jim
>>
>>   Caleb
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>>>
>>
>>
>