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Xiangrui Meng commented on DATAFU21:

You need to have both bounds of the bin to compute q1 and q2.
1) You need two jobs. The first computes the thresholds and the second does the sampling.
This is different from SRS. In SRS' streaming case, when p is fixed, the interval [p1, p2]
is always expanding. But I don't think this is true for weighted case. For the scalability,
if you make 1000 bins and there are 1000 partitions, the reducer only need a few MBs.
2) No. I think we can figure out some reasonable discretization as the default. Users should
not be aware of it. For example [2^i, 2^{i+1}], i = 100,99,...,100. You need to work out
the math.
> Probability weighted sampling without reservoir
> 
>
> Key: DATAFU21
> URL: https://issues.apache.org/jira/browse/DATAFU21
> Project: DataFu
> Issue Type: New Feature
> Environment: Mac OS, Linux
> Reporter: jian wang
> Assignee: jian wang
>
> This issue is used to track investigation on finding a weighted sampler without using
internal reservoir.
> At present, the SimpleRandomSample has implemented a good acceptancerejection sampling
algo on probability random sampling. The weighted sampler could utilize the simple random
sample with slight modification.
> One slight modification is: the present simple random sample generates a uniform random
number lies between (0, 1) as the random variable to accept or reject an item. The weighted
sample may generate this random variable based on the item's weight and this random number
still lies between (0, 1) and each item's random variable remain independent between each
other.
> Need further think and experiment the correctness of this solution and how to implement
it in an effective way.

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