Cantor provides utilities for estimating the cardinality of large sets.
The algorithms herein are parallelizable, and a Hadoop wrapper class is provided for convenience.
It employs most of the HyperLogLog++ algorithm as seen in this paper, excluding the sparse scheme, and using a simple linear interpolation instead of kNN. In addition, it can use MinHash structures to estimate cardinalities of intersections of these sets, as described in this blog post.
Both HyperLogLog and MinHash require a precision
parameter. Basic guidelines are available as follows,
and HLLCounter.MIN_P = 4 <= p <= 18 = HLLCounter.MAX_P.
####HyperLogLog p @ 99.7% Confidence
| p | Relative Error |
|---|---|
| 4 | 75% |
| 5 | 65% |
| 6 | 47% |
| 7 | 32% |
| 8 | 23% |
| 9 | 16% |
| 10 | 10% |
| 11 | 8% |
| 12 | 5% |
| 13 | 4% |
| 14 | 2.5% |
| 15 | 2% |
| 16 | 1.3% |
| 17 | 1% |
| 18 | 0.7% |
####MinHash k @ 99% Confidence
| Relative Error | Intersection Size --> | * |
|---|
-
| 0.01% | 0.1% |1.0% | 5.0% |10.0%
100% | 90000 | 9000 |900 | 170 |75 50% | 313334 | 31334 |3134 | 587 |280 25% | - | 116800 |11520 | 2208 |1040 10% | - | - |68455 | 13128 |6210
This MinHash k table can be generated by using minhash_k.py in the utils
directory. For now, the only requirement is scipy, which you can install with
pip install -r utils/requirements.txt. Then, for example, you can do:
%> ./utils/minhash_k.py --jaccard 0.0001 --error 1 --confidence 0.99
MinHash k: 90000
Error at k: 1.0
%> ./utils/minhash_k.py --jaccard 0.01 --error 0.25 --confidence 0.99
MinHash k: 11520
Error at k: 0.25
%> ./utils/minhash_k.py --jaccard 0.01 --error 0.25 --confidence 0.90
MinHash k: 4800
Error at k: 0.25
Additional information is available with ./utils/minhash_k.py --help.
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