commit | 7be614faf45efcf7c4e883ba8b56b71005228c78 | [log] [tgz] |
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author | AlexanderSaydakov <AlexanderSaydakov@users.noreply.github.com> | Tue Jun 11 12:05:33 2019 -0700 |

committer | AlexanderSaydakov <AlexanderSaydakov@users.noreply.github.com> | Tue Jun 11 12:05:33 2019 -0700 |

tree | bc5cfbb57345c3c3c8c18b3acf731f05b171e05d | |

parent | e80b587b786d755e74b2ffb7584826d9ebd23eae [diff] |

support lg_k in non-aggregate union

1 file changed

tree: bc5cfbb57345c3c3c8c18b3acf731f05b171e05d

README.md

Module for PostgreSQL to support approximate algorithms based on the Datasketches core library sketches-core-cpp. See https://datasketches.github.io/ for details.

This module currently supports two sketches:

- CPC (Compressed Probabilistic Counting) sketch - very compact (when serialized) distinct-counting sketch
- KLL float quantiles sketch - for estimating distributions: quantile, rank, PMF (histogram), CDF

Suppose 100 million random integer values uniformly distributed in the range from 1 to 100M have been generated and inserted into a table

Exact count distinct:

$ time psql test -c "select count(distinct id) from random_ints_100m;" count ---------- 63208457 (1 row) real 1m59.060s

Approximate count distinct:

$ time psql test -c "select cpc_sketch_distinct(id) from random_ints_100m;" cpc_sketch_distinct --------------------- 63423695.9451363 (1 row) real 0m20.680s

Note that the above one-off distinct count is just to show the basic usage. Most importantly, the sketch can be used as an “additive” distinct count metric in a data cube.

Merging sketches:

create table cpc_sketch_test(sketch cpc_sketch); insert into cpc_sketch_test select cpc_sketch_build(1); insert into cpc_sketch_test select cpc_sketch_build(2); insert into cpc_sketch_test select cpc_sketch_build(3); select cpc_sketch_get_estimate(cpc_sketch_merge(sketch)) from cpc_sketch_test; cpc_sketch_get_estimate ------------------------- 3.00024414612919

Table “normal” has 1 million values from the normal distribution with mean=0 and stddev=1. We can build a sketch, which represents the distribution (create table kll_float_sketch_test(sketch kll_float_sketch)):

$ psql test -c "insert into kll_float_sketch_test select kll_float_sketch_build(value) from normal" INSERT 0 1

We expect the value with rank 0.5 (median) to be approximately 0:

$ psql test -c "select kll_float_sketch_get_quantile(sketch, 0.5) from kll_float_sketch_test" kll_float_sketch_get_quantile ------------------------------- 0.00648344

In reverse: we expect the rank of value 0 (true median) to be approximately 0.5:

$ psql test -c "select kll_float_sketch_get_rank(sketch, 0) from kll_float_sketch_test" kll_float_sketch_get_rank --------------------------- 0.496289

Getting several quantiles at once:

$ psql test -c "select kll_float_sketch_get_quantiles(sketch, ARRAY[0, 0.25, 0.5, 0.75, 1]) from kll_float_sketch_test" kll_float_sketch_get_quantiles -------------------------------------------------- {-4.72317,-0.658811,0.00648344,0.690616,4.91773}

Getting the probability mass function (PMF):

$ psql test -c "select kll_float_sketch_get_pmf(sketch, ARRAY[-2, -1, 0, 1, 2]) from kll_float_sketch_test" kll_float_sketch_get_pmf ------------------------------------------------------ {0.022966,0.135023,0.3383,0.343186,0.13466,0.025865}

The ARRAY[-2, -1, 0, 1, 2] of 5 split points defines 6 intervals (bins): (-inf,-2), [-2,-1), [-1,0), [0,1), [1,2), [2,inf). The result is 6 estimates of probability mass in these bins (fractions of input values that fall into the bins). These fractions can be transformed to counts (histogram) by scaling them by the factor of N (the total number of input values), which can be obtained from the sketch:

$ psql test -c "select kll_float_sketch_get_n(sketch) from kll_float_sketch_test" kll_float_sketch_get_n ------------------------ 1000000

In this simple example we know the value of N since we constructed this sketch, but in a general case sketches are merged across dimensions of data hypercube, so the vale of N is not known in advance.

Note that the normal distribution was used just to show the basic usage. The sketch does not make any assumptions about the distribution.