commit | 33e60d71fa773bf8376415f2f3325785886725ff | [log] [tgz] |
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author | saydakov <saydakov@yahoo-inc.com> | Fri Jan 25 16:56:44 2019 -0800 |

committer | saydakov <saydakov@yahoo-inc.com> | Fri Jan 25 16:56:44 2019 -0800 |

tree | 10ea3eb7d4420ef468475f7991376e523b19ad71 | |

parent | eb48fe47f10407e83f8daf0df299f417b90829e9 [diff] |

fixed URLs, added mandatory versions

1 file changed

tree: 10ea3eb7d4420ef468475f7991376e523b19ad71

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

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