datasketches 1.0.0

This Release
datasketches 1.0.0
Date
Status
Stable
Latest Stable
datasketches 1.1.0 —
Other Releases
Abstract
approximate algorithms for big data analysis
Released By
saydakov
License
The PostgreSQL License
Resources
Special Files

Extensions

cpc_sketch 1.0.0
CPC sketch for approximate distinct counting
kll_float_sketch 1.0.0
KLL quantiles sketch for approximating distributions of float values (quanitles, ranks, histograms)

README

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

Examples

Distinct counting with CPC sketch

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

Estimating quanitles and ranks with KLL sketch

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.