commit | ca7e8a6650d324835c91414a4cd04073283e1a4c | [log] [tgz] |
---|---|---|

author | AlexanderSaydakov <AlexanderSaydakov@users.noreply.github.com> | Tue Jun 18 12:46:35 2019 -0700 |

committer | AlexanderSaydakov <AlexanderSaydakov@users.noreply.github.com> | Tue Jun 18 12:46:35 2019 -0700 |

tree | 0f5a96434037a115fee18b661084e1f237b7b343 | |

parent | b2783cf50ffe2268aaab1d85223087efd5744494 [diff] |

check if version is specified, cleanup

1 file changed

tree: 0f5a96434037a115fee18b661084e1f237b7b343

README.md

Module for PostgreSQL to support approximate algorithms based on the Datasketches core library datasketches-cpp. See Datasketches documentation for details.

This module currently supports the following sketches:

- CPC (Compressed Probabilistic Counting) sketch - very compact (when serialized) distinct-counting sketch
- Theta sketch - distinct counting with set operations (intersection, a-not-b)
- KLL float quantiles sketch - for estimating distributions: quantile, rank, PMF (histogram), CDF
- Frequent strings sketch - capture the heaviest items (strings) by count or by some other weight

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_union(sketch)) from cpc_sketch_test; cpc_sketch_get_estimate ------------------------- 3.00024414612919

See above for the exact distinct count of 100 million random integers

Approximate distinct count:

$ time psql test -c "select theta_sketch_distinct(id) from random_ints_100m" theta_sketch_distinct ----------------------- 64593262.4373193 (1 row) real 0m19.701s

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.

Aggregate union:

create table theta_sketch_test(sketch theta_sketch); insert into theta_sketch_test select theta_sketch_build(1); insert into theta_sketch_test select theta_sketch_build(2); insert into theta_sketch_test select theta_sketch_build(3); select theta_sketch_get_estimate(theta_sketch_union(sketch)) from theta_sketch_test; theta_sketch_get_estimate --------------------------- 3

Non-aggregate set operations:

create table theta_set_op_test(sketch1 theta_sketch, sketch2 theta_sketch); insert into theta_set_op_test select theta_sketch_build(1), theta_sketch_build(1); insert into theta_set_op_test select theta_sketch_build(1), theta_sketch_build(2); select theta_sketch_get_estimate(theta_sketch_union(sketch1, sketch2)) from theta_set_op_test; theta_sketch_get_estimate --------------------------- 1 2 (2 rows) select theta_sketch_get_estimate(theta_sketch_intersection(sketch1, sketch2)) from theta_set_op_test; theta_sketch_get_estimate --------------------------- 1 0 (2 rows) select theta_sketch_get_estimate(theta_sketch_a_not_b(sketch1, sketch2)) from theta_set_op_test; theta_sketch_get_estimate --------------------------- 0 1 (2 rows)

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.

Consider a numeric Zipfian distribution with parameter alpha=1.1 (high skew) and range of 213, so that the number 1 has the highest frequency, the number 2 appears substantially less frequently and so on. Suppose zipf_1p1_8k_100m table has 100 million random values drawn from such a distribution, and the values are converted to strings.

Suppose the goal is to get the most frequent strings from this table. In terms of the frequent items sketch we have to chose a threshold. Let's try to capture values that repeat more than 1 million times, or more than 1% of the 100 million entries in the table. According to the error table, frequent items sketch of size 29 must capture all values more frequent then about 0.7% of the input.

The following query is to build a sketch with lg_k=9 and get results with estimated weight above 1 million using “no false negatives” policy. The output format is: value, estimate, lower bound, upper bound.

$ time psql test -c "select frequent_strings_sketch_result_no_false_negatives(frequent_strings_sketch_build(9, value), 1000000) from zipf_1p1_8k_100m" frequent_strings_sketch_result_no_false_negatives --------------------------------------------------- (1,15328953,15209002,15328953) (2,7156065,7036114,7156065) (3,4578361,4458410,4578361) (4,3334808,3214857,3334808) (5,2608563,2488612,2608563) (6,2135715,2015764,2135715) (7,1801961,1682010,1801961) (8,1557433,1437482,1557433) (9,1368446,1248495,1368446) (10,1216532,1096581,1216532) (11,1098304,978353,1098304) (11 rows) real 0m38.178s

Here is an equivalent exact computation:

$ time psql test -c "select value, weight from (select value, count(*) as weight from zipf_1p1_8k_100m group by value) t where weight > 1000000 order by weight desc" value | weight -------+---------- 1 | 15328953 2 | 7156065 3 | 4578361 4 | 3334808 5 | 2608563 6 | 2135715 7 | 1801961 8 | 1557433 9 | 1368446 10 | 1216532 11 | 1098304 (11 rows) real 0m18.362s

In this particular case the exact computation happens to be faster. This is just to show the basic usage. Most importantly, the sketch can be used as an “additive” metric in a data cube, and can be easily merged across dimensions.