mirror of
https://git.postgresql.org/git/postgresql.git
synced 2026-02-22 22:37:01 +08:00
faf071b553
This adds 3 new variants of the random() function:
random(min date, max date) returns date
random(min timestamp, max timestamp) returns timestamp
random(min timestamptz, max timestamptz) returns timestamptz
Each returns a random value x in the range min <= x <= max.
Author: Damien Clochard <damien@dalibo.info>
Reviewed-by: Greg Sabino Mullane <htamfids@gmail.com>
Reviewed-by: Dean Rasheed <dean.a.rasheed@gmail.com>
Reviewed-by: Vik Fearing <vik@postgresfriends.org>
Reviewed-by: Chao Li <li.evan.chao@gmail.com>
Discussion: https://postgr.es/m/f524d8cab5914613d9e624d9ce177d3d@dalibo.info
306 lines
11 KiB
PL/PgSQL
306 lines
11 KiB
PL/PgSQL
--
|
|
-- RANDOM
|
|
-- Test random() and allies
|
|
--
|
|
-- Tests in this file may have a small probability of failure,
|
|
-- since we are dealing with randomness. Try to keep the failure
|
|
-- risk for any one test case under 1e-9.
|
|
--
|
|
|
|
-- There should be no duplicates in 1000 random() values.
|
|
-- (Assuming 52 random bits in the float8 results, we could
|
|
-- take as many as 3000 values and still have less than 1e-9 chance
|
|
-- of failure, per https://en.wikipedia.org/wiki/Birthday_problem)
|
|
SELECT r, count(*)
|
|
FROM (SELECT random() r FROM generate_series(1, 1000)) ss
|
|
GROUP BY r HAVING count(*) > 1;
|
|
|
|
-- The range should be [0, 1). We can expect that at least one out of 2000
|
|
-- random values is in the lowest or highest 1% of the range with failure
|
|
-- probability less than about 1e-9.
|
|
|
|
SELECT count(*) FILTER (WHERE r < 0 OR r >= 1) AS out_of_range,
|
|
(count(*) FILTER (WHERE r < 0.01)) > 0 AS has_small,
|
|
(count(*) FILTER (WHERE r > 0.99)) > 0 AS has_large
|
|
FROM (SELECT random() r FROM generate_series(1, 2000)) ss;
|
|
|
|
-- Check for uniform distribution using the Kolmogorov-Smirnov test.
|
|
|
|
CREATE FUNCTION ks_test_uniform_random()
|
|
RETURNS boolean AS
|
|
$$
|
|
DECLARE
|
|
n int := 1000; -- Number of samples
|
|
c float8 := 1.94947; -- Critical value for 99.9% confidence
|
|
ok boolean;
|
|
BEGIN
|
|
ok := (
|
|
WITH samples AS (
|
|
SELECT random() r FROM generate_series(1, n) ORDER BY 1
|
|
), indexed_samples AS (
|
|
SELECT (row_number() OVER())-1.0 i, r FROM samples
|
|
)
|
|
SELECT max(abs(i/n-r)) < c / sqrt(n) FROM indexed_samples
|
|
);
|
|
RETURN ok;
|
|
END
|
|
$$
|
|
LANGUAGE plpgsql;
|
|
|
|
-- As written, ks_test_uniform_random() returns true about 99.9%
|
|
-- of the time. To get down to a roughly 1e-9 test failure rate,
|
|
-- just run it 3 times and accept if any one of them passes.
|
|
SELECT ks_test_uniform_random() OR
|
|
ks_test_uniform_random() OR
|
|
ks_test_uniform_random() AS uniform;
|
|
|
|
-- now test random_normal()
|
|
|
|
-- As above, there should be no duplicates in 1000 random_normal() values.
|
|
SELECT r, count(*)
|
|
FROM (SELECT random_normal() r FROM generate_series(1, 1000)) ss
|
|
GROUP BY r HAVING count(*) > 1;
|
|
|
|
-- ... unless we force the range (standard deviation) to zero.
|
|
-- This is a good place to check that the mean input does something, too.
|
|
SELECT r, count(*)
|
|
FROM (SELECT random_normal(10, 0) r FROM generate_series(1, 100)) ss
|
|
GROUP BY r;
|
|
SELECT r, count(*)
|
|
FROM (SELECT random_normal(-10, 0) r FROM generate_series(1, 100)) ss
|
|
GROUP BY r;
|
|
|
|
-- Check standard normal distribution using the Kolmogorov-Smirnov test.
|
|
|
|
CREATE FUNCTION ks_test_normal_random()
|
|
RETURNS boolean AS
|
|
$$
|
|
DECLARE
|
|
n int := 1000; -- Number of samples
|
|
c float8 := 1.94947; -- Critical value for 99.9% confidence
|
|
ok boolean;
|
|
BEGIN
|
|
ok := (
|
|
WITH samples AS (
|
|
SELECT random_normal() r FROM generate_series(1, n) ORDER BY 1
|
|
), indexed_samples AS (
|
|
SELECT (row_number() OVER())-1.0 i, r FROM samples
|
|
)
|
|
SELECT max(abs((1+erf(r/sqrt(2)))/2 - i/n)) < c / sqrt(n)
|
|
FROM indexed_samples
|
|
);
|
|
RETURN ok;
|
|
END
|
|
$$
|
|
LANGUAGE plpgsql;
|
|
|
|
-- As above, ks_test_normal_random() returns true about 99.9%
|
|
-- of the time, so try it 3 times and accept if any test passes.
|
|
SELECT ks_test_normal_random() OR
|
|
ks_test_normal_random() OR
|
|
ks_test_normal_random() AS standard_normal;
|
|
|
|
-- Test random(min, max)
|
|
|
|
-- invalid range bounds
|
|
SELECT random(1, 0);
|
|
SELECT random(1000000000001, 1000000000000);
|
|
SELECT random(-2.0, -3.0);
|
|
SELECT random('NaN'::numeric, 10);
|
|
SELECT random('-Inf'::numeric, 0);
|
|
SELECT random(0, 'NaN'::numeric);
|
|
SELECT random(0, 'Inf'::numeric);
|
|
|
|
-- empty range is OK
|
|
SELECT random(101, 101);
|
|
SELECT random(1000000000001, 1000000000001);
|
|
SELECT random(3.14, 3.14);
|
|
|
|
-- There should be no triple duplicates in 1000 full-range 32-bit random()
|
|
-- values. (Each of the C(1000, 3) choices of triplets from the 1000 values
|
|
-- has a probability of 1/(2^32)^2 of being a triple duplicate, so the
|
|
-- average number of triple duplicates is 1000 * 999 * 998 / 6 / 2^64, which
|
|
-- is roughly 9e-12.)
|
|
SELECT r, count(*)
|
|
FROM (SELECT random(-2147483648, 2147483647) r
|
|
FROM generate_series(1, 1000)) ss
|
|
GROUP BY r HAVING count(*) > 2;
|
|
|
|
-- There should be no duplicates in 1000 full-range 64-bit random() values.
|
|
SELECT r, count(*)
|
|
FROM (SELECT random_normal(-9223372036854775808, 9223372036854775807) r
|
|
FROM generate_series(1, 1000)) ss
|
|
GROUP BY r HAVING count(*) > 1;
|
|
|
|
-- There should be no duplicates in 1000 15-digit random() numeric values.
|
|
SELECT r, count(*)
|
|
FROM (SELECT random_normal(0, 1 - 1e-15) r
|
|
FROM generate_series(1, 1000)) ss
|
|
GROUP BY r HAVING count(*) > 1;
|
|
|
|
-- Expect at least one out of 2000 random values to be in the lowest and
|
|
-- highest 1% of the range.
|
|
SELECT (count(*) FILTER (WHERE r < -2104533975)) > 0 AS has_small,
|
|
(count(*) FILTER (WHERE r > 2104533974)) > 0 AS has_large
|
|
FROM (SELECT random(-2147483648, 2147483647) r FROM generate_series(1, 2000)) ss;
|
|
|
|
SELECT count(*) FILTER (WHERE r < -1500000000 OR r > 1500000000) AS out_of_range,
|
|
(count(*) FILTER (WHERE r < -1470000000)) > 0 AS has_small,
|
|
(count(*) FILTER (WHERE r > 1470000000)) > 0 AS has_large
|
|
FROM (SELECT random(-1500000000, 1500000000) r FROM generate_series(1, 2000)) ss;
|
|
|
|
SELECT (count(*) FILTER (WHERE r < -9038904596117680292)) > 0 AS has_small,
|
|
(count(*) FILTER (WHERE r > 9038904596117680291)) > 0 AS has_large
|
|
FROM (SELECT random(-9223372036854775808, 9223372036854775807) r
|
|
FROM generate_series(1, 2000)) ss;
|
|
|
|
SELECT count(*) FILTER (WHERE r < -1500000000000000 OR r > 1500000000000000) AS out_of_range,
|
|
(count(*) FILTER (WHERE r < -1470000000000000)) > 0 AS has_small,
|
|
(count(*) FILTER (WHERE r > 1470000000000000)) > 0 AS has_large
|
|
FROM (SELECT random(-1500000000000000, 1500000000000000) r
|
|
FROM generate_series(1, 2000)) ss;
|
|
|
|
SELECT count(*) FILTER (WHERE r < -1.5 OR r > 1.5) AS out_of_range,
|
|
(count(*) FILTER (WHERE r < -1.47)) > 0 AS has_small,
|
|
(count(*) FILTER (WHERE r > 1.47)) > 0 AS has_large
|
|
FROM (SELECT random(-1.500000000000000, 1.500000000000000) r
|
|
FROM generate_series(1, 2000)) ss;
|
|
|
|
-- Every possible value should occur at least once in 2500 random() values
|
|
-- chosen from a range with 100 distinct values.
|
|
SELECT min(r), max(r), count(r) FROM (
|
|
SELECT DISTINCT random(-50, 49) r FROM generate_series(1, 2500));
|
|
|
|
SELECT min(r), max(r), count(r) FROM (
|
|
SELECT DISTINCT random(123000000000, 123000000099) r
|
|
FROM generate_series(1, 2500));
|
|
|
|
SELECT min(r), max(r), count(r) FROM (
|
|
SELECT DISTINCT random(-0.5, 0.49) r FROM generate_series(1, 2500));
|
|
|
|
-- Check for uniform distribution using the Kolmogorov-Smirnov test.
|
|
|
|
CREATE FUNCTION ks_test_uniform_random_int_in_range()
|
|
RETURNS boolean AS
|
|
$$
|
|
DECLARE
|
|
n int := 1000; -- Number of samples
|
|
c float8 := 1.94947; -- Critical value for 99.9% confidence
|
|
ok boolean;
|
|
BEGIN
|
|
ok := (
|
|
WITH samples AS (
|
|
SELECT random(0, 999999) / 1000000.0 r FROM generate_series(1, n) ORDER BY 1
|
|
), indexed_samples AS (
|
|
SELECT (row_number() OVER())-1.0 i, r FROM samples
|
|
)
|
|
SELECT max(abs(i/n-r)) < c / sqrt(n) FROM indexed_samples
|
|
);
|
|
RETURN ok;
|
|
END
|
|
$$
|
|
LANGUAGE plpgsql;
|
|
|
|
SELECT ks_test_uniform_random_int_in_range() OR
|
|
ks_test_uniform_random_int_in_range() OR
|
|
ks_test_uniform_random_int_in_range() AS uniform_int;
|
|
|
|
CREATE FUNCTION ks_test_uniform_random_bigint_in_range()
|
|
RETURNS boolean AS
|
|
$$
|
|
DECLARE
|
|
n int := 1000; -- Number of samples
|
|
c float8 := 1.94947; -- Critical value for 99.9% confidence
|
|
ok boolean;
|
|
BEGIN
|
|
ok := (
|
|
WITH samples AS (
|
|
SELECT random(0, 999999999999) / 1000000000000.0 r FROM generate_series(1, n) ORDER BY 1
|
|
), indexed_samples AS (
|
|
SELECT (row_number() OVER())-1.0 i, r FROM samples
|
|
)
|
|
SELECT max(abs(i/n-r)) < c / sqrt(n) FROM indexed_samples
|
|
);
|
|
RETURN ok;
|
|
END
|
|
$$
|
|
LANGUAGE plpgsql;
|
|
|
|
SELECT ks_test_uniform_random_bigint_in_range() OR
|
|
ks_test_uniform_random_bigint_in_range() OR
|
|
ks_test_uniform_random_bigint_in_range() AS uniform_bigint;
|
|
|
|
CREATE FUNCTION ks_test_uniform_random_numeric_in_range()
|
|
RETURNS boolean AS
|
|
$$
|
|
DECLARE
|
|
n int := 1000; -- Number of samples
|
|
c float8 := 1.94947; -- Critical value for 99.9% confidence
|
|
ok boolean;
|
|
BEGIN
|
|
ok := (
|
|
WITH samples AS (
|
|
SELECT random(0, 0.999999) r FROM generate_series(1, n) ORDER BY 1
|
|
), indexed_samples AS (
|
|
SELECT (row_number() OVER())-1.0 i, r FROM samples
|
|
)
|
|
SELECT max(abs(i/n-r)) < c / sqrt(n) FROM indexed_samples
|
|
);
|
|
RETURN ok;
|
|
END
|
|
$$
|
|
LANGUAGE plpgsql;
|
|
|
|
SELECT ks_test_uniform_random_numeric_in_range() OR
|
|
ks_test_uniform_random_numeric_in_range() OR
|
|
ks_test_uniform_random_numeric_in_range() AS uniform_numeric;
|
|
|
|
-- setseed() should produce a reproducible series of random() values.
|
|
|
|
SELECT setseed(0.5);
|
|
|
|
SELECT random() FROM generate_series(1, 10);
|
|
|
|
-- Likewise for random_normal(); however, since its implementation relies
|
|
-- on libm functions that have different roundoff behaviors on different
|
|
-- machines, we have to round off the results a bit to get consistent output.
|
|
SET extra_float_digits = -1;
|
|
|
|
SELECT random_normal() FROM generate_series(1, 10);
|
|
SELECT random_normal(mean => 1, stddev => 0.1) r FROM generate_series(1, 10);
|
|
|
|
-- Reproducible random(min, max) values.
|
|
SELECT random(1, 6) FROM generate_series(1, 10);
|
|
SELECT random(-2147483648, 2147483647) FROM generate_series(1, 10);
|
|
SELECT random(-9223372036854775808, 9223372036854775807) FROM generate_series(1, 10);
|
|
SELECT random(-1e30, 1e30) FROM generate_series(1, 10);
|
|
SELECT random(-0.4, 0.4) FROM generate_series(1, 10);
|
|
SELECT random(0, 1 - 1e-30) FROM generate_series(1, 10);
|
|
SELECT n, random(0, trim_scale(abs(1 - 10.0^(-n)))) FROM generate_series(-20, 20) n;
|
|
|
|
-- random dates
|
|
SELECT random('1979-02-08'::date,'2025-07-03'::date) AS random_date_multiple_years;
|
|
SELECT random('4714-11-24 BC'::date,'5874897-12-31 AD'::date) AS random_date_maximum_range;
|
|
SELECT random('1979-02-08'::date,'1979-02-08'::date) AS random_date_empty_range;
|
|
SELECT random('2024-12-31'::date, '2024-01-01'::date); -- fail
|
|
SELECT random('-infinity'::date, '2024-01-01'::date); -- fail
|
|
SELECT random('2024-12-31'::date, 'infinity'::date); -- fail
|
|
|
|
-- random timestamps
|
|
SELECT random('1979-02-08'::timestamp,'2025-07-03'::timestamp) AS random_timestamp_multiple_years;
|
|
SELECT random('4714-11-24 BC'::timestamp,'294276-12-31 23:59:59.999999'::timestamp) AS random_timestamp_maximum_range;
|
|
SELECT random('2024-07-01 12:00:00.000001'::timestamp, '2024-07-01 12:00:00.999999'::timestamp) AS random_narrow_range;
|
|
SELECT random('1979-02-08'::timestamp,'1979-02-08'::timestamp) AS random_timestamp_empty_range;
|
|
SELECT random('2024-12-31'::timestamp, '2024-01-01'::timestamp); -- fail
|
|
SELECT random('-infinity'::timestamp, '2024-01-01'::timestamp); -- fail
|
|
SELECT random('2024-12-31'::timestamp, 'infinity'::timestamp); -- fail
|
|
|
|
-- random timestamps with timezone
|
|
SELECT random('1979-02-08 +01'::timestamptz,'2025-07-03 +02'::timestamptz) AS random_timestamptz_multiple_years;
|
|
SELECT random('4714-11-24 BC +00'::timestamptz,'294276-12-31 23:59:59.999999 +00'::timestamptz) AS random_timestamptz_maximum_range;
|
|
SELECT random('2024-07-01 12:00:00.000001 +04'::timestamptz, '2024-07-01 12:00:00.999999 +04'::timestamptz) AS random_timestamptz_narrow_range;
|
|
SELECT random('1979-02-08 +05'::timestamptz,'1979-02-08 +05'::timestamptz) AS random_timestamptz_empty_range;
|
|
SELECT random('2024-01-01 +06'::timestamptz, '2024-01-01 +07'::timestamptz); -- fail
|
|
SELECT random('-infinity'::timestamptz, '2024-01-01 +07'::timestamptz); -- fail
|
|
SELECT random('2024-01-01 +06'::timestamptz, 'infinity'::timestamptz); -- fail
|