368 lines
11 KiB
Plaintext
368 lines
11 KiB
Plaintext
# 2013-05-28
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#
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# The author disclaims copyright to this source code. In place of
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# a legal notice, here is a blessing:
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#
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# May you do good and not evil.
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# May you find forgiveness for yourself and forgive others.
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# May you share freely, never taking more than you give.
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#
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#***********************************************************************
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# This file implements regression tests for SQLite library. The
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# focus of this file is percentile.c extension
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#
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set testdir [file dirname $argv0]
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source $testdir/tester.tcl
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# Basic test of the percentile() function.
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#
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do_test percentile-1.0 {
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load_static_extension db percentile
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execsql {
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CREATE TABLE t1(x);
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INSERT INTO t1 VALUES(1),(4),(6),(7),(8),(9),(11),(11),(11);
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}
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execsql {SELECT percentile(x,0) FROM t1}
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} {1.0}
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foreach {in out} {
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100 11.0
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50 8.0
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12.5 4.0
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15 4.4
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20 5.2
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80 11.0
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89 11.0
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} {
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do_test percentile-1.1.$in {
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execsql {SELECT percentile(x,$in) FROM t1}
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} $out
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}
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do_execsql_test percentile-1.1.median {
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SELECT median(x) FROM t1;
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} 8.0
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# Add some NULL values.
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#
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do_test percentile-1.2 {
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execsql {INSERT INTO t1 VALUES(NULL),(NULL);}
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} {}
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foreach {in out} {
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100 11.0
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50 8.0
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12.5 4.0
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15 4.4
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20 5.2
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80 11.0
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89 11.0
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} {
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do_test percentile-1.3.$in {
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execsql {SELECT percentile(x,$in) FROM t1}
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} $out
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}
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# The second argument to percentile can change some, but not much.
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#
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do_test percentile-1.4 {
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catchsql {SELECT round(percentile(x, 15+0.000001*rowid),1) FROM t1}
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} {0 4.4}
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do_test percentile-1.5 {
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catchsql {SELECT round(percentile(x, 15+0.1*rowid),1) FROM t1}
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} {1 {2nd argument to percentile() is not the same for all input rows}}
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# Input values in a random order
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#
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do_test percentile-1.6 {
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execsql {
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CREATE TABLE t2(x);
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INSERT INTO t2 SELECT x+0.0 FROM t1 ORDER BY random();
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}
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} {}
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foreach {in out} {
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100 11.0
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50 8.0
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12.5 4.0
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15 4.4
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20 5.2
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80 11.0
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89 11.0
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} {
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do_test percentile-1.7.$in {
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execsql {SELECT percentile(x,$in) FROM t2}
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} $out
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}
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# Wrong number of arguments
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#
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do_test percentile-1.8 {
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catchsql {SELECT percentile(x,0,1) FROM t1}
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} {1 {wrong number of arguments to function percentile()}}
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do_test percentile-1.9 {
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catchsql {SELECT percentile(x) FROM t1}
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} {1 {wrong number of arguments to function percentile()}}
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# Second argument must be numeric
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#
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do_test percentile-1.10 {
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catchsql {SELECT percentile(x,null) FROM t1}
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} {1 {2nd argument to percentile() is not a number between 0.0 and 100.0}}
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do_test percentile-1.11 {
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catchsql {SELECT percentile(x,'fifty') FROM t1}
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} {1 {2nd argument to percentile() is not a number between 0.0 and 100.0}}
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do_test percentile-1.12 {
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catchsql {SELECT percentile(x,x'3530') FROM t1}
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} {1 {2nd argument to percentile() is not a number between 0.0 and 100.0}}
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# Second argument is out of range
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#
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do_test percentile-1.13 {
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catchsql {SELECT percentile(x,-0.0000001) FROM t1}
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} {1 {2nd argument to percentile() is not a number between 0.0 and 100.0}}
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do_test percentile-1.14 {
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catchsql {SELECT percentile(x,100.0000001) FROM t1}
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} {1 {2nd argument to percentile() is not a number between 0.0 and 100.0}}
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# First argument is not NULL and is not NUMERIC
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#
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do_test percentile-1.15 {
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catchsql {
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BEGIN;
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UPDATE t1 SET x='50' WHERE x IS NULL;
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SELECT percentile(x, 50) FROM t1;
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}
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} {1 {1st argument to percentile() is not numeric}}
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do_test percentile-1.16 {
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catchsql {
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ROLLBACK;
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BEGIN;
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UPDATE t1 SET x=x'3530' WHERE x IS NULL;
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SELECT percentile(x, 50) FROM t1;
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}
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} {1 {1st argument to percentile() is not numeric}}
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do_test percentile-1.17 {
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catchsql {
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ROLLBACK;
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SELECT percentile(x, 50) FROM t1;
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}
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} {0 8.0}
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# No non-NULL entries.
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#
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do_test percentile-1.18 {
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execsql {
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UPDATE t1 SET x=NULL;
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SELECT ifnull(percentile(x, 50),'NULL') FROM t1
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}
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} {NULL}
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# Exactly one non-NULL entry
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#
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do_test percentile-1.19 {
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execsql {
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UPDATE t1 SET x=12345 WHERE rowid=5;
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SELECT percentile(x, 0), percentile(x, 50), percentile(x,100) FROM t1
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}
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} {12345.0 12345.0 12345.0}
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# Infinity as an input
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#
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do_test percentile-1.20 {
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catchsql {
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DELETE FROM t1;
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INSERT INTO t1 SELECT x+0.0 FROM t2;
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UPDATE t1 SET x=1.0e300*1.0e300 WHERE rowid=5;
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SELECT percentile(x,50) from t1;
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}
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} {1 {Inf input to percentile()}}
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do_test percentile-1.21 {
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catchsql {
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UPDATE t1 SET x=-1.0e300*1.0e300 WHERE rowid=5;
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SELECT percentile(x,50) from t1;
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}
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} {1 {Inf input to percentile()}}
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# Million-row Inputs
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#
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ifcapable vtab {
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do_test percentile-2.0 {
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load_static_extension db wholenumber
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execsql {
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CREATE VIRTUAL TABLE nums USING wholenumber;
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CREATE TABLE t3(x);
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INSERT INTO t3 SELECT value-1 FROM nums WHERE value BETWEEN 1 AND 500000;
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INSERT INTO t3 SELECT value*10 FROM nums
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WHERE value BETWEEN 500000 AND 999999;
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SELECT count(*) FROM t3;
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}
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} {1000000}
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foreach {in out} {
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0 0.0
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100 9999990.0
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50 2749999.5
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10 99999.9
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} {
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do_test percentile-2.1.$in {
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execsql {
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SELECT round(percentile(x, $in),1) from t3;
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}
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} $out
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}
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}
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# median() as a window function. (2024-08-31)
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#
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do_execsql_test percentile-3.0 {
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DROP TABLE IF EXISTS t1;
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CREATE TABLE t1(a INTEGER PRIMARY KEY, b, c, d);
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INSERT INTO t1 VALUES (1, 'A', 'one', 8.4),
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(2, 'B', 'two', 7.1),
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(3, 'C', 'three', 5.9),
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(4, 'D', 'one', 11.0),
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(5, 'E', 'two', 12.5),
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(6, 'F', 'three', 0.0),
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(7, 'G', 'one', 2.7);
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}
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do_execsql_test percentile-3.1 {
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SELECT a, b, c, d,
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group_concat(b,'.') OVER W1 AS 'elements',
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median(d) OVER W1 AS 'median'
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FROM t1
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WINDOW W1 AS (ORDER BY c, a ROWS BETWEEN 1 PRECEDING AND 1 FOLLOWING)
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} {
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1 A one 8.4 A.D 9.7
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4 D one 11.0 A.D.G 8.4
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7 G one 2.7 D.G.C 5.9
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3 C three 5.9 G.C.F 2.7
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6 F three 0.0 C.F.B 5.9
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2 B two 7.1 F.B.E 7.1
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5 E two 12.5 B.E 9.8
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}
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do_execsql_test percentile-3.2 {
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SELECT a, b, c, d,
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group_concat(b,'.') OVER W1 AS 'elements',
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median(d) OVER W1 AS 'median'
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FROM t1
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WINDOW W1 AS (ORDER BY c, a ROWS BETWEEN UNBOUNDED PRECEDING AND 1 FOLLOWING)
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} {
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1 A one 8.4 A.D 9.7
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4 D one 11.0 A.D.G 8.4
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7 G one 2.7 A.D.G.C 7.15
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3 C three 5.9 A.D.G.C.F 5.9
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6 F three 0.0 A.D.G.C.F.B 6.5
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2 B two 7.1 A.D.G.C.F.B.E 7.1
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5 E two 12.5 A.D.G.C.F.B.E 7.1
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}
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do_execsql_test percentile-3.3 {
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SELECT a, b, c, d,
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group_concat(b,'.') OVER W1 AS 'elements',
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median(d) OVER W1 AS 'median'
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FROM t1
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WINDOW W1 AS (ORDER BY c, a ROWS BETWEEN 1 PRECEDING AND UNBOUNDED FOLLOWING)
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} {
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1 A one 8.4 A.D.G.C.F.B.E 7.1
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4 D one 11.0 A.D.G.C.F.B.E 7.1
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7 G one 2.7 D.G.C.F.B.E 6.5
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3 C three 5.9 G.C.F.B.E 5.9
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6 F three 0.0 C.F.B.E 6.5
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2 B two 7.1 F.B.E 7.1
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5 E two 12.5 B.E 9.8
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}
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# Test case adapted from examples shown at
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# https://database.guide/3-functions-to-calculate-the-median-in-sql/
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#
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do_execsql_test percential-4.0 {
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CREATE TABLE products(
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vendorId INT,
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productId INTEGER PRIMARY KEY,
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productName REAL,
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price REAL
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);
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INSERT INTO products VALUES
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(1001, 17, 'Left-handed screwdriver', 25.99),
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(1001, 49, 'Right-handed screwdriver', 25.99),
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(1001, 216, 'Long weight (blue)', 14.75),
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(1001, 31, 'Long weight (green)', 11.99),
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(1002, 37, 'Sledge hammer', 33.49),
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(1003, 7, 'Chainsaw', 245.00),
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(1003, 8, 'Straw dog box', 55.99),
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(1003, 12, 'Hammock', 11.01),
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(1004, 113, 'Teapot', 12.45),
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(1004, 117, 'Bottomless coffee mug', 9.99);
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}
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do_execsql_test percentile-4.1 {
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SELECT VendorId, ProductId, /* ProductName,*/ Price,
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avg(price) OVER (PARTITION BY vendorId) AS "Average",
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median(price) OVER (PARTITION BY vendorId) AS "Median"
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FROM products
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ORDER BY vendorId, productId;
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} {
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1001 17 25.99 19.68 20.37
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1001 31 11.99 19.68 20.37
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1001 49 25.99 19.68 20.37
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1001 216 14.75 19.68 20.37
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1002 37 33.49 33.49 33.49
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1003 7 245.0 104.0 55.99
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1003 8 55.99 104.0 55.99
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1003 12 11.01 104.0 55.99
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1004 113 12.45 11.22 11.22
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1004 117 9.99 11.22 11.22
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}
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do_execsql_test percentile-4.2 {
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SELECT vendorId, median(price) FROM products
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GROUP BY 1 ORDER BY 1;
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} {1001 20.37 1002 33.49 1003 55.99 1004 11.22}
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do_execsql_test percentile-5.0 {
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CREATE TABLE user(name TEXT, class TEXT, cost REAL);
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INSERT INTO user VALUES
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('Alice', 'Y', 3578.27),
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('Bob', 'X', 3399.99),
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('Cindy', 'Z', 699.10),
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('Dave', 'Y', 3078.27),
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('Emma', 'Z', 2319.99),
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('Fred', 'Y', 539.99),
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('Gina', 'X', 2320.49),
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('Hank', 'W', 24.99),
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('Irma', 'W', 24.99),
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('Jake', 'X', 2234.99),
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('Kim', 'Y', 4319.99),
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('Liam', 'X', 4968.59),
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('Mia', 'W', 59.53),
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('Nate', 'W', 23.50);
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}
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do_execsql_test percentile-5.1 {
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SELECT name, class, cost,
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percentile(cost, 0) OVER w1 AS 'P0',
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percentile(cost, 25) OVER w1 AS 'P1',
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percentile(cost, 50) OVER w1 AS 'P2',
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percentile(cost, 75) OVER w1 AS 'P3',
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percentile(cost, 100) OVER w1 AS 'P4'
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FROM user
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WINDOW w1 AS (PARTITION BY class)
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ORDER BY class, cost;
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} {
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Nate W 23.5 23.5 24.6175 24.99 33.625 59.53
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Hank W 24.99 23.5 24.6175 24.99 33.625 59.53
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Irma W 24.99 23.5 24.6175 24.99 33.625 59.53
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Mia W 59.53 23.5 24.6175 24.99 33.625 59.53
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Jake X 2234.99 2234.99 2299.115 2860.24 3792.14 4968.59
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Gina X 2320.49 2234.99 2299.115 2860.24 3792.14 4968.59
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Bob X 3399.99 2234.99 2299.115 2860.24 3792.14 4968.59
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Liam X 4968.59 2234.99 2299.115 2860.24 3792.14 4968.59
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Fred Y 539.99 539.99 2443.7 3328.27 3763.7 4319.99
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Dave Y 3078.27 539.99 2443.7 3328.27 3763.7 4319.99
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Alice Y 3578.27 539.99 2443.7 3328.27 3763.7 4319.99
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Kim Y 4319.99 539.99 2443.7 3328.27 3763.7 4319.99
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Cindy Z 699.1 699.1 1104.3225 1509.545 1914.7675 2319.99
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Emma Z 2319.99 699.1 1104.3225 1509.545 1914.7675 2319.99
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}
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# Fuzzer find.
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do_execsql_test percentile-6.0 {
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WITH RECURSIVE c(n) AS (VALUES(1) UNION ALL SELECT n+1 FROM c WHERE n<12)
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SELECT median(iif(n%2,0.1,1.0)) FROM c;
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} 0.55
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finish_test
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