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Use chi-square for random distributivity verification in test (#8709)

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Problem:
Currently, when performing random distribution verification, we determine
the probability of each element occurring in the sum, but the probability is
only an estimate, these tests had rare sporadic failures, and we cannot verify
what the probability of failure will be.

Solution:
Using the chi-square distribution instead of the original random distribution
validation makes the test more reasonable and easier to find problems.
This commit is contained in:
sundb 2021-04-01 13:20:15 +08:00 committed by GitHub
parent 636aa8de76
commit 569a3f4548
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GPG Key ID: 4AEE18F83AFDEB23
4 changed files with 33 additions and 13 deletions
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28
tests/support/util.tcl
View File

@ -682,20 +682,34 @@ proc string2printable s {
return $res
}
# Check that probability of each element are between {min_prop} and {max_prop}.
proc check_histogram_distribution {res min_prop max_prop} {
# Calculation value of Chi-Square Distribution. By this value
# we can verify the random distribution sample confidence.
# Based on the following wiki:
# https://en.wikipedia.org/wiki/Chi-square_distribution
#
# param res Random sample list
# return Value of Chi-Square Distribution
#
# x2_value: return of chi_square_value function
# df: Degrees of freedom, Number of independent values minus 1
#
# By using x2_value and df to back check the cardinality table,
# we can know the confidence of the random sample.
proc chi_square_value {res} {
unset -nocomplain mydict
foreach key $res {
dict incr mydict $key 1
}
set x2_value 0
set p [expr [llength $res] / [dict size $mydict]]
foreach key [dict keys $mydict] {
set value [dict get $mydict $key]
set probability [expr {double($value) / [llength $res]}]
if {$probability < $min_prop || $probability > $max_prop} {
return false
}
# Aggregate the chi-square value of each element
set v [expr {pow($value - $p, 2) / $p}]
set x2_value [expr {$x2_value + $v}]
}
return true
return $x2_value
}

6
tests/unit/type/hash.tcl
View File

@ -105,8 +105,9 @@ start_server {tags {"hash"}} {
assert_equal [llength $res] 2002
# Test random uniform distribution
# df = 9, 40 means 0.00001 probability
set res [r hrandfield myhash -1000]
assert_equal [check_histogram_distribution $res 0.05 0.15] true
assert_lessthan [chi_square_value $res] 40
# 2) Check that all the elements actually belong to the original hash.
foreach {key val} $res {
@ -199,7 +200,8 @@ start_server {tags {"hash"}} {
}
}
assert_equal $all_ele_return true
assert_equal [check_histogram_distribution $allkey 0.05 0.15] true
# df = 9, 40 means 0.00001 probability
assert_lessthan [chi_square_value $allkey] 40
}
}
r config set hash-max-ziplist-value $original_max_value

6
tests/unit/type/set.tcl
View File

@ -533,8 +533,9 @@ start_server {
}
# Use negative count (PATH 1).
# df = 9, 40 means 0.00001 probability
set res [r srandmember myset -1000]
assert_equal [check_histogram_distribution $res 0.05 0.15] true
assert_lessthan [chi_square_value $res] 40
# Use positive count (both PATH 3 and PATH 4).
foreach size {8 2} {
@ -547,7 +548,8 @@ start_server {
lappend allkey $ele
}
}
assert_equal [check_histogram_distribution $allkey 0.05 0.15] true
# df = 9, 40 means 0.00001 probability
assert_lessthan [chi_square_value $allkey] 40
}
}
}

6
tests/unit/type/zset.tcl
View File

@ -1655,8 +1655,9 @@ start_server {tags {"zset"}} {
assert_equal [llength $res] 2002
# Test random uniform distribution
# df = 9, 40 means 0.00001 probability
set res [r zrandmember myzset -1000]
assert_equal [check_histogram_distribution $res 0.05 0.15] true
assert_lessthan [chi_square_value $res] 40
# 2) Check that all the elements actually belong to the original zset.
foreach {key val} $res {
@ -1749,7 +1750,8 @@ start_server {tags {"zset"}} {
}
}
assert_equal $all_ele_return true
assert_equal [check_histogram_distribution $allkey 0.05 0.15] true
# df = 9, 40 means 0.00001 probability
assert_lessthan [chi_square_value $allkey] 40
}
}
r config set zset-max-ziplist-value $original_max_value