Commit dee11f96 authored by Sergey Petrunya's avatar Sergey Petrunya

MDEV-4362: {division by zero when lookup constant is outside the value table}

- Fix Histogram::point_selectivity() to work in the case where the 
  passed value_pos=0 (or 1) and the first (or the last) bucket in the 
  histogram has zero value-range (i.e one value).
parent ad842b5f
......@@ -1356,6 +1356,37 @@ id select_type table type possible_keys key key_len ref rows filtered Extra
Warnings:
Note 1003 select `test`.`t1`.`a` AS `a` from `test`.`t1` where (`test`.`t1`.`a` = <cache>(-(1)))
drop table t0, t1;
#
# MDEV-4362: Selectivity estimates for IN (...) do not depend on whether the values are in range
#
create table t1 (col1 int);
set @a=-1;
create table t2 (a int) select (@a:=@a+1) as a from information_schema.session_variables A limit 100;
insert into t1 select A.a from t2 A, t2 B where A.a < 100 and B.a < 100;
select min(col1), max(col1), count(*) from t1;
min(col1) max(col1) count(*)
0 99 10000
set histogram_size=100;
analyze table t1 persistent for all;
Table Op Msg_type Msg_text
test.t1 analyze status OK
explain extended select * from t1 where col1 in (1,2,3);
id select_type table type possible_keys key key_len ref rows filtered Extra
1 SIMPLE t1 ALL NULL NULL NULL NULL 10000 3.37 Using where
Warnings:
Note 1003 select `test`.`t1`.`col1` AS `col1` from `test`.`t1` where (`test`.`t1`.`col1` in (1,2,3))
# Must not cause fp division by zero, or produce nonsense numbers:
explain extended select * from t1 where col1 in (-1,-2,-3);
id select_type table type possible_keys key key_len ref rows filtered Extra
1 SIMPLE t1 ALL NULL NULL NULL NULL 10000 3.00 Using where
Warnings:
Note 1003 select `test`.`t1`.`col1` AS `col1` from `test`.`t1` where (`test`.`t1`.`col1` in (<cache>(-(1)),<cache>(-(2)),<cache>(-(3))))
explain extended select * from t1 where col1<=-1;
id select_type table type possible_keys key key_len ref rows filtered Extra
1 SIMPLE t1 ALL NULL NULL NULL NULL 10000 1.00 Using where
Warnings:
Note 1003 select `test`.`t1`.`col1` AS `col1` from `test`.`t1` where (`test`.`t1`.`col1` <= <cache>(-(1)))
drop table t1, t2;
set histogram_type=@save_histogram_type;
set histogram_size=@save_histogram_size;
set optimizer_use_condition_selectivity=@save_optimizer_use_condition_selectivity;
......
......@@ -1366,6 +1366,37 @@ id select_type table type possible_keys key key_len ref rows filtered Extra
Warnings:
Note 1003 select `test`.`t1`.`a` AS `a` from `test`.`t1` where (`test`.`t1`.`a` = <cache>(-(1)))
drop table t0, t1;
#
# MDEV-4362: Selectivity estimates for IN (...) do not depend on whether the values are in range
#
create table t1 (col1 int);
set @a=-1;
create table t2 (a int) select (@a:=@a+1) as a from information_schema.session_variables A limit 100;
insert into t1 select A.a from t2 A, t2 B where A.a < 100 and B.a < 100;
select min(col1), max(col1), count(*) from t1;
min(col1) max(col1) count(*)
0 99 10000
set histogram_size=100;
analyze table t1 persistent for all;
Table Op Msg_type Msg_text
test.t1 analyze status OK
explain extended select * from t1 where col1 in (1,2,3);
id select_type table type possible_keys key key_len ref rows filtered Extra
1 SIMPLE t1 ALL NULL NULL NULL NULL 10000 3.37 Using where
Warnings:
Note 1003 select `test`.`t1`.`col1` AS `col1` from `test`.`t1` where (`test`.`t1`.`col1` in (1,2,3))
# Must not cause fp division by zero, or produce nonsense numbers:
explain extended select * from t1 where col1 in (-1,-2,-3);
id select_type table type possible_keys key key_len ref rows filtered Extra
1 SIMPLE t1 ALL NULL NULL NULL NULL 10000 3.00 Using where
Warnings:
Note 1003 select `test`.`t1`.`col1` AS `col1` from `test`.`t1` where (`test`.`t1`.`col1` in (<cache>(-(1)),<cache>(-(2)),<cache>(-(3))))
explain extended select * from t1 where col1<=-1;
id select_type table type possible_keys key key_len ref rows filtered Extra
1 SIMPLE t1 ALL NULL NULL NULL NULL 10000 1.00 Using where
Warnings:
Note 1003 select `test`.`t1`.`col1` AS `col1` from `test`.`t1` where (`test`.`t1`.`col1` <= <cache>(-(1)))
drop table t1, t2;
set histogram_type=@save_histogram_type;
set histogram_size=@save_histogram_size;
set optimizer_use_condition_selectivity=@save_optimizer_use_condition_selectivity;
......
......@@ -908,6 +908,22 @@ explain extended select * from t1 where a=-1;
drop table t0, t1;
--echo #
--echo # MDEV-4362: Selectivity estimates for IN (...) do not depend on whether the values are in range
--echo #
create table t1 (col1 int);
set @a=-1;
create table t2 (a int) select (@a:=@a+1) as a from information_schema.session_variables A limit 100;
insert into t1 select A.a from t2 A, t2 B where A.a < 100 and B.a < 100;
select min(col1), max(col1), count(*) from t1;
set histogram_size=100;
analyze table t1 persistent for all;
explain extended select * from t1 where col1 in (1,2,3);
--echo # Must not cause fp division by zero, or produce nonsense numbers:
explain extended select * from t1 where col1 in (-1,-2,-3);
explain extended select * from t1 where col1<=-1;
drop table t1, t2;
set histogram_type=@save_histogram_type;
set histogram_size=@save_histogram_size;
set optimizer_use_condition_selectivity=@save_optimizer_use_condition_selectivity;
......
......@@ -302,16 +302,27 @@ class Histogram
(max + 1 == get_width() ? 1.0 : (get_value(max) * inv_prec_factor)) -
(min == 0 ? 0.0 : (get_value(min-1) * inv_prec_factor));
/*
So:
- each bucket has the same #rows
- values are unformly distributed across the [min_value,max_value] domain.
If a bucket has value range that's N times bigger then average, than
each value will have to have N times fewer rows than average.
*/
DBUG_ASSERT(current_bucket_width);
sel= avg_sel * avg_bucket_width / current_bucket_width;
if (current_bucket_width < 1e-16)
{
/*
A special case: we are at the first (or the last) bucket in the
histogram, the bucket's value range is a singlepoint [x,x], and
pos_value=0 (for the first bucket) or pos_value=1 (for the last).
*/
sel= avg_sel;
}
else
{
/*
So:
- each bucket has the same #rows
- values are unformly distributed across the [min_value,max_value] domain.
If a bucket has value range that's N times bigger then average, than
each value will have to have N times fewer rows than average.
*/
sel= avg_sel * avg_bucket_width / current_bucket_width;
}
/*
(Q: if we just follow this proportion we may end up in a situation
......
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