Re: Optimize MCV stats for sortable types and utilize sorted-order properties - Mailing list pgsql-hackers

Hi, hackers

(Rebase it.     CC’ing all, hoping for your sincere assistance.)

Optimize MCV statistics for sortable types by leveraging sorted-order properties

1. Preserve ascending-ordered MCV values (new statistic kind STATISTIC_KIND_MCV_VALUE_SORTED)
for sort-comparable types when populating pg_statistic.
In compute_scalar_stats(), keep existing logic and allocate an extra ScalarMCVItem
workspace to store sorted MCV entries.

2. Use the pre-sorted MCV list during selectivity estimation:
   - Check against min/max boundaries; boundary cases complete with only 1-2 comparisons.
   - Entries inside the MCV range use binary search, reducing cost from average N/2 to log(N).
   - Entries outside the MCV range skip full MCV iteration, limiting comparisons to at most 2.

   This optimization is implemented for **var_eq_const()** (equality comparisons) and
   **mcv_selectivity()** (inequalities: <, <=, >, >=), fully exploiting sorted MCV properties.
   Further functions that can benefit from sorted MCV will be considered later.

3. Completed work:
   - Compatibility support for non-sortable types and sorted-state detection.
   - pg_stats view updates to expose STATISTIC_KIND_MCV_VALUE_SORTED MCV values
     via most_common_vals and most_common_freqs.

4. TODO:
   - Avoid storing STATISTIC_KIND_MCV_VALUE_SORTED alongside legacy STATISTIC_KIND_MCV.
     When compute_scalar_stats() generates the new sorted MCV for sortable types,
     remove or overwrite any existing STATISTIC_KIND_MCV entry.
   - Audit functions for performance benefits or regressions introduced by sorted MCV,
     and apply necessary fixes.
  - Add comparison of performance test results.


Attach test SQL and patch files:
drop table if exists t_analyze_mcv;
create table t_analyze_mcv(id int);
insert into t_analyze_mcv select (g+45) % 10 from generate_series(1, 90) g;
insert into t_analyze_mcv select 12 from generate_series(1, 10) g;
insert into t_analyze_mcv select * from t_analyze_mcv;
analyze t_analyze_mcv;
select attname,null_frac,n_distinct,most_common_vals,most_common_freqs,correlation
 from pg_catalog.pg_stats where tablename = 't_analyze_mcv'\gx
-[ RECORD 1 ]-----+--------------------------------------------------------
attname           | id
null_frac         | 0
n_distinct        | 11
most_common_vals  | {0,1,2,3,4,5,6,7,8,9,12}
most_common_freqs | {0.09,0.09,0.09,0.09,0.09,0.09,0.09,0.09,0.09,0.09,0.1}
correlation       | 0.20327759
xman7=# select id,count(*) from t_analyze_mcv group by id ;
 id | count
----+-------
  8 |    18
  9 |    18
  7 |    18
  1 |    18
  5 |    18
  4 |    18
  2 |    18
  0 |    18
  6 |    18
 12 |    20
  3 |    18
(11 rows)
xman7=#
--for var_eq_const()
explain select * from t_analyze_mcv where id = -1;   --1  rows,  low-out-off-mcv-range, directly compute sumcommon with
comparingOTHER MCV VALUES 
explain select * from t_analyze_mcv where id = 0;    --18 rows,  compare first one,directly complete
explain select * from t_analyze_mcv where id = 5;    --18 rows,  in mcv rang,one of list,binary search found
explain select * from t_analyze_mcv where id = 10;   --1  rows,  in mcv rang,one of list,binary search not found,
directlycompute sumcommon with comparing OTHER MCV VALUES 
explain select * from t_analyze_mcv where id = 12;   --20 rows,  compare last one,directly complete
explain select * from t_analyze_mcv where id = 13;   --1  rows,  high-out-off-mcv-range, directly compute sumcommon
withcomparing OTHER MCV VALUES 
--for mcv_selectivity()
--< <=
-- 1 row, low-out-of-mcv-range, 1 compare with [0]. Directly compute sumcommon without comparing other MCV values;
mcv_selec= 0.0 
explain select * from t_analyze_mcv where id <  -1; -- 1 rows
--or
explain select * from t_analyze_mcv where id <= -1; -- 1 rows
-- 1 compare with [0]. Directly compute sumcommon without comparing other MCV values;
explain select * from t_analyze_mcv where id <  0; -- 1 rows
--or
explain select * from t_analyze_mcv where id <= 0; -- 18 rows
-- compare with [0] and [nvlaues - 1], and binary search
explain select * from t_analyze_mcv where id <  1; -- 18 rows
explain select * from t_analyze_mcv where id <= 1; --36 rows
explain select * from t_analyze_mcv where id <  10; --180 rows
explain select * from t_analyze_mcv where id <= 10; --180 rows
-- compare with [0] and [nvlaues - 1], not need binary search
explain select * from t_analyze_mcv where id <  12; --180 rows
explain select * from t_analyze_mcv where id <= 12; --200 rows
-- compare with [0] and [nvlaues - 1], not need binary search
explain select * from t_analyze_mcv where id <  12; --1 rows
explain select * from t_analyze_mcv where id <= 12; --1 rows
--> >=
--only compare with [0] and and [nvlaues - 1],not need binary search
explain select * from t_analyze_mcv where id > -1;  --200 rows
explain select * from t_analyze_mcv where id >= -1; --200 row
explain select * from t_analyze_mcv where id > 0;  --182 rows
explain select * from t_analyze_mcv where id >= 0; --200 rows
--only compare with [0] and and [nvlaues - 1],and binary search
explain select * from t_analyze_mcv where id > 5;  -- 92 rows
explain select * from t_analyze_mcv where id >= 5; -- 110 rows
--only compare with [nvlaues - 1]
explain select * from t_analyze_mcv where id > 12;  -- 1 rows
explain select * from t_analyze_mcv where id >= 12;
regards,
--
ZizhuanLiu (X-MAN)
44973863@qq.com