database/mariadb
1
0
Fork 0
mirror of https://github.com/MariaDB/server.git synced 2025-07-29 05:21:33 +03:00
Code Activity

Added test cases for preceding test

Browse Source 
This includes all test changes from
"Changing all cost calculation to be given in milliseconds"
and forwards.

Some of the things that caused changes in the result files:

- As part of fixing tests, I added 'echo' to some comments to be able to
  easier find out where things where wrong.
- MATERIALIZED has now a higher cost compared to X than before. Because
  of this some MATERIALIZED types have changed to DEPENDEND SUBQUERY.
  - Some test cases that required MATERIALIZED to repeat a bug was
    changed by adding more rows to force MATERIALIZED to happen.
- 'Filtered' in SHOW EXPLAIN has in many case changed from 100.00 to
  something smaller. This is because now filtered also takes into
  account the smallest possible ref access and filters, even if they
  where not used. Another reason for 'Filtered' being smaller is that
  we now also take into account implicit filtering done for subqueries
  using FIRSTMATCH.
  (main.subselect_no_exists_to_in)
  This is caluculated in best_access_path() and stored in records_out.
- Table orders has changed because more accurate costs.
- 'index' and 'ALL' for small tables has changed to use 'range' or
   'ref' because of optimizer_scan_setup_cost.
- index can be changed to 'range' as 'range' optimizer assumes we don't
  have to read the blocks from disk that range optimizer has already read.
  This can be confusing in the case where there is no obvious where clause
  but instead there is a hidden 'key_column > NULL' added by the optimizer.
  (main.subselect_no_exists_to_in)
- Scan on primary clustered key does not report 'Using Index' anymore
  (It's a table scan, not an index scan).
- For derived tables, the number of rows is now 100 instead of 2,
  which can be seen in EXPLAIN.
- More tests have "Using index for group by" as the cost of this
  optimization is now more correct (lower).
- A primary key could be preferred for a normal key, even if it would
  access more rows, as it's faster to do 1 lokoup and 3 'index_next' on a
  clustered primary key than one lookup trough a secondary.
  (main.stat_tables_innodb)

Notes:

- There was a 4.7% more calls to best_extension_by_limited_search() in
  the main.greedy_optimizer test.  However examining the test results
  it looked that the plans where slightly better (eq_ref where more
  chained together) so I assume this is ok.
- I have verified a few test cases where there was notable/unexpected
  changes in the plan and in all cases the new optimizer plans where
  faster.  (main.greedy_optimizer and some others)
This commit is contained in:
Monty
2022-10-04 16:16:06 +03:00
committed by Sergei Petrunia
parent eb68023c8e
commit 727491b72a
290 changed files with 10927 additions and 8874 deletions
Download Patch File Download Diff File Expand all files Collapse all files

59
mysql-test/main/stat_tables_innodb.result
View File

@ -77,12 +77,12 @@ and r_name = 'AMERICA' and o_orderdate >= date '1995-01-01'
group by n_name
order by revenue desc;
id select_type table type possible_keys key key_len ref rows Extra
1 SIMPLE region ALL PRIMARY NULL NULL NULL 5 Using where; Using temporary; Using filesort
1 SIMPLE nation ref PRIMARY,i_n_regionkey i_n_regionkey 5 dbt3_s001.region.r_regionkey 5
1 SIMPLE supplier ref PRIMARY,i_s_nationkey i_s_nationkey 5 dbt3_s001.nation.n_nationkey 1 Using index
1 SIMPLE customer ref PRIMARY,i_c_nationkey i_c_nationkey 5 dbt3_s001.nation.n_nationkey 6 Using index
1 SIMPLE supplier index PRIMARY,i_s_nationkey i_s_nationkey 5 NULL 10 Using where; Using index; Using temporary; Using filesort
1 SIMPLE nation eq_ref PRIMARY,i_n_regionkey PRIMARY 4 dbt3_s001.supplier.s_nationkey 1 Using where
1 SIMPLE region eq_ref PRIMARY PRIMARY 4 dbt3_s001.nation.n_regionkey 1 Using where
1 SIMPLE customer ref PRIMARY,i_c_nationkey i_c_nationkey 5 dbt3_s001.supplier.s_nationkey 6 Using index
1 SIMPLE orders ref|filter PRIMARY,i_o_orderdate,i_o_custkey i_o_custkey|i_o_orderdate 5|4 dbt3_s001.customer.c_custkey 15 (14%) Using where; Using rowid filter
1 SIMPLE lineitem ref PRIMARY,i_l_suppkey,i_l_orderkey,i_l_orderkey_quantity i_l_suppkey 9 dbt3_s001.supplier.s_suppkey,dbt3_s001.orders.o_orderkey 1
1 SIMPLE lineitem ref PRIMARY,i_l_suppkey,i_l_orderkey,i_l_orderkey_quantity PRIMARY 4 dbt3_s001.orders.o_orderkey 4 Using where
select n_name, sum(l_extendedprice * (1 - l_discount)) as revenue
from customer, orders, lineitem, supplier, nation, region
where c_custkey = o_custkey and l_orderkey = o_orderkey
@ -208,12 +208,12 @@ and r_name = 'AMERICA' and o_orderdate >= date '1995-01-01'
group by n_name
order by revenue desc;
id select_type table type possible_keys key key_len ref rows Extra
1 SIMPLE region ALL PRIMARY NULL NULL NULL 5 Using where; Using temporary; Using filesort
1 SIMPLE nation ref PRIMARY,i_n_regionkey i_n_regionkey 5 dbt3_s001.region.r_regionkey 5
1 SIMPLE supplier ref PRIMARY,i_s_nationkey i_s_nationkey 5 dbt3_s001.nation.n_nationkey 1 Using index
1 SIMPLE customer ref PRIMARY,i_c_nationkey i_c_nationkey 5 dbt3_s001.nation.n_nationkey 6 Using index
1 SIMPLE supplier index PRIMARY,i_s_nationkey i_s_nationkey 5 NULL 10 Using where; Using index; Using temporary; Using filesort
1 SIMPLE nation eq_ref PRIMARY,i_n_regionkey PRIMARY 4 dbt3_s001.supplier.s_nationkey 1 Using where
1 SIMPLE region eq_ref PRIMARY PRIMARY 4 dbt3_s001.nation.n_regionkey 1 Using where
1 SIMPLE customer ref PRIMARY,i_c_nationkey i_c_nationkey 5 dbt3_s001.supplier.s_nationkey 6 Using index
1 SIMPLE orders ref|filter PRIMARY,i_o_orderdate,i_o_custkey i_o_custkey|i_o_orderdate 5|4 dbt3_s001.customer.c_custkey 15 (14%) Using where; Using rowid filter
1 SIMPLE lineitem ref PRIMARY,i_l_suppkey,i_l_orderkey,i_l_orderkey_quantity i_l_suppkey 9 dbt3_s001.supplier.s_suppkey,dbt3_s001.orders.o_orderkey 1
1 SIMPLE lineitem ref PRIMARY,i_l_suppkey,i_l_orderkey,i_l_orderkey_quantity PRIMARY 4 dbt3_s001.orders.o_orderkey 4 Using where
select n_name, sum(l_extendedprice * (1 - l_discount)) as revenue
from customer, orders, lineitem, supplier, nation, region
where c_custkey = o_custkey and l_orderkey = o_orderkey
@ -246,7 +246,7 @@ order by o_year;
id select_type table type possible_keys key key_len ref rows Extra
1 SIMPLE region ALL PRIMARY NULL NULL NULL 5 Using where; Using temporary; Using filesort
1 SIMPLE part ALL PRIMARY NULL NULL NULL 200 Using where; Using join buffer (flat, BNL join)
1 SIMPLE lineitem ref PRIMARY,i_l_suppkey_partkey,i_l_partkey,i_l_suppkey,i_l_orderkey,i_l_orderkey_quantity i_l_partkey 5 dbt3_s001.part.p_partkey 30 Using where
1 SIMPLE lineitem ref PRIMARY,i_l_suppkey_partkey,i_l_partkey,i_l_suppkey,i_l_orderkey,i_l_orderkey_quantity i_l_suppkey_partkey 5 dbt3_s001.part.p_partkey 30 Using index condition
1 SIMPLE supplier eq_ref PRIMARY,i_s_nationkey PRIMARY 4 dbt3_s001.lineitem.l_suppkey 1 Using where
1 SIMPLE n2 eq_ref PRIMARY PRIMARY 4 dbt3_s001.supplier.s_nationkey 1
1 SIMPLE orders eq_ref PRIMARY,i_o_orderdate,i_o_custkey PRIMARY 4 dbt3_s001.lineitem.l_orderkey 1 Using where
@ -289,6 +289,26 @@ id select_type table type possible_keys key key_len ref rows Extra
1 SIMPLE part eq_ref PRIMARY PRIMARY 4 dbt3_s001.partsupp.ps_partkey 1 Using where
1 SIMPLE lineitem ref PRIMARY,i_l_suppkey_partkey,i_l_partkey,i_l_suppkey,i_l_orderkey,i_l_orderkey_quantity i_l_suppkey_partkey 10 dbt3_s001.partsupp.ps_partkey,dbt3_s001.supplier.s_suppkey 8
1 SIMPLE orders eq_ref PRIMARY PRIMARY 4 dbt3_s001.lineitem.l_orderkey 1
EXPLAIN EXTENDED select nation, o_year, sum(amount) as sum_profit
from (select n_name as nation,
extract(year from o_orderdate) as o_year,
l_extendedprice * (1 - l_discount) - ps_supplycost * l_quantity as amount
from part, supplier, lineitem, partsupp, orders, nation
where s_suppkey = l_suppkey and ps_suppkey = l_suppkey
and ps_partkey = l_partkey and p_partkey = l_partkey
and o_orderkey = l_orderkey and s_nationkey = n_nationkey
and p_name like '%green%') as profit
group by nation, o_year
order by nation, o_year desc;
id select_type table type possible_keys key key_len ref rows filtered Extra
1 SIMPLE supplier index PRIMARY,i_s_nationkey i_s_nationkey 5 NULL 10 100.00 Using where; Using index; Using temporary; Using filesort
1 SIMPLE nation eq_ref PRIMARY PRIMARY 4 dbt3_s001.supplier.s_nationkey 1 100.00
1 SIMPLE partsupp ref PRIMARY,i_ps_partkey,i_ps_suppkey i_ps_suppkey 4 dbt3_s001.supplier.s_suppkey 70 100.00
1 SIMPLE part eq_ref PRIMARY PRIMARY 4 dbt3_s001.partsupp.ps_partkey 1 100.00 Using where
1 SIMPLE lineitem ref PRIMARY,i_l_suppkey_partkey,i_l_partkey,i_l_suppkey,i_l_orderkey,i_l_orderkey_quantity i_l_suppkey_partkey 10 dbt3_s001.partsupp.ps_partkey,dbt3_s001.supplier.s_suppkey 8 100.00
1 SIMPLE orders eq_ref PRIMARY PRIMARY 4 dbt3_s001.lineitem.l_orderkey 1 100.00
Warnings:
Note 1003 select `dbt3_s001`.`nation`.`n_name` AS `nation`,extract(year from `dbt3_s001`.`orders`.`o_orderDATE`) AS `o_year`,sum(`dbt3_s001`.`lineitem`.`l_extendedprice` * (1 - `dbt3_s001`.`lineitem`.`l_discount`) - `dbt3_s001`.`partsupp`.`ps_supplycost` * `dbt3_s001`.`lineitem`.`l_quantity`) AS `sum_profit` from `dbt3_s001`.`part` join `dbt3_s001`.`supplier` join `dbt3_s001`.`lineitem` join `dbt3_s001`.`partsupp` join `dbt3_s001`.`orders` join `dbt3_s001`.`nation` where `dbt3_s001`.`partsupp`.`ps_suppkey` = `dbt3_s001`.`supplier`.`s_suppkey` and `dbt3_s001`.`lineitem`.`l_suppkey` = `dbt3_s001`.`supplier`.`s_suppkey` and `dbt3_s001`.`part`.`p_partkey` = `dbt3_s001`.`partsupp`.`ps_partkey` and `dbt3_s001`.`lineitem`.`l_partkey` = `dbt3_s001`.`partsupp`.`ps_partkey` and `dbt3_s001`.`orders`.`o_orderkey` = `dbt3_s001`.`lineitem`.`l_orderkey` and `dbt3_s001`.`nation`.`n_nationkey` = `dbt3_s001`.`supplier`.`s_nationkey` and `dbt3_s001`.`part`.`p_name` like '%green%' group by `dbt3_s001`.`nation`.`n_name`,extract(year from `dbt3_s001`.`orders`.`o_orderDATE`) desc order by `dbt3_s001`.`nation`.`n_name`,extract(year from `dbt3_s001`.`orders`.`o_orderDATE`) desc
select nation, o_year, sum(amount) as sum_profit
from (select n_name as nation,
extract(year from o_orderdate) as o_year,
@ -623,8 +643,8 @@ set @@use_stat_tables= PREFERABLY;
explain
SELECT * FROM INFORMATION_SCHEMA.PROFILING, mysql.user;
id select_type table type possible_keys key key_len ref rows Extra
1 SIMPLE PROFILING ALL NULL NULL NULL NULL NULL
1 SIMPLE global_priv ALL NULL NULL NULL NULL 5 Using join buffer (flat, BNL join)
1 SIMPLE global_priv ALL NULL NULL NULL NULL 5
1 SIMPLE PROFILING ALL NULL NULL NULL NULL NULL Using join buffer (flat, BNL join)
set @@optimizer_use_condition_selectivity=@save_optimizer_use_condition_selectivity;
set use_stat_tables=@save_use_stat_tables;
#
@ -884,7 +904,6 @@ set histogram_size=0;
#
create table t1 (a int, b int);
insert into t1(a,b) values (1,2),(1,3),(1,4),(1,5),(2,6),(2,7),(3,8),(3,9),(3,9),(4,10);
insert into t1 select seq,seq from seq_10_to_100;
set use_stat_tables= preferably_for_queries;
#
# with use_stat_tables= PREFERABLY_FOR_QUERIES
@ -898,7 +917,7 @@ db_name table_name column_name min_value max_value nulls_ratio avg_length avg_fr
analyze
select * from t1 where a = 1 and b=3;
id select_type table type possible_keys key key_len ref rows r_rows filtered r_filtered Extra
1 SIMPLE t1 ALL NULL NULL NULL NULL 101 101.00 100.00 0.99 Using where
1 SIMPLE t1 ALL NULL NULL NULL NULL 10 10.00 100.00 10.00 Using where
#
# with use_stat_tables= PREFERABLY_FOR_QUERIES
# analyze table t1 will collect statistics if we use PERSISTENT
@ -910,12 +929,12 @@ test.t1 analyze status Engine-independent statistics collected
test.t1 analyze status OK
select * from mysql.column_stats;
db_name table_name column_name min_value max_value nulls_ratio avg_length avg_frequency hist_size hist_type histogram
test t1 a 1 100 0.0000 4.0000 1.0632 0 NULL NULL
test t1 a 1 4 0.0000 4.0000 2.5000 0 NULL NULL
# filtered shows that we used the data from stat tables
analyze
select * from t1 where a = 1 and b=3;
id select_type table type possible_keys key key_len ref rows r_rows filtered r_filtered Extra
1 SIMPLE t1 ALL NULL NULL NULL NULL 101 101.00 1.05 0.99 Using where
1 SIMPLE t1 ALL NULL NULL NULL NULL 10 10.00 25.00 10.00 Using where
#
# with use_stat_tables= PREFERABLY
# analyze table t1 will collect statistics
@ -927,13 +946,13 @@ test.t1 analyze status Engine-independent statistics collected
test.t1 analyze status OK
select * from mysql.column_stats;
db_name table_name column_name min_value max_value nulls_ratio avg_length avg_frequency hist_size hist_type histogram
test t1 a 1 100 0.0000 4.0000 1.0632 0 NULL NULL
test t1 b 2 100 0.0000 4.0000 1.0202 0 NULL NULL
test t1 a 1 4 0.0000 4.0000 2.5000 0 NULL NULL
test t1 b 2 10 0.0000 4.0000 1.1111 0 NULL NULL
# filtered shows that we used the data from stat tables
analyze
select * from t1 where a=1 and b=3;
id select_type table type possible_keys key key_len ref rows r_rows filtered r_filtered Extra
1 SIMPLE t1 ALL NULL NULL NULL NULL 101 101.00 0.99 0.99 Using where
1 SIMPLE t1 ALL NULL NULL NULL NULL 10 10.00 2.78 10.00 Using where
drop table t1;
set @@global.histogram_size=@save_histogram_size;
# End of 10.4 tests