Subquery Cache

The goal of the subquery cache is to optimize the evaluation of correlated subqueries by storing results together with correlation parameters in a cache and avoiding re-execution of the subquery in cases where the result is already in the cache.

Administration

The cache is on by default. One can switch it off using the optimizer_switch subquery_cache setting, like so:

SET optimizer_switch='subquery_cache=off';

The efficiency of the subquery cache is visible in 2 statistical variables:

Subquery_cache_hit - Global counter for all subquery cache hits.
Subquery_cache_miss - Global counter for all subquery cache misses.

The session variables tmp_table_size and max_heap_table_size influence the size of in-memory temporary tables in the table used for caching. It cannot grow more than the minimum of the above variables values (see the Implementation section for details).

Visibility

Your usage of the cache is visible in EXTENDED EXPLAIN output (warnings) as "<expr_cache><//list of parameters//>(//cached expression//)". For example:

EXPLAIN EXTENDED SELECT * FROM t1 WHERE a IN (SELECT b FROM t2);
+----+--------------------+-------+------+---------------+------+---------+------+------+----------+-------------+
| id | select_type        | table | type | possible_keys | key  | key_len | ref  | rows | filtered | Extra       |
+----+--------------------+-------+------+---------------+------+---------+------+------+----------+-------------+
|  1 | PRIMARY            | t1    | ALL  | NULL          | NULL | NULL    | NULL |    2 |   100.00 | Using where |
|  2 | DEPENDENT SUBQUERY | t2    | ALL  | NULL          | NULL | NULL    | NULL |    2 |   100.00 | Using where |
+----+--------------------+-------+------+---------------+------+---------+------+------+----------+-------------+
2 rows in set, 1 warning (0.00 sec)

SHOW WARNINGS;
+-------+------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| Level | Code | Message                                                                                                                                                                                                    |
+-------+------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| Note  | 1003 | SELECT `test`.`t1`.`a` AS `a` from `test`.`t1` WHERE <expr_cache><`test`.`t1`.`a`>(<in_optimizer>(`test`.`t1`.`a`,<exists>(SELECT 1 FROM `test`.`t2` WHERE (<cache>(`test`.`t1`.`a`) = `test`.`t2`.`b`)))) |
+-------+------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
1 row in set (0.00 sec)

In the example above the presence of "<expr_cache><`test`.`t1`.`a`>(...)" is how you know you are using the subquery cache.

Implementation

Every subquery cache creates a temporary table where the results and all parameters are stored. It has a unique index over all parameters. First the cache is created in a MEMORY table (if doing this is impossible the cache becomes disabled for that expression). When the table grows up to the minimum of tmp_table_size and max_heap_table_size, the hit rate will be checked:

if the hit rate is really small (<0.2) the cache will be disabled.
if the hit rate is moderate (<0.7) the table will be cleaned (all records deleted) to keep the table in memory
if the hit rate is high the table will be converted to a disk table (for 5.3.0 it can only be converted to a disk table).

hit rate = hit / (hit + miss)

Performance Impact

Here are some examples that show the performance impact of the subquery cache (these tests were made on a 2.53 GHz Intel Core 2 Duo MacBook Pro with dbt-3 scale 1 data set).

example	cache on	cache off	gain	hit	miss	hit rate
1	1.01sec	1 hour 31 min 43.33sec	5445x	149975	25	99.98%
2	0.21sec	1.41sec	6.71x	6285	220	96.6%
3	2.54sec	2.55sec	1.00044x	151	461	24.67%
4	1.87sec	1.95sec	0.96x	0	23026	0%

Example 1

Dataset from DBT-3 benchmark, a query to find customers with balance near top in their nation:

select count(*) from customer 
where 
   c_acctbal > 0.8 * (select max(c_acctbal) 
                      from customer C 
                      where C.c_nationkey=customer.c_nationkey
                      group by c_nationkey);

Example 2

DBT-3 benchmark, Query #17

select sum(l_extendedprice) / 7.0 as avg_yearly 
from lineitem, part 
where 
  p_partkey = l_partkey and 
  p_brand = 'Brand#42' and p_container = 'JUMBO BAG' and 
  l_quantity < (select 0.2 * avg(l_quantity) from lineitem 
                where l_partkey = p_partkey);

Example 3

DBT-3 benchmark, Query #2

select
        s_acctbal, s_name, n_name, p_partkey, p_mfgr, s_address, s_phone, s_comment
from
        part, supplier, partsupp, nation, region
where
        p_partkey = ps_partkey and s_suppkey = ps_suppkey and p_size = 33
        and p_type like '%STEEL' and s_nationkey = n_nationkey
        and n_regionkey = r_regionkey and r_name = 'MIDDLE EAST'
        and ps_supplycost = (
                select
                        min(ps_supplycost)
                from
                        partsupp, supplier, nation, region
                where
                        p_partkey = ps_partkey and s_suppkey = ps_suppkey
                        and s_nationkey = n_nationkey and n_regionkey = r_regionkey
                        and r_name = 'MIDDLE EAST'
        )
order by
        s_acctbal desc, n_name, s_name, p_partkey;

Example 4

DBT-3 benchmark, Query #20

select
        s_name, s_address
from
        supplier, nation
where
        s_suppkey in (
                select
                        distinct (ps_suppkey)
                from
                        partsupp, part
                where
                        ps_partkey=p_partkey
                        and p_name like 'indian%'
                        and ps_availqty > (
                                select
                                        0.5 * sum(l_quantity)
                                from
                                        lineitem
                                where
                                        l_partkey = ps_partkey
                                        and l_suppkey = ps_suppkey
                                        and l_shipdate >= '1995-01-01'
                                        and l_shipdate < date_ADD('1995-01-01',interval 1 year)
                                )
        )
        and s_nationkey = n_nationkey and n_name = 'JAPAN'
order by
        s_name;