ICP简介
Index Condition Pushdown (ICP) is an optimization for the case where MySQL retrieves rows from a table using an index. Without ICP, the storage engine traverses the index to locate rows in the base table and returns them to the MySQL server which evaluates the WHEREcondition for the rows. With ICP enabled, and if parts of the WHERE condition can be evaluated by using only fields from the index, the MySQL server pushes this part of the WHERE condition down to the storage engine. The storage engine then evaluates the pushed index condition by using the index entry and only if this is satisfied is the row read from the table. ICP can reduce the number of times the storage engine must access the base table and the number of times the MySQL server must access the storage engine.
也就说：利用索引（二级索引）来过滤一部分where条件
测试
导入数据库
wget https://launchpad.net/test-db/employees-db-1/1.0.6/+download/employees_db-full-1.0.6.tar.bz2 tar jxf employees_db-full-1.0.6.tar.bz2 cd employees_db mysql -uroot -p < employees.sql
表结构
mysql> show create table employees \G
*************************** 1. row ***************************
Table: employees
Create Table: CREATE TABLE `employees` (
`emp_no` int(11) NOT NULL,
`birth_date` date NOT NULL,
`first_name` varchar(14) NOT NULL,
`last_name` varchar(16) NOT NULL,
`gender` enum(‘M‘,‘F‘) NOT NULL,
`hire_date` date NOT NULL,
PRIMARY KEY (`emp_no`),
KEY `index_bh` (`birth_date`,`hire_date`)
) ENGINE=InnoDB DEFAULT CHARSET=utf8
1 row in set (0.00 sec)
一些表数据
mysql> select @@optimizer_switch like ‘%index_condition_pushdown%‘ \G *************************** 1. row *************************** @@optimizer_switch like ‘%index_condition_pushdown%‘: 1 1 row in set (0.00 sec) mysql> select @@optimizer_switch like ‘%index_condition_pushdown%‘ \G *************************** 1. row *************************** @@optimizer_switch like ‘%index_condition_pushdown%‘: 1 1 row in set (0.00 sec) mysql> select @@query_cache_type; +--------------------+ | @@query_cache_type | +--------------------+ | OFF | +--------------------+ 1 row in set (0.01 sec) mysql> select count(*) from employees; +----------+ | count(*) | +----------+ | 300024 | +----------+ 1 row in set (0.17 sec) mysql> set profiling=1; Query OK, 0 rows affected, 1 warning (0.00 sec)
建立索引
alter table employees add index index_bh (`birth_date`,`hire_date`);
查询分析
mysql> explain select * from employees where birth_date between ‘1955-01-01‘ and ‘1955-12-31‘ and datediff(hire_date,birth_date)>12300 and first_name like ‘S%b%‘; +----+-------------+-----------+-------+---------------+----------+---------+------+-------+-------------+ | id | select_type | table | type | possible_keys | key | key_len | ref | rows | Extra | +----+-------------+-----------+-------+---------------+----------+---------+------+-------+-------------+ | 1 | SIMPLE | employees | range | index_bh | index_bh | 3 | NULL | 46318 | Using where | +----+-------------+-----------+-------+---------------+----------+---------+------+-------+-------------+ 1 row in set (0.00 sec) mysql> SET optimizer_switch=‘index_condition_pushdown=on‘; Query OK, 0 rows affected (0.00 sec) mysql> explain select * from employees where birth_date between ‘1955-01-01‘ and ‘1955-12-31‘ and datediff(hire_date,birth_date)>12300 and first_name like ‘S%b%‘; +----+-------------+-----------+-------+---------------+----------+---------+------+-------+------------------------------------+ | id | select_type | table | type | possible_keys | key | key_len | ref | rows | Extra | +----+-------------+-----------+-------+---------------+----------+---------+------+-------+------------------------------------+ | 1 | SIMPLE | employees | range | index_bh | index_bh | 3 | NULL | 46318 | Using index condition; Using where | +----+-------------+-----------+-------+---------------+----------+---------+------+-------+------------------------------------+ 1 row in set (0.01 sec)
执行查询
mysql> show profiles; +----------+------------+------------------------------------------------------------------------------------------------------------------------------------------------------+ | Query_ID | Duration | Query | +----------+------------+------------------------------------------------------------------------------------------------------------------------------------------------------+ | 1 | 0.00278025 | desc employees | | 2 | 0.00049775 | show create table employees | | 3 | 0.07444550 | select * from employees where birth_date between ‘1955-01-01‘ and ‘1955-12-31‘ and datediff(hire_date,birth_date)>12300 and first_name like ‘S%b%‘ | | 4 | 0.00027500 | SET optimizer_switch=‘index_condition_pushdown=off‘ | | 5 | 0.12347025 | select * from employees where birth_date between ‘1955-01-01‘ and ‘1955-12-31‘ and datediff(hire_date,birth_date)>12300 and first_name like ‘S%b%‘ | +----------+------------+------------------------------------------------------------------------------------------------------------------------------------------------------+
从结果可以看出来开启ICP之后确实快不少
启用ICP之后，可以用索引来筛选 datediff(hire_date,birth_date)>12300 记录，不需要读出整条记录
ICP原理
如下图所示（图来自MariaDB）
1、优化器没有使用ICP时
- 在存储引擎层，首先读取索引元组（index tuple），然后使用（index tuple）在基表中（base table）定位和读取整行数据
- 到服务器层，匹配where条件，如果该行数据满足where条件则使用，否则丢弃
- 指针向下一行移动，重复以上过程
2、使用ICP的时候
- 如果where条件的一部分能够通过使用索引中的字段进行过滤，那么服务器层将把这部分where条件Pushdown到存储引擎层
- 到存储引擎层，从索引中读取索引元组（index tuple），使用索引元组进行判断，如果没有满足where条件，则处理下一条索引元组（index tuple），只有当索引元组满足条件的时候，才会去基表中读取数据
ICP的使用条件
1、只能用于二级索引(secondary index)
2、explain显示的执行计划中type值（join 类型）为range、 ref、 eq_ref或者ref_or_null。且查询需要访问表的整行数据，即不能直接通过二级索引的元组数据获得查询结果(索引覆盖)
3、ICP可以用于MyISAM和InnnoDB存储引擎，不支持分区表（5.7将会解决这个问题）
4、ICP的加速效果取决于在存储引擎内通过ICP筛选掉的数据的比例
参考文章
https://mariadb.com/kb/en/index-condition-pushdown/
http://dev.mysql.com/doc/refman/5.6/en/index-condition-pushdown-optimization.html
http://blog.codinglabs.org/articles/index-condition-pushdown.html