mysql explain type connection type example
For obtaining the MySQL execution plan, we can view it through the explain method. The explain method seems simple, but it actually contains a lot of content, especially the type in the output result. Type column. Understanding these different types is very important for our SQL optimization. This article only describes the type column in the explian output results and gives its demonstration.
For a full description of explian output, please refer to: MySQL EXPLAIN SQL output information description
1. The value of the type column in the EXPLAIN statement
type：
连接类型
system 表只有一行 const 表最多只有一行匹配，通用用于主键或者唯一索引比较时
eq_ref 每次与之前的表合并行都只在该表读取一行，这是除了system，const之外最好的一种，
特点是使用=，而且索引的所有部分都参与join且索引是主键或非空唯一键的索引
ref 如果每次只匹配少数行，那就是比较好的一种，使用=或<=>，可以是左覆盖索引或非主键或非唯一键
fulltext 全文搜索
ref_or_null 与ref类似，但包括NULL
index_merge 表示出现了索引合并优化(包括交集，并集以及交集之间的并集)，但不包括跨表和全文索引。
这个比较复杂，目前的理解是合并单表的范围索引扫描（如果成本估算比普通的range要更优的话）
unique_subquery 在in子查询中，就是value in (select...)把形如“select unique_key_column”的子查询替换。
PS：所以不一定in子句中使用子查询就是低效的！
index_subquery 同上，但把形如”select non_unique_key_column“的子查询替换
range 常数值的范围 index a.当查询是索引覆盖的，即所有数据均可从索引树获取的时候（Extra中有Using Index）；
b.以索引顺序从索引中查找数据行的全表扫描（无 Using Index）；
c.如果Extra中Using Index与Using Where同时出现的话，则是利用索引查找键值的意思；
d.如单独出现，则是用读索引来代替读行，但不用于查找
all 全表扫描2. Connection Type part example
1、all-- 环境描述
(root@localhost) [sakila]> show variables like 'version';
+---------------+--------+
| Variable_name | Value |
+---------------+--------+
| version | 5.6.26 |
+---------------+--------+
MySQL采取全表遍历的方式来返回数据行，等同于Oracle的full table scan
(root@localhost) [sakila]> explain select count(description) from film;
+----+-------------+-------+------+---------------+------+---------+------+------+-------+
| id | select_type | table | type | possible_keys | key | key_len | ref | rows | Extra |
+----+-------------+-------+------+---------------+------+---------+------+------+-------+
| 1 | SIMPLE | film | ALL | NULL | NULL | NULL | NULL | 1000 | NULL |
+----+-------------+-------+------+---------------+------+---------+------+------+-------+
2、index
MySQL采取索引全扫描的方式来返回数据行，等同于Oracle的full index scan
(root@localhost) [sakila]> explain select title from film \G
*************************** 1. row ***************************
id: 1
select_type: SIMPLE
table: film
type: indexpossible_keys: NULL
key: idx_title
key_len: 767
ref: NULL
rows: 1000
Extra: Using index1 row in set (0.00 sec)
3、 range
索引范围扫描，对索引的扫描开始于某一点，返回匹配值域的行，常见于between、<、>等的查询
等同于Oracle的index range scan
(root@localhost) [sakila]> explain select * from payment where customer_id>300 and customer_id<400\G
*************************** 1. row ***************************
id: 1
select_type: SIMPLE
table: payment
type: rangepossible_keys: idx_fk_customer_id
key: idx_fk_customer_id
key_len: 2
ref: NULL
rows: 2637
Extra: Using where1 row in set (0.00 sec)
(root@localhost) [sakila]> explain select * from payment where customer_id in (200,300,400)\G
*************************** 1. row ***************************
id: 1
select_type: SIMPLE
table: payment
type: rangepossible_keys: idx_fk_customer_id
key: idx_fk_customer_id
key_len: 2
ref: NULL
rows: 86
Extra: Using index condition1 row in set (0.00 sec)
4、ref
非唯一性索引扫描或者，返回匹配某个单独值的所有行。常见于使用非唯一索引即唯一索引的非唯一前缀进行的查找
(root@localhost) [sakila]> explain select * from payment where customer_id=305\G
*************************** 1. row ***************************
id: 1
select_type: SIMPLE
table: payment
type: refpossible_keys: idx_fk_customer_id
key: idx_fk_customer_id
key_len: 2
ref: const
rows: 25
Extra: 1 row in set (0.00 sec)
idx_fk_customer_id为表payment上的外键索引，且存在多个不不唯一的值，如下查询
(root@localhost) [sakila]> select customer_id,count(*) from payment group by customer_id
-> limit 2;
+-------------+----------+
| customer_id | count(*) |+-------------+----------+
| 1 | 32 || 2 | 27 |
+-------------+----------+-- 下面是非唯一前缀索引使用ref的示例
(root@localhost) [sakila]> create index idx_fisrt_last_name on customer(first_name,last_name);
Query OK, 599 rows affected (0.09 sec)
Records: 599 Duplicates: 0 Warnings: 0(root@localhost) [sakila]> select first_name,count(*) from customer group by first_name
-> having count(*)>1 limit 2;
+------------+----------+| first_name | count(*) |
+------------+----------+| JAMIE | 2 || JESSIE | 2 |
+------------+----------+2 rows in set (0.00 sec)
(root@localhost) [sakila]> explain select first_name from customer where first_name='JESSIE'\G
*************************** 1. row ***************************
id: 1 select_type: SIMPLE
table: customer
type: refpossible_keys: idx_fisrt_last_name
key: idx_fisrt_last_name
key_len: 137
ref: const
rows: 2
Extra: Using where; Using index1 row in set (0.00 sec)
(root@localhost) [sakila]> alter table customer drop index idx_fisrt_last_name;
Query OK, 599 rows affected (0.03 sec)
Records: 599 Duplicates: 0 Warnings: 0--下面演示出现在join是ref的示例
(root@localhost) [sakila]> explain select b.*,a.* from payment a inner join -> customer b on a.customer_id=b.customer_id\G
*************************** 1. row ***************************
id: 1
select_type: SIMPLE
table: b
type: ALLpossible_keys: PRIMARY
key: NULL
key_len: NULL
ref: NULL
rows: 599
Extra: NULL
*************************** 2. row ***************************
id: 1
select_type: SIMPLE
table: a
type: refpossible_keys: idx_fk_customer_id
key: idx_fk_customer_id
key_len: 2
ref: sakila.b.customer_id
rows: 13
Extra: NULL2 rows in set (0.01 sec)
5、eq_ref
类似于ref，其差别在于使用的索引为唯一索引，对于每个索引键值，表中只有一条记录与之匹配。
多见于主键扫描或者索引唯一扫描。
(root@localhost) [sakila]> explain select * from film a join film_text b
-> on a.film_id=b.film_id;
+----+-------------+-------+--------+---------------+---------+---------+------------------+------+-------------+
| id | select_type | table | type | possible_keys | key | key_len | ref | rows | Extra |
+----+-------------+-------+--------+---------------+---------+---------+------------------+------+-------------+
| 1 | SIMPLE | b | ALL | PRIMARY | NULL | NULL | NULL | 1000 | NULL |
| 1 | SIMPLE | a | eq_ref | PRIMARY | PRIMARY | 2 | sakila.b.film_id | 1 | Using where |
+----+-------------+-------+--------+---------------+---------+---------+------------------+------+-------------+
(root@localhost) [sakila]> explain select title from film where film_id=5;
+----+-------------+-------+-------+---------------+---------+---------+-------+------+-------+
| id | select_type | table | type | possible_keys | key | key_len | ref | rows | Extra |
+----+-------------+-------+-------+---------------+---------+---------+-------+------+-------+| 1 | SIMPLE
| film | const | PRIMAR | PRIMARY | 2 | const | 1 | NULL |
+----+-------------+-------+-------+---------------+---------+---------+-------+------+-------+6、const、system：
当MySQL对查询某部分进行优化，这个匹配的行的其他列值可以转换为一个常量来处理。
如将主键或者唯一索引置于where列表中，MySQL就能将该查询转换为一个常量
(root@localhost) [sakila]> create table t1(id int,ename varchar(20) unique);
Query OK, 0 rows affected (0.05 sec)
(root@localhost) [sakila]> insert into t1 values(1,'robin'),(2,'jack'),(3,'henry');
Query OK, 3 rows affected (0.00 sec)
Records: 3 Duplicates: 0 Warnings: 0
(root@localhost) [sakila]> explain select * from (select * from t1 where ename='robin')x;
+----+-------------+------------+--------+---------------+-------+---------+-------+------+-------+
| id | select_type | table | type | possible_keys | key | key_len | ref | rows | Extra |
+----+-------------+------------+--------+---------------+-------+---------+-------+------+-------+
| 1 | PRIMARY | <derived2> | system | NULL | NULL | NULL | NULL | 1 | NULL |
| 2 | DERIVED | t1 | const | ename | ename | 23 | const | 1 | NULL |
+----+-------------+------------+--------+---------------+-------+---------+-------+------+-------+
2 rows in set (0.00 sec)
7、type=NULL
MySQL不用访问表或者索引就可以直接得到结果
(root@localhost) [sakila]> explain select sysdate();+----+-------------+-------+------+---------------+------+---------+------+------+----------------+
| id | select_type | table | type | possible_keys | key | key_len | ref | rows | Extra |
+----+-------------+-------+------+---------------+------+---------+------+------+----------------+
| 1 | SIMPLE | NULL | NULL | NULL | NULL | NULL | NULL | NULL | No tables used |
+----+-------------+-------+------+---------------+------+---------+------+------+----------------+
1 row in set (0.00 sec)For obtaining the MySQL execution plan, we can view it through the explain method. The explain method seems simple, but actually contains a lot of content. Especially the type column in the output result. Understanding these different types is very important for our SQL optimization. This article only describes the type column in the explian output results and gives its demonstration.
For a full description of explian output, please refer to: MySQL EXPLAIN SQL output information description
1. The value of the type column in the EXPLAIN statement
type：
连接类型
system 表只有一行 const 表最多只有一行匹配，通用用于主键或者唯一索引比较时
eq_ref 每次与之前的表合并行都只在该表读取一行，这是除了system，const之外最好的一种，
特点是使用=，而且索引的所有部分都参与join且索引是主键或非空唯一键的索引
ref 如果每次只匹配少数行，那就是比较好的一种，使用=或<=>，可以是左覆盖索引或非主键或非唯一键
fulltext 全文搜索
ref_or_null 与ref类似，但包括NULL
index_merge 表示出现了索引合并优化(包括交集，并集以及交集之间的并集)，但不包括跨表和全文索引。
这个比较复杂，目前的理解是合并单表的范围索引扫描（如果成本估算比普通的range要更优的话）
unique_subquery 在in子查询中，就是value in (select...)把形如“select unique_key_column”的子查询替换。
PS：所以不一定in子句中使用子查询就是低效的！
index_subquery 同上，但把形如”select non_unique_key_column“的子查询替换
range 常数值的范围 index a.当查询是索引覆盖的，即所有数据均可从索引树获取的时候（Extra中有Using Index）；
b.以索引顺序从索引中查找数据行的全表扫描（无 Using Index）；
c.如果Extra中Using Index与Using Where同时出现的话，则是利用索引查找键值的意思；
d.如单独出现，则是用读索引来代替读行，但不用于查找
all 全表扫描2. Connection Type part example
1、all-- 环境描述
(root@localhost) [sakila]> show variables like 'version';
+---------------+--------+
| Variable_name | Value |
+---------------+--------+
| version | 5.6.26 |
+---------------+--------+MySQL采取全表遍历的方式来返回数据行，等同于Oracle的full table scan
(root@localhost) [sakila]> explain select count(description) from film;
+----+-------------+-------+------+---------------+------+---------+------+------+-------+
| id | select_type | table | type | possible_keys | key | key_len | ref | rows | Extra |
+----+-------------+-------+------+---------------+------+---------+------+------+-------+
| 1 | SIMPLE | film | ALL | NULL | NULL | NULL | NULL | 1000 | NULL |
+----+-------------+-------+------+---------------+------+---------+------+------+-------+
2、index
MySQL采取索引全扫描的方式来返回数据行，等同于Oracle的full index scan
(root@localhost) [sakila]> explain select title from film \G
*************************** 1. row ***************************
id: 1
select_type: SIMPLE
table: film
type: indexpossible_keys: NULL
key: idx_title
key_len: 767
ref: NULL
rows: 1000
Extra: Using index1 row in set (0.00 sec)
3、 range
索引范围扫描，对索引的扫描开始于某一点，返回匹配值域的行，常见于between、<、>等的查询
等同于Oracle的index range scan
(root@localhost) [sakila]> explain select * from payment where customer_id>300 and customer_id<400\G
*************************** 1. row ***************************
id: 1
select_type: SIMPLE
table: payment
type: rangepossible_keys: idx_fk_customer_id
key: idx_fk_customer_id
key_len: 2
ref: NULL
rows: 2637
Extra: Using where1 row in set (0.00 sec)
(root@localhost) [sakila]> explain select * from payment where customer_id in (200,300,400)\G
*************************** 1. row ***************************
id: 1
select_type: SIMPLE
table: payment
type: rangepossible_keys: idx_fk_customer_id
key: idx_fk_customer_id
key_len: 2
ref: NULL
rows: 86
Extra: Using index condition1 row in set (0.00 sec)
4、ref
非唯一性索引扫描或者，返回匹配某个单独值的所有行。常见于使用非唯一索引即唯一索引的非唯一前缀进行的查找
(root@localhost) [sakila]> explain select * from payment where customer_id=305\G
*************************** 1. row ***************************
id: 1
select_type: SIMPLE
table: payment
type: refpossible_keys: idx_fk_customer_id
key: idx_fk_customer_id
key_len: 2
ref: const
rows: 25
Extra: 1 row in set (0.00 sec)
idx_fk_customer_id为表payment上的外键索引，且存在多个不不唯一的值，如下查询
(root@localhost) [sakila]> select customer_id,count(*) from payment group by customer_id
-> limit 2;
+-------------+----------+
| customer_id | count(*) |+-------------+----------+
| 1 | 32 || 2 | 27 |
+-------------+----------+-- 下面是非唯一前缀索引使用ref的示例
(root@localhost) [sakila]> create index idx_fisrt_last_name on customer(first_name,last_name);
Query OK, 599 rows affected (0.09 sec)
Records: 599 Duplicates: 0 Warnings: 0(root@localhost) [sakila]> select first_name,count(*) from customer group by first_name
-> having count(*)>1 limit 2;
+------------+----------+| first_name | count(*) |
+------------+----------+| JAMIE | 2 || JESSIE | 2 |
+------------+----------+2 rows in set (0.00 sec)
(root@localhost) [sakila]> explain select first_name from customer where first_name='JESSIE'\G
*************************** 1. row ***************************
id: 1
select_type: SIMPLE
table: customer
type: refpossible_keys: idx_fisrt_last_name
key: idx_fisrt_last_name
key_len: 137
ref: const
rows: 2
Extra: Using where; Using index1 row in set (0.00 sec)
(root@localhost) [sakila]> alter table customer drop index idx_fisrt_last_name;
Query OK, 599 rows affected (0.03 sec)
Records: 599 Duplicates: 0 Warnings: 0--下面演示出现在join是ref的示例
(root@localhost) [sakila]> explain select b.*,a.* from payment a inner join
-> customer b on a.customer_id=b.customer_id\G
*************************** 1. row ***************************
id: 1
select_type:
SIMPLE
table: b
type: ALLpossible_keys: PRIMARY
key: NULL
key_len: NULL
ref: NULL
rows: 599
Extra: NULL
*************************** 2. row ***************************
id: 1
select_type: SIMPLE
table: a
type: refpossible_keys: idx_fk_customer_id
key: idx_fk_customer_id
key_len: 2
ref: sakila.b.customer_id
rows: 13
Extra: NULL2 rows in set (0.01 sec)
5、eq_ref
类似于ref，其差别在于使用的索引为唯一索引，对于每个索引键值，表中只有一条记录与之匹配。
多见于主键扫描或者索引唯一扫描。
(root@localhost) [sakila]> explain select * from film a join film_text b
-> on a.film_id=b.film_id;
+----+-------------+-------+--------+---------------+---------+---------+------------------+------+-------------+
| id | select_type | table | type | possible_keys | key | key_len | ref | rows | Extra |
+----+-------------+-------+--------+---------------+---------+---------+------------------+------+-------------+
| 1 | SIMPLE | b | ALL | PRIMARY | NULL | NULL | NULL | 1000 | NULL |
| 1 | SIMPLE | a | eq_ref | PRIMARY | PRIMARY | 2 | sakila.b.film_id | 1 | Using where |
+----+-------------+-------+--------+---------------+---------+---------+------------------+------+-------------+
(root@localhost) [sakila]> explain select title from film where film_id=5;
+----+-------------+-------+-------+---------------+---------+---------+-------+------+-------+
| id | select_type | table | type | possible_keys | key | key_len | ref | rows | Extra |
+----+-------------+-------+-------+---------------+---------+---------+-------+------+-------+
| 1 | SIMPLE | film | const | PRIMARY | PRIMARY | 2 | const | 1 | NULL |
+----+-------------+-------+-------+---------------+---------+---------+-------+------+-------+
6、const、system：
当MySQL对查询某部分进行优化，这个匹配的行的其他列值可以转换为一个常量来处理。
如将主键或者唯一索引置于where列表中，MySQL就能将该查询转换为一个常量
(root@localhost) [sakila]> create table t1(id int,ename varchar(20) unique);
Query OK, 0 rows affected (0.05 sec)
(root@localhost) [sakila]> insert into t1 values(1,'robin'),(2,'jack'),(3,'henry');
Query OK, 3 rows affected (0.00 sec)
Records: 3 Duplicates: 0 Warnings: 0
(root@localhost) [sakila]> explain select * from (select * from t1 where ename='robin')x;
+----+-------------+------------+--------+---------------+-------+---------+-------+------+-------+
| id | select_type | table | type | possible_keys | key | key_len | ref | rows | Extra |
+----+-------------+------------+--------+---------------+-------+---------+-------+------+-------+|
1 | PRIMARY | <derived2> | system | NULL | NULL | NULL | NULL | 1 | NULL ||
2 | DERIVED | t1 | const | ename | ename | 2
3 | const | 1 | NULL |
+----+-------------+------------+--------+---------------+-------+---------+-------+------+-------+
2 rows in set (0.00 sec)
7、type=NULL
MySQL不用访问表或者索引就可以直接得到结果
(root@localhost) [sakila]> explain select sysdate();
+----+-------------+-------+------+---------------+------+---------+------+------+----------------+
| id | select_type | table | type | possible_keys | key | key_len | ref | rows | Extra |
+----+-------------+-------+------+---------------+------+---------+------+------+----------------+
| 1 | SIMPLE | NULL | NULL | NULL | NULL | NULL | NULL | NULL | No tables used |
+----+-------------+-------+------+---------------+------+---------+------+------+----------------+
1 row in set (0.00 sec)The above is the content of mysql explain type connection type example. For more related content, please pay attention to the PHP Chinese website (m.sbmmt.com)!
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PHP plays the role of connector and brain center in intelligent customer service, responsible for connecting front-end input, database storage and external AI services; 2. When implementing it, it is necessary to build a multi-layer architecture: the front-end receives user messages, the PHP back-end preprocesses and routes requests, first matches the local knowledge base, and misses, call external AI services such as OpenAI or Dialogflow to obtain intelligent reply; 3. Session management is written to MySQL and other databases by PHP to ensure context continuity; 4. Integrated AI services need to use Guzzle to send HTTP requests, safely store APIKeys, and do a good job of error handling and response analysis; 5. Database design must include sessions, messages, knowledge bases, and user tables, reasonably build indexes, ensure security and performance, and support robot memory
How to make PHP container support automatic construction? Continuously integrated CI configuration method of PHP environment
Jul 25, 2025 pm 08:54 PM
To enable PHP containers to support automatic construction, the core lies in configuring the continuous integration (CI) process. 1. Use Dockerfile to define the PHP environment, including basic image, extension installation, dependency management and permission settings; 2. Configure CI/CD tools such as GitLabCI, and define the build, test and deployment stages through the .gitlab-ci.yml file to achieve automatic construction, testing and deployment; 3. Integrate test frameworks such as PHPUnit to ensure that tests are automatically run after code changes; 4. Use automated deployment strategies such as Kubernetes to define deployment configuration through the deployment.yaml file; 5. Optimize Dockerfile and adopt multi-stage construction
