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MySQL performance optimization--Explain usage introduction

零下一度Original: 2017-05-05 16:31:241206browse

Introduction

MySQL provides an EXPLAIN command, which can analyze the SELECT statement and output the detailed information of the SELECT execution for Targeted optimization for developers.
The usage of the EXPLAIN command is very simple. Just add Explain before the SELECT statement, for example:

EXPLAIN SELECT * from user_info WHERE  id < 300;

Preparation

In order to facilitate the subsequent demonstration of the use of EXPLAIN , First we need to create two tables for testing and add corresponding data:

CREATE TABLE `user_info` (
  `id`   BIGINT(20)  NOT NULL AUTO_INCREMENT,
  `name` VARCHAR(50) NOT NULL DEFAULT &#39;&#39;,
  `age`  INT(11)              DEFAULT NULL,
  PRIMARY KEY (`id`),
  KEY `name_index` (`name`)
)
  ENGINE = InnoDB
  DEFAULT CHARSET = utf8

INSERT INTO user_info (name, age) VALUES (&#39;xys&#39;, 20);
INSERT INTO user_info (name, age) VALUES (&#39;a&#39;, 21);
INSERT INTO user_info (name, age) VALUES (&#39;b&#39;, 23);
INSERT INTO user_info (name, age) VALUES (&#39;c&#39;, 50);
INSERT INTO user_info (name, age) VALUES (&#39;d&#39;, 15);
INSERT INTO user_info (name, age) VALUES (&#39;e&#39;, 20);
INSERT INTO user_info (name, age) VALUES (&#39;f&#39;, 21);
INSERT INTO user_info (name, age) VALUES (&#39;g&#39;, 23);
INSERT INTO user_info (name, age) VALUES (&#39;h&#39;, 50);
INSERT INTO user_info (name, age) VALUES (&#39;i&#39;, 15);

CREATE TABLE `order_info` (
  `id`           BIGINT(20)  NOT NULL AUTO_INCREMENT,
  `user_id`      BIGINT(20)           DEFAULT NULL,
  `product_name` VARCHAR(50) NOT NULL DEFAULT &#39;&#39;,
  `productor`    VARCHAR(30)          DEFAULT NULL,
  PRIMARY KEY (`id`),
  KEY `user_product_detail_index` (`user_id`, `product_name`, `productor`)
)
  ENGINE = InnoDB
  DEFAULT CHARSET = utf8

INSERT INTO order_info (user_id, product_name, productor) VALUES (1, &#39;p1&#39;, &#39;WHH&#39;);
INSERT INTO order_info (user_id, product_name, productor) VALUES (1, &#39;p2&#39;, &#39;WL&#39;);
INSERT INTO order_info (user_id, product_name, productor) VALUES (1, &#39;p1&#39;, &#39;DX&#39;);
INSERT INTO order_info (user_id, product_name, productor) VALUES (2, &#39;p1&#39;, &#39;WHH&#39;);
INSERT INTO order_info (user_id, product_name, productor) VALUES (2, &#39;p5&#39;, &#39;WL&#39;);
INSERT INTO order_info (user_id, product_name, productor) VALUES (3, &#39;p3&#39;, &#39;MA&#39;);
INSERT INTO order_info (user_id, product_name, productor) VALUES (4, &#39;p1&#39;, &#39;WHH&#39;);
INSERT INTO order_info (user_id, product_name, productor) VALUES (6, &#39;p1&#39;, &#39;WHH&#39;);
INSERT INTO order_info (user_id, product_name, productor) VALUES (9, &#39;p8&#39;, &#39;TE&#39;);

EXPLAIN output format

The output content of the EXPLAIN command is roughly as follows:

mysql> explain select * from user_info where id = 2\G
*************************** 1. row ***************************
           id: 1
  select_type: SIMPLE
        table: user_info
   partitions: NULL
         type: const
possible_keys: PRIMARY
          key: PRIMARY
      key_len: 8
          ref: const
         rows: 1
     filtered: 100.00
        Extra: NULL
1 row in set, 1 warning (0.00 sec)

Each The meaning of the columns is as follows:

id: The identifier of the SELECT query. Each SELECT is automatically assigned a unique identifier.
select_type : SELECT query type.
table: Which table is queried
partitions: Matching partitions
type: join type
possible_keys: possible indexes used in this query
key: exact used in this query The index reached.
ref: Which field or constant is used with key
rows: Displays how many rows were scanned by this query. This is an estimate.
filtered: Indicates the percentage of data filtered by this query condition
extra: Additional information

Next let’s take a look at the more important fields.

select_type

select_type represents the type of query, which Commonly used values are:

SIMPLE, which indicates that this query does not contain UNION queries or subqueries.
PRIMARY, which indicates that this query is the outermost Layer query
UNION, indicating that this query is the second or subsequent query of UNION
DEPENDENT UNION, the second of UNION or subsequent query statements, depending on the outer query
UNION RESULT, the result of UNION
SUBQUERY, the first subquery SELECT
DEPENDENT SUBQUERY: The first SELECT in the subquery depends on the outer query. That is, the subquery depends on the result of the outer query.

The most common query type should be SIMPLE. For example, when our query has no subquery or UNION query, it is usually the SIMPLE type, for example:

mysql> explain select * from user_info where id = 2\G
*************************** 1. row ***************************
           id: 1
  select_type: SIMPLE
        table: user_info
   partitions: NULL
         type: const
possible_keys: PRIMARY
          key: PRIMARY
      key_len: 8
          ref: const
         rows: 1
     filtered: 100.00
        Extra: NULL
1 row in set, 1 warning (0.00 sec)

If we use a UNION query, the results output by EXPLAIN are similar to the following:

mysql> EXPLAIN (SELECT * FROM user_info  WHERE id IN (1, 2, 3))
    -> UNION
    -> (SELECT * FROM user_info WHERE id IN (3, 4, 5));
+----+--------------+------------+------------+-------+---------------+---------+---------+------+------+----------+-----------------+
| id | select_type  | table      | partitions | type  | possible_keys | key     | key_len | ref  | rows | filtered | Extra           |
+----+--------------+------------+------------+-------+---------------+---------+---------+------+------+----------+-----------------+
|  1 | PRIMARY      | user_info  | NULL       | range | PRIMARY       | PRIMARY | 8       | NULL |    3 |   100.00 | Using where     |
|  2 | UNION        | user_info  | NULL       | range | PRIMARY       | PRIMARY | 8       | NULL |    3 |   100.00 | Using where     |
| NULL | UNION RESULT | <union1,2> | NULL       | ALL   | NULL          | NULL    | NULL    | NULL | NULL |     NULL | Using temporary |
+----+--------------+------------+------------+-------+---------------+---------+---------+------+------+----------+-----------------+
3 rows in set, 1 warning (0.00 sec)

table

Indicates the table or derived table involved in the query

type The

type field is more important. It provides an important basis for judging whether the query is efficient. Through the type field, we judge that this query is a full table Scan or index scan etc.

type Common types

type Common values are:

system : There is only one piece of data in the table. This type is a special const type.

const: Equivalent query scan for primary key or unique index, only one row is returned at most Data. const query is very fast because it only reads it once.
For example, the query below uses the primary key index, so type is of const type .

mysql> explain select * from user_info where id = 2\G
*************************** 1. row ***************************
        id: 1
select_type: SIMPLE
     table: user_info
partitions: NULL
      type: const
possible_keys: PRIMARY
       key: PRIMARY
   key_len: 8
       ref: const
      rows: 1
  filtered: 100.00
     Extra: NULL
1 row in set, 1 warning (0.00 sec)

eq_ref: This type usually appears in multi-table join queries, which means that each result in the previous table can only match one row of results in the latter table. And the query The comparison operation is usually =, which has higher query efficiency. For example:

mysql> EXPLAIN SELECT * FROM user_info, order_info WHERE user_info.id = order_info.user_id\G
*************************** 1. row ***************************
        id: 1
select_type: SIMPLE
     table: order_info
partitions: NULL
      type: index
possible_keys: user_product_detail_index
       key: user_product_detail_index
   key_len: 314
       ref: NULL
      rows: 9
  filtered: 100.00
     Extra: Using where; Using index
*************************** 2. row ***************************
        id: 1
select_type: SIMPLE
     table: user_info
partitions: NULL
      type: eq_ref
possible_keys: PRIMARY
       key: PRIMARY
   key_len: 8
       ref: test.order_info.user_id
      rows: 1
  filtered: 100.00
     Extra: NULL
2 rows in set, 1 warning (0.00 sec)

ref: This type usually appears in join queries of multiple tables, and is aimed at non-unique or Non-primary key index, or query using leftmost prefix rule index.
For example, in the following example, a ref type of query is used:

mysql> EXPLAIN SELECT * FROM user_info, order_info WHERE user_info.id = order_info.user_id AND order_info.user_id = 5\G
*************************** 1. row ***************************
        id: 1
select_type: SIMPLE
     table: user_info
partitions: NULL
      type: const
possible_keys: PRIMARY
       key: PRIMARY
   key_len: 8
       ref: const
      rows: 1
  filtered: 100.00
     Extra: NULL
*************************** 2. row ***************************
        id: 1
select_type: SIMPLE
     table: order_info
partitions: NULL
      type: ref
possible_keys: user_product_detail_index
       key: user_product_detail_index
   key_len: 9
       ref: const
      rows: 1
  filtered: 100.00
     Extra: Using index
2 rows in set, 1 warning (0.01 sec)

range: Indicates the use of index range query to obtain some data records in the table through the index field range. This type usually appears in =, a8093152e673feb7aba1828c43532094, >, >=, 0bd892685d35f3a0a1bfb7508ab9ca59, BETWEEN, IN() operation.
When type is range, then EXPLAIN outputs ref The field is NULL, and the key_len field is the longest of the indexes used in this query.
For example, the following example is a range query:
```
mysql> EXPLAIN SELECT *
 ->         FROM user_info
 ->         WHERE id BETWEEN 2 AND 8 \G
*************************** 1. row ***************************
        id: 1
select_type: SIMPLE
     table: user_info
partitions: NULL
      type: range
possible_keys: PRIMARY
       key: PRIMARY
   key_len: 8
       ref: NULL
      rows: 7
  filtered: 100.00
     Extra: Using where
1 row in set, 1 warning (0.00 sec)
```
index: Represents a full index scan, which is similar to the ALL type, except that the ALL type is a full table scan, while the index type only scans all indexes without scanning the data.
index Type usually appears when: the data to be queried can be obtained directly in the index tree without scanning the data. When this is the case, the Extra field will display Using index.

For example:

mysql> EXPLAIN SELECT name FROM  user_info \G
*************************** 1. row ***************************
           id: 1
  select_type: SIMPLE
        table: user_info
   partitions: NULL
         type: index
possible_keys: NULL
          key: name_index
      key_len: 152
          ref: NULL
         rows: 10
     filtered: 100.00
        Extra: Using index
1 row in set, 1 warning (0.00 sec)

In the above example, the name field we query happens to be an index, so we can directly obtain the data from the index to meet the query needs, without Query the data in the table. So in this case, the value of type is index, and the value of Extra is Using index.

ALL: 表示全表扫描, 这个类型的查询是性能最差的查询之一. 通常来说, 我们的查询不应该出现 ALL 类型的查询, 因为这样的查询在数据量大的情况下, 对数据库的性能是巨大的灾难. 如一个查询是 ALL 类型查询, 那么一般来说可以对相应的字段添加索引来避免.
下面是一个全表扫描的例子, 可以看到, 在全表扫描时, possible_keys 和 key 字段都是 NULL, 表示没有使用到索引, 并且 rows 十分巨大, 因此整个查询效率是十分低下的.
```
mysql> EXPLAIN SELECT age FROM  user_info WHERE age = 20 \G
*************************** 1. row ***************************
        id: 1
select_type: SIMPLE
     table: user_info
partitions: NULL
      type: ALL
possible_keys: NULL
       key: NULL
   key_len: NULL
       ref: NULL
      rows: 10
  filtered: 10.00
     Extra: Using where
1 row in set, 1 warning (0.00 sec)
```
type 类型的性能比较

通常来说, 不同的 type 类型的性能关系如下:
ALL < index < range ~ index_merge < ref < eq_ref < const < system
ALL 类型因为是全表扫描, 因此在相同的查询条件下, 它是速度最慢的.
而 index 类型的查询虽然不是全表扫描, 但是它扫描了所有的索引, 因此比 ALL 类型的稍快.
后面的几种类型都是利用了索引来查询数据, 因此可以过滤部分或大部分数据, 因此查询效率就比较高了.

possible_keys

possible_keys 表示 MySQL 在查询时, 能够使用到的索引. 注意, 即使有些索引在 possible_keys 中出现, 但是并不表示此索引会真正地被 MySQL 使用到. MySQL 在查询时具体使用了哪些索引, 由 key 字段决定.

key

此字段是 MySQL 在当前查询时所真正使用到的索引.

key_len

表示查询优化器使用了索引的字节数. 这个字段可以评估组合索引是否完全被使用, 或只有最左部分字段被使用到.
key_len 的计算规则如下:

字符串

char(n): n 字节长度
varchar(n): 如果是 utf8 编码, 则是 3 n + 2字节; 如果是 utf8mb4 编码, 则是 4 n + 2 字节.

数值类型:

TINYINT: 1字节
SMALLINT: 2字节
MEDIUMINT: 3字节
INT: 4字节
BIGINT: 8字节

时间类型

DATE: 3字节
TIMESTAMP: 4字节
DATETIME: 8字节

字段属性: NULL 属性占用一个字节. 如果一个字段是 NOT NULL 的, 则没有此属性.

我们来举两个简单的栗子:

mysql> EXPLAIN SELECT * FROM order_info WHERE user_id < 3 AND product_name = &#39;p1&#39; AND productor = &#39;WHH&#39; \G
*************************** 1. row ***************************
           id: 1
  select_type: SIMPLE
        table: order_info
   partitions: NULL
         type: range
possible_keys: user_product_detail_index
          key: user_product_detail_index
      key_len: 9
          ref: NULL
         rows: 5
     filtered: 11.11
        Extra: Using where; Using index
1 row in set, 1 warning (0.00 sec)

上面的例子是从表 order_info 中查询指定的内容, 而我们从此表的建表语句中可以知道, 表 order_info 有一个联合索引:

KEY `user_product_detail_index` (`user_id`, `product_name`, `productor`)

不过此查询语句 WHERE user_id < 3 AND product_name = 'p1' AND productor = 'WHH' 中, 因为先进行 user_id 的范围查询, 而根据 最左前缀匹配 原则, 当遇到范围查询时, 就停止索引的匹配, 因此实际上我们使用到的索引的字段只有 user_id, 因此在 EXPLAIN 中, 显示的 key_len 为 9. 因为 user_id 字段是 BIGINT, 占用 8 字节, 而 NULL 属性占用一个字节, 因此总共是 9 个字节. 若我们将user_id 字段改为 BIGINT(20) NOT NULL DEFAULT '0', 则 key_length 应该是8.

上面因为 最左前缀匹配 原则, 我们的查询仅仅使用到了联合索引的 user_id 字段, 因此效率不算高.

接下来我们来看一下下一个例子:

mysql> EXPLAIN SELECT * FROM order_info WHERE user_id = 1 AND product_name = &#39;p1&#39; \G;
*************************** 1. row ***************************
           id: 1
  select_type: SIMPLE
        table: order_info
   partitions: NULL
         type: ref
possible_keys: user_product_detail_index
          key: user_product_detail_index
      key_len: 161
          ref: const,const
         rows: 2
     filtered: 100.00
        Extra: Using index
1 row in set, 1 warning (0.00 sec)

这次的查询中, 我们没有使用到范围查询, key_len 的值为 161. 为什么呢? 因为我们的查询条件 WHERE user_id = 1 AND product_name = 'p1' 中, 仅仅使用到了联合索引中的前两个字段, 因此 keyLen(user_id) + keyLen(product_name) = 9 + 50 * 3 + 2 = 161

rows

rows 也是一个重要的字段. MySQL 查询优化器根据统计信息, 估算 SQL 要查找到结果集需要扫描读取的数据行数.
这个值非常直观显示 SQL 的效率好坏, 原则上 rows 越少越好.

Extra

EXplain 中的很多额外的信息会在 Extra 字段显示, 常见的有以下几种内容:

Using filesort
当 Extra 中有 Using filesort 时, 表示 MySQL 需额外的排序操作, 不能通过索引顺序达到排序效果. 一般有 Using filesort, 都建议优化去掉, 因为这样的查询 CPU 资源消耗大.
例如下面的例子:

mysql> EXPLAIN SELECT * FROM order_info ORDER BY product_name \G
*************************** 1. row ***************************
        id: 1
select_type: SIMPLE
     table: order_info
partitions: NULL
      type: index
possible_keys: NULL
       key: user_product_detail_index
   key_len: 253
       ref: NULL
      rows: 9
  filtered: 100.00
     Extra: Using index; Using filesort
1 row in set, 1 warning (0.00 sec)

我们的索引是

KEY `user_product_detail_index` (`user_id`, `product_name`, `productor`)

但是上面的查询中根据 product_name 来排序, 因此不能使用索引进行优化, 进而会产生 Using filesort.
如果我们将排序依据改为 ORDER BY user_id, product_name, 那么就不会出现 Using filesort 了. 例如:

mysql> EXPLAIN SELECT * FROM order_info ORDER BY user_id, product_name \G
*************************** 1. row ***************************
           id: 1
  select_type: SIMPLE
        table: order_info
   partitions: NULL
         type: index
possible_keys: NULL
          key: user_product_detail_index
      key_len: 253
          ref: NULL
         rows: 9
     filtered: 100.00
        Extra: Using index
1 row in set, 1 warning (0.00 sec)

Using index
"覆盖索引扫描", 表示查询在索引树中就可查找所需数据, 不用扫描表数据文件, 往往说明性能不错
Using temporary
查询有使用临时表, 一般出现于排序, 分组和多表 join 的情况, 查询效率不高, 建议优化.

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