Execution effect:
##COUNT(*)MySQL performs count(*) In order to optimize, count(*) directly scans the primary key index record, does not take out all fields, and directly accumulates them by row.
COUNT(1)The InnoDB engine traverses the entire table, but does not take a value. The server layer puts a number "1" in each row returned. Accumulate by row.
COUNT(field)If this "field" is defined as NOT NULL, then the InnoDB engine will read this field from the record line by line, the server layer The judgment cannot be NULL and is accumulated row by row; if the "field" definition allows NULL, then the InnoDB engine will read this field from the record row by row, and then take out the value and judge it again. If it is not NULL, it will be accumulated.
The environment used for testing in this article:
[root@zhyno1 ~]# cat /etc/system-release CentOS Linux release 7.9.2009 (Core) [root@zhyno1 ~]# uname -a Linux zhyno1 3.10.0-1160.62.1.el7.x86_64 #1 SMP Tue Apr 5 16:57:59 UTC 2022 x86_64 x86_64 x86_64 GNU/Linux
The test database uses (storage engine Using InnoDB, other parameters are default):
(Mon Jul 25 09:41:39 2022)[root@GreatSQL][(none)]>select version(); +-----------+ | version() | +-----------+ | 8.0.25-16 | +-----------+ 1 row in set (0.00 sec)
Experiment start:
#首先我们创建一个实验表
CREATE TABLE test_count (
`id` int(10) NOT NULL AUTO_INCREMENT PRIMARY KEY,
`name` varchar(20) NOT NULL,
`salary` int(1) NOT NULL,
KEY `idx_salary` (`salary`)
) ENGINE=InnoDB DEFAULT CHARSET=utf8;
#插入1000W条数据
DELIMITER //
CREATE PROCEDURE insert_1000w()
BEGIN
DECLARE i INT;
SET i=1;
WHILE i<=10000000 DO
INSERT INTO test_count(name,salary) VALUES('KAiTO',1);
SET i=i+1;
END WHILE;
END//
DELIMITER ;
#执行存储过程
call insert_1000w();Next, let’s experiment separately:
COUNT(1)It took 4.19 seconds
(Sat Jul 23 22:56:04 2022)[root@GreatSQL][test]>select count(1) from test_count; +----------+ | count(1) | +----------+ | 10000000 | +----------+ 1 row in set (4.19 sec)
COUNT(*)It took 4.16 seconds
(Sat Jul 23 22:57:41 2022)[root@GreatSQL][test]>select count(*) from test_count; +----------+ | count(*) | +----------+ | 10000000 | +----------+ 1 row in set (4.16 sec)
COUNT (Field)It took 4.23 seconds
(Sat Jul 23 22:58:56 2022)[root@GreatSQL][test]>select count(id) from test_count; +-----------+ | count(id) | +-----------+ | 10000000 | +-----------+ 1 row in set (4.23 sec)
COUNT(*)
(Sat Jul 23 22:59:16 2022)[root@GreatSQL][test]>explain select count(*) from test_count; +----+-------------+------------+------------+-------+---------------+------------+---------+------+---------+----------+-------------+ | id | select_type | table | partitions | type | possible_keys | key | key_len | ref | rows | filtered | Extra | +----+-------------+------------+------------+-------+---------------+------------+---------+------+---------+----------+-------------+ | 1 | SIMPLE | test_count | NULL | index | NULL | idx_salary | 4 | NULL | 9980612 | 100.00 | Using index | +----+-------------+------------+------------+-------+---------------+------------+---------+------+---------+----------+-------------+ 1 row in set, 1 warning (0.01 sec) (Sat Jul 23 22:59:48 2022)[root@GreatSQL][test]>show warnings; +-------+------+-----------------------------------------------------------------------+ | Level | Code | Message | +-------+------+-----------------------------------------------------------------------+ | Note | 1003 | /* select#1 */ select count(0) AS `count(*)` from `test`.`test_count` | +-------+------+-----------------------------------------------------------------------+ 1 row in set (0.00 sec)
COUNT(1)
(Sat Jul 23 23:12:45 2022)[root@GreatSQL][test]>explain select count(1) from test_count; +----+-------------+------------+------------+-------+---------------+------------+---------+------+---------+----------+-------------+ | id | select_type | table | partitions | type | possible_keys | key | key_len | ref | rows | filtered | Extra | +----+-------------+------------+------------+-------+---------------+------------+---------+------+---------+----------+-------------+ | 1 | SIMPLE | test_count | NULL | index | NULL | idx_salary | 4 | NULL | 9980612 | 100.00 | Using index | +----+-------------+------------+------------+-------+---------------+------------+---------+------+---------+----------+-------------+ 1 row in set, 1 warning (0.00 sec) (Sat Jul 23 23:13:02 2022)[root@GreatSQL][test]>show warnings; +-------+------+-----------------------------------------------------------------------+ | Level | Code | Message | +-------+------+-----------------------------------------------------------------------+ | Note | 1003 | /* select#1 */ select count(1) AS `count(1)` from `test`.`test_count` | +-------+------+-----------------------------------------------------------------------+ 1 row in set (0.00 sec)
COUNT(field)
(Sat Jul 23 23:13:14 2022)[root@GreatSQL][test]>explain select count(id) from test_count; +----+-------------+------------+------------+-------+---------------+------------+---------+------+---------+----------+-------------+ | id | select_type | table | partitions | type | possible_keys | key | key_len | ref | rows | filtered | Extra | +----+-------------+------------+------------+-------+---------------+------------+---------+------+---------+----------+-------------+ | 1 | SIMPLE | test_count | NULL | index | NULL | idx_salary | 4 | NULL | 9980612 | 100.00 | Using index | +----+-------------+------------+------------+-------+---------------+------------+---------+------+---------+----------+-------------+ 1 row in set, 1 warning (0.00 sec) (Sat Jul 23 23:13:29 2022)[root@GreatSQL][test]>show warnings; +-------+------+-----------------------------------------------------------------------------------------------+ | Level | Code | Message | +-------+------+-----------------------------------------------------------------------------------------------+ | Note | 1003 | /* select#1 */ select count(`test`.`test_count`.`id`) AS `count(id)` from `test`.`test_count` | +-------+------+-----------------------------------------------------------------------------------------------+ 1 row in set (0.00 sec)
It should be noted that if there is a non-primary key field in COUNT
(Tue Jul 26 14:01:57 2022)[root@GreatSQL][test]>explain select count(name) from test_count where id <100 ; +----+-------------+------------+------------+-------+---------------+---------+---------+------+------+----------+-------------+ | id | select_type | table | partitions | type | possible_keys | key | key_len | ref | rows | filtered | Extra | +----+-------------+------------+------------+-------+---------------+---------+---------+------+------+----------+-------------+ | 1 | SIMPLE | test_count | NULL | range | PRIMARY | PRIMARY | 4 | NULL | 99 | 100.00 | Using where | +----+-------------+------------+------------+-------+---------------+---------+---------+------+------+----------+-------------+ 1 row in set, 1 warning (0.00 sec)
COUNT(*) and COUNT(1) is the fastest, followed by COUNT(id).
count(*) was rewritten by the MySQL query optimizer into count(0), and the idx_salary index was selected.
count(1) and count(id) both select the idx_salary index.
COUNT(*)=COUNT(1)>COUNT(id)
MySQL's official documentation also says:
InnoDB handles SELECT COUNT(*) and SELECT COUNT(1) operations in the same way. There is no performance difference
So it means that forTranslation: InnoDB handles SELECT COUNT(*) and SELECT COUNT(1) operations in the same way. There is no performance difference
COUNT(1) or COUNT(*), the optimization of MySQL is actually exactly the same, there is no There is no performance difference.
COUNT(*), because this is the standard syntax for counting rows defined by MySQL92.
information_schema database . There is a TABLES table in this library. The main fields of this table are:
TABLE_SCHEMA: Database name
TABLE_NAME：Table name
ENGINE：Storage engine used
TABLES_ROWS: Number of records
count(*)?
We use TABLES_ROWS to query the number of table records:
(Sat Jul 23 23:15:14 2022)[root@GreatSQL][test]>SELECT TABLE_ROWS
-> FROM INFORMATION_SCHEMA.TABLES
-> WHERE TABLE_NAME = 'test_count';
+------------+
| TABLE_ROWS |
+------------+
| 9980612 |
+------------+
1 row in set (0.03 sec)count(*) There are different implementation methods. This article mainly introduces the execution process on the InnoDB engine.
count(*) function first reads from the memory. Get the data in the table into the memory buffer, and then scan the entire table to get the number of row records. To put it simply, it is a full table scan. A loop solves the problem. Within the loop: First read a row, and then decide whether the row is included in count. The loop counts row by row.
count(*), this number will be returned directly, which is very efficient.
Despite this, InnoDB has optimized the count(*) operation. InnoDB is an index-organized table. The leaf nodes of the primary key index tree are data, while the leaf nodes of the ordinary index tree are primary key values. Therefore, the ordinary index tree is much smaller than the primary key index tree. For operations like count(*), the results obtained by traversing any index tree are logically the same. Therefore, the MySQL optimizer will find the smallest tree to traverse.
It should be noted that What we discuss in this article is count(*) without filter conditions. If the WHERE condition is added, the MyISAM engine The table cannot return so quickly.
COUNT(*)=COUNT(1)>COUNT(id)
COUNT(*), COUNT(field) and COUNT(1)
is SQL92 The defined standard syntax for counting the number of rows, so MySQL has made a lot of optimizations for it. MyISAM will directly record the total number of rows in the table for COUNT(*) query, while InnoDB will scan the table When choosing the smallest index to reduce costs. The premise of these optimizations is that there are no WHERE and GROUP conditional queries.
and COUNT(1), and the efficiency is the same, but COUNT(field)Needs to judge whether the field is NULL, so the efficiency will be lower.
is the standard syntax for counting rows defined by SQL92 and is highly efficient, it is recommended to use COUNT(* )The number of rows in the query table.
, during the table creation process, it is necessary to establish a high-performance index according to business needs, and also pay attention to Avoid unnecessary indexing.
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