mysql数据库索引的建立以及性能测试

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mysql数据库索引的建立以及性能测试

来源: 作者: 时间:2016-02-02 08:56 【

---------mysql学习(四)索引的建立-------- 今天突然开窍了,所以补充点索引方面的知识。 创建索引,这里仍然以数据较少的mytab表为例: 原数据为:mysql set names gbk;Query OK, 0 rows a

##---------mysql学习(四)索引的建立--------###
#今天突然开窍了,所以补充点索引方面的知识。
#创建索引,这里仍然以数据较少的mytab表为例:
#原数据为:
mysql> set names gbk;
Query OK, 0 rows affected (0.00 sec)

mysql> select * from mytab;
+----+--------+-----+--------+
| id | name | age | salary |
+----+--------+-----+--------+
| 1 | ?阿琼 | 23 | 1000 |
| 2 | 秋水虾 | 24 | 500 |
| 3 | 害人精 | 22 | 100 |
+----+--------+-----+--------+
3 rows in set (0.00 sec)
#alter table table_name add index index_name (column)==
#create index index_name on table_name(column);
#alter创建索引示例

mysql> alter table mytab add index mytab_name (name);
Query OK, 3 rows affected (0.15 sec)
Records: 3 Duplicates: 0 Warnings: 0

#create创建索引示例:
mysql> create index mytab_id on mytab (id);
Query OK, 3 rows affected (0.16 sec)
Records: 3 Duplicates: 0 Warnings: 0

#查看索引
mysql> show index from mytab;
+-------+------------+----------+--------------+-------------+-----------+------
-------+----------+--------+------+------------+---------+
| Table | Non_unique | Key_name | Seq_in_index | Column_name | Collation | Cardi
nality | Sub_part | Packed | Null | Index_type | Comment |
+-------+------------+----------+--------------+-------------+-----------+------
-------+----------+--------+------+------------+---------+
| mytab | 0 | PRIMARY | 1 | id | A |
3 | NULL | NULL | | BTREE | |
| mytab | 1 | mytab_id | 1 | id | A |
3 | NULL | NULL | | BTREE | |
+-------+------------+----------+--------------+-------------+-----------+------
-------+----------+--------+------+------------+---------+
2 rows in set (0.00 sec)

#创建unique索引
mysql> alter table mytab add unique (name);
Query OK, 3 rows affected (0.20 sec)
Records: 3 Duplicates: 0 Warnings: 0

#创建联合索引:
mysql> create index mytab_id_name on mytab (id,name);
Query OK, 3 rows affected (0.20 sec)
Records: 3 Duplicates: 0 Warnings: 0
mysql> show index from mytab;
+-------+------------+---------------+--------------+-------------+-----------+-
------------+----------+--------+------+------------+---------+
| Table | Non_unique | Key_name | Seq_in_index | Column_name | Collation |
Cardinality | Sub_part | Packed | Null | Index_type | Comment |
+-------+------------+---------------+--------------+-------------+-----------+-
------------+----------+--------+------+------------+---------+
| mytab | 0 | PRIMARY | 1 | id | A |
3 | NULL | NULL | | BTREE | |
| mytab | 0 | name | 1 | name | A |
3 | NULL | NULL | | BTREE | |
| mytab | 1 | mytab_name | 1 | name | A |
3 | NULL | NULL | | BTREE | |
| mytab | 1 | mytab_id_name | 1 | id | A |
3 | NULL | NULL | | BTREE | |
| mytab | 1 | mytab_id_name | 2 | name | A |
3 | NULL | NULL | | BTREE | |
+-------+------------+---------------+--------------+-------------+-----------+-
------------+----------+--------+------+------------+---------+
5 rows in set (0.00 sec)

#下面我们尝试一下删除索引,删除用drop
#drop index index_name on table_name==
#alter table table_name drop index index_name;

#drop示例:
mysql> drop index mytab_id on mytab;
Query OK, 3 rows affected (0.17 sec)
Records: 3 Duplicates: 0 Warnings: 0

#alter示例:
mysql> alter table mytab drop index mytab_id_name;
Query OK, 3 rows affected (0.17 sec)
Records: 3 Duplicates: 0 Warnings: 0

#现在发现由于数据数量较小,根本无法判断索引存在的价值。
#
#这里我打算向其中添加3000行数据,这里需要用到Java代码:
#
| 3001 | yiha_2997 | 22 | 5997 |
| 3002 | yiha_2998 | 22 | 5998 |
| 3003 | yiha_2999 | 22 | 5999 |
+------+-----------+-----+--------+
3003 rows in set (0.01 sec)
#######################java代码段##############################
public static void main(String[] args) {
Connection conn=DBConnection.getConnection();
try {
conn.setAutoCommit(false);
PreparedStatement state=conn.prepareStatement
("insert into mytab(name,age,salary) values (?,?,?)");
for(int i=0;i<3000;i++){
state.setString(1,"yiha_"+i );
state.setInt(2, 22);
state.setInt(3, 3000+i);
state.addBatch();
}
state.executeBatch();
conn.commit();
state.close();
} catch (SQLException e) {
e.printStackTrace();
}
}
######################连接connection######################
private static String url="jdbc:://" +
"localhost:3306/mydb?useUnicode=true&characterEncoding=UTF-8";
private static String driver="com.mysql.jdbc.Driver";
private static String name="root";
private static String pwd="root";
public static Connection getConnection(){
Connection conn;
try {
Class.forName(driver).newInstance();
conn = DriverManager.getConnection(url, name, pwd);
return conn;

###################################################################

##现在数据库中有3003条数据,我们看一下检索数据时间。
#如检索:
id NAME age salary
| 2894 | yiha_2890 | 22 | 5890 |
#id以及name为索引,但是age和salary为非索引

mysql> select * from mytab where id=2894;
+------+-----------+-----+--------+
| id | name | age | salary |
+------+-----------+-----+--------+
| 2894 | yiha_2890 | 22 | 5890 |
+------+-----------+-----+--------+
1 row in set (0.00 sec)

mysql> select * from mytab where salary=5890;
+------+-----------+-----+--------+
| id | name | age | salary |
+------+-----------+-----+--------+
| 2894 | yiha_2890 | 22 | 5890 |
+------+-----------+-----+--------+
1 row in set (0.00 sec)
#可以看出无差别,也许数据仍旧太少,现在将数据提升到30000;

mysql> select * from mytab where id=30000; #id为索引
+-------+------------+-----+--------+
| id | name | age | salary |
+-------+------------+-----+--------+
| 30000 | yiha_29996 | 23 | 32996 |
+-------+------------+-----+--------+
1 row in set (0.00 sec)

mysql> select * from mytab where salary=32996;#salary为非索引
+-------+------------+-----+--------+
| id | name | age | salary |
+-------+------------+-----+--------+
| 30000 | yiha_29996 | 23 | 32996 |
+-------+------------+-----+--------+
1 row in set (0.02 sec)
#由于name也是索引,所以这里试一下用name查找数据:

mysql> select * from mytab where name='yiha_29996';#name为索引
+-------+------------+-----+--------+
| id | name | age | salary |
+-------+------------+-----+--------+
| 30000 | yiha_29996 | 23 | 32996 |
+-------+------------+-----+--------+
1 row in set (0.00 sec)

##虽然在数据多次实验中能够看出索引的作用,但是并不是很明显。以上每一组所耗费时间都是
#个人寻找的出现次数最多的时间。

##个人感觉测试索引效果挺无聊的,索引的作用很多文章都只写了可以精确查找,至于索引如何
#运用貌似很少有相关的东西。数据库中的数据还可以随意扩大,个人感觉先这样吧。

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