下面我来详细讲解一下“Sharding-JDBC自动实现MySQL读写分离的示例代码”的完整攻略。
Sharding-JDBC是一个基于JDBC的轻量级数据库中间件,能够完成Java应用程序中的数据分片,水平分片和读写分离等功能。
在项目的pom.xml
文件中添加以下依赖:
<dependency>
<groupId>org.apache.shardingsphere</groupId>
<artifactId>sharding-jdbc-core</artifactId>
<version>4.1.1</version>
</dependency>
<dependency>
<groupId>org.apache.shardingsphere</groupId>
<artifactId>sharding-jdbc-core-spring-boot-starter</artifactId>
<version>4.1.1</version>
</dependency>
<dependency>
<groupId>org.apache.shardingsphere</groupId>
<artifactId>sharding-jdbc-orchestration-spring-boot-starter</artifactId>
<version>4.1.1</version>
</dependency>
在application.yml
文件中配置数据源,如下所示:
spring:
datasource:
names: ds0,ds1
ds0:
url: jdbc:mysql://127.0.0.1:3306/test0?useSSL=false&useUnicode=true&characterEncoding=utf8&allowMultiQueries=true&serverTimezone=UTC
username: root
password: root
ds1:
url: jdbc:mysql://127.0.0.1:3306/test1?useSSL=false&useUnicode=true&characterEncoding=utf8&allowMultiQueries=true&serverTimezone=UTC
username: root
password: root
在application.yml
文件中添加Sharding-JDBC配置,如下所示:
spring:
shardingsphere:
datasource:
names: myshard
myshard:
type: com.zaxxer.hikari.HikariDataSource
driver-class-name: com.mysql.jdbc.Driver
jdbc-url: jdbc:mysql://localhost:3306
username: root
password:
hikari:
minimum-idle: 5
maximum-pool-size: 50
sharding:
tables:
user:
actual-data-nodes: myshard.user_${0..1}
table-strategy:
inline:
sharding-column: id
algorithm-expression: user_${id % 2}
key-generator:
column: id
type: SNOWFLAKE
order:
actual-data-nodes: myshard.order_${0..1}
table-strategy:
inline:
sharding-column: id
algorithm-expression: order_${id % 2}
key-generator:
column: id
type: SNOWFLAKE
default-database-strategy:
inline:
sharding-column: ds_name
algorithm-expression: ds$->{id % 2}
default-table-strategy:
none:
添加UserMapper和UserService,如下所示:
@Mapper
public interface UserMapper {
@Insert("INSERT INTO user (name, age, gender) VALUES (#{name},#{age},#{gender})")
int insert(User user);
@Select("SELECT * from user WHERE id = #{id}")
User getById(long id);
}
@Service
public class UserService {
@Autowired
UserMapper userMapper;
@Transactional
public void addUser(User user){
userMapper.insert(user);
}
public User getUserById(long id){
return userMapper.getById(id);
}
}
在测试类中添加以下测试代码,测试Sharding-JDBC是否能够实现MySQL读写分离:
@SpringBootTest
class ShardingJdbcApplicationTests {
@Autowired
UserService userService;
@Test
void contextLoads() {
User user = new User();
user.setName("Tom");
user.setAge(18);
user.setGender("male");
userService.addUser(user);
User user1 = userService.getUserById(1);
System.out.println(user1);
}
}
以上代码就是Sharding-JDBC自动实现MySQL读写分离的示例代码。通过配置Sharding-JDBC实现数据分片和读写分离,从而提高数据库的读写性能和吞吐量。
在Sharding-JDBC自动实现MySQL读写分离的示例代码中,添加以下测试代码,测试Sharding-JDBC是否能够实现MySQL读写分离:
@SpringBootTest
class ShardingJdbcApplicationTests {
@Autowired
UserService userService;
@Test
void contextLoads() {
User user = new User();
user.setName("Tom");
user.setAge(18);
user.setGender("male");
userService.addUser(user);
User user1 = userService.getUserById(1);
System.out.println(user1);
}
}
以上代码执行后,会向数据库插入一条记录,并从数据库中查询出这条记录。
在Sharding-JDBC自动实现MySQL读写分离的示例代码中,修改application.yml
文件中的分片策略,如下所示:
spring:
shardingsphere:
datasource:
names: myshard
myshard:
...
hikari:
minimum-idle: 5
maximum-pool-size: 50
sharding:
tables:
user:
actual-data-nodes: myshard.user${0..1}.user_${0..1}
table-strategy:
complex:
sharding-columns: id,gender
algorithm-class-name: com.example.demo.algorithm.ModuloShardingAlgorithm
key-generator:
column: id
type: SNOWFLAKE
...
同时,修改ModuloShardingAlgorithm
类的具体实现,如下所示:
public class ModuloShardingAlgorithm implements ComplexKeysShardingAlgorithm<Long> {
@Override
public Collection<String> doSharding(Collection<String> availableTargetNames, ComplexKeysShardingValue<Long> shardingValue) {
Set<String> result = new HashSet<>();
for (Long value : shardingValue.getColumnNameAndShardingValuesMap().get("gender")) {
String suffix = value % 2 + "_" + shardingValue.getColumnNameAndShardingValuesMap().get("id").iterator().next() % 2;
for (String name : availableTargetNames) {
if (name.endsWith(suffix)) {
result.add(name);
}
}
}
return result;
}
}
以上修改的分片策略是基于id和gender两个字段进行分片,其中id字段按照2个一组进行分片,gender字段按照值的模2进行分片。
修改完成后,重新执行测试类中的测试方法,验证Sharding-JDBC是否能够按照新的分片策略进行数据分片。
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