由于当当发布了最新的Sharding-Sphere,所以本文已经过时,不日将推出新的版本
项目中遇到了分库分表的问题,找到了shrding-jdbc,于是就搞了一个springboot+sharding-jdbc+mybatis的增量分片的应用。今天写博客总结一下遇到的坑。
其实,我自己写了一个increament-jdbc组件的,当我读了sharding-jdbc的源码之后,发现思路和原理差不多,sharding这个各方面要比我的强,毕竟我是一天之内赶出来的东东。
示例代码地址:https://gitee.com/spartajet/s…
demo没有写日志,也没有各种异常判断,只是说明问题
一、需求背景
我的项目背景就不说了,现在举一个例子吧:A,B两支股票都在上海,深圳上市,需要实时记录这两支股票的交易tick(不懂tick也没有关系)。现在的分片策略是:上海、深圳分别建库,每个库都存各自交易所的两支股票的ticktick,且按照月分表。如图:
db_sh
- tick_a_2017_01
- tick_b_2017_01
- ……..
- tick_a_2017_12
- tick_b_2017_12
db_sz
tick_a_2017_01
- tick_b_2017_01
- ……..
- tick_a_2017_12
- tick_b_2017_12
分库分表就是这样的。根据这个建库。
**千万不要讨论这样分库分表是否合适,这里这样分片只是举个栗子,说明分库分表这个事情。**
**Sharding-jdbc是不支持建库的SQL,如果像我这样增量的数据库和数据表,那就要一次性把一段时期的数据库和数据表都要建好。**
二、建库
考虑到表确实多,所以我就只建1,2月份的表。语句见demo文件。
三、springboot集成sharding-jdbc
mvn配置pom如下:
<groupId>com.spartajet</groupId>
<artifactId>springboot-sharding-jdbc-demo</artifactId>
<version>0.0.1-SNAPSHOT</version>
<packaging>jar</packaging>
<name>springboot-sharding-jdbc-demo</name>
<description>Springboot integrate Sharding-jdbc Demo</description>
<properties>
<project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
<project.reporting.outputEncoding>UTF-8</project.reporting.outputEncoding>
<project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
<project.build.locales>zh_CN</project.build.locales>
<java.version>1.8</java.version>
<project.build.jdk>${java.version}</project.build.jdk>
<spring.boot.version>1.4.1.RELEASE</spring.boot.version>
<com.alibaba.druid.version>1.0.13</com.alibaba.druid.version>
<mysql-connector-java.version>5.1.36</mysql-connector-java.version>
<sharding-jdbc.version>1.4.1</sharding-jdbc.version>
<com.google.code.gson.version>2.8.0</com.google.code.gson.version>
<joda-trade.version>2.9.7</joda-trade.version>
<commons-dbcp.version>1.4</commons-dbcp.version>
<commons-io.version>2.5</commons-io.version>
<mybatis-spring-boot-starter.version>1.2.0</mybatis-spring-boot-starter.version>
</properties>
<dependencies>
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-jdbc</artifactId>
<version>${spring.boot.version}</version>
</dependency>
<dependency>
<groupId>org.mybatis.spring.boot</groupId>
<artifactId>mybatis-spring-boot-starter</artifactId>
<version>${mybatis-spring-boot-starter.version}</version>
</dependency>
<dependency>
<groupId>commons-dbcp</groupId>
<artifactId>commons-dbcp</artifactId>
<version>${commons-dbcp.version}</version>
</dependency>
<dependency>
<groupId>com.dangdang</groupId>
<artifactId>sharding-jdbc-core</artifactId>
<version>${sharding-jdbc.version}</version>
</dependency>
<dependency>
<groupId>com.dangdang</groupId>
<artifactId>sharding-jdbc-config-spring</artifactId>
<version>${sharding-jdbc.version}</version>
</dependency>
<dependency>
<groupId>com.dangdang</groupId>
<artifactId>sharding-jdbc-self-id-generator</artifactId>
<version>${sharding-jdbc.version}</version>
</dependency>
<dependency>
<groupId>com.google.code.gson</groupId>
<artifactId>gson</artifactId>
<version>${com.google.code.gson.version}</version>
</dependency>
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-web</artifactId>
<version>${spring.boot.version}</version>
<exclusions>
<exclusion>
<artifactId>org.springframework.boot</artifactId>
<groupId>spring-boot-start-logging</groupId>
</exclusion>
</exclusions>
</dependency>
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-test</artifactId>
<version>${spring.boot.version}</version>
<scope>test</scope>
</dependency>
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-log4j2</artifactId>
<version>${spring.boot.version}</version>
<exclusions>
<exclusion>
<groupId>log4j</groupId>
<artifactId>log4j</artifactId>
</exclusion>
</exclusions>
</dependency>
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter</artifactId>
<version>${spring.boot.version}</version>
<exclusions>
<exclusion>
<artifactId>org.springframework.boot</artifactId>
<groupId>spring-boot-start-logging</groupId>
</exclusion>
<exclusion>
<artifactId>logback-classic</artifactId>
<groupId>ch.qos.logback</groupId>
</exclusion>
<exclusion>
<artifactId>log4j-over-slf4j</artifactId>
<groupId>org.slf4j</groupId>
</exclusion>
</exclusions>
</dependency>
<dependency>
<groupId>mysql</groupId>
<artifactId>mysql-connector-java</artifactId>
<version>${mysql-connector-java.version}</version>
</dependency>
</dependencies>
<build>
<plugins>
<plugin>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-maven-plugin</artifactId>
<version>${spring.boot.version}</version>
</plugin>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-compiler-plugin</artifactId>
<version>3.1</version>
<configuration>
<source>${project.build.jdk}</source>
<target>${project.build.jdk}</target>
<encoding>${project.build.sourceEncoding}</encoding>
</configuration>
</plugin>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-jar-plugin</artifactId>
<version>2.4</version>
</plugin>
</plugins>
</build>
其实这个和sharding-jdbc的官网差不多。其实我想写一个sharding-jdbc-spring-boot-starter
的pom的,等项目业务都做完再说吧。
四、配置数据源
我想将数据库做成可配置的,所以我没有在application.properties
文件中直接配置数据库,而是写在了database.json
文件中。
[
{
"name": "db_sh",
"url": "jdbc:mysql://localhost:3306/db_sh",
"username": "root",
"password": "root",
"driveClassName":"com.mysql.jdbc.Driver"
},
{
"name": "db_sz",
"url": "jdbc:mysql://localhost:3306/db_sz",
"username": "root",
"password": "root",
"driveClassName":"com.mysql.jdbc.Driver"
}
]
然后在springboot读取database文件,加载方式如下:
@Value("classpath:database.json")
private Resource databaseFile;
@Bean
public List<Database> databases() throws IOException {
String databasesString = IOUtils.toString(databaseFile.getInputStream(), Charset.forName("UTF-8"));
List<Database> databases = new Gson().fromJson(databasesString, new TypeToken<List<Database>>() {
}.getType());
return databases;
}
加载完database信息之后,可以通过工厂方法配置逻辑数据库:
@Bean
public HashMap<String, DataSource> dataSourceMap(List<Database> databases) {
Map<String, DataSource> dataSourceMap = new HashMap<>();
for (Database database : databases) {
DataSourceBuilder dataSourceBuilder = DataSourceBuilder.create();
dataSourceBuilder.url(database.getUrl());
dataSourceBuilder.driverClassName(database.getDriveClassName());
dataSourceBuilder.username(database.getUsername());
dataSourceBuilder.password(database.getPassword());
DataSource dataSource = dataSourceBuilder.build();
dataSourceMap.put(database.getName(), dataSource);
}
return dataSourceMap;
}
这样就把各个逻辑数据库就加载好了。
五、配置分片策略
5.1数据库分片策略
在这个实例中,数据库的分库就是根据上海(sh)和深圳(sz)来分的,在sharding-jdbc中是单键分片。根据官方文档实现接口SingleKeyDatabaseShardingAlgorithm
就可以
@service
public class DatabaseShardingAlgorithm implements SingleKeyDatabaseShardingAlgorithm<String> {
/**
* 根据分片值和SQL的=运算符计算分片结果名称集合.
*
* @param availableTargetNames 所有的可用目标名称集合, 一般是数据源或表名称
* @param shardingValue 分片值
*
* @return 分片后指向的目标名称, 一般是数据源或表名称
*/
@Override
public String doEqualSharding(Collection<String> availableTargetNames, ShardingValue<String> shardingValue) {
String databaseName = "";
for (String targetName : availableTargetNames) {
if (targetName.endsWith(shardingValue.getValue())) {
databaseName = targetName;
break;
}
}
return databaseName;
}
}
此接口还有另外两个方法,doInSharding
和doBetweenSharding
,因为我暂时不用IN和BETWEEN方法,所以就没有写,直接返回null。
5.2数据表分片策略
数据表的分片策略是根据股票和时间共同决定的,在sharding-jdbc中是多键分片。根据官方文档,实现MultipleKeysTableShardingAlgorithm
接口就OK了
@service
public class TableShardingAlgorithm implements MultipleKeysTableShardingAlgorithm {
/**
* 根据分片值计算分片结果名称集合.
*
* @param availableTargetNames 所有的可用目标名称集合, 一般是数据源或表名称
* @param shardingValues 分片值集合
*
* @return 分片后指向的目标名称集合, 一般是数据源或表名称
*/
@Override
public Collection<String> doSharding(Collection<String> availableTargetNames, Collection<ShardingValue<?>> shardingValues) {
String name = null;
Date time = null;
for (ShardingValue<?> shardingValue : shardingValues) {
if (shardingValue.getColumnName().equals("name")) {
name = ((ShardingValue<String>) shardingValue).getValue();
}
if (shardingValue.getColumnName().equals("time")) {
time = ((ShardingValue<Date>) shardingValue).getValue();
}
if (name != null && time != null) {
break;
}
}
String timeString = new SimpleDateFormat("yyyy_MM").format(time);
String suffix = name + "_" + timeString;
Collection<String> result = new LinkedHashSet<>();
for (String targetName : availableTargetNames) {
if (targetName.endsWith(suffix)) {
result.add(targetName);
}
}
return result;
}
}
这些方法的使用可以查官方文档。
5.3注入分片策略
以上只是定义了分片算法,还没有形成策略,还没有告诉shrding将哪个字段给分片算法:
@Configuration
public class ShardingStrategyConfig {
@Bean
public DatabaseShardingStrategy databaseShardingStrategy(DatabaseShardingAlgorithm databaseShardingAlgorithm) {
DatabaseShardingStrategy databaseShardingStrategy = new DatabaseShardingStrategy("exchange", databaseShardingAlgorithm);
return databaseShardingStrategy;
}
@Bean
public TableShardingStrategy tableShardingStrategy(TableShardingAlgorithm tableShardingAlgorithm) {
Collection<String> columns = new LinkedList<>();
columns.add("name");
columns.add("time");
TableShardingStrategy tableShardingStrategy = new TableShardingStrategy(columns, tableShardingAlgorithm);
return tableShardingStrategy;
}
}
这样才能形成完成的分片策略。
六、配置Sharding-jdbc的DataSource
sharding-jdbc的原理其实很简单,就是自己做一个DataSource给上层应用使用,这个DataSource包含所有的逻辑库和逻辑表,应用增删改查时,他自己再修改sql,然后选择合适的数据库继续操作。所以这个DataSource创建很重要。
@Bean
@Primary
public DataSource shardingDataSource(HashMap<String, DataSource> dataSourceMap, DatabaseShardingStrategy databaseShardingStrategy, TableShardingStrategy tableShardingStrategy) {
DataSourceRule dataSourceRule = new DataSourceRule(dataSourceMap);
TableRule tableRule = TableRule.builder("tick").actualTables(Arrays.asList("db_sh.tick_a_2017_01", "db_sh.tick_a_2017_02", "db_sh.tick_b_2017_01", "db_sh.tick_b_2017_02", "db_sz.tick_a_2017_01", "db_sz.tick_a_2017_02", "db_sz.tick_b_2017_01", "db_sz.tick_a_2017_02")).dataSourceRule(dataSourceRule).build();
ShardingRule shardingRule = ShardingRule.builder().dataSourceRule(dataSourceRule).tableRules(Arrays.asList(tableRule)).databaseShardingStrategy(databaseShardingStrategy).tableShardingStrategy(tableShardingStrategy).build();
DataSource shardingDataSource = ShardingDataSourceFactory.createDataSource(shardingRule);
return shardingDataSource;
}
这里要着重说一下为什么要用@Primary这个注解,没有这个注解是会报错的,错误大致意思就是DataSource太多了,mybatis不知道用哪个。加上这个mybatis就知道用sharding的DataSource了。这里参考的是jpa的多数据源配置
七、配置mybatis
7.1 Bean
public class Tick {
private long id;
private String name;
private String exchange;
private int ask;
private int bid;
private Date time;
}
7.2 Mapper
很简单,只实现一个插入方法
@Mapper
public interface TickMapper {
@Insert("insert into tick (id,name,exchange,ask,bid,time) values (#{id},#{name},#{exchange},#{ask},#{bid},#{time})")
void insertTick(Tick tick);
}
7.3 SessionFactory配置
还要设置一下tick的SessionFactory:
@Configuration
@MapperScan(basePackages = "com.spartajet.shardingboot.mapper", sqlSessionFactoryRef = "sessionFactory")
public class TickSessionFactoryConfig {
@Bean
public SqlSessionFactory sessionFactory(DataSource shardingDataSource) throws Exception {
final SqlSessionFactoryBean sessionFactory = new SqlSessionFactoryBean();
sessionFactory.setDataSource(shardingDataSource);
return sessionFactory.getObject();
}
@Bean
public CommonSelfIdGenerator commonSelfIdGenerator() {
CommonSelfIdGenerator.setClock(AbstractClock.systemClock());
CommonSelfIdGenerator commonSelfIdGenerator = new CommonSelfIdGenerator();
return commonSelfIdGenerator;
}
}
这里添加了一个CommonSelfIdGenerator
,sharding自带的id生成器,看了下代码和facebook
的snowflake
类似。我又不想把数据库的主键设置成自增的,否则数据双向同步会死的很惨的。
八、测试写入
@RunWith(SpringJUnit4ClassRunner.class)
@SpringBootTest
public class SpringbootShardingJdbcDemoApplicationTests {
@Autowired
private TickMapper tickMapper;
@Autowired
private CommonSelfIdGenerator commonSelfIdGenerator;
@Test
public void contextLoads() {
Tick tick = new Tick(commonSelfIdGenerator.generateId().longValue(), "a", "sh", 100, 200, new Date());
this.tickMapper.insertTick(tick);
}
}
成功实现增量分库分表!!!