这篇文章主要介绍“怎么使用sharding-jdbc读写分离”，在日常操作中，相信很多人在怎么使用sharding-jdbc读写分离问题上存在疑惑，小编查阅了各式资料，整理出简单好用的操作方法，希望对大家解答”怎么使用sharding-jdbc读写分离”的疑惑有所帮助！接下来，请跟着小编一起来学习吧！
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核心概念
源码分析
1.启动入口:
public class JavaConfigurationExample {
// private static ShardingType shardingType = ShardingType.SHARDING_DATABASES;
// private static ShardingType shardingType = ShardingType.SHARDING_TABLES;
// private static ShardingType shardingType = ShardingType.SHARDING_DATABASES_AND_TABLES;
private static ShardingType shardingType = ShardingType.MASTER_SLAVE;
// private static ShardingType shardingType = ShardingType.SHARDING_MASTER_SLAVE;
// private static ShardingType shardingType = ShardingType.SHARDING_VERTICAL;
public static void main(final String[] args) throws SQLException {
DataSource dataSource = DataSourceFactory.newInstance(shardingType);
CommonService commonService = getCommonService(dataSource);
commonService.initEnvironment();
commonService.processSuccess();
commonService.cleanEnvironment();
}
private static CommonService getCommonService(final DataSource dataSource) {
return new CommonServiceImpl(new OrderRepositoryImpl(dataSource), new OrderItemRepositoryImpl(dataSource));
}
}2.以sharding-jdbc为例，配置主从读写分离代码如下：
@Override
public DataSource getDataSource() throws SQLException {
//主从配置
MasterSlaveRuleConfiguration masterSlaveRuleConfig = new MasterSlaveRuleConfiguration(/*主从命名*/"demo_ds_master_slave", /*主库*/"demo_ds_master", /*从库*/Arrays.asList("demo_ds_slave_0", "demo_ds_slave_1"));
//打印sql
Properties props = new Properties();
props.put("sql.show", true);
//创建MasterSlaveDataSource数据源
return MasterSlaveDataSourceFactory.createDataSource(createDataSourceMap(), masterSlaveRuleConfig, props);
}
private Map createDataSourceMap() {
Map result = new HashMap<>();
//主库
result.put("demo_ds_master", DataSourceUtil.createDataSource("demo_ds_master"));
//两从库
result.put("demo_ds_slave_0", DataSourceUtil.createDataSource("demo_ds_slave_0"));
result.put("demo_ds_slave_1", DataSourceUtil.createDataSource("demo_ds_slave_1"));
return result;
}创建sharding主从数据源MasterSlaveDataSource
public MasterSlaveDataSource(final Map dataSourceMap, final MasterSlaveRuleConfiguration masterSlaveRuleConfig, final Properties props) throws SQLException {
super(dataSourceMap);
//缓存MySQL元数据
cachedDatabaseMetaData = createCachedDatabaseMetaData(dataSourceMap);
//主从规则配置
this.masterSlaveRule = new MasterSlaveRule(masterSlaveRuleConfig);
//主从sql解析
parseEngine = new MasterSlaveSQLParseEntry(getDatabaseType());
shardingProperties = new ShardingProperties(null == props ? new Properties() : props);
}
3.执行insert插入方法
@Override
public Long insert(final Order order) throws SQLException {
String sql = "INSERT INTO t_order (user_id, status) VALUES (?, ?)";
//获取MasterSlaveDataSource数据源连接，同时创建MasterSlavePreparedStatement
//这里有两个Statement分别含义
//1.MasterSlaveStatement：执行sql时候才路由
//2.MasterSlavePreparedStatement：创建Statement时就路由
//Statement.RETURN_GENERATED_KEYS 自动生成主键并返回生成的主键
try (Connection connection = dataSource.getConnection();
PreparedStatement preparedStatement = connection.prepareStatement(sql, Statement.RETURN_GENERATED_KEYS)) {
preparedStatement.setInt(1, order.getUserId());
preparedStatement.setString(2, order.getStatus());
//MasterSlavePreparedStatement执行sql
preparedStatement.executeUpdate();
try (ResultSet resultSet = preparedStatement.getGeneratedKeys()) {
if (resultSet.next()) {
order.setOrderId(resultSet.getLong(1));
}
}
}
return order.getOrderId();
} 获取数据库连接MasterSlaveConnection->AbstractConnectionAdapter#getConnection
/**
* Get database connection.
*
* @param dataSourceName data source name
* @return database connection
* @throws SQLException SQL exception
*/
//MEMORY_STRICTLY：Proxy会保持一个数据库中所有被路由到的表的连接，这种方式的好处是利用流式ResultSet来节省内存
//
//CONNECTION_STRICTLY：代理在取出ResultSet中的所有数据后会释放连接，同时，内存的消耗将会增加
//
public final Connection getConnection(final String dataSourceName) throws SQLException {
return getConnections(ConnectionMode.MEMORY_STRICTLY, dataSourceName, 1).get(0);
}
/**
* Get database connections.
*
* @param connectionMode connection mode
* @param dataSourceName data source name
* @param connectionSize size of connection list to be get
* @return database connections
* @throws SQLException SQL exception
*/
public final List getConnections(final ConnectionMode connectionMode, final String dataSourceName, final int connectionSize) throws SQLException {
//获取数据源
DataSource dataSource = getDataSourceMap().get(dataSourceName);
Preconditions.checkState(null != dataSource, "Missing the data source name: '%s'", dataSourceName);
Collection connections;
//并发从cache中获取连接
synchronized (cachedConnections) {
connections = cachedConnections.get(dataSourceName);
}
List result;
//如果cache中连接数大于指定连接数时，返回指定连接数量
if (connections.size() >= connectionSize) {
result = new ArrayList<>(connections).subList(0, connectionSize);
} else if (!connections.isEmpty()) {
result = new ArrayList<>(connectionSize);
result.addAll(connections);
//创建缺少的指定连接数
List newConnections = createConnections(dataSourceName, connectionMode, dataSource, connectionSize - connections.size());
result.addAll(newConnections);
synchronized (cachedConnections) {
cachedConnections.putAll(dataSourceName, newConnections);
}
} else {
result = new ArrayList<>(createConnections(dataSourceName, connectionMode, dataSource, connectionSize));
synchronized (cachedConnections) {
cachedConnections.putAll(dataSourceName, result);
}
}
return result;
}
@SuppressWarnings("SynchronizationOnLocalVariableOrMethodParameter")
private List createConnections(final String dataSourceName, final ConnectionMode connectionMode, final DataSource dataSource, final int connectionSize) throws SQLException {
//为1时不存在并发获取连接情况，直接返回单个连接
if (1 == connectionSize) {
return Collections.singletonList(createConnection(dataSourceName, dataSource));
}
//TODO 不处理并发
if (ConnectionMode.CONNECTION_STRICTLY == connectionMode) {
return createConnections(dataSourceName, dataSource, connectionSize);
}
//并发
synchronized (dataSource) {
return createConnections(dataSourceName, dataSource, connectionSize);
}
}
private List createConnections(final String dataSourceName, final DataSource dataSource, final int connectionSize) throws SQLException {
List result = new ArrayList<>(connectionSize);
for (int i = 0; i < connectionSize; i++) {
try {
result.add(createConnection(dataSourceName, dataSource));
} catch (final SQLException ex) {
for (Connection each : result) {
each.close();
}
throw new SQLException(String.format("Could't get %d connections one time, partition succeed connection(%d) have released!", connectionSize, result.size()), ex);
}
}
return result;
}
private Connection createConnection(final String dataSourceName, final DataSource dataSource) throws SQLException {
//判断是否是sharding事物
Connection result = isInShardingTransaction() ? shardingTransactionManager.getConnection(dataSourceName) : dataSource.getConnection();
replayMethodsInvocation(result);
return result;
}预准备路由并缓存Statement
public MasterSlavePreparedStatement(final MasterSlaveConnection connection, final String sql, final int autoGeneratedKeys) throws SQLException {
this.connection = connection;
//创建router对象
masterSlaveRouter = new MasterSlaveRouter(connection.getMasterSlaveDataSource().getMasterSlaveRule(), connection.getParseEngine(),
connection.getMasterSlaveDataSource().getShardingProperties().getValue(ShardingPropertiesConstant.SQL_SHOW));
//缓存路由后的Statement，useCache缓存解析后的sql Statement
for (String each : masterSlaveRouter.route(sql, true)) {
//获取数据库连接
PreparedStatement preparedStatement = connection.getConnection(each).prepareStatement(sql, autoGeneratedKeys);
routedStatements.add(preparedStatement);
}
}执行MasterSlaveRouter#route方法获取路由库
public Collection route(final String sql, final boolean useCache) {
//解析sql，这里不分析sql如何使用antlr4解析
Collection result = route(parseEngine.parse(sql, useCache));
//是否打印sql
if (showSQL) {
SQLLogger.logSQL(sql, result);
}
return result;
}
private Collection route(final SQLStatement sqlStatement) {
//判断是否master
if (isMasterRoute(sqlStatement)) {
//设置当前线程是否允许访问主库
MasterVisitedManager.setMasterVisited();
//返回主库
return Collections.singletonList(masterSlaveRule.getMasterDataSourceName());
}
//根据配置的算法获取从库，两种算法：
//1、随机
//2、轮询
return Collections.singletonList(masterSlaveRule.getLoadBalanceAlgorithm().getDataSource(
masterSlaveRule.getName(), masterSlaveRule.getMasterDataSourceName(), new ArrayList<>(masterSlaveRule.getSlaveDataSourceNames())));
}
执行MasterSlavePreparedStatement#executeUpdate
@Override
public int executeUpdate() throws SQLException {
int result = 0;
//从本地缓存遍历执行
for (PreparedStatement each : routedStatements) {
result += each.executeUpdate();
}
return result;
}4.获取从库算法策略
@Getter
@Setter
public final class RandomMasterSlaveLoadBalanceAlgorithm implements MasterSlaveLoadBalanceAlgorithm {
private Properties properties = new Properties();
@Override
public String getType() {
return "RANDOM";
}
@Override
public String getDataSource(final String name, final String masterDataSourceName, final List slaveDataSourceNames) {
//从slave.size()中获取一个随机数
return slaveDataSourceNames.get(new Random().nextInt(slaveDataSourceNames.size()));
}
}@Getter
@Setter
public final class RoundRobinMasterSlaveLoadBalanceAlgorithm implements MasterSlaveLoadBalanceAlgorithm {
//并发map
private static final ConcurrentHashMap COUNTS = new ConcurrentHashMap<>();
private Properties properties = new Properties();
@Override
public String getType() {
return "ROUND_ROBIN";
}
@Override
public String getDataSource(final String name, final String masterDataSourceName, final List slaveDataSourceNames) {
//查看对应名称的计数器，没有则初始化一个
AtomicInteger count = COUNTS.containsKey(name) ? COUNTS.get(name) : new AtomicInteger(0);
COUNTS.putIfAbsent(name, count);
// 采用cas轮询，如果计数器长到slave.size()，那么归零（防止计数器不断增长下去）
count.compareAndSet(slaveDataSourceNames.size(), 0);
//绝对值，计数器%slave.size()取模
return slaveDataSourceNames.get(Math.abs(count.getAndIncrement()) % slaveDataSourceNames.size());
}
}SPI扩展机制，load加载第一个算法作为默认算法；ss默认是随机
到此，关于“怎么使用sharding-jdbc读写分离”的学习就结束了，希望能够解决大家的疑惑。理论与实践的搭配能更好的帮助大家学习，快去试试吧！若想继续学习更多相关知识，请继续关注创新互联网站，小编会继续努力为大家带来更多实用的文章！
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