从源码理解Druid连接池原理
前言
在我们平时开发中，使用数据库连接池时使用阿里的Druid连接池已经比较常见了，但是我们在集成到Springboot时似乎非常简单，只需要简单的配置即可使用，那么Druid是怎么加载的呢，本文就从源码层面进行揭秘
使用
首先简单的介绍下如何使用
1、pom.xml加载jar包，直接使用集成springboot的jar
<dependency>
<groupId>com.alibaba</groupId>
<artifactId>druid-spring-boot-starter</artifactId>
<version>1.1.10</version>
</dependency>
2、application.properties进行配置
spring.datasource.url=jdbc:mysql://localhost:3306/mynote spring.datasource.username=root spring.datasource.password=root # 使用阿里的DruidDataSource数据源 spring.datasource.type=com.alibaba.druid.pool.DruidDataSource spring.datasource.driverClassName=com.mysql.cj.jdbc.Driver # 初始化连接数，默认为0 spring.datasource.druid.initial-size=0 # 最大连接数，默认为8 spring.datasource.druid.max-active=8
主要配置参数就是初始化连接数和最大连接数，最大连接数一般不需要配置的太大，一般8核cpu使用8个线程就可以了，原因是8核cpu同时可以处理的线程数只有8，设置的太大反而会造成CPU时间片的频繁切换
源码
首先我们没有做任何代码上的配置，为什么druid可以加载呢？那么就很容易联想到springboot的自动装配机制，所以我们看druid-spring-boot-starter jar包，这是一个start组件，所以我们直接看他的spring.factories文件，自动装配的机制这里不做介绍，可以看这篇文章
1 @Configuration 2 @ConditionalOnClass(DruidDataSource.class) 3 @AutoConfigureBefore(DataSourceAutoConfiguration.class) 4 @EnableConfigurationProperties({DruidStatProperties.class, DataSourceProperties.class}) 5 @Import({DruidSpringAopConfiguration.class, 6 DruidStatViewServletConfiguration.class, 7 DruidWebStatFilterConfiguration.class, 8 DruidFilterConfiguration.class}) 9 public class DruidDataSourceAutoConfigure { 10 11 private static final Logger LOGGER = LoggerFactory.getLogger(DruidDataSourceAutoConfigure.class); 12 13 @Bean(initMethod = "init") 14 @ConditionalOnMissingBean 15 public DataSource dataSource() { 16 LOGGER.info("Init DruidDataSource"); 17 return new DruidDataSourceWrapper(); 18 } 19 }
初始化了一个DataSource，实现类是DruidDataSourceWrapper，这个DataSource就是我们jdk提供jdbc操作的一个很重要的接口
到这里DataSource已经初始化完成了
我们开始从使用的地方入手，我的项目是基于Mybatis查询数据库的，这里从Mybatis查询开始入手
我们都知道Mybatis查询最终必定会从mybatis的Executor的query开始执行
所以我们在BaseExecutor的query方法打上断点，果然进来了，然后我们继续看
1 @Override 2 public <E> List<E> query(MappedStatement ms, Object parameter, RowBounds rowBounds, ResultHandler resultHandler, CacheKey key, BoundSql boundSql) throws SQLException { 3 ErrorContext.instance().resource(ms.getResource()).activity("executing a query").object(ms.getId()); 4 if (closed) { 5 throw new ExecutorException("Executor was closed."); 6 } 7 if (queryStack == 0 && ms.isFlushCacheRequired()) { 8 clearLocalCache(); 9 } 10 List<E> list; 11 try { 12 queryStack++; 13 list = resultHandler == null ? (List<E>) localCache.getObject(key) : null; 14 if (list != null) { 15 handleLocallyCachedOutputParameters(ms, key, parameter, boundSql); 16 } else { 17 // 核心代码 18 list = queryFromDatabase(ms, parameter, rowBounds, resultHandler, key, boundSql); 19 } 20 } finally { 21 queryStack--; 22 } 23 ...... 24 return list; 25 }
我们只看核心代码，进入queryFromDatabase
private <E> List<E> queryFromDatabase(MappedStatement ms, Object parameter, RowBounds rowBounds, ResultHandler resultHandler, CacheKey key, BoundSql boundSql) throws SQLException { List<E> list; localCache.putObject(key, EXECUTION_PLACEHOLDER); try { // 核心代码 list = doQuery(ms, parameter, rowBounds, resultHandler, boundSql); } finally { localCache.removeObject(key); } localCache.putObject(key, list); if (ms.getStatementType() == StatementType.CALLABLE) { localOutputParameterCache.putObject(key, parameter); } return list; }
继续跟
1 @Override 2 public <E> List<E> doQuery(MappedStatement ms, Object parameter, RowBounds rowBounds, ResultHandler resultHandler, BoundSql boundSql) throws SQLException { 3 Statement stmt = null; 4 try { 5 Configuration configuration = ms.getConfiguration(); 6 StatementHandler handler = configuration.newStatementHandler(wrapper, ms, parameter, rowBounds, resultHandler, boundSql); 7 // 核心代码 8 stmt = prepareStatement(handler, ms.getStatementLog()); 9 return handler.query(stmt, resultHandler); 10 } finally { 11 closeStatement(stmt); 12 } 13 }
这里我们看到获取了一个Statement ，这个Statement 是我们java原生操作数据库的一个很重要的类，这个Statement 应该是需要从一个数据库连接（Connection）上获取的，这里就很重要了，所以我们就需要看在里面是怎么获取Connection的就可以了
1 private Statement prepareStatement(StatementHandler handler, Log statementLog) throws SQLException { 2 Statement stmt; 3 // 核心 4 Connection connection = getConnection(statementLog); 5 stmt = handler.prepare(connection, transaction.getTimeout()); 6 handler.parameterize(stmt); 7 return stmt; 8 }
继续
1 protected Connection getConnection(Log statementLog) throws SQLException { 2 // 核心代码 3 Connection connection = transaction.getConnection(); 4 if (statementLog.isDebugEnabled()) { 5 return ConnectionLogger.newInstance(connection, statementLog, queryStack); 6 } else { 7 return connection; 8 } 9 }
核心代码，获取Connection，进入了SpringManagedTransaction的getConnection方法
1 @Override 2 public Connection getConnection() throws SQLException { 3 if (this.connection == null) { 4 // 核心代码 5 openConnection(); 6 } 7 return this.connection; 8 }
继续
private void openConnection() throws SQLException { // 核心代码 this.connection = DataSourceUtils.getConnection(this.dataSource); this.autoCommit = this.connection.getAutoCommit(); this.isConnectionTransactional = DataSourceUtils.isConnectionTransactional(this.connection, this.dataSource); LOGGER.debug(() -> "JDBC Connection [" + this.connection + "] will" + (this.isConnectionTransactional ? " " : " not ") + "be managed by Spring"); }
核心代码处，这个this.dataSource就是我们一开始通过自动装配初始化的。
DataSourceUtils这个类是spring提供的，也就是最终数据源的策略是通过spring提供的扩展机制，实现不同的dataSource来实现不同功能的
继续
public static Connection getConnection(DataSource dataSource) throws CannotGetJdbcConnectionException { try { // 核心代码 return doGetConnection(dataSource); } catch (SQLException ex) { throw new CannotGetJdbcConnectionException("Failed to obtain JDBC Connection", ex); } catch (IllegalStateException ex) { throw new CannotGetJdbcConnectionException("Failed to obtain JDBC Connection: " + ex.getMessage()); } }
继续
public static Connection doGetConnection(DataSource dataSource) throws SQLException { Assert.notNull(dataSource, "No DataSource specified"); ConnectionHolder conHolder = (ConnectionHolder) TransactionSynchronizationManager.getResource(dataSource); if (conHolder != null && (conHolder.hasConnection() || conHolder.isSynchronizedWithTransaction())) { conHolder.requested(); if (!conHolder.hasConnection()) { logger.debug("Fetching resumed JDBC Connection from DataSource"); conHolder.setConnection(fetchConnection(dataSource)); } return conHolder.getConnection(); } // Else we either got no holder or an empty thread-bound holder here. logger.debug("Fetching JDBC Connection from DataSource"); // 核心代码 Connection con = fetchConnection(dataSource); ...... return con; }
private static Connection fetchConnection(DataSource dataSource) throws SQLException { // 核心代码 Connection con = dataSource.getConnection(); if (con == null) { throw new IllegalStateException("DataSource returned null from getConnection(): " + dataSource); } return con; }
public DruidPooledConnection getConnection(long maxWaitMillis) throws SQLException { // 核心代码1 init(); if (filters.size() > 0) { FilterChainImpl filterChain = new FilterChainImpl(this); // 核心代码2 return filterChain.dataSource_connect(this, maxWaitMillis); } else { return getConnectionDirect(maxWaitMillis); } }
这里的核心代码1也很重要的，这里我们后续再看
继续看dataSource_connect
@Override public DruidPooledConnection dataSource_connect(DruidDataSource dataSource, long maxWaitMillis) throws SQLException { if (this.pos < filterSize) { // 核心代码 DruidPooledConnection conn = nextFilter().dataSource_getConnection(this, dataSource, maxWaitMillis); return conn; } return dataSource.getConnectionDirect(maxWaitMillis); }
继续，进入了StatFilter的dataSource_getConnection
@Override
public DruidPooledConnection dataSource_getConnection(FilterChain chain, DruidDataSource dataSource,
long maxWaitMillis) throws SQLException {
// 核心代码
DruidPooledConnection conn = chain.dataSource_connect(dataSource, maxWaitMillis);
if (conn != null) {
conn.setConnectedTimeNano();
StatFilterContext.getInstance().pool_connection_open();
}
return conn;
}
继续，然后又回到了FilterChainImpl的dataSource_connect
@Override
public DruidPooledConnection dataSource_connect(DruidDataSource dataSource, long maxWaitMillis) throws SQLException {
if (this.pos < filterSize) {
DruidPooledConnection conn = nextFilter().dataSource_getConnection(this, dataSource, maxWaitMillis);
return conn;
}
// 核心代码
return dataSource.getConnectionDirect(maxWaitMillis);
}
这个时候走了下面这个方法
public DruidPooledConnection getConnectionDirect(long maxWaitMillis) throws SQLException {
int notFullTimeoutRetryCnt = 0;
for (;;) {
// handle notFullTimeoutRetry
DruidPooledConnection poolableConnection;
try {
// 核心代码
poolableConnection = getConnectionInternal(maxWaitMillis);
} catch (GetConnectionTimeoutException ex) {
if (notFullTimeoutRetryCnt <= this.notFullTimeoutRetryCount && !isFull()) {
notFullTimeoutRetryCnt++;
if (LOG.isWarnEnabled()) {
LOG.warn("get connection timeout retry : " + notFullTimeoutRetryCnt);
}
continue;
}
throw ex;
}
......
}
private DruidPooledConnection getConnectionInternal(long maxWait) throws SQLException {
DruidConnectionHolder holder;
......
// 上面做了各种逻辑判断，此处不关注
if (maxWait > 0) {
holder = pollLast(nanos);
} else {
// 核心代码1
holder = takeLast();
}
......
holder.incrementUseCount();
// 核心代码2
DruidPooledConnection poolalbeConnection = new DruidPooledConnection(holder);
return poolalbeConnection;
}
核心代码1处获取了一个DruidConnectionHolder，DruidConnectionHolder里面有个关键的成员变量，就是我们的连接Connection
DruidConnectionHolder takeLast() throws InterruptedException, SQLException {
try {
while (poolingCount == 0) {
emptySignal(); // send signal to CreateThread create connection
if (failFast && failContinuous.get()) {
throw new DataSourceNotAvailableException(createError);
}
notEmptyWaitThreadCount++;
if (notEmptyWaitThreadCount > notEmptyWaitThreadPeak) {
notEmptyWaitThreadPeak = notEmptyWaitThreadCount;
}
try {
notEmpty.await(); // signal by recycle or creator
} finally {
notEmptyWaitThreadCount--;
}
notEmptyWaitCount++;
if (!enable) {
connectErrorCountUpdater.incrementAndGet(this);
throw new DataSourceDisableException();
}
}
} catch (InterruptedException ie) {
notEmpty.signal(); // propagate to non-interrupted thread
notEmptySignalCount++;
throw ie;
}
// 核心代码1
decrementPoolingCount();
// 核心代码2
DruidConnectionHolder last = connections[poolingCount];
connections[poolingCount] = null;
return last;
}
这里的decrementPoolingCount就是把一个int的变量poolingCount-1，然后在connections数组里面取某一个Connection
这里就已经看到核心代码了，connections就是我们的线程池了，是一个数组类型，里面存放了我们需要的连接，依靠一个指针poolingCount来控制当前应该可以取哪一个下标的Connection
查看断点，可以看到里面有8个Connection，也就是我们初始线程池数量
接下来再看下之前没看的init
public void init() throws SQLException {
......
// 核心代码1
connections = new DruidConnectionHolder[maxActive];
evictConnections = new DruidConnectionHolder[maxActive];
keepAliveConnections = new DruidConnectionHolder[maxActive];
SQLException connectError = null;
if (createScheduler != null) {
for (int i = 0; i < initialSize; ++i) {
createTaskCount++;
CreateConnectionTask task = new CreateConnectionTask(true);
this.createSchedulerFuture = createScheduler.submit(task);
}
} else if (!asyncInit) {
try {
// init connections
for (int i = 0; i < initialSize; ++i) {
// 核心代码2
PhysicalConnectionInfo pyConnectInfo = createPhysicalConnection();
DruidConnectionHolder holder = new DruidConnectionHolder(this, pyConnectInfo);
connections[poolingCount] = holder;
incrementPoolingCount();
}
if (poolingCount > 0) {
poolingPeak = poolingCount;
poolingPeakTime = System.currentTimeMillis();
}
} catch (SQLException ex) {
LOG.error("init datasource error, url: " + this.getUrl(), ex);
connectError = ex;
}
}
......
}
}
核心代码1，初始化了一个最大连接数的数组
核心代码2，初始化初始连接数数量的线程池连接
到这里，核心代码就全部看完了，本文是从Mybatis查询开始看代码的，实际上核心代码可以直接从DataSource的getConnection方法开始看
总结
Druid连接池的核心功能主要就是注册一个DataSource的bean，连接池、获取连接等都依赖于DataSource的实现类DruidDataSourceWrapper，连接池功能主要是维护了一个数组，在项目启动时提前创建了一些数据库连接放到了里面复用
参考：https://blog.csdn.net/qq_31086797/article/details/114631032
