配置读取
druid连接池支持的所有连接参数可在类com.alibaba.druid.pool.DruidDataSourceFactory中查看。
配置读取代码：
public void configFromPropety(Properties properties) {
}
整体代码比较简单，就是把配置内容，读取到dataSource。
连接池初始化
首先是简单的判断，加锁：
if (inited) {
return;
}
DruidDriver.getInstance();
final ReentrantLock lock = this.lock;
try {
lock.lockInterruptibly();
} catch (InterruptedException e) {
throw new SQLException("interrupt", e);
}
之后会更新一些JMX的监控指标：
this.connectionIdSeedUpdater.addAndGet(this, delta);
this.statementIdSeedUpdater.addAndGet(this, delta);
this.resultSetIdSeedUpdater.addAndGet(this, delta);
this.transactionIdSeedUpdater.addAndGet(this, delta);
druid的监控指标都是通过jmx实现的。
解析连接串：
if (this.jdbcUrl != null) {
//解析连接串
this.jdbcUrl = this.jdbcUrl.trim();
initFromWrapDriverUrl();
}
initFromWrapDriverUrl方法，除了从jdbc url中解析出连接和驱动信息，后面还把filters的名字，解析成了对应的filter类。
private void initFromWrapDriverUrl() throws SQLException {
if (!jdbcUrl.startsWith(DruidDriver.DEFAULT_PREFIX)) {
return;
}
DataSourceProxyConfig config = DruidDriver.parseConfig(jdbcUrl, null);
this.driverClass = config.getRawDriverClassName();
LOG.error("error url : '" + jdbcUrl + "', it should be : '" + config.getRawUrl() + "'");
this.jdbcUrl = config.getRawUrl();
if (this.name == null) {
this.name = config.getName();
}
for (Filter filter : config.getFilters()) {
addFilter(filter);
}
}
之后在init方法里面，会进行filters的初始化：
//初始化filter 属性
for (Filter filter : filters) {
filter.init(this);
}
之后解析数据库类型：
if (this.dbTypeName == null || this.dbTypeName.length() == 0) {
this.dbTypeName = JdbcUtils.getDbType(jdbcUrl, null);
}
注意枚举值： com.alibaba.druid.DbType，这个里面包含了目前durid连接池支持的所有数据源 类型，另外，druid还额外提供了一些驱动类，例如：
elastic_search (1 << 25), // com.alibaba.xdriver.elastic.jdbc.ElasticDriver
clickhouse还提供了负载均衡的驱动类：
com.alibaba.druid.support.clickhouse.BalancedClickhouseDriver 。
在回到init方法，之后是一堆参数解析，不再说，跳过了。
之后是通过SPI加载自定义的filter:
private void initFromSPIServiceLoader() {
if (loadSpifilterSkip) {
return;
}
if (autoFilters == null) {
List<Filter> filters = new ArrayList<Filter>();
ServiceLoader<Filter> autoFilterLoader = ServiceLoader.load(Filter.class);
for (Filter filter : autoFilterLoader) {
AutoLoad autoLoad = filter.getClass().getAnnotation(AutoLoad.class);
if (autoLoad != null && autoLoad.value()) {
filters.add(filter);
}
}
autoFilters = filters;
}
for (Filter filter : autoFilters) {
if (LOG.isInfoEnabled()) {
LOG.info("load filter from spi :" + filter.getClass().getName());
}
addFilter(filter);
}
}
注意自定义的filter,要使用com.alibaba.druid.filter.AutoLoad。
解析驱动：
protected void resolveDriver() throws SQLException {
if (this.driver == null) {
if (this.driverClass == null || this.driverClass.isEmpty()) {
this.driverClass = JdbcUtils.getDriverClassName(this.jdbcUrl);
}
if (MockDriver.class.getName().equals(driverClass)) {
driver = MockDriver.instance;
} else if ("com.alibaba.druid.support.clickhouse.BalancedClickhouseDriver".equals(driverClass)) {
Properties info = new Properties();
info.put("user", username);
info.put("password", password);
info.putAll(connectProperties);
driver = new BalancedClickhouseDriver(jdbcUrl, info);
} else {
if (jdbcUrl == null && (driverClass == null || driverClass.length() == 0)) {
throw new SQLException("url not set");
}
driver = JdbcUtils.createDriver(driverClassLoader, driverClass);
}
} else {
if (this.driverClass == null) {
this.driverClass = driver.getClass().getName();
}
}
}
其中durid自己的mock驱动和clickhouse的负载均衡的驱动，特殊判断了下，其他走的都是class forname.
之后是exception sorter和checker的一些东西，跟主线剧情关系不大，skip.
之后是一些初始化JdbcDataSourceStat,没啥东西。
之后是核心:
connections = new DruidConnectionHolder[maxActive]; //连接数组
evictConnections = new DruidConnectionHolder[maxActive]; //销毁的连接数组
keepAliveConnections = new DruidConnectionHolder[maxActive]; //保持活跃可用的数组
dataSource的连接，都被包装在类DruidConnectionHolder中，之后是一个同步去初始化连接还是异步去初始化的连接，总之，是去初始化 连接的过程:
if (createScheduler != null && asyncInit) {
for (int i = 0; i < initialSize; ++i) {
submitCreateTask(true);
}
} else if (!asyncInit) {
// init connections
while (poolingCount < initialSize) {
try {
PhysicalConnectionInfo pyConnectInfo = createPhysicalConnection();
DruidConnectionHolder holder = new DruidConnectionHolder(this, pyConnectInfo);
connections[poolingCount++] = holder;
} catch (SQLException ex) {
LOG.error("init datasource error, url: " + this.getUrl(), ex);
if (initExceptionThrow) {
connectError = ex;
break;
} else {
Thread.sleep(3000);
}
}
}
if (poolingCount > 0) {
poolingPeak = poolingCount;
poolingPeakTime = System.currentTimeMillis();
}
}
初始化的连接个数为连接串里面配置的initialSize.
核心初始化方法com.alibaba.druid.pool.DruidAbstractDataSource#createPhysicalConnection(),在这方法里面，会拿用户名密码，之后执行真正的获取connection:
public Connection createPhysicalConnection(String url, Properties info) throws SQLException {
Connection conn;
if (getProxyFilters().size() == 0) {
conn = getDriver().connect(url, info);
} else {
conn = new FilterChainImpl(this).connection_connect(info);
}
createCountUpdater.incrementAndGet(this);
return conn;
}
注意，如果配置了filters,则所有操作，都会在操作前执行filter处理链。
public ConnectionProxy connection_connect(Properties info) throws SQLException {
if (this.pos < filterSize) {
return nextFilter()
.connection_connect(this, info);
}
Driver driver = dataSource.getRawDriver();
String url = dataSource.getRawJdbcUrl();
Connection nativeConnection = driver.connect(url, info);
if (nativeConnection == null) {
return null;
}
return new ConnectionProxyImpl(dataSource, nativeConnection, info, dataSource.createConnectionId());
}
再回到主流程init方法，connections数组初始化完成之后，
开启额外线程：
createAndLogThread(); //打印连接信息
createAndStartCreatorThread(); //创建连接线程
createAndStartDestroyThread(); //销毁连接线程
先看注释，具体里面的内容后面单独拉出来讲。
之后：
initedLatch.await(); //初始化 latch -1
init = true; //标记已经初始化完成
initedTime = new Date(); //时间
registerMbean(); //为datasource 注册jmx监控指标
最后的最后，如果配置了keepAlive:
if (keepAlive) {
// async fill to minIdle
if (createScheduler != null) {
for (int i = 0; i < minIdle; ++i) {
submitCreateTask(true);
}
} else {
this.emptySignal();
}
}
这时候，会根据配置的活跃连接数minIdle,去给datasource的连接，做个保持活跃连接个数，具体后面再说。
连接池使用的核心逻辑
首先，使用数组作为连接的容器，对于真实连接的加入和移除，使用lock就行同步，另外，在加入和移除连接时候，对比生产消费模型，通过lock上的条件，来通知是否可以获取或者加入连接。
public DruidAbstractDataSource(boolean lockFair){
lock = new ReentrantLock(lockFair);
notEmpty = lock.newCondition(); //非空，有连接
empty = lock.newCondition(); //空的
}
另外，默认的fairlock为false
public DruidDataSource(){
this(false);
}
public DruidDataSource(boolean fairLock){
super(fairLock);
configFromPropety(System.getProperties());
}
创建连接
在线程com.alibaba.druid.pool.DruidDataSource.CreateConnectionThread中：
if (emptyWait) {
// 必须存在线程等待，才创建连接
if (poolingCount >= notEmptyWaitThreadCount //
&& (!(keepAlive && activeCount + poolingCount < minIdle))
&& !isFailContinuous()
) {
empty.await();
}
// 防止创建超过maxActive数量的连接
if (activeCount + poolingCount >= maxActive) {
empty.await();
continue;
}
}
必须存在线程等待获取连接时候，才能创建连接，并且要保持总的连接数，不能超过配置的最大连接。
创建完连接之后，执行notEmpty.signalAll();通知消费者。
获取连接
外层代码：
@Override
public DruidPooledConnection getConnection() throws SQLException {
return getConnection(maxWait);
}
public DruidPooledConnection getConnection(long maxWaitMillis) throws SQLException {
init();
if (filters.size() > 0) {
FilterChainImpl filterChain = new FilterChainImpl(this);
return filterChain.dataSource_connect(this, maxWaitMillis);
} else {
return getConnectionDirect(maxWaitMillis);
}
}
忽略掉filter chain,其实最后执行的还是com.alibaba.druid.pool.DruidDataSource#getConnectionDirect：
方法内部：
poolableConnection = getConnectionInternal(maxWaitMillis);
- 1 , 连接不足，需要直接去创建新的，跟我们初始化一样
- 2，从connections里面拿
if (maxWait > 0) {
holder = pollLast(nanos);
} else {
holder = takeLast();
}
其中，maxWait默认为-1，配置在init里面：
String property = properties.getProperty("druid.maxWait");
if (property != null && property.length() > 0) {
try {
int value = Integer.parseInt(property);
this.setMaxWait(value);
} catch (NumberFormatException e) {
LOG.error("illegal property 'druid.maxWait'", e);
}
}
这个用于配置拿连接时候，是否在这个时间上进行等待，默认是否，即一直等到拿到连接为止。
直接看下阻塞拿的过程：
DruidConnectionHolder takeLast() throws InterruptedException, SQLException {
try {
//没连接了
while (poolingCount == 0) {
//暗示下创建线程没连接了
emptySignal(); // send signal to CreateThread create connection
if (failFast && isFailContinuous()) {
throw new DataSourceNotAvailableException(createError);
}
notEmptyWaitThreadCount++;
if (notEmptyWaitThreadCount > notEmptyWaitThreadPeak) {
notEmptyWaitThreadPeak = notEmptyWaitThreadCount;
}
try {
//傻等着创建或者回收，能给整出来点儿连接
notEmpty.await(); // signal by recycle or creator
} finally {
notEmptyWaitThreadCount--;
}
notEmptyWaitCount++;
if (!enable) {
connectErrorCountUpdater.incrementAndGet(this);
if (disableException != null) {
throw disableException;
}
throw new DataSourceDisableException();
}
}
} catch (InterruptedException ie) {
notEmpty.signal(); // propagate to non-interrupted thread
notEmptySignalCount++;
throw ie;
}
//拿数组的最后一个连接
decrementPoolingCount();
DruidConnectionHolder last = connections[poolingCount];
connections[poolingCount] = null;
return last;
}
连接回收
protected void createAndStartDestroyThread() {
destroyTask = new DestroyTask();
//自定义配置销毁 ，适用于连接数非常多的 情况
if (destroyScheduler != null) {
long period = timeBetweenEvictionRunsMillis;
if (period <= 0) {
period = 1000;
}
destroySchedulerFuture = destroyScheduler.scheduleAtFixedRate(destroyTask, period, period,
TimeUnit.MILLISECONDS);
initedLatch.countDown();
return;
}
String threadName = "Druid-ConnectionPool-Destroy-" + System.identityHashCode(this);
//单线程销毁
destroyConnectionThread = new DestroyConnectionThread(threadName);
destroyConnectionThread.start();
}
实际的销毁：
public class DestroyTask implements Runnable {
public DestroyTask() {
}
@Override
public void run() {
shrink(true, keepAlive);
if (isRemoveAbandoned()) {
removeAbandoned();
}
}
}
最终 执行的还是 shrink方法。
public void shrink(boolean checkTime, boolean keepAlive) {
try {
lock.lockInterruptibly();
} catch (InterruptedException e) {
return;
}
boolean needFill = false;
int evictCount = 0;
int keepAliveCount = 0;
int fatalErrorIncrement = fatalErrorCount - fatalErrorCountLastShrink;
fatalErrorCountLastShrink = fatalErrorCount;
try {
if (!inited) {
return;
}
final int checkCount = poolingCount - minIdle; //需要检测连接的数量
final long currentTimeMillis = System.currentTimeMillis();
for (int i = 0; i < poolingCount; ++i) { //检测目前connections数组中的连接
DruidConnectionHolder connection = connections[i];
if ((onFatalError || fatalErrorIncrement > 0) && (lastFatalErrorTimeMillis > connection.connectTimeMillis)) {
keepAliveConnections[keepAliveCount++] = connection;
continue;
}
if (checkTime) {
//是否设置了物理连接的超时时间phyTimoutMills。假如设置了该时间，
// 判断连接时间存活时间是否已经超过phyTimeoutMills，是则放入evictConnections中
if (phyTimeoutMillis > 0) {
long phyConnectTimeMillis = currentTimeMillis - connection.connectTimeMillis;
if (phyConnectTimeMillis > phyTimeoutMillis) {
evictConnections[evictCount++] = connection;
continue;
}
}
long idleMillis = currentTimeMillis - connection.lastActiveTimeMillis;//获取连接空闲时间
//如果某条连接空闲时间小于minEvictableIdleTimeMillis，则不用继续检查剩下的连接了
if (idleMillis < minEvictableIdleTimeMillis
&& idleMillis < keepAliveBetweenTimeMillis
) {
break;
}
if (idleMillis >= minEvictableIdleTimeMillis) {
// check checkTime is silly code
//检测检查了几个连接了
if (checkTime && i < checkCount) {
//超时了
evictConnections[evictCount++] = connection;
continue;
} else if (idleMillis > maxEvictableIdleTimeMillis) {
//超时了
evictConnections[evictCount++] = connection;
continue;
}
}
if (keepAlive && idleMillis >= keepAliveBetweenTimeMillis) {
//配置了keepAlive，并且在存活时间内，放到keepAlive数组
keepAliveConnections[keepAliveCount++] = connection;
}
} else {
//不需要检查时间的，直接移除
if (i < checkCount) {
evictConnections[evictCount++] = connection;
} else {
break;
}
}
}
int removeCount = evictCount + keepAliveCount; //移除了几个
//由于使用connections连接时候，都是取后面的，后面 的是最新的连接，只考虑前面过期就行，所以只需要挪动前面的连接
if (removeCount > 0) {
System.arraycopy(connections, removeCount, connections, 0, poolingCount - removeCount);
Arrays.fill(connections, poolingCount - removeCount, poolingCount, null);
poolingCount -= removeCount;
}
keepAliveCheckCount += keepAliveCount;
if (keepAlive && poolingCount + activeCount < minIdle) {
//不够核心的活跃连接时候，需要去创建啦
needFill = true;
}
} finally {
lock.unlock();
}
if (evictCount > 0) {
for (int i = 0; i < evictCount; ++i) {
//销毁连接
DruidConnectionHolder item = evictConnections[i];
Connection connection = item.getConnection();
JdbcUtils.close(connection);
destroyCountUpdater.incrementAndGet(this);
}
Arrays.fill(evictConnections, null);
}
if (keepAliveCount > 0) {
// keep order
for (int i = keepAliveCount - 1; i >= 0; --i) {
DruidConnectionHolder holer = keepAliveConnections[i];
Connection connection = holer.getConnection();
holer.incrementKeepAliveCheckCount();
boolean validate = false;
try {
this.validateConnection(connection);
validate = true;
} catch (Throwable error) {
if (LOG.isDebugEnabled()) {
LOG.debug("keepAliveErr", error);
}
// skip
}
boolean discard = !validate; //没通过validate
if (validate) {
//通过keep alive检查，更新时间
holer.lastKeepTimeMillis = System.currentTimeMillis();
//这里还会尝试放回connections数组
boolean putOk = put(holer, 0L, true);
if (!putOk) {
//没放入，标记要丢弃了
discard = true;
}
}
if (discard) {
try {
connection.close();
} catch (Exception e) {
// skip
}
lock.lock();
try {
discardCount++;
if (activeCount + poolingCount <= minIdle) {
//发信号让创建线程去创建
emptySignal();
}
} finally {
lock.unlock();
}
}
}
this.getDataSourceStat().addKeepAliveCheckCount(keepAliveCount);
Arrays.fill(keepAliveConnections, null);
}
if (needFill) {
//又要去创建了
lock.lock();
try {
int fillCount = minIdle - (activeCount + poolingCount + createTaskCount);
for (int i = 0; i < fillCount; ++i) {
emptySignal();
}
} finally {
lock.unlock();
}
} else if (onFatalError || fatalErrorIncrement > 0) {
lock.lock();
try {
emptySignal();
} finally {
lock.unlock();
}
}
}
工具数组evictConnections,keepAliveConnections 用完即被置空，老工具人了。
一波操作下来，完成了对connections数组的大清洗。
小结
- 只写了核心逻辑，很多validate,checker,filter省略了。
- druid连接池源码里面还有很多好用的工具，比如数据库驱动工具，jdbc工具，解析SQL的语法树，ibatis的支持，wall过滤，多数据源…
- 最新的代码我看还有支持配套ZK的高可用方案，用到的话后期我会继续更新源码解析。
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