要想写出高性能高并发的应用，自然有许多关键，如io，算法，异步，语言特性，操作系统特性，队列，内存，cpu，分布式，网络，数据结构，高性能组件。
胡说一通先。
回到主题，线程池。如果说多线程是提高系统并发能力的利器之一，那么线程池就是让这个利器更容易控制的一种工具。如果我们自己纯粹使用多线程基础特性编写，那么，必然需要相当老道的经验，才能够驾驭复杂的环境。而线程池则不需要，你只需知道如何使用，即可轻松掌控多线程，安全地为你服务。
1. 常见线程池的应用样例
线程池，不说本身很简单，但应用一定是简单的。
线程池有许多的实现，但我们只说 ThreadPoolExecutor 版本，因其应用最广泛，别无其他。当然了，还有一个定时调度线程池 ScheduledThreadPoolExecutor 另说，因其需求场景不同，无法比较。
下面，我就几个应用级别，说明下我们如何快速使用线程池。（走走过场而已，无关其他）
1.1. 初级线程池
初级版本的使用线程池，只需要借助一个工具类即可： Executors . 它提供了许多静态方法，你只需随便选一个就可以使用线程池了。比如：
// 创建固定数量的线程池
Executors.newFixedThreadPool(8);
// 创建无限动态创建的线程池
Executors.newCachedThreadPool();
// 创建定时调度线程池
Executors.newScheduledThreadPool(2);
// 还有个创建单线程的就不说了，都一样
使用上面这些方法创建好的线程池，直接调用其 execute() 或者 submit() 方法，就可以实现多线程编程了。没毛病！
1.2. 中级线程池
我这里所说的中级，实际就是不使用以上超级简单方式使用线程池的方式。即你已经知道了 ThreadPoolExecutor 这个东东了。这不管你的出发点是啥！
// 自定义各线程参数
ThreadPoolExecutor threadPoolExecutor = new ThreadPoolExecutor(4, 20, 20, TimeUnit.MILLISECONDS, new LinkedBlockingQueue<>());
具体参数解释就不说了，咱们不扫盲。总之，使用这玩意儿，说明你已经开始有点门道了。
1.3. 高级线程池
实际上，这个版本就没法具体说如何做了。
但它可能是，你知道你的线程池应用场景的，你清楚你的硬件运行环境的，你会使用线程池命名的，你会定义你的队列大小的，你会考虑上下文切换的，你会考虑线程安全的，你会考虑锁性能的，你可能会自己造个轮子的。。。
2. 这不是线程池
我们通常理解的线程池，就是能够同时跑多个任务的地方。但有时候线程池不一像线程池，而像一个单线程。来看一个具体的简单的线程池的使用场景：
// 初始化线程池
private ExecutorService executor
= new ThreadPoolExecutor(Runtime.getRuntime().availableProcessors(),
Runtime.getRuntime().availableProcessors(),
0L, TimeUnit.SECONDS,
new ArrayBlockingQueue<>(50),
new NamedThreadFactory("test-pool"),
new ThreadPoolExecutor.CallerRunsPolicy());
// 使用线程池处理任务
public Integer doTask(String updateIntervalDesc) throws Exception {
long startTime = System.currentTimeMillis();
List<TestDto> testList;
AtomicInteger affectNum = new AtomicInteger(0);
int pageSize = 1000;
AtomicInteger pageNo = new AtomicInteger(1);
Map<String, Object> condGroupLabel = new HashMap<>();
log.info("start do sth:{}", updateIntervalDesc);
List<Future<?>> futureList = new ArrayList<>();
do {
PageHelper.startPage(pageNo.getAndIncrement(), pageSize);
List<TestDto> list
= testDao.getLabelListNew(condGroupLabel);
testList = list;
// 循环向线程池中提交任务
for (TestDto s : list) {
Future<?> future = executor.submit(() -> {
try {
// do sth...
affectNum.incrementAndGet();
}
catch (Throwable e) {
log.error("error:{}", pageNo.get(), e);
}
});
futureList.add(future);
}
} while (testList.size() >= pageSize);
// 等待任务完成
int i = 0;
for (Future<?> future : futureList) {
future.get();
log.info("done:+{} ", i++);
}
log.info("doTask done:{}, num:{}, cost:{}ms",
updateIntervalDesc, affectNum.get(), System.currentTimeMillis() - startTime);
return affectNum.get();
}
主要业务就是，从数据库中取出许多任务，放入线程池中运行。因为任务又涉及到db等的io操作，所以使用多线程处理，非常合理。
然而，有一种情况的出现，也许会打破这个平衡：那就是当单个任务能够快速执行完成时，而且快到刚上一任务提交完成，还没等下一次提交时，就任务就已被执行完成。这时，你就可能会看到一个神奇的现象，即一直只有一个线程在运行任务。这不是线程池该干的事，更像是单线程任务在跑。
然后，我们可能开始怀疑：某个线程被阻塞了？线程调度不公平了？队列选择不正确了？触发jdk bug了？线程池未完全利用的线程了？等等。。。
然而结果并非如此，究其原因只是当我们向线程池提交任务时，实际上只是向线程池的队列中添加了任务。即上面显示的 ArrayBlockingQueue 添加了任务，而线程池中的各worker负责从队列中获取任务进行执行。而当任务数很少时，自然只有一部分worker会处理执行中了。至于为什么一直是同一个线程在执行，则可能是由于jvm的调度机制导致。事实上，是受制于 ArrayBlockingQueue.poll() 的公平性。而这个poll()的实现原理，则是由 wait/notify 机制的公平性决定的。
如下，是线程池的worker工作原理：
// java.util.concurrent.ThreadPoolExecutor#runWorker
/**
* Main worker run loop. Repeatedly gets tasks from queue and
* executes them, while coping with a number of issues:
*
* 1. We may start out with an initial task, in which case we
* don't need to get the first one. Otherwise, as long as pool is
* running, we get tasks from getTask. If it returns null then the
* worker exits due to changed pool state or configuration
* parameters. Other exits result from exception throws in
* external code, in which case completedAbruptly holds, which
* usually leads processWorkerExit to replace this thread.
*
* 2. Before running any task, the lock is acquired to prevent
* other pool interrupts while the task is executing, and then we
* ensure that unless pool is stopping, this thread does not have
* its interrupt set.
*
* 3. Each task run is preceded by a call to beforeExecute, which
* might throw an exception, in which case we cause thread to die
* (breaking loop with completedAbruptly true) without processing
* the task.
*
* 4. Assuming beforeExecute completes normally, we run the task,
* gathering any of its thrown exceptions to send to afterExecute.
* We separately handle RuntimeException, Error (both of which the
* specs guarantee that we trap) and arbitrary Throwables.
* Because we cannot rethrow Throwables within Runnable.run, we
* wrap them within Errors on the way out (to the thread's
* UncaughtExceptionHandler). Any thrown exception also
* conservatively causes thread to die.
*
* 5. After task.run completes, we call afterExecute, which may
* also throw an exception, which will also cause thread to
* die. According to JLS Sec 14.20, this exception is the one that
* will be in effect even if task.run throws.
*
* The net effect of the exception mechanics is that afterExecute
* and the thread's UncaughtExceptionHandler have as accurate
* information as we can provide about any problems encountered by
* user code.
*
* @param w the worker
*/
final void runWorker(Worker w) {
Thread wt = Thread.currentThread();
Runnable task = w.firstTask;
w.firstTask = null;
w.unlock(); // allow interrupts
boolean completedAbruptly = true;
try {
// worker 不停地向队列中获取任务，然后执行
// 其中获取任务的过程，可能被中断，也可能不会，受到线程池伸缩配置的影响
while (task != null || (task = getTask()) != null) {
w.lock();
// If pool is stopping, ensure thread is interrupted;
// if not, ensure thread is not interrupted. This
// requires a recheck in second case to deal with
// shutdownNow race while clearing interrupt
if ((runStateAtLeast(ctl.get(), STOP) ||
(Thread.interrupted() &&
runStateAtLeast(ctl.get(), STOP))) &&
!wt.isInterrupted())
wt.interrupt();
try {
beforeExecute(wt, task);
Throwable thrown = null;
try {
task.run();
} catch (RuntimeException x) {
thrown = x; throw x;
} catch (Error x) {
thrown = x; throw x;
} catch (Throwable x) {
thrown = x; throw new Error(x);
} finally {
afterExecute(task, thrown);
}
} finally {
task = null;
w.completedTasks++;
w.unlock();
}
}
completedAbruptly = false;
} finally {
processWorkerExit(w, completedAbruptly);
}
}
/**
* Performs blocking or timed wait for a task, depending on
* current configuration settings, or returns null if this worker
* must exit because of any of:
* 1. There are more than maximumPoolSize workers (due to
* a call to setMaximumPoolSize).
* 2. The pool is stopped.
* 3. The pool is shutdown and the queue is empty.
* 4. This worker timed out waiting for a task, and timed-out
* workers are subject to termination (that is,
* {@code allowCoreThreadTimeOut || workerCount > corePoolSize})
* both before and after the timed wait, and if the queue is
* non-empty, this worker is not the last thread in the pool.
*
* @return task, or null if the worker must exit, in which case
* workerCount is decremented
*/
private Runnable getTask() {
boolean timedOut = false; // Did the last poll() time out?
for (;;) {
int c = ctl.get();
int rs = runStateOf(c);
// Check if queue empty only if necessary.
if (rs >= SHUTDOWN && (rs >= STOP || workQueue.isEmpty())) {
decrementWorkerCount();
return null;
}
int wc = workerCountOf(c);
// Are workers subject to culling?
boolean timed = allowCoreThreadTimeOut || wc > corePoolSize;
if ((wc > maximumPoolSize || (timed && timedOut))
&& (wc > 1 || workQueue.isEmpty())) {
if (compareAndDecrementWorkerCount(c))
return null;
continue;
}
try {
// 可能调用超时方法，也可能调用阻塞方法
// 固定线程池的情况下，调用阻塞 take() 方法
Runnable r = timed ?
workQueue.poll(keepAliveTime, TimeUnit.NANOSECONDS) :
workQueue.take();
if (r != null)
return r;
timedOut = true;
} catch (InterruptedException retry) {
timedOut = false;
}
}
}
即线程池worker持续向队列获取任务，执行即可。而队列任务的获取，则由两个读写锁决定：
// java.util.concurrent.ArrayBlockingQueue#take
public E take() throws InterruptedException {
final ReentrantLock lock = this.lock;
// 此处锁，保证执行线程安全性
lock.lockInterruptibly();
try {
while (count == 0)
// 此处释放锁等待，再次唤醒时，要求必须重新持有锁
notEmpty.await();
return dequeue();
} finally {
lock.unlock();
}
}
//
/**
* Inserts the specified element at the tail of this queue, waiting
* for space to become available if the queue is full.
*
* @throws InterruptedException {@inheritDoc}
* @throws NullPointerException {@inheritDoc}
*/
public void put(E e) throws InterruptedException {
checkNotNull(e);
final ReentrantLock lock = this.lock;
lock.lockInterruptibly();
try {
while (count == items.length)
notFull.await();
enqueue(e);
} finally {
lock.unlock();
}
}
/**
* Inserts element at current put position, advances, and signals.
* Call only when holding lock.
*/
private void enqueue(E x) {
// assert lock.getHoldCount() == 1;
// assert items[putIndex] == null;
final Object[] items = this.items;
items[putIndex] = x;
if (++putIndex == items.length)
putIndex = 0;
count++;
// 通知取等线程，唤醒
notEmpty.signal();
}
所以，具体谁取到任务，就是要看谁抢到了锁。而这，可能又涉及到jvm的高效调度策略啥的了吧。（虽然不确定，但感觉像） 至少，任务运行的表象是，所有任务被某个线程一直抢到。
3. 回归线程池
线程池的目的，在于处理一些异步的任务，或者并发的执行多个无关联的任务。在于让系统减负。而当任务的提交消耗，大于了任务的执行消耗，那就没必要使用多线程了，或者说这是错误的用法了。我们应该线程池做更重的活，而不是轻量级的。如上问题，执行性能必然很差。但我们稍做转变，也许就不一样了。
// 初始化线程池
private ExecutorService executor
= new ThreadPoolExecutor(Runtime.getRuntime().availableProcessors(),
Runtime.getRuntime().availableProcessors(),
0L, TimeUnit.SECONDS,
new ArrayBlockingQueue<>(50),
new NamedThreadFactory("test-pool"),
new ThreadPoolExecutor.CallerRunsPolicy());
// 使用线程池处理任务
public Integer doTask(String updateIntervalDesc) throws Exception {
long startTime = System.currentTimeMillis();
List<TestDto> testList;
AtomicInteger affectNum = new AtomicInteger(0);
int pageSize = 1000;
AtomicInteger pageNo = new AtomicInteger(1);
Map<String, Object> condGroupLabel = new HashMap<>();
log.info("start do sth:{}", updateIntervalDesc);
List<Future<?>> futureList = new ArrayList<>();
do {
PageHelper.startPage(pageNo.getAndIncrement(), pageSize);
List<TestDto> list
= testDao.getLabelListNew(condGroupLabel);
testList = list;
// 一批任务只向线程池中提交任务
Future<?> future = executor.submit(() -> {
for (TestDto s : list) {
try {
// do sth...
affectNum.incrementAndGet();
}
catch (Throwable e) {
log.error("error:{}", pageNo.get(), e);
}
}
});
futureList.add(future);
} while (testList.size() >= pageSize);
// 等待任务完成
int i = 0;
for (Future<?> future : futureList) {
future.get();
log.info("done:+{} ", i++);
}
log.info("doTask done:{}, num:{}, cost:{}ms",
updateIntervalDesc, affectNum.get(), System.currentTimeMillis() - startTime);
return affectNum.get();
}
即，让每个线程执行的任务足够重，以至于完全忽略提交的消耗。这样才能够发挥多线程的作用。
原文链接： http://www.cnblogs.com/yougewe/p/14421826.html
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