java线程池源码的理解
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线程池
新建线程和切换线程的开销太大了，使用线程池可以避免频繁的创建和销毁线程，让创建的线程进行复用，节省系统资源。
线程池的关键类：ThreadPoolExecutor。
该类中包含了大量的多线程与并发处理工具，包括ReentrantLock、AtomicInteger、AQS、CAS、BlockingQueue等
主要流程
execute() –> addWorker() –>runWorker() -> getTask()
execute()： 将任务提交到线程池
execute(Runnable command)用于执行任务，参数command为将要执行的任务。
根据线程池的运行状态，以及线程池中的线程数量，决定执行addWorker()，还是拒绝策略reject()。
如果线程数小于核心线程数，则创建worker线程任务并执行。
如果线程数大于核心线程数，只有线程池处于running状态，才会将任务加入到工作队列中。
如果线程数大于最大线程数，或者线程池处于非running状态，就会执行拒绝策略。
核心线程数、最大线程数、拒绝策略等相关参数的解析，详情见：https://www.cnblogs.com/expiator/p/9053754.html
/**
* Executes the given task sometime in the future. The task
* may execute in a new thread or in an existing pooled thread.
*
* If the task cannot be submitted for execution, either because this
* executor has been shutdown or because its capacity has been reached,
* the task is handled by the current {@code RejectedExecutionHandler}.
*
* @param command the task to execute
* @throws RejectedExecutionException at discretion of
* {@code RejectedExecutionHandler}, if the task
* cannot be accepted for execution
* @throws NullPointerException if {@code command} is null
*/
public void execute(Runnable command) {
//execute()的参数command为即要执行的任务
if (command == null)
throw new NullPointerException();
/*
* Proceed in 3 steps:
*
* 1. If fewer than corePoolSize threads are running, try to
* start a new thread with the given command as its first
* task. The call to addWorker atomically checks runState and
* workerCount, and so prevents false alarms that would add
* threads when it shouldn\'t, by returning false.
*
* 2. If a task can be successfully queued, then we still need
* to double-check whether we should have added a thread
* (because existing ones died since last checking) or that
* the pool shut down since entry into this method. So we
* recheck state and if necessary roll back the enqueuing if
* stopped, or start a new thread if there are none.
*
* 3. If we cannot queue task, then we try to add a new
* thread. If it fails, we know we are shut down or saturated
* and so reject the task.
*/
int c = ctl.get();
//如果工作线程数小于核心线程数，则创建worker线程任务并执行
if (workerCountOf(c) < corePoolSize) {
if (addWorker(command, true))
return;
c = ctl.get();
}
//在阻塞队列 BlockingQueue 中 add() 和 offer()都是用来向队列添加一个元素。
//在容量已满的情况下，add() 方法会抛出IllegalStateException异常，offer() 方法只会返回 false 。
//如果工作线程数大于核心线程数，只有线程池处于running状态，才会将任务加入到工作队列中。
if (isRunning(c) && workQueue.offer(command)) {
int recheck = ctl.get();
if (! isRunning(recheck) && remove(command))
reject(command);
else if (workerCountOf(recheck) == 0)
addWorker(null, false);
}
else if (!addWorker(command, false))
reject(command);
}
重要参数及变量
- 控制状态的变量 ctl：
ctl是一个AtomicInteger原子操作类，能够保证线程安全。
ctl变量定义如下：
private final AtomicInteger ctl = new AtomicInteger(ctlOf(RUNNING, 0));
private static int ctlOf(int rs, int wc) { return rs | wc; }
详细讲解如下：
The main pool control state, ctl, is an atomic integer packing
two conceptual fields
workerCount, indicating the effective number of threads
runState, indicating whether running, shutting down etc
大概意思是：通过对ctl的运算，能够得到两个重要的变量，workerCount(worker线程数量)和runState(线程池运行状态)。
- 线程池运行状态 runState：
runState由几个整型常量RUNNING 、SHUTDOWN 、STOP、TIDYING、TERMINATED表示。
The runState provides the main lifecycle control, taking on values:
RUNNING: Accept new tasks and process queued tasks
SHUTDOWN: Don\'t accept new tasks, but process queued tasks
STOP: Don\'t accept new tasks, don\'t process queued tasks,
and interrupt in-progress tasks
TIDYING: All tasks have terminated, workerCount is zero,
the thread transitioning to state TIDYING
will run the terminated() hook method
TERMINATED: terminated() has completed
内部类Worker：
Worker类，继承AQS，并实现了Runnable。
这个类主要维护线程运行任务的拦截控制状态，用于简化每个Task(任务)执行时获取和释放锁的过程。
Worker类内部有一个thread线程变量，在Worker类实例化时，thread对象也会随之创建。
Worker类和Task(任务)有什么区别？
Task只实现了Runnable接口，而Worker类还继承了AQS，Worker还会协助获取和释放锁。
worker是线程池中的线程，而Task虽然是runnable，但是并没有真正执行，只是被Worker调用了run方法，只有Worker(工人)真正开启并执行。
/**
* Class Worker mainly maintains interrupt control state for
* threads running tasks, along with other minor bookkeeping.
* This class opportunistically extends AbstractQueuedSynchronizer
* to simplify acquiring and releasing a lock surrounding each
* task execution. This protects against interrupts that are
* intended to wake up a worker thread waiting for a task from
* instead interrupting a task being run. We implement a simple
* non-reentrant mutual exclusion lock rather than use
* ReentrantLock because we do not want worker tasks to be able to
* reacquire the lock when they invoke pool control methods like
* setCorePoolSize. Additionally, to suppress interrupts until
* the thread actually starts running tasks, we initialize lock
* state to a negative value, and clear it upon start (in
* runWorker).
*/
private final class Worker
extends AbstractQueuedSynchronizer
implements Runnable
{
/**
* This class will never be serialized, but we provide a
* serialVersionUID to suppress a javac warning.
*/
private static final long serialVersionUID = 6138294804551838833L;
/** Thread this worker is running in. Null if factory fails. */
//非常重要的线程变量
final Thread thread;
/** Initial task to run. Possibly null. */
Runnable firstTask;
/** Per-thread task counter */
volatile long completedTasks;
/**
* Creates with given first task and thread from ThreadFactory.
* @param firstTask the first task (null if none)
*/
Worker(Runnable firstTask) {
setState(-1); // inhibit interrupts until runWorker
this.firstTask = firstTask;
//在Worker类实例化时，thread对象也会随之创建。
this.thread = getThreadFactory().newThread(this);
}
/** Delegates main run loop to outer runWorker */
public void run() {
runWorker(this);
}
// Lock methods
//
// The value 0 represents the unlocked state.
// The value 1 represents the locked state.
protected boolean isHeldExclusively() {
return getState() != 0;
}
protected boolean tryAcquire(int unused) {
if (compareAndSetState(0, 1)) {
setExclusiveOwnerThread(Thread.currentThread());
return true;
}
return false;
}
protected boolean tryRelease(int unused) {
setExclusiveOwnerThread(null);
setState(0);
return true;
}
public void lock() { acquire(1); }
public boolean tryLock() { return tryAcquire(1); }
public void unlock() { release(1); }
public boolean isLocked() { return isHeldExclusively(); }
void interruptIfStarted() {
Thread t;
if (getState() >= 0 && (t = thread) != null && !t.isInterrupted()) {
try {
t.interrupt();
} catch (SecurityException ignore) {
}
}
}
}
addWorker()： 添加工作线程Worker。
addWorker()方法的布尔参数core，取决了workerCount(也就是worker数量)的边界范围。
该方法实例化Worker对象worker，worker内部的线程变量thread获取可重入锁ReentrantLock。
通过ReentrantLock加锁，保证线程安全。
接着会将新建的worker对象添加到HashSet
最后开启线程，会自动执行worker对象内部的run()方法，run()方法内部会执行runWorker()。
/**
* Checks if a new worker can be added with respect to current
* pool state and the given bound (either core or maximum). If so,
* the worker count is adjusted accordingly, and, if possible, a
* new worker is created and started, running firstTask as its
* first task. This method returns false if the pool is stopped or
* eligible to shut down. It also returns false if the thread
* factory fails to create a thread when asked. If the thread
* creation fails, either due to the thread factory returning
* null, or due to an exception (typically OutOfMemoryError in
* Thread.start()), we roll back cleanly.
*
* @param firstTask the task the new thread should run first (or
* null if none). Workers are created with an initial first task
* (in method execute()) to bypass queuing when there are fewer
* than corePoolSize threads (in which case we always start one),
* or when the queue is full (in which case we must bypass queue).
* Initially idle threads are usually created via
* prestartCoreThread or to replace other dying workers.
*
* @param core if true use corePoolSize as bound, else
* maximumPoolSize. (A boolean indicator is used here rather than a
* value to ensure reads of fresh values after checking other pool
* state).
* @return true if successful
*/
private boolean addWorker(Runnable firstTask, boolean core) {
retry:
for (;;) {
int c = ctl.get();
int rs = runStateOf(c);
// Check if queue empty only if necessary.
if (rs >= SHUTDOWN &&
! (rs == SHUTDOWN &&
firstTask == null &&
! workQueue.isEmpty()))
return false;
for (;;) {
int wc = workerCountOf(c);
//方法的布尔参数core，取决了workerCount(也就是worker数量)的边界范围
if (wc >= CAPACITY ||
wc >= (core ? corePoolSize : maximumPoolSize))
return false;
//通过CAS机制，进行加1操作。具体内容见下文。
if (compareAndIncrementWorkerCount(c))
break retry;
c = ctl.get(); // Re-read ctl
if (runStateOf(c) != rs)
continue retry;
// else CAS failed due to workerCount change; retry inner loop
}
}
boolean workerStarted = false;
boolean workerAdded = false;
Worker w = null;
try {
//实例化Worker对象，Worker对象内部的线程变量thread获取可重入锁ReentrantLock，操作完毕就释放锁，保证线程安全。
w = new Worker(firstTask);
final Thread t = w.thread;
if (t != null) {
final ReentrantLock mainLock = this.mainLock;
mainLock.lock();
try {
// Recheck while holding lock.
// Back out on ThreadFactory failure or if
// shut down before lock acquired.
int rs = runStateOf(ctl.get());
if (rs < SHUTDOWN ||
(rs == SHUTDOWN && firstTask == null)) {
if (t.isAlive()) // precheck that t is startable
throw new IllegalThreadStateException();
//将worker对象添加到HashSet<Worker>对象workers里面。这个HashSet集合workers的size()，其实就是线程池的大小。
workers.add(w);
int s = workers.size();
if (s > largestPoolSize)
largestPoolSize = s;
workerAdded = true;
}
} finally {
mainLock.unlock();
}
if (workerAdded) {
//开启线程，会自动执行Worker对象内部的run()方法，run()方法内部会执行runWorker()。
t.start();
workerStarted = true;
}
}
} finally {
if (! workerStarted)
addWorkerFailed(w);
}
return workerStarted;
}
- AtomicInteger和CAS：
在多线程中操作基本类型变量，为了保证线程安全，使用AtomicInteger是一个非常好的选择。
ctl是一个AtomicInteger对象。AtomiInteger对象，可以通过CAS机制，对变量进行操作，如自增等。
CAS就是CompareAndSwap，比较和替换。当变量的值为期望值时，将其修改为对应的更新值。
关于AtomicInteger和CAS，详情参考：https://www.cnblogs.com/expiator/p/9449298.html
上面的addWorker()中调用的compareAndIncrementWorkerCount()方法如下：
/**
* Attempts to CAS-increment the workerCount field of ctl.
*/
private boolean compareAndIncrementWorkerCount(int expect) {
return ctl.compareAndSet(expect, expect + 1);
}
/**
* Atomically sets the value to the given updated value
* if the current value {@code ==} the expected value.
*
* @param expect the expected value
* @param update the new value
* @return {@code true} if successful. False return indicates that
* the actual value was not equal to the expected value.
*/
public final boolean compareAndSet(int expect, int update) {
return unsafe.compareAndSwapInt(this, valueOffset, expect, update);
}
runWorker(): 运行工作线程Worker。
通过task.run();执行任务。
/**
* 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 {
//获取任务
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 {
//执行任务
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);
}
}
getTask()：获取任务
从工作队列workQueue中取出任务task。
workQueue是一个BlockingQueue(阻塞队列)，使用take()和poll()函数都可以从队列中取数。
区别是：如果队列中没有数据时，使用take()则线程await()等待。而poll()则不会等待，直接返回null。
/**
* 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?
//是否需要计时处理，如果设置了allowCoreThreadTimeOut或当前工作线程数量大于corePoolSize 则需要计时处理
boolean timed = allowCoreThreadTimeOut || wc > corePoolSize;
if ((wc > maximumPoolSize || (timed && timedOut))
&& (wc > 1 || workQueue.isEmpty())) {
if (compareAndDecrementWorkerCount(c))
return null;
continue;
}
try {
//workQueue是一个BlockingQueue(阻塞队列)，使用take()和poll()函数都可以从队列中取数。
//区别是：如果队列中没有数据时，使用take()则线程await()等待。而poll()则不会等待，直接返回null。
Runnable r = timed ?
workQueue.poll(keepAliveTime, TimeUnit.NANOSECONDS) :
workQueue.take();
if (r != null)
return r;
timedOut = true;
} catch (InterruptedException retry) {
timedOut = false;
}
}
}
线程池size
前文提到，在addWorker()中，会将新建的worker对象添加到HashSet
而线程池中的线程数量，就是指workers这个Set集合的size。
/**
* Returns the current number of threads in the pool.
*
* @return the number of threads
*/
public int getPoolSize() {
final ReentrantLock mainLock = this.mainLock;
mainLock.lock();
try {
// Remove rare and surprising possibility of
// isTerminated() && getPoolSize() > 0
return runStateAtLeast(ctl.get(), TIDYING) ? 0
: workers.size();
} finally {
mainLock.unlock();
}
}
线程池的总结：
主要流程：execute() –> addWorker() –>runWorker() -> getTask() -> run()
execute()： 将任务提交到线程池
addWorker()： 添加工作线程Worker。
runWorker(): 运行工作线程Worker。
getTask()：获取任务Task
Worker：工作线程worker ， worker是线程池中的线程。
Task： 就是具体的任务。
Worker(工人)就是负责干活的，用来运行Task(任务)。
通过复用Worker工作线程，不断去从WorkQueue(工作队列)中获取Task(任务)并执行Task (任务)，达到复用线程的目的，减少系统的资源开销。
参考资料
《码出高效》
https://www.cnblogs.com/sxkgeek/p/9343519.html
https://blog.csdn.net/programmer_at/article/details/79799267
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