笔记文章，没有废话，句句关键
线程池的优点
- 重用线程池里的线程，避免创建和销毁线程所带来的性能开销
- 有效控制最大并发数，避免造成线程间抢占系统资源而造成阻塞
- 提高线程可管理性，可以统一进行分配，调优和监控的能力
Android中的线程池
复用Java中的Executor接口，具体实现类为ThreadPoolExecutor，它有以下几个参数：
| 参数 | 说明 | 注释 |
|---|---|---|
| corePoolSize | 线程池中核心线程数量 | 一直存活，即使处于闲置状态 |
| maximumPoolSize | 最大能创建的线程数量(非核心线程，包含核心线程个数) | 达到这个值后，后续任务会阻塞 |
| keepAliveTime | 非核心线程最大存活时间 | 当设置allowCoreThreadTimeOut=true 同样会做用于核心线程，但通常不会这么做 |
| unit | keepAliveTime的时间单位 | TimeUnit中的枚举(时间单位) |
| workQueue | 等待队列。execute 方法提交的Runnable存储在其中 | 如果线程池中的线程数量大于等于corePoolSize的时候，把该任务放入等待队列 |
| threadFactory | 线程创建工程厂(用来创建线程的) | 默认使用Executors.defaultThreadFactory() 来创建线程，线程具有相同的NORM_PRIORITY优先级并且是非守护线程 |
| handler | 线程池的饱和拒绝策略(不常用) | 阻塞队列已且没有空闲的线程，此时继续提交任务，就需要采取一种策略处理该任务，默认会抛出异常 |
常用与关键方法
- void execute(Runnable run)//提交任务,交由线程池调度
- void shutdown()//关闭线程池,等待任务执行完成
- void shutdownNow()//关闭线程池，不等待任务执行完成
- int getTaskCount()//返回线程池找中所有任务的数量
(已完成的任务+阻塞队列中的任务)- int getCompletedTaskCount()//返回线程池中已执行完成的任务数量
(已完成的任务)- int getPoolSize()//返回线程池中已创建线程数量
- int getActiveCount()//返回当前正在运行的线程数量
- void terminated() 线程池终止时执行的策略
线程池的运行状态
线程存在运行状态，如下：
| 状态 | 说明 |
|---|---|
| NEW | 初始状态,线程被新建,还没调用start方法 |
| RUNNABLE | 运行状态,把"运行中"和"就绪"统称为运行状态 |
| BLOCKED | 阻塞状态，表示线程阻塞于锁 |
| WAITING | 等待状态,需要其他线程通知唤醒 |
| TIME_WAITING | 超时等待状态，表示可以在指定的时间超时后自行返回 |
| TERMINATED | 终止状态，表示当前线程已经执行完毕 |
在这里插入图片描述
根据线程的运行状态思想，我们来展开线程池的运行状态：
| 状态 | 说明 |
|---|---|
| RUNNABLE | 表示当前线程池，存在正在运行的线程 |
| SHUTDOWN | 关闭线程池，不在执行新的任务，但会执行完线程池正在运行的任务，和添加到队列中的任务，对应shutDown()方法 |
| STOP | 立即关闭线程池，打断正在运行的任务，且不再处理等待队列中已添加的任务，对应shutDownNow()方法 |
| TIDYING | shutDown() / shutDownNow()后进入此状态，表示队列和线程池为空，之后进入TERMINATED状态 |
| TERMINATED | 终止状态，表示当前线程已经执行完毕，并调用 terminated() 通知外界 |
在这里插入图片描述
安卓中常用的四种线程池
Executors.newFixedThreadPool()
public static ExecutorService newFixedThreadPool(int nThreads) {
return new ThreadPoolExecutor(nThreads, nThreads,
0L, TimeUnit.MILLISECONDS,
new LinkedBlockingQueue<Runnable>());
}
- 线程数量固定的线程池
- 核心线程与非核心线程数量相等，意味着只有核心线程。
- keepAliveTime = 0L，即使线程池中的线程空闲，也不会被回收。除非调用shutDown()或shutDownNow()去关闭线程池。
- 可以更快响应外界的请求，且任务阻塞队列为无边界队列(LinkedBlockingQueue()链表结构的阻塞队列)，意味着任务可以无限加入线程池
Executors.newScheduledThreadPool()
public static ScheduledExecutorService newScheduledThreadPool(int corePoolSize) {
return new ScheduledThreadPoolExecutor(corePoolSize);
}
public ScheduledThreadPoolExecutor(int corePoolSize) {
super(corePoolSize, Integer.MAX_VALUE,
10L, MILLISECONDS,
new DelayedWorkQueue());
}
- 核心线程数量固定，非核心线程是无限的，但非核心线程存活时间非常短(10毫秒)，闲置后会被回收
- 适合执行定时任务和固定周期的重复任务
- DelayedWorkQueue()优先级队列(堆结构)，会根据定时/延时时间进行排序，延时少，先执行
Executors.newSingleThreadExecutor() 常用
public static ExecutorService newSingleThreadExecutor() {
return new FinalizableDelegatedExecutorService
(new ThreadPoolExecutor(1, 1,
0L, TimeUnit.MILLISECONDS,
new LinkedBlockingQueue<Runnable>()));
}
- 核心线程数为1的线程池。确保所有任务在同一线程执行，因此不用考虑线程同步问题
- 阻塞队列是无界的，可以一直接收任务 、
Executors.newCachedThreadPool()
public static ExecutorService newCachedThreadPool() {
return new ThreadPoolExecutor(0, Integer.MAX_VALUE,
60L, TimeUnit.SECONDS,
new SynchronousQueue<Runnable>());
}
- 没有核心线程，非核心线程无限，但执行完任务后，线程存活60秒
- SynchronousQueue() 是个特殊的队列，可以理解为无法存储元素的队列，因此线程池接收到任务就会创建线程去执行。当整个线程都处于空闲状态，60秒后，线程池中的线程都会因超时而被停止，不占用系统资源。
- 根据以上两点，此线程池适合执行耗时较少但频繁的任务执行
线程池源码分析
在分析源码前，先把线程池的工作流程图放出来。这样再去阅读源码会更加深刻
在这里插入图片描述
阅读源码的目的：
- 了解线程池如何复用
- 如何巧妙的使用线程池，根据需求去自定义线程池，以达到暂停/恢复，任务具有优先级，监控，分配，调优等
- 学习线程池优秀的设计思想
- 面试吹牛逼
源码分析
分析源码前看下ThreadPoolExecutor类的头注释：
public class ThreadPoolExecutor extends AbstractExecutorService {
/**
* 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
*
* In order to pack them into one int, we limit workerCount to
* (2^29)-1 (about 500 million) threads rather than (2^31)-1 (2
* billion) otherwise representable. If this is ever an issue in
* the future, the variable can be changed to be an AtomicLong,
* and the shift/mask constants below adjusted. But until the need
* arises, this code is a bit faster and simpler using an int.
*
* The workerCount is the number of workers that have been
* permitted to start and not permitted to stop. The value may be
* transiently different from the actual number of live threads,
* for example when a ThreadFactory fails to create a thread when
* asked, and when exiting threads are still performing
* bookkeeping before terminating. The user-visible pool size is
* reported as the current size of the workers set.
*
* 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
*
* The numerical order among these values matters, to allow
* ordered comparisons. The runState monotonically increases over
* time, but need not hit each state. The transitions are:
*
* RUNNING -> SHUTDOWN
* On invocation of shutdown(), perhaps implicitly in finalize()
* (RUNNING or SHUTDOWN) -> STOP
* On invocation of shutdownNow()
* SHUTDOWN -> TIDYING
* When both queue and pool are empty
* STOP -> TIDYING
* When pool is empty
* TIDYING -> TERMINATED
* When the terminated() hook method has completed
*
* Threads waiting in awaitTermination() will return when the
* state reaches TERMINATED.
*
* Detecting the transition from SHUTDOWN to TIDYING is less
* straightforward than you'd like because the queue may become
* empty after non-empty and vice versa during SHUTDOWN state, but
* we can only terminate if, after seeing that it is empty, we see
* that workerCount is 0 (which sometimes entails a recheck -- see
* below).
*/
这里面有几段比较重要 ：
* 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
*
* In order to pack them into one int, we limit workerCount to
* (2^29)-1 (about 500 million) threads rather than (2^31)-1 (2
* billion) otherwise representable. If this is ever an issue in
* the future, the variable can be changed to be an AtomicLong,
* and the shift/mask constants below adjusted. But until the need
* arises, this code is a bit faster and simpler using an int.
线程池控制状态：clt是一个int类型的原子数，它表示两个概念，workerCount表示有效线程，笼统来讲，相当于线程池运行的线程个数，runState，表示线程池运行的状态，如RUNNING SHUTDOWN等等。
将这两个字段打包成一个int，int为4字节，有32位，后29位表示线程池的数量，最大可为5个亿，如果不够可以使用AtomicLong代替。当然，运行线程池根本达不到这个数量。
* 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
五种状态的含义，上面提到过。
* RUNNING -> SHUTDOWN
* On invocation of shutdown(), perhaps implicitly in finalize()
* (RUNNING or SHUTDOWN) -> STOP
* On invocation of shutdownNow()
* SHUTDOWN -> TIDYING
* When both queue and pool are empty
* STOP -> TIDYING
* When pool is empty
* TIDYING -> TERMINATED
* When the terminated() hook method has completed
线程池状态间的变化。等于上面我们画的图：
在这里插入图片描述
结论：阅读源码可以适当看下类头说明，尤其是Android源码的类头。可以帮我们更好的理解源码
从线程池入口进入，理解前面提到ctl是什么？
private final AtomicInteger ctl = new AtomicInteger(ctlOf(RUNNING, 0));
private static final int COUNT_BITS = Integer.SIZE - 3;
private static final int CAPACITY = (1 << COUNT_BITS) - 1;
// runState is stored in the high-order bits
private static final int RUNNING = -1 << COUNT_BITS;
private static final int SHUTDOWN = 0 << COUNT_BITS;
private static final int STOP = 1 << COUNT_BITS;
private static final int TIDYING = 2 << COUNT_BITS;
private static final int TERMINATED = 3 << COUNT_BITS;
// Packing and unpacking ctl
private static int runStateOf(int c) { return c & ~CAPACITY; }
private static int workerCountOf(int c) { return c & CAPACITY; }
private static int ctlOf(int rs, int wc) { return rs | wc; }
Integer.SIZE为32，因此 COUNT_BITS = Integer.SIZE - 3 = 29 CAPACITY表示线程池的容量：
(1 << COUNT_BITS) - 1 ---> 100000000000000000000000000000(30位) -1 -->11111111111111111111111111111(29位)
这代表int32位中，前3位表示线程池状态，后面29位表示容量
计算结果的值如下：
在这里插入图片描述
总结来说，状态值自上而下，数值越来越大
AtomicInteger ctl = new AtomicInteger(ctlOf(RUNNING, 0)); 初始状态为RUNNING，线程数为0
ctlOf(int rs, int wc) 用来获取int中的值，用来调用下面两个方法 ：
private static int runStateOf(int c) { return c & ~CAPACITY; } 获取线程池状态
private static int workerCountOf(int c) { return c & CAPACITY; } 获取线程池中线程的有效线程数量
计算方式如下：
在这里插入图片描述
理解上面的计算方式后，开始真正进入源码阅读
==入口函数：== Executor.execute()
public void execute(Runnable command) {
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();
int num=workerCountOf(c);
//第一步
if (workerCountOf(c) < corePoolSize) {
if (addWorker(command, true))
return;
c = ctl.get();
}
//第二步
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);
}//第三步
else if (!addWorker(command, false))
reject(command);
}
// firstTask 待执行的任务 core 是否要启动核心线程
private boolean addWorker(Runnable firstTask, boolean core)
注释翻译： 线程池的执行分3步 ：
if (workerCountOf(c) < corePoolSize) {
if (addWorker(command, true))
return;
c = ctl.get();
}
- 如果小于
corePoolSize核心线程数，直接创先新的线程，并将任务作为该线程的第一个任务(firstTask),因为是往新线程执行任务，肯定是第一个任务addWorker()方法的返回值，表示是否添加任务成功，core = true启动核心线程 ，并直接return返回，不再向下执行
// if (workerCountOf(c) < corePoolSize)不满足，也就是运行线程大于等于核心线程，
// 此时可能为核心线程达到最大，或核心与非核心共存
// 那么继续判断，线程池为运行状态，同时加入队列是否成功，
if (isRunning(c) && workQueue.offer(command)) {
// 成功！ 进行二次校验，因为此时线程池中的线程可能销毁
// (比如非核心到达keepAliveTime指定存活时间而销毁)或某个线程调用shutDown()方法
int recheck = ctl.get();
// 如果不是RUNNING状态 ，删除刚才offer进队列的任务
if (!isRunning(recheck) && remove(command))
// 执行拒绝策略 调用创建时的handle，执行 handler.rejectedExecution(command, this); ，默认抛出异常
reject(command);
// 如果线程池 没有正在运行的线程，那么这个线程可能调用了shutDown()方法，要关闭了，那么就加入个null任务，
//也就是不执行 新任务，去创建非核心线程 core = false 执行等待队列中的任务
else if (workerCountOf(recheck) == 0)
// 添加个null任务
addWorker(null, false);
}//(isRunning(c) && workQueue.offer(command))不满足，表示等待队列已满，添加任务失败，那么就创建非核心线程
//如果创建失败。执行拒绝策略
else if (!addWorker(command, false))
reject(command);
boolean addWorker(Runnable firstTask, boolean core) 方法逻辑：
private boolean addWorker(Runnable firstTask, boolean core) {
retry: //双层for循环 retry外层循环终止标识
for (;;) {
int c = ctl.get();
int rs = runStateOf(c);
// Check if queue empty only if necessary.
// 如果rs >= SHUTDOWN不再添加新执行 后面的判断条件可以不关心
if (rs >= SHUTDOWN &&
! (rs == SHUTDOWN &&
firstTask == null &&
! workQueue.isEmpty()))
return false;
for (;;) {
//获取线程池中，正在执行任务的线程
int wc = workerCountOf(c);
// 大于容量，不可能，不用看， wc >= (core ? corePoolSize : maximumPoolSize 这个判断是
// core = true wc >= corePoolSize 核心线程到达上限 还要创建核心线程，不可以 返回false
// core = false wc >= maximumPoolSize 达到非核心(包含核心)线程上限 还要创建线程 返回false
if (wc >= CAPACITY ||
wc >= (core ? corePoolSize : maximumPoolSize))
return false;
//workerCount +1
if (compareAndIncrementWorkerCount(c))
//返回外层循环 ，循环结束
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 {
// 创建 Worker包装我们的任务 firstTask
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());
// rs < SHUTDOWN 表示是RUNNING 状态
// (rs == SHUTDOWN && firstTask == null) 表示SHUTDOWN状态 且没有不再执行新的任务
//2种情况都会继续执行
if (rs < SHUTDOWN ||
(rs == SHUTDOWN && firstTask == null)) {
if (t.isAlive()) // precheck that t is startable
throw new IllegalThreadStateException();
//上面2中情况 都会加入workers集合
workers.add(w);
int s = workers.size();
if (s > largestPoolSize)
largestPoolSize = s;
// 设置 workerAdded = true;
workerAdded = true;
}
} finally {
mainLock.unlock();
}
//workerAdded = true; 上面设置过了
if (workerAdded) {
// 启动线程
t.start();
workerStarted = true;
}
}
} finally {
if (! workerStarted)
addWorkerFailed(w);
}
return workerStarted;
}
private final class Worker
extends AbstractQueuedSynchronizer
implements Runnable
{
Worker(Runnable firstTask) {
setState(-1); // inhibit interrupts until runWorker
//赋值 firstTask 并用getThreadFactory()得到我们创建时传入的线程工厂调用newThread(this);创建线程，并将自己作为Runnable传入
this.firstTask = firstTask;
this.thread = getThreadFactory().newThread(this);
}
｝
Worker是个Runnable，上面的w = new Worker(firstTask); 方法其实就是对任务的包装 在构造方法中赋值 firstTask 并用getThreadFactory()得到我们传入的线程工厂调用newThread(this);创建线程，并将自己作为Runnable传入。当addWorker()方法调用 t.start() 就会执行Worker类中的run()方法。
Worker类
/** Delegates main run loop to outer runWorker. */
public void run() {
runWorker(this);
}
final void runWorker(Worker w) {
Thread wt = Thread.currentThread();
//赋值firstTask给task
Runnable task = w.firstTask;
w.firstTask = null;
w.unlock(); // allow interrupts
boolean completedAbruptly = true;
try {
//要执行的任务不为空直接执行，如果等于空调用getTask()去等待队列取出任务
//对应了最开始 (workerCountOf(recheck) == 0) addWorker(null, false); 这段判断条件
//之前提过 添加空任务的目的就是不再执行新任务，去执行等待队列的任务。因此getTask()得以执行
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 {
//执行任务前调用此时已经在线程中运行 ，因为这是在Worker中的run方法中
beforeExecute(wt, task);
Throwable thrown = null;
try {
//执行我们传入的Runnable
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 {
//执行任务后调用
afterExecute(task, thrown);
}
} finally {
task = null;
w.completedTasks++;
w.unlock();
}
}
completedAbruptly = false;
} finally {
processWorkerExit(w, completedAbruptly);
}
}
getTask()
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?
//allowCoreThreadTimeOut 表示非核心线程的超时时间keepAliveTime是否作用于核心线程，默认是false
// 所以只看后面的判断。 wc > corePoolSize;表示大于核心线程 true
boolean timed = allowCoreThreadTimeOut || wc > corePoolSize;
if ((wc > maximumPoolSize || (timed && timedOut))
&& (wc > 1 || workQueue.isEmpty())) {
if (compareAndDecrementWorkerCount(c))
return null;
continue;
}
try {
//wc > corePoolSize;表示大于核心线程 true 那么就是创建非核心线程，非核心线程取等待队列的任务有超时时间
//keepAliveTime时间内阻塞。取不到 继续向下执行 任务完成 非核心线程执行完销毁
// wc <= corePoolSize; 小于或等于核心线程 workQueue.take();一直阻塞，当队列有任务时立马执行，这也是为什么核心线程一直存在的原因
Runnable r = timed ?
workQueue.poll(keepAliveTime, TimeUnit.NANOSECONDS) :
workQueue.take();
if (r != null)
return r;
timedOut = true;
} catch (InterruptedException retry) {
timedOut = false;
}
}
}
总结：
- 核心线程一直存在的原因的是
workQueue.take();，非核心线程keepAliveTime时间后销毁的原因是workQueue.poll(keepAliveTime, TimeUnit.NANOSECONDS) - 从以下代码可以看出。线程池中的任务达到corePoolSize线程数时，所有任务都会先添加到队列，当调用
workQueue.offer(command)时，被workQueue.take();阻塞的核心线程就会拿到任务，然后去执行，这很关键 - if (isRunning(c) && workQueue.offer(command)) 里的条件当都都不满足时，什么都不会做，之后会往队列中添加元素。当核心线程有空间是getTask中take()阻塞的核心线程会立马取队列取出个任务执行
-
beforeExecute(wt, task);和afterExecute(task, thrown);会在任务执行前后执行 - 核心线程与非核心线程没有本质上的区别。只是在获取任务时，通过此判断
boolean timed = allowCoreThreadTimeOut || wc > corePoolSize;,非核心超时会结束，而非核心则会take()阻塞住
int c = ctl.get();
int num=workerCountOf(c);
if (workerCountOf(c) < corePoolSize) {
if (addWorker(command, true))
return;
c = ctl.get();
}
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);
通过源码 得出前面提到的结论
在这里插入图片描述
实战
根据学习到的源码，封装自己的线程池工具类，它有一下几个特点：
/**
* 1. 支持按任务的优先级去执行,
* 2. 支持线程池暂停.恢复(批量文件下载，上传) ，
* 3. 异步结果主动回调主线程
* todo 线程池能力监控,耗时任务检测,定时,延迟,
*
* 根据源码分析 ：当工作线程小于核心线程是不会进入队列
* 当核心线程启动后 每次都会进入队列
*/
object MyExecutor {
private var hiExecutor: ThreadPoolExecutor
//暂停线程表示：hint：这里为啥不用volatile修饰？
private var isPause: Boolean = false
private val lock: ReentrantLock = ReentrantLock()
private val condition: Condition = lock.newCondition()
//主线程回调能力的Handler
private val handler: Handler = Handler(Looper.getMainLooper())
private val seq = AtomicLong()
/**
* 初始化构建参数
*/
init {
//获取cpu数
val cpuCount = Runtime.getRuntime().availableProcessors()
//核心线程数为 cpu+1
val corePoolSize = cpuCount + 1
//非核心线程数
val maxPoolSize = corePoolSize * 2 + 1
//非核心线程超时销毁时间
val keepAliveTime = 30L
//超时时间单位 s
val unit = TimeUnit.SECONDS
//相当于默认实现 创建一个线程
val threadFactory = ThreadFactory() {
val thread = Thread(it)
//hi-executor-0
thread.name = "hi-executor-" + seq.getAndIncrement()
return@ThreadFactory Thread(it)
}
val priorityBlockingQueue: PriorityBlockingQueue<out Runnable> = PriorityBlockingQueue()
hiExecutor = object : ThreadPoolExecutor(
corePoolSize,
maxPoolSize,
keepAliveTime,
unit,
priorityBlockingQueue as BlockingQueue<Runnable>,
threadFactory
) {
override fun beforeExecute(t: Thread?, r: Runnable?) {
priorityBlockingQueue.size
if (isPause) {
try {
//条件唤醒线程标准写法
lock.lock()
condition.await()
} finally {
lock.unlock()
}
}
}
override fun afterExecute(r: Runnable?, t: Throwable?) {
//监控线程池耗时任务,线程创建数量,正在运行的数量
HiLog.e("已执行完的任务的优先级是：" + (r as PriorityRunnable).priority.toString() + Thread.currentThread().name)
priorityBlockingQueue.size
}
}
}
/**
* priority越小 优先级越高
* @IntRange 优先级范围
* @JvmOverloads 重载方法 生成多种重载方法
* 无返回结果 与正常线程池一样
*/
@JvmOverloads
fun execute(@IntRange(from = 0, to = 10) priority: Int = 0, runnable: Runnable) {
hiExecutor.execute(PriorityRunnable(priority, runnable))
}
@JvmOverloads
fun execute(@IntRange(from = 0, to = 10) priority: Int = 0, callable: Callable<*>) {
val newFixedThreadPool = Executors.newCachedThreadPool()
hiExecutor.execute(PriorityRunnable(priority, callable))
}
/**
* 提供任务执行后返回结果
* 并回调主线程的能力
*
* <T> 返回结果类型
*/
abstract class Callable<T> : Runnable {
override fun run() {
handler.post {
onPrepare()
}
//真正在线程中执行
val t = onBackground()
handler.post {
onCompleted(t)
}
}
/**
* UI线程执行
*/
open fun onPrepare() {
//预处理 加载动画
}
/**
* 线程池中执行
*/
abstract fun onBackground(): T
/**
* UI线程执行
* 执行完成
*/
abstract fun onCompleted(t: T)
}
/**
* 重写的意义在于 PriorityBlockingQueue 这个具有排序的堆结构 每个元素要有比较大小的能力
* priority 线程优先级 runnable执行任务的
*/
class PriorityRunnable(val priority: Int, private val runnable: Runnable) : Runnable,
Comparable<PriorityRunnable> {
override fun run() {
runnable.run()
}
override fun compareTo(other: PriorityRunnable): Int {
//倒序 priority越小 优先级越高
return if (this.priority < other.priority) 1 else if (this.priority > other.priority) -1 else 0
}
}
//可能多个线程调用此方法
@Synchronized
fun pause() {
isPause = true
HiLog.e("hiexecutor is paused")
}
@Synchronized
fun resume() {
isPause = false
lock.lock()
try {
condition.signalAll()
} finally {
lock.unlock()
}
HiLog.e("hiexecutor is resume")
}
}
测试代码：
private void priorityExecutor() {
for (int priority = 0; priority < 10; priority++) {
int finalPriority = priority;
HiExecutor.INSTANCE.execute(priority, () -> {
try {
Thread.sleep(1000 - finalPriority * 100);
} catch (InterruptedException e) {
e.printStackTrace();
}
});
}
}
利用源码分析后，我们可以起送封装自己的线程池，它具有以下几种能力：
- 支持按任务的优先级去执行,
- 支持线程池暂停.恢复(批量文件下载，上传) ，
- 异步结果主动回调主线程
- 线程池能力监控,耗时任务检测,定时,延迟,
但执行中有2个问题：
- 明明是使用优先队列，但为什么第一次的时候，任务优先级却是无序的，第二次就好了？
- 使用pause()方法，线程还在执行？
对于问题1。
在这里插入图片描述
首先线程池的调度是，系统Cpu利用时间碎片随机去调度的。我们的设置的只能尽可能的去满足，按照优先级去执行，但不能保证。更重要的是。通过源码得知。当线程池中，核心线程数未到最大值时(测试例子中是5)，是不会加入到队列的，因此也就不会排序。当第二次执行任务，线程池就会先加入队列。然后被take()阻塞的队列取到任务，然后执行，此时就有了优先级。 解决：可以在应用启动白屏页启动线程。异步去初始化第三方库，或其他不需要优先级的任务。这样之后的任务就都具有优先级了
对于问题2：
在这里插入图片描述
通过源码分析：
final void runWorker(Worker w) {
Thread wt = Thread.currentThread();
Runnable task = w.firstTask;
w.firstTask = null;
w.unlock(); // allow interrupts
boolean completedAbruptly = true;
try {
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);
}
}
我们重写的方法
override fun beforeExecute(t: Thread?, r: Runnable?) {
priorityBlockingQueue.size
if (isPause) {
try {
//条件唤醒线程标准写法
lock.lock()
condition.await()
} finally {
lock.unlock()
}
}
}
beforeExecute(wt, task);中的方法，只能阻塞当前线程，和后续要执行的线程，已经在线程中开始执行的任务是无法暂停的。 这其实不是问题。只是种误解
总结
通过学习源码，我们的收获：
- 学习了int字段的多重利用，试想下，如果不采用源码中的方式，我们至少要维护这几种字段：
sunStatus，runningWorkCount，maxWorkCount，shutDownWorkCount，还要保证他们的线程安全。这是种值得借鉴的优秀思想。ARouter中也用到类似思想(extars) - 清楚线程池的状态和工作流程，理解线程池如何复用与非核心线程的销毁
- 找到插手线程池的思路，扩展自己的线程池
- 通过学习源码，还解决了2个看似不合理，但却在源码中有答案的问题。
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