【多线程与高并发】6-线程池
Executor
public class T01_MyExecutor implements Executor{
public static void main(String[] args) {
new T01_MyExecutor().execute(()->System.out.println("hello executor"));
}
@Override
public void execute(Runnable command) {
//new Thread(command).run();
command.run();
}
}
ExecutorService
/**
* 认识ExecutorService,阅读API文档
* 认识submit方法，扩展了execute方法，具有一个返回值
*/
package com.mashibing.juc.c_026_01_ThreadPool;
import java.util.concurrent.ExecutorService;
import java.util.concurrent.Executors;
public class T02_ExecutorService {
public static void main(String[] args) {
ExecutorService e = Executors.newCachedThreadPool();
}
}
Callable
/**
* 认识Callable，对Runnable进行了扩展
* 对Callable的调用，可以有返回值
*/
public static void main(String[] args) throws ExecutionException, InterruptedException {
Callable<String> c = new Callable() {
@Override
public String call() throws Exception {
return "Hello Callable";
}
};
ExecutorService service = Executors.newCachedThreadPool();
// Future 存储执行的 将来才会产生的结果
Future<String> future = service.submit(c); //异步
System.out.println(future.get());//阻塞
service.shutdown();
}
FutureTask
Future+Runnable结合
FutureTask<Integer> task = new FutureTask<>(()->{
TimeUnit.MILLISECONDS.sleep(500);
return 1000;
}); //new Callable () { Integer call();}
new Thread(task).start();
System.out.println(task.get()); //阻塞
CompletableFuture 任务管理
管理多个Future的结果
/**
* 假设你能够提供一个服务
* 这个服务查询各大电商网站同一类产品的价格并汇总展示
*/
public class T06_01_CompletableFuture {
public static void main(String[] args) throws ExecutionException, InterruptedException {
long start, end;
/*start = System.currentTimeMillis();
priceOfTM();
priceOfTB();
priceOfJD();
end = System.currentTimeMillis();
System.out.println("use serial method call! " + (end - start));*/
start = System.currentTimeMillis();
CompletableFuture<Double> futureTM = CompletableFuture.supplyAsync(()->priceOfTM());
CompletableFuture<Double> futureTB = CompletableFuture.supplyAsync(()->priceOfTB());
CompletableFuture<Double> futureJD = CompletableFuture.supplyAsync(()->priceOfJD());
CompletableFuture.allOf(futureTM, futureTB, futureJD).join();
CompletableFuture.supplyAsync(()->priceOfTM())
.thenApply(String::valueOf)
.thenApply(str-> "price " + str)
.thenAccept(System.out::println);
end = System.currentTimeMillis();
System.out.println("use completable future! " + (end - start));
try {
System.in.read();
} catch (IOException e) {
e.printStackTrace();
}
}
private static double priceOfTM() {
delay();
return 1.00;
}
private static double priceOfTB() {
delay();
return 2.00;
}
private static double priceOfJD() {
delay();
return 3.00;
}
/*private static double priceOfAmazon() {
delay();
throw new RuntimeException("product not exist!");
}*/
private static void delay() {
int time = new Random().nextInt(500);
try {
TimeUnit.MILLISECONDS.sleep(time);
} catch (InterruptedException e) {
e.printStackTrace();
}
System.out.printf("After %s sleep!\n", time);
}
}
线程池
- ThreadPoolExecutor
static class Task implements Runnable {
private int i;
public Task(int i) {
this.i = i;
}
@Override
public void run() {
System.out.println(Thread.currentThread().getName() + " Task " + i);
try {
System.in.read();
} catch (IOException e) {
e.printStackTrace();
}
}
@Override
public String toString() {
return "Task{" +
"i=" + i +
'}';
}
}
public static void main(String[] args) {
ThreadPoolExecutor tpe = new ThreadPoolExecutor(2, 4,
60, TimeUnit.SECONDS,
new ArrayBlockingQueue<Runnable>(4),
Executors.defaultThreadFactory(),
new ThreadPoolExecutor.CallerRunsPolicy());
for (int i = 0; i < 8; i++) {
tpe.execute(new Task(i));
}
System.out.println(tpe.getQueue());
tpe.execute(new Task(100));
System.out.println(tpe.getQueue());
tpe.shutdown();
}
- ForkJoinPool
分解汇总的任务，用很少的线程可以执行很多的任务（子任务）。
ForkJoinPool 不是为了替代 ExecutorService，而是它的补充，在某些应用场景下性能比 ExecutorService 更好。ForkJoinPool 主要用于实现“分而治之”的算法，特别是分治之后递归调用的函数，例如 quick sort 等。ForkJoinPool 最适合的是计算密集型的任务，如果存在 I/O，线程间同步，sleep() 等会造成线程长时间阻塞的情况时，最好配合使用 ManagedBlocker。
- WorkStealingPool，当有线程执行完，去其他线程偷一个执行，poll需要加锁
public class T11_WorkStealingPool {
public static void main(String[] args) throws IOException {
ExecutorService service = Executors.newWorkStealingPool();
System.out.println(Runtime.getRuntime().availableProcessors());
service.execute(new R(1000));
service.execute(new R(2000));
service.execute(new R(2000));
service.execute(new R(2000)); //daemon
service.execute(new R(2000));
//由于产生的是精灵线程（守护线程、后台线程），主线程不阻塞的话，看不到输出
System.in.read();
}
static class R implements Runnable {
int time;
R(int t) {
this.time = t;
}
@Override
public void run() {
try {
TimeUnit.MILLISECONDS.sleep(time);
} catch (InterruptedException e) {
e.printStackTrace();
}
System.out.println(time + " " + Thread.currentThread().getName());
}
}
}
- ForkJoinPool 范例
public class T12_ForkJoinPool {
static int[] nums = new int[1000000];
static final int MAX_NUM = 50000;
static Random r = new Random();
static {
for(int i=0; i<nums.length; i++) {
nums[i] = r.nextInt(100);
}
System.out.println("---" + Arrays.stream(nums).sum()); //stream api
}
static class AddTask extends RecursiveAction {
int start, end;
AddTask(int s, int e) {
start = s;
end = e;
}
@Override
protected void compute() {
if(end-start <= MAX_NUM) {
long sum = 0L;
for(int i=start; i<end; i++) sum += nums[i];
System.out.println("from:" + start + " to:" + end + " = " + sum);
} else {
int middle = start + (end-start)/2;
AddTask subTask1 = new AddTask(start, middle);
AddTask subTask2 = new AddTask(middle, end);
subTask1.fork();
subTask2.fork();
}
}
}
static class AddTaskRet extends RecursiveTask<Long> {
private static final long serialVersionUID = 1L;
int start, end;
AddTaskRet(int s, int e) {
start = s;
end = e;
}
@Override
protected Long compute() {
if(end-start <= MAX_NUM) {
long sum = 0L;
for(int i=start; i<end; i++) sum += nums[i];
return sum;
}
int middle = start + (end-start)/2;
AddTaskRet subTask1 = new AddTaskRet(start, middle);
AddTaskRet subTask2 = new AddTaskRet(middle, end);
subTask1.fork();
subTask2.fork();
return subTask1.join() + subTask2.join();
}
}
public static void main(String[] args) throws IOException {
/*ForkJoinPool fjp = new ForkJoinPool();
AddTask task = new AddTask(0, nums.length);
fjp.execute(task);*/
T12_ForkJoinPool temp = new T12_ForkJoinPool();
ForkJoinPool fjp = new ForkJoinPool();
AddTaskRet task = new AddTaskRet(0, nums.length);
fjp.execute(task);
long result = task.join();
System.out.println(result);
//System.in.read();
}
}
类型
并发concurrent和并行parallel：
并发是指任务提交（线程级），并行是指任务执行（多CPU并行），并行是并发的子集。
调整线程池的大小
线程池大小与处理器的利用率之比可以用下面公式估算：
N(threads)=N(cpu) * U(cpu) * (1+W/C)
- N(cpu)：是处理器的核的数目，可以通过Runtime.getRuntime().availableProce-ssorw()得到。
- U(cpu)：是期望的CPU利用率（该值应介于0-1之间）
- W/C：是等待时间与计算时间的比率
ThreadPoolExecutor源码解析
1、常用变量的解释
// 1. `ctl`，可以看做一个int类型的数字，高3位表示线程池状态，低29位表示worker数量
private final AtomicInteger ctl = new AtomicInteger(ctlOf(RUNNING, 0));
// 2. `COUNT_BITS`，`Integer.SIZE`为32，所以`COUNT_BITS`为29
private static final int COUNT_BITS = Integer.SIZE - 3;
// 3. `CAPACITY`，线程池允许的最大线程数。1左移29位，然后减1，即为 2^29 - 1
private static final int CAPACITY = (1 << COUNT_BITS) - 1;
// runState is stored in the high-order bits
// 4. 线程池有5种状态，按大小排序如下：RUNNING < SHUTDOWN < STOP < TIDYING < TERMINATED
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
// 5. `runStateOf()`，获取线程池状态，通过按位与操作，低29位将全部变成0
private static int runStateOf(int c) { return c & ~CAPACITY; }
// 6. `workerCountOf()`，获取线程池worker数量，通过按位与操作，高3位将全部变成0
private static int workerCountOf(int c) { return c & CAPACITY; }
// 7. `ctlOf()`，根据线程池状态和线程池worker数量，生成ctl值
private static int ctlOf(int rs, int wc) { return rs | wc; }
/*
* Bit field accessors that don't require unpacking ctl.
* These depend on the bit layout and on workerCount being never negative.
*/
// 8. `runStateLessThan()`，线程池状态小于xx
private static boolean runStateLessThan(int c, int s) {
return c < s;
}
// 9. `runStateAtLeast()`，线程池状态大于等于xx
private static boolean runStateAtLeast(int c, int s) {
return c >= s;
}
2、构造方法
public ThreadPoolExecutor(int corePoolSize,
int maximumPoolSize,
long keepAliveTime,
TimeUnit unit,
BlockingQueue<Runnable> workQueue,
ThreadFactory threadFactory,
RejectedExecutionHandler handler) {
// 基本类型参数校验
if (corePoolSize < 0 ||
maximumPoolSize <= 0 ||
maximumPoolSize < corePoolSize ||
keepAliveTime < 0)
throw new IllegalArgumentException();
// 空指针校验
if (workQueue == null || threadFactory == null || handler == null)
throw new NullPointerException();
this.corePoolSize = corePoolSize;
this.maximumPoolSize = maximumPoolSize;
this.workQueue = workQueue;
// 根据传入参数`unit`和`keepAliveTime`，将存活时间转换为纳秒存到变量`keepAliveTime `中
this.keepAliveTime = unit.toNanos(keepAliveTime);
this.threadFactory = threadFactory;
this.handler = handler;
}
3、提交执行task的过程
execute执行过程：
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();
// worker数量比核心线程数小，直接创建worker执行任务
if (workerCountOf(c) < corePoolSize) {
if (addWorker(command, true))
return;
c = ctl.get();
}
// worker数量超过核心线程数，任务直接进入队列
if (isRunning(c) && workQueue.offer(command)) {
int recheck = ctl.get();
// 线程池状态不是RUNNING状态，说明执行过shutdown命令，需要对新加入的任务执行reject()操作。
// 这儿为什么需要recheck，是因为任务入队列前后，线程池的状态可能会发生变化。
if (! isRunning(recheck) && remove(command))
reject(command);
// 这儿为什么需要判断0值，主要是在线程池构造方法中，核心线程数允许为0
else if (workerCountOf(recheck) == 0)
addWorker(null, false);
}
// 如果线程池不是运行状态，或者任务进入队列失败，则尝试创建worker执行任务。
// 这儿有3点需要注意：
// 1. 线程池不是运行状态时，addWorker内部会判断线程池状态
// 2. addWorker第2个参数表示是否创建核心线程
// 3. addWorker返回false，则说明任务执行失败，需要执行reject操作
else if (!addWorker(command, false))
reject(command);
}
4、addworker源码解析
Worker 继承了AQS，实现了runnable
private boolean addWorker(Runnable firstTask, boolean core) {
retry:
// 外层自旋
for (;;) {
int c = ctl.get();
int rs = runStateOf(c);
// 这个条件写得比较难懂，我对其进行了调整，和下面的条件等价
// (rs > SHUTDOWN) ||
// (rs == SHUTDOWN && firstTask != null) ||
// (rs == SHUTDOWN && workQueue.isEmpty())
// 1. 线程池状态大于SHUTDOWN时，直接返回false
// 2. 线程池状态等于SHUTDOWN，且firstTask不为null，直接返回false
// 3. 线程池状态等于SHUTDOWN，且队列为空，直接返回false
// Check if queue empty only if necessary.
if (rs >= SHUTDOWN &&
! (rs == SHUTDOWN &&
firstTask == null &&
! workQueue.isEmpty()))
return false;
// 内层自旋
for (;;) {
int wc = workerCountOf(c);
// worker数量超过容量，直接返回false
if (wc >= CAPACITY ||
wc >= (core ? corePoolSize : maximumPoolSize))
return false;
// 使用CAS的方式增加worker数量。
// 若增加成功，则直接跳出外层循环进入到第二部分
if (compareAndIncrementWorkerCount(c))
break retry;
c = ctl.get(); // Re-read ctl
// 线程池状态发生变化，对外层循环进行自旋
if (runStateOf(c) != rs)
continue retry;
// 其他情况，直接内层循环进行自旋即可
// else CAS failed due to workerCount change; retry inner loop
}
}
boolean workerStarted = false;
boolean workerAdded = false;
Worker w = null;
try {
w = new Worker(firstTask);
final Thread t = w.thread;
if (t != null) {
final ReentrantLock mainLock = this.mainLock;
// worker的添加必须是串行的，因此需要加锁
mainLock.lock();
try {
// Recheck while holding lock.
// Back out on ThreadFactory failure or if
// shut down before lock acquired.
// 这儿需要重新检查线程池状态
int rs = runStateOf(ctl.get());
if (rs < SHUTDOWN ||
(rs == SHUTDOWN && firstTask == null)) {
// worker已经调用过了start()方法，则不再创建worker
if (t.isAlive()) // precheck that t is startable
throw new IllegalThreadStateException();
// worker创建并添加到workers成功
workers.add(w);
// 更新`largestPoolSize`变量
int s = workers.size();
if (s > largestPoolSize)
largestPoolSize = s;
workerAdded = true;
}
} finally {
mainLock.unlock();
}
// 启动worker线程
if (workerAdded) {
t.start();
workerStarted = true;
}
}
} finally {
// worker线程启动失败，说明线程池状态发生了变化（关闭操作被执行），需要进行shutdown相关操作
if (! workerStarted)
addWorkerFailed(w);
}
return workerStarted;
}
5、线程池worker任务单元
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. */
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;
// 这儿是Worker的关键所在，使用了线程工厂创建了一个线程。传入的参数为当前worker
this.thread = getThreadFactory().newThread(this);
}
/** Delegates main run loop to outer runWorker */
public void run() {
runWorker(this);
}
// 省略代码...
}
6、核心线程执行逻辑-runworker
final void runWorker(Worker w) {
Thread wt = Thread.currentThread();
Runnable task = w.firstTask;
w.firstTask = null;
// 调用unlock()是为了让外部可以中断
w.unlock(); // allow interrupts
// 这个变量用于判断是否进入过自旋（while循环）
boolean completedAbruptly = true;
try {
// 这儿是自旋
// 1. 如果firstTask不为null，则执行firstTask；
// 2. 如果firstTask为null，则调用getTask()从队列获取任务。
// 3. 阻塞队列的特性就是：当队列为空时，当前线程会被阻塞等待
while (task != null || (task = getTask()) != null) {
// 这儿对worker进行加锁，是为了达到下面的目的
// 1. 降低锁范围，提升性能
// 2. 保证每个worker执行的任务是串行的
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
// 如果线程池正在停止，则对当前线程进行中断操作
if ((runStateAtLeast(ctl.get(), STOP) ||
(Thread.interrupted() &&
runStateAtLeast(ctl.get(), STOP))) &&
!wt.isInterrupted())
wt.interrupt();
// 执行任务，且在执行前后通过`beforeExecute()`和`afterExecute()`来扩展其功能。
// 这两个方法在当前类里面为空实现。
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 {
// 帮助gc
task = null;
// 已完成任务数加一
w.completedTasks++;
w.unlock();
}
}
completedAbruptly = false;
} finally {
// 自旋操作被退出，说明线程池正在结束
processWorkerExit(w, completedAbruptly);
}
}
浙公网安备 33010602011771号