JavaSE之JUC线程池

线程池

1.池化技术

程序运行的本质：占用系统资源！ 提高程序的使用率，降低我们一个性能消耗

线程池、连接池、内存池、对象池 …

• 为什么要用线程池：线程复用

2.线程池属性

三大方法、七大参数、4种拒绝策略

a.三大方法

package com.coding.pool;

import java.util.concurrent.ExecutorService;
import java.util.concurrent.Executors;

public class ThreadPoolDemo01 {
    public static void main(String[] args) {
        // 单例，只能有一个线程！
//        ExecutorService threadPool = Executors.newSingleThreadExecutor();
        // 固定的线程数
//         ExecutorService threadPool = Executors.newFixedThreadPool(8);
        // 遇强则强！可伸缩！
//         ExecutorService threadPool = Executors.newCachedThreadPool();

        try {
            // 线程池的使用方式！
            for (int i = 0; i < 30; i++) {
                threadPool.execute(()->{
                    System.out.println(Thread.currentThread().getName() + " ok");
                });
            }
        } catch (Exception e) {
            e.printStackTrace();
        } finally {
            // 使用完毕后需要关闭！
            threadPool.shutdown();
        }

    }
}

b.七大参数

1、先看看这个三个方法的源码

 new ThreadPoolExecutor(0, Integer.MAX_VALUE, // 约等于21亿
                                      60L, TimeUnit.SECONDS,
                                      new SynchronousQueue<Runnable>());

new ThreadPoolExecutor(nThreads, nThreads,
                                      0L, TimeUnit.MILLISECONDS,
                                      new LinkedBlockingQueue<Runnable>());

ThreadPoolExecutor(1, 1, 0L, TimeUnit.MILLISECONDS,
                                    new LinkedBlockingQueue<Runnable>())

public ThreadPoolExecutor(int corePoolSize, // 核心池线程数大小 (常用)
                              int maximumPoolSize,  // 最大的线程数大小 (常用)
                              long keepAliveTime, // 超时等待时间 (常用)
                              TimeUnit unit, // 时间单位 (常用)
                              BlockingQueue<Runnable> workQueue, // 阻塞队列(常用)
                              ThreadFactory threadFactory, // 线程工厂
                              RejectedExecutionHandler handler // 拒绝策略(常用)) {
    if (corePoolSize < 0 ||
        maximumPoolSize <= 0 ||
        maximumPoolSize < corePoolSize ||
        keepAliveTime < 0)
        throw new IllegalArgumentException();
    if (workQueue == null || threadFactory == null || handler == null)
        throw new NullPointerException();
    this.acc = System.getSecurityManager() == null ?
        null :
    AccessController.getContext();
    this.corePoolSize = corePoolSize;
    this.maximumPoolSize = maximumPoolSize;
    this.workQueue = workQueue;
    this.keepAliveTime = unit.toNanos(keepAliveTime);
    this.threadFactory = threadFactory;
    this.handler = handler;
}

• 自定义线程池的策略

package com.coding.pool;

import java.util.concurrent.*;

public class ThreadPoolDemo01 {
    public static void main(String[] args) {
        // 单例，只能有一个线程！
//        ExecutorService threadPool = Executors.newSingleThreadExecutor();
        // 固定的线程数
//         ExecutorService threadPool = Executors.newFixedThreadPool(1);
        //遇强则强！可伸缩！
//         ExecutorService threadPool = Executors.newCachedThreadPool();

        ExecutorService threadPool = new ThreadPoolExecutor(
                2,
                5,
                3L,
                TimeUnit.SECONDS,
                new LinkedBlockingDeque<>(3),
                Executors.defaultThreadFactory(),
                new ThreadPoolExecutor.CallerRunsPolicy());

        try {
            // 线程池的使用方式！
            for (int i = 0; i < 100; i++) {
                threadPool.execute(()->{
                    System.out.println(Thread.currentThread().getName() + " ok");
                });
            }
        } catch (Exception e) {
            e.printStackTrace();
        } finally {
            // 使用完毕后需要关闭！
            threadPool.shutdown();
        }

    }
}

c.四大拒绝策略

• 1、ThreadPoolExecutor.AbortPolicy()； 抛出异常，丢弃任务
• 2、ThreadPoolExecutor.DiscardPolicy()；不抛出异常，丢弃任务
• 3、ThreadPoolExecutor.DiscardOldestPolicy()； 尝试获取任务，不一定执行！
• 4、ThreadPoolExecutor.CallerRunsPolicy()； 哪来的去哪里找对应的线程执行！

请你谈谈 最大线程池 该如何设置啊？

CPU密集型： 根据CPU的处理器数量来定！保证最大效率

IO密集型： 50 个线程都是进程操作大io资源, 比较耗时！ > 这个常用的 IO 任务数！