Java多线程之线程池(ThreadPoolExecutor)实现原理分析(一)

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小怪聊职场
2018.05.14 23:11 字数 1177

在上一篇文章Java中实现多线程的3种方法介绍和比较中,我们讲解了Java中实现多线程的3种方法。使用多线程,就必须要考虑使用线程池,今天我们来聊聊线程池的那些事。
注:源码都是基于JDK1.8

一、为什么要使用线程池?

如果并发的线程数量很多,并且每个线程都是执行一个时间很短的任务就结束了,这样频繁创建线程就会大大降低系统的效率,因为频繁创建线程和销毁线程需要时间。

那么有没有一种办法使得线程可以复用,就是执行完一个任务,并不被销毁,而是可以继续执行其他的任务?

在Java中可以通过线程池来达到这样的效果。今天我们就来详细讲解一下Java的线程池,首先我们从最核心的ThreadPoolExecutor类中的方法讲起,然后再讲述它的实现原理,接着给出了它的使用示例,最后讨论了一下如何合理配置线程池的大小。

二、Java中的ThreadPoolExecutor类

java.uitl.concurrent.ThreadPoolExecutor类是线程池中最核心的一个类,因此如果要透彻地了解Java中的线程池,必须先了解这个类。下面我们来看一下ThreadPoolExecutor类的具体实现源码。

在ThreadPoolExecutor类中提供了四个构造方法:

public class ThreadPoolExecutor extends AbstractExecutorService {
    ...
    public ThreadPoolExecutor(int corePoolSize,
                              int maximumPoolSize,
                              long keepAliveTime,
                              TimeUnit unit,
                              BlockingQueue<Runnable> workQueue) {
        this(corePoolSize, maximumPoolSize, keepAliveTime, unit, workQueue,
             Executors.defaultThreadFactory(), defaultHandler);
    }

    public ThreadPoolExecutor(int corePoolSize,
                              int maximumPoolSize,
                              long keepAliveTime,
                              TimeUnit unit,
                              BlockingQueue<Runnable> workQueue,
                              ThreadFactory threadFactory) {
        this(corePoolSize, maximumPoolSize, keepAliveTime, unit, workQueue,
             threadFactory, defaultHandler);
    }

    public ThreadPoolExecutor(int corePoolSize,
                              int maximumPoolSize,
                              long keepAliveTime,
                              TimeUnit unit,
                              BlockingQueue<Runnable> workQueue,
                              RejectedExecutionHandler handler) {
        this(corePoolSize, maximumPoolSize, keepAliveTime, unit, workQueue,
             Executors.defaultThreadFactory(), handler);
    }

    public ThreadPoolExecutor(int corePoolSize,
                              int maximumPoolSize,
                              long keepAliveTime,
                              TimeUnit unit,
                              BlockingQueue<Runnable> workQueue,
                              ThreadFactory threadFactory,
                              RejectedExecutionHandler handler) {
        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;
    }

从上面的代码可以得知,ThreadPoolExecutor继承了AbstractExecutorService类,并提供了四个构造器,事实上,通过观察每个构造器的源码具体实现,发现前面三个构造器都是调用的第四个构造器进行的初始化工作。

下面解释下一下构造器中各个参数的含义:

  • corePoolSize:核心池的大小。
  • maximumPoolSize:线程池最大线程数,它表示在线程池中最多能创建多少个线程,注意与corePoolSize区分,后面会讲到。
  • keepAliveTime:表示线程没有任务执行时最多保持多久时间会终止。
  • unit:参数keepAliveTime的时间单位,有7种取值,在TimeUnit类中有7种静态属性:
    /**
     * Time unit representing one thousandth of a microsecond
     */
    NANOSECONDS {
        public long toNanos(long d)   { return d; }
        public long toMicros(long d)  { return d/(C1/C0); }
        public long toMillis(long d)  { return d/(C2/C0); }
        public long toSeconds(long d) { return d/(C3/C0); }
        public long toMinutes(long d) { return d/(C4/C0); }
        public long toHours(long d)   { return d/(C5/C0); }
        public long toDays(long d)    { return d/(C6/C0); }
        public long convert(long d, TimeUnit u) { return u.toNanos(d); }
        int excessNanos(long d, long m) { return (int)(d - (m*C2)); }
    },

    /**
     * Time unit representing one thousandth of a millisecond
     */
    MICROSECONDS {
        public long toNanos(long d)   { return x(d, C1/C0, MAX/(C1/C0)); }
        public long toMicros(long d)  { return d; }
        public long toMillis(long d)  { return d/(C2/C1); }
        public long toSeconds(long d) { return d/(C3/C1); }
        public long toMinutes(long d) { return d/(C4/C1); }
        public long toHours(long d)   { return d/(C5/C1); }
        public long toDays(long d)    { return d/(C6/C1); }
        public long convert(long d, TimeUnit u) { return u.toMicros(d); }
        int excessNanos(long d, long m) { return (int)((d*C1) - (m*C2)); }
    },

    /**
     * Time unit representing one thousandth of a second
     */
    MILLISECONDS {
        public long toNanos(long d)   { return x(d, C2/C0, MAX/(C2/C0)); }
        public long toMicros(long d)  { return x(d, C2/C1, MAX/(C2/C1)); }
        public long toMillis(long d)  { return d; }
        public long toSeconds(long d) { return d/(C3/C2); }
        public long toMinutes(long d) { return d/(C4/C2); }
        public long toHours(long d)   { return d/(C5/C2); }
        public long toDays(long d)    { return d/(C6/C2); }
        public long convert(long d, TimeUnit u) { return u.toMillis(d); }
        int excessNanos(long d, long m) { return 0; }
    },

    /**
     * Time unit representing one second
     */
    SECONDS {
        public long toNanos(long d)   { return x(d, C3/C0, MAX/(C3/C0)); }
        public long toMicros(long d)  { return x(d, C3/C1, MAX/(C3/C1)); }
        public long toMillis(long d)  { return x(d, C3/C2, MAX/(C3/C2)); }
        public long toSeconds(long d) { return d; }
        public long toMinutes(long d) { return d/(C4/C3); }
        public long toHours(long d)   { return d/(C5/C3); }
        public long toDays(long d)    { return d/(C6/C3); }
        public long convert(long d, TimeUnit u) { return u.toSeconds(d); }
        int excessNanos(long d, long m) { return 0; }
    },

    /**
     * Time unit representing sixty seconds
     */
    MINUTES {
        public long toNanos(long d)   { return x(d, C4/C0, MAX/(C4/C0)); }
        public long toMicros(long d)  { return x(d, C4/C1, MAX/(C4/C1)); }
        public long toMillis(long d)  { return x(d, C4/C2, MAX/(C4/C2)); }
        public long toSeconds(long d) { return x(d, C4/C3, MAX/(C4/C3)); }
        public long toMinutes(long d) { return d; }
        public long toHours(long d)   { return d/(C5/C4); }
        public long toDays(long d)    { return d/(C6/C4); }
        public long convert(long d, TimeUnit u) { return u.toMinutes(d); }
        int excessNanos(long d, long m) { return 0; }
    },

    /**
     * Time unit representing sixty minutes
     */
    HOURS {
        public long toNanos(long d)   { return x(d, C5/C0, MAX/(C5/C0)); }
        public long toMicros(long d)  { return x(d, C5/C1, MAX/(C5/C1)); }
        public long toMillis(long d)  { return x(d, C5/C2, MAX/(C5/C2)); }
        public long toSeconds(long d) { return x(d, C5/C3, MAX/(C5/C3)); }
        public long toMinutes(long d) { return x(d, C5/C4, MAX/(C5/C4)); }
        public long toHours(long d)   { return d; }
        public long toDays(long d)    { return d/(C6/C5); }
        public long convert(long d, TimeUnit u) { return u.toHours(d); }
        int excessNanos(long d, long m) { return 0; }
    },

    /**
     * Time unit representing twenty four hours
     */
    DAYS {
        public long toNanos(long d)   { return x(d, C6/C0, MAX/(C6/C0)); }
        public long toMicros(long d)  { return x(d, C6/C1, MAX/(C6/C1)); }
        public long toMillis(long d)  { return x(d, C6/C2, MAX/(C6/C2)); }
        public long toSeconds(long d) { return x(d, C6/C3, MAX/(C6/C3)); }
        public long toMinutes(long d) { return x(d, C6/C4, MAX/(C6/C4)); }
        public long toHours(long d)   { return x(d, C6/C5, MAX/(C6/C5)); }
        public long toDays(long d)    { return d; }
        public long convert(long d, TimeUnit u) { return u.toDays(d); }
        int excessNanos(long d, long m) { return 0; }
    };
  • workQueue:一个阻塞队列,用来存储等待执行的任务。
  • threadFactory:线程工厂,主要用来创建线程。
  • handler:表示当拒绝处理任务时的策略。

从源码可以得知ThreadPoolExecutor继承了AbstractExecutorService,我们看下AbstractExecutorService的实现:

public abstract class AbstractExecutorService implements ExecutorService {

    protected <T> RunnableFuture<T> newTaskFor(Runnable runnable, T value) {
        return new FutureTask<T>(runnable, value);
    }

    protected <T> RunnableFuture<T> newTaskFor(Callable<T> callable) {
        return new FutureTask<T>(callable);
    }

    public Future<?> submit(Runnable task) {
        if (task == null) throw new NullPointerException();
        RunnableFuture<Void> ftask = newTaskFor(task, null);
        execute(ftask);
        return ftask;
    }

    public <T> Future<T> submit(Runnable task, T result) {
        if (task == null) throw new NullPointerException();
        RunnableFuture<T> ftask = newTaskFor(task, result);
        execute(ftask);
        return ftask;
    }

    public <T> Future<T> submit(Callable<T> task) {
        if (task == null) throw new NullPointerException();
        RunnableFuture<T> ftask = newTaskFor(task);
        execute(ftask);
        return ftask;
    }

    private <T> T doInvokeAny(Collection<? extends Callable<T>> tasks,
                              boolean timed, long nanos)
        throws InterruptedException, ExecutionException, TimeoutException {
        ...
    }

    public <T> T invokeAny(Collection<? extends Callable<T>> tasks)
        throws InterruptedException, ExecutionException {
        try {
            return doInvokeAny(tasks, false, 0);
        } catch (TimeoutException cannotHappen) {
            assert false;
            return null;
        }
    }

    public <T> T invokeAny(Collection<? extends Callable<T>> tasks,
                           long timeout, TimeUnit unit)
        throws InterruptedException, ExecutionException, TimeoutException {
        return doInvokeAny(tasks, true, unit.toNanos(timeout));
    }

    public <T> List<Future<T>> invokeAll(Collection<? extends Callable<T>> tasks)
        throws InterruptedException {
        ...
    }

    public <T> List<Future<T>> invokeAll(Collection<? extends Callable<T>> tasks,
                                         long timeout, TimeUnit unit)
        throws InterruptedException {
        ...
    }

AbstractExecutorService是一个抽象类,它实现了ExecutorService接口,我们看下ExecutorService接口的实现:

public interface ExecutorService extends Executor {
    void shutdown();

    List<Runnable> shutdownNow();

    boolean isShutdown();

    boolean isTerminated();

    boolean awaitTermination(long timeout, TimeUnit unit)
        throws InterruptedException;

    <T> Future<T> submit(Callable<T> task);

    <T> Future<T> submit(Runnable task, T result);

    Future<?> submit(Runnable task);

    <T> List<Future<T>> invokeAll(Collection<? extends Callable<T>> tasks)
        throws InterruptedException;

    <T> List<Future<T>> invokeAll(Collection<? extends Callable<T>> tasks,
                                  long timeout, TimeUnit unit)
        throws InterruptedException;

    <T> T invokeAny(Collection<? extends Callable<T>> tasks)
        throws InterruptedException, ExecutionException;

    <T> T invokeAny(Collection<? extends Callable<T>> tasks,
                    long timeout, TimeUnit unit)
        throws InterruptedException, ExecutionException, TimeoutException;
}

而ExecutorService又是继承了Executor接口,我们看一下Executor接口的实现:

public interface Executor {
    void execute(Runnable command);
}

到这里,大家应该明白了ThreadPoolExecutor、AbstractExecutorService、ExecutorService和Executor几个之间的关系了。
1、Executor是一个顶层接口,在它里面只声明了一个方法execute(Runnable),返回值为void,参数为Runnable类型,从字面意思可以理解,就是用来执行传进去的任务的。
2、然后ExecutorService接口继承了Executor接口,并声明了一些方法:submit、invokeAll、invokeAny以及shutDown等;
3、抽象类AbstractExecutorService实现了ExecutorService接口,基本实现了ExecutorService中声明的所有方法;
4、然后ThreadPoolExecutor继承了类AbstractExecutorService。

在ThreadPoolExecutor类中有几个非常重要的方法:
1、public void execute(Runnable command)
2、public void shutdown()
3、public List<Runnable> shutdownNow()
4、 submit

public Future<?> submit(Runnable task) 
public <T> Future<T> submit(Runnable task, T result)
public <T> Future<T> submit(Callable<T> task)
  • execute()方法实际上是Executor中声明的方法,在ThreadPoolExecutor进行了具体的实现,这个方法是ThreadPoolExecutor的核心方法,通过这个方法可以向线程池提交一个任务,交由线程池去执行。
  • shutdown()和shutdownNow()是用来关闭线程池的。
  • submit()方法是在ExecutorService中声明的方法,在AbstractExecutorService就已经有了具体的实现,在ThreadPoolExecutor中并没有对其进行重写,这个方法也是用来向线程池提交任务的,但是它和execute()方法不同,它能够返回任务执行的结果,去看submit()方法的实现,会发现它实际上还是调用的execute()方法,只不过它利用了Future来获取任务执行结果。

本文只对ThreadPoolExecutor类做一个宏观的介绍,下一篇文章将会深入剖析ThreadPoolExecutor类,以此去深入了解线程池的实现原理。

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