盘一盘 synchronized （二）—— 偏向锁批量重偏向与批量撤销

在本文讲解之前，先来简单了解一下为什么会有批量重偏向和批量撤销。

批量重偏向：当一个线程创建了大量对象并执行了初始的同步操作，后来另一个线程也来将这些对象作为锁对象进行操作，会导偏向锁重偏向的操作。

批量撤销：在多线程竞争剧烈的情况下，使用偏向锁将会降低效率，于是乎产生了批量撤销机制。

JVM的默认参数值

通过JVM的默认参数值，找一找批量重偏向和批量撤销的阈值。

设置JVM参数-XX:+PrintFlagsFinal，在项目启动时即可输出JVM的默认参数值

intx BiasedLockingBulkRebiasThreshold = 20 默认偏向锁批量重偏向阈值

intx BiasedLockingBulkRevokeThreshold = 40 默认偏向锁批量撤销阈值

当然我们可以通过-XX:BiasedLockingBulkRebiasThreshold 和 -XX:BiasedLockingBulkRevokeThreshold 来手动设置阈值

批量重偏向

public static void main(String[] args) throws Exception {

        //延时产生可偏向对象

        Thread.sleep(5000);

        //创造100个偏向线程t1的偏向锁

        List<A> listA = new ArrayList<>();

        Thread t1 = new Thread(() -> {

            for (int i = 0; i <100 ; i++) {

                A a = new A();

                synchronized (a){

                    listA.add(a);

                }

            }

            try {

                //为了防止JVM线程复用，在创建完对象后，保持线程t1状态为存活

                Thread.sleep(100000000);

            } catch (InterruptedException e) {

                e.printStackTrace();

            }

        });

        t1.start();

        //睡眠3s钟保证线程t1创建对象完成

        Thread.sleep(3000);

        out.println("打印t1线程，list中第20个对象的对象头：");

        out.println((ClassLayout.parseInstance(listA.get(19)).toPrintable()));

        //创建线程t2竞争线程t1中已经退出同步块的锁

        Thread t2 = new Thread(() -> {

            //这里面只循环了30次！！！

            for (int i = 0; i < 30; i++) {

                A a =listA.get(i);

                synchronized (a){

                    //分别打印第19次和第20次偏向锁重偏向结果

                    if(i==18||i==19){

                        out.println("第"+ ( i + 1) + "次偏向结果");

                        out.println((ClassLayout.parseInstance(a).toPrintable()));

                    }

                }

            }

            try {

                Thread.sleep(10000000);

            } catch (InterruptedException e) {

                e.printStackTrace();

            }

        });

        t2.start();

        Thread.sleep(3000);

        out.println("打印list中第11个对象的对象头：");

        out.println((ClassLayout.parseInstance(listA.get(10)).toPrintable()));

        out.println("打印list中第26个对象的对象头：");

        out.println((ClassLayout.parseInstance(listA.get(25)).toPrintable()));

        out.println("打印list中第41个对象的对象头：");

        out.println((ClassLayout.parseInstance(listA.get(40)).toPrintable()));

    }

我们一步一步来分析打印结果

首先，创造了100个偏向线程t1的偏向锁，结果没什么好说的，100个偏向锁嘛，偏向的线程ID信息为531939333

再来看看重偏向的结果，线程t2，前19次偏向均产生了轻量锁，而到第20次的时候，达到了批量重偏向的阈值20，此时锁并不是轻量级锁，而变成了偏向锁，此时偏向的线程t2，线程t2的ID信息为5322162821

最后我们再来看一下偏向结束后的对象头信息。

前20个对象，并没有触发了批量重偏向机制，线程t2执行释放同步锁后，转变为无锁形态

第20~30个对象，触发了批量重偏向机制，对象为偏向锁状态，偏向线程t2，线程t2的ID信息为5322162821

而31个对象之后，也没有触发了批量重偏向机制，对象仍偏向线程t1，线程t1的ID信息为531939333

批量撤销

我们再来看看批量撤销

public static void main(String[] args) throws Exception {

    Thread.sleep(5000);

    List<A> listA = new ArrayList<>();

    Thread t1 = new Thread(() -> {

        for (int i = 0; i <100 ; i++) {

            A a = new A();

            synchronized (a){

                listA.add(a);

            }

        }

        try {

            Thread.sleep(100000000);

        } catch (InterruptedException e) {

            e.printStackTrace();

        }

    });

    t1.start();

    Thread.sleep(3000);

    Thread t2 = new Thread(() -> {

        //这里循环了40次。达到了批量撤销的阈值

        for (int i = 0; i < 40; i++) {

            A a =listA.get(i);

            synchronized (a){

            }

        }

        try {

            Thread.sleep(10000000);

        } catch (InterruptedException e) {

            e.printStackTrace();

        }

    });

    t2.start();

    //———————————分割线，前面代码不再赘述——————————————————————————————————————————

    Thread.sleep(3000);

    out.println("打印list中第11个对象的对象头：");

    out.println((ClassLayout.parseInstance(listA.get(10)).toPrintable()));

    out.println("打印list中第26个对象的对象头：");

    out.println((ClassLayout.parseInstance(listA.get(25)).toPrintable()));

    out.println("打印list中第90个对象的对象头：");

    out.println((ClassLayout.parseInstance(listA.get(89)).toPrintable()));

    Thread t3 = new Thread(() -> {

        for (int i = 20; i < 40; i++) {

            A a =listA.get(i);

            synchronized (a){

                if(i==20||i==22){

                    out.println("thread3 第"+ i + "次");

                    out.println((ClassLayout.parseInstance(a).toPrintable()));

                }

            }

        }

    });

    t3.start();

    Thread.sleep(10000);

    out.println("重新输出新实例A");

    out.println((ClassLayout.parseInstance(new A()).toPrintable()));

}

来看看输出结果，这部分和上面批量偏向结果的大相径庭。重点关注记录的线程ID信息

前20个对象，并没有触发了批量重偏向机制，线程t2执行释放同步锁后，转变为无锁形态

第20~40个对象，触发了批量重偏向机制，对象为偏向锁状态，偏向线程t2，线程t2的ID信息为540039429

而41个对象之后，也没有触发了批量重偏向机制，对象仍偏向线程t1，线程t1的ID信息为540002309

重头戏来了！线程t3也来竞争锁。因为已经达到了批量撤销的阈值，且对象listA.get(20)和listA.get(22)已经进行过偏向锁的重偏向，并不会再次重偏向线程t3。

此时触发批量撤销，此时对象锁膨胀变为轻量级锁。

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" alt="" width="1051" height="495" />

再来看看最后新生成的对象A。值得注意的是：本应该为可偏向状态偏向锁的新对象，在经历过批量重偏向和批量撤销后直接在实例化后转为无锁。

简单总结

1、批量重偏向和批量撤销是针对类的优化，和对象无关。

2、偏向锁重偏向一次之后不可再次重偏向。

3、当某个类已经触发批量撤销机制后，JVM会默认当前类产生了严重的问题，剥夺了该类的新实例对象使用偏向锁的权利

巴特西

盘一盘 synchronized （二）—— 偏向锁批量重偏向与批量撤销

JVM的默认参数值

批量重偏向

批量撤销

简单总结

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