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做网站的好处,好网站的标准,修改wordpress 表格,做视频网站虚拟主机怎么选上述规约是一个关于Java并发编程的重要问题。本篇博文我来详细解释一下#xff1a; 1. volatile的内存可见性 volatile的作用#xff1a; 保证可见性#xff1a;当一个线程修改了volatile变量的值#xff0c;新值会立即刷新到主内存#xff0c;其他线程读取时会从主内存重…上述规约是一个关于Java并发编程的重要问题。本篇博文我来详细解释一下1.volatile的内存可见性volatile的作用保证可见性当一个线程修改了volatile变量的值新值会立即刷新到主内存其他线程读取时会从主内存重新获取最新值禁止指令重排序防止JVM的指令重排序优化示例publicclassVolatileExample{privatevolatilebooleanflagfalse;publicvoidwriter(){flagtrue;// 写操作}publicvoidreader(){if(flag){// 读操作// 一定能看到最新值}}}2.为什么volatile不能解决多写问题publicclassCounter{privatevolatileintcount0;// 多线程调用会有问题publicvoidincrement(){count;// 不是原子操作读→改→写}}问题分析线程A读取count0 线程B读取count0 线程Acount11写回 线程Bcount11写回 // 结果应该是2实际是13.解决方案对比方案1AtomicIntegerimportjava.util.concurrent.atomic.AtomicInteger;publicclassAtomicExample{privateAtomicIntegercountnewAtomicInteger(0);publicvoidincrement(){count.incrementAndGet();// 原子操作}publicintget(){returncount.get();}}原理基于CASCompare And SwappublicfinalintincrementAndGet(){returnunsafe.getAndAddInt(this,valueOffset,1)1;}// 实际是自旋CAS操作方案2LongAdderJDK8推荐importjava.util.concurrent.atomic.LongAdder;publicclassLongAdderExample{privateLongAddercountnewLongAdder();publicvoidincrement(){count.increment();// 性能更好}publiclongsum(){returncount.sum();}}4.性能对比分析特性AtomicLongLongAdder原理CAS自旋分段CAS高并发写性能一般优秀读性能O(1)O(N)需要合并内存占用低较高适用场景读写均衡写多读少5.完整示例importjava.util.concurrent.atomic.AtomicInteger;importjava.util.concurrent.atomic.LongAdder;importjava.util.concurrent.CountDownLatch;publicclassCounterComparison{// 方案1volatile不安全privatevolatileintvolatileCount0;// 方案2AtomicIntegerprivateAtomicIntegeratomicCountnewAtomicInteger(0);// 方案3LongAdderJDK8推荐privateLongAdderadderCountnewLongAdder();publicvoidtestVolatile()throwsInterruptedException{CountDownLatchlatchnewCountDownLatch(100);for(inti0;i100;i){newThread(()-{for(intj0;j1000;j){volatileCount;// 线程不安全}latch.countDown();}).start();}latch.await();System.out.println(Volatile结果可能不正确: volatileCount);}publicvoidtestAtomic()throwsInterruptedException{CountDownLatchlatchnewCountDownLatch(100);for(inti0;i100;i){newThread(()-{for(intj0;j1000;j){atomicCount.incrementAndGet();}latch.countDown();}).start();}latch.await();System.out.println(AtomicInteger结果: atomicCount.get());}publicvoidtestLongAdder()throwsInterruptedException{CountDownLatchlatchnewCountDownLatch(100);for(inti0;i100;i){newThread(()-{for(intj0;j1000;j){adderCount.increment();}latch.countDown();}).start();}latch.await();System.out.println(LongAdder结果: adderCount.sum());}publicstaticvoidmain(String[]args)throwsInterruptedException{CounterComparisontestnewCounterComparison();test.testAtomic();test.testLongAdder();test.testVolatile();}}6.使用建议单写多读使用volatile低竞争环境使用AtomicInteger/AtomicLong高并发写场景使用LongAdderJDK8精确统计场景使用Atomic系列LongAdder的sum()可能不是实时精确值总结volatile只解决可见性问题不解决原子性问题对于count这类复合操作必须使用原子类在JDK8的高并发写场景下LongAdder性能通常优于AtomicLong选择哪个工具取决于具体的读写比例和并发程度