首先给出一个并发场景下,错误的计数器方法:
public class CountExample {
// 请求总数
public static int clientTotal = 5000;
// 同时并发执行的线程数
public static int threadTotal = 200;
public static int count = 0;
public static void main(String[] args) throws Exception {
ExecutorService executorService = Executors.newCachedThreadPool();
final Semaphore semaphore = new Semaphore(threadTotal);
final CountDownLatch countDownLatch = new CountDownLatch(clientTotal);
for (int i = 0; i < clientTotal ; i++) {
executorService.execute(() -> {
try {
semaphore.acquire();
add();
semaphore.release();
} catch (Exception e) {
log.error("exception", e);
}
countDownLatch.countDown();
});
}
countDownLatch.await();
executorService.shutdown();
log.info("count:{}", count);
}
private static void add() {
count++;
}
}运行多次输出的结果不一样:
第一次输出:
21:45:05.233 [main] INFO com.tim.concurrency.example.CountExample - count:4975.
第二次输出:
21:47:08.113 [main] INFO com.tim.concurrency.example.CountExample - count:4972.
分析:
上述代码想实现的功能是200个并发线程进行5000次请求,并对cout值进行加1的操作。
改进策略1 (通过AtomicLong):
@Slf4j
public class CountExample {
// 请求总数
public static int clientTotal = 5000;
// 同时并发执行的线程数
public static int threadTotal = 200;
public static AtomicLong count = new AtomicLong(0);
public static void main(String[] args) throws Exception {
ExecutorService executorService = Executors.newCachedThreadPool();
final Semaphore semaphore = new Semaphore(threadTotal);
final CountDownLatch countDownLatch = new CountDownLatch(clientTotal);
for (int i = 0; i < clientTotal ; i++) {
executorService.execute(() -> {
try {
semaphore.acquire();
add();
semaphore.release();
} catch (Exception e) {
log.error("exception", e);
}
countDownLatch.countDown();
});
}
countDownLatch.await();
executorService.shutdown();
log.info("count:{}", count);
}
private static void add() {
count.getAndAdd(1);
}
}改进策略2(通过synchronized同步字段):
public class CountExample {
// 请求总数
public static int clientTotal = 5000;
// 同时并发执行的线程数
public static int threadTotal = 200;
public static int count = 0;
public static void main(String[] args) throws Exception {
ExecutorService executorService = Executors.newCachedThreadPool();
final Semaphore semaphore = new Semaphore(threadTotal);
final CountDownLatch countDownLatch = new CountDownLatch(clientTotal);
for (int i = 0; i < clientTotal ; i++) {
executorService.execute(() -> {
try {
semaphore.acquire();
add();
semaphore.release();
} catch (Exception e) {
log.error("exception", e);
}
countDownLatch.countDown();
});
}
countDownLatch.await();
executorService.shutdown();
log.info("count:{}", count);
}
private synchronized static void add() {
count++;
}
}改进策略3(通过LongAdder):
@Slf4j
public class CountExample {
// 请求总数
public static int clientTotal = 5000;
// 同时并发执行的线程数
public static int threadTotal = 200;
public static LongAdder count = new LongAdder();
public static void main(String[] args) throws Exception {
ExecutorService executorService = Executors.newCachedThreadPool();
final Semaphore semaphore = new Semaphore(threadTotal);
final CountDownLatch countDownLatch = new CountDownLatch(clientTotal);
for (int i = 0; i < clientTotal ; i++) {
executorService.execute(() -> {
try {
semaphore.acquire();
add();
semaphore.release();
} catch (Exception e) {
log.error("exception", e);
}
countDownLatch.countDown();
});
}
countDownLatch.await();
executorService.shutdown();
log.info("count:{}", count);
}
private static void add() {
count.increment();
}
}
本文详细介绍了如何在多线程环境下编写一个并发计数器,通过使用原子操作和锁机制确保计数的准确性。内容包括Java的AtomicInteger类的使用、线程同步以及互斥锁的概念,帮助读者理解在并发编程中确保数据一致性的关键技巧。
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