一、ReentrantReadWriteLock
当读操作远远高于写操作时，这时候使用 读写锁 让 读-读 可以并发，提高性能。
类似于数据库中的 select ... from ... lock in share mode
示例
提供一个 数据容器类 内部分别使用读锁保护数据的 read() 方法，写锁保护数据的 write() 方法
class DataContainer {
private Object data;
private ReentrantReadWriteLock rw = new ReentrantReadWriteLock();
private ReentrantReadWriteLock.ReadLock r = rw.readLock();
private ReentrantReadWriteLock.WriteLock w = rw.writeLock();
public Object read() {
log.debug("获取读锁...");
r.lock();
try {
log.debug("读取");
sleep(1);
return data;
} finally {
log.debug("释放读锁...");
r.unlock();
}
}
public void write() {
log.debug("获取写锁...");
w.lock();
try {
log.debug("写入");
sleep(1);
} finally {
log.debug("释放写锁...");
w.unlock();
}
}
}
读-读 可并发
测试 读锁-读锁 可以并发
DataContainer dataContainer = new DataContainer();
new Thread(() -> {
dataContainer.read();
}, "t1").start();
new Thread(() -> {
dataContainer.read();
}, "t2").start();
输出结果，从这里可以看到 Thread-0 锁定期间，Thread-1 的读操作不受影响
14:05:14.341 c.DataContainer [t2] - 获取读锁...
14:05:14.341 c.DataContainer [t1] - 获取读锁...
14:05:14.345 c.DataContainer [t1] - 读取
14:05:14.345 c.DataContainer [t2] - 读取
14:05:15.365 c.DataContainer [t2] - 释放读锁...
14:05:15.386 c.DataContainer [t1] - 释放读锁...
读-写 / 写-写 互斥
测试 读锁-写锁 相互阻塞
DataContainer dataContainer = new DataContainer();
new Thread(() -> {
dataContainer.read();
}, "t1").start();
Thread.sleep(100);
new Thread(() -> {
dataContainer.write();
}, "t2").start();
输出结果
14:04:21.838 c.DataContainer [t1] - 获取读锁...
14:04:21.838 c.DataContainer [t2] - 获取写锁...
14:04:21.841 c.DataContainer [t2] - 写入
14:04:22.843 c.DataContainer [t2] - 释放写锁...
14:04:22.843 c.DataContainer [t1] - 读取
14:04:23.843 c.DataContainer [t1] - 释放读锁...
写锁-写锁 也是相互阻塞的，这里就不测试了
注意事项
- 读锁不支持条件变量,写锁支持
重入时不支持升级：即持有读锁的情况下去获取写锁，会导致获取写锁永久等待
r.lock();
try {
// ...
w.lock();
try {
// ...
} finally{
w.unlock();
}
} finally{
r.unlock();
}
重入时支持降级：即持有写锁的情况下去获取读锁
class CachedData {
Object data;
// 是否有效，如果失效，需要重新计算 data
volatile boolean cacheValid;
final ReentrantReadWriteLock rwl = new ReentrantReadWriteLock();
void processCachedData() {
rwl.readLock().lock();
if (!cacheValid) {
// 获取写锁前必须释放读锁
rwl.readLock().unlock();
rwl.writeLock().lock();
try {
// 判断是否有其它线程已经获取了写锁、更新了缓存, 避免重复更新
if (!cacheValid) {
data = ...
cacheValid = true;
}
// 降级为读锁, 释放写锁, 这样能够让其它线程读取缓存
rwl.readLock().lock();
} finally {
rwl.writeLock().unlock();
}
}
// 自己用完数据, 释放读锁
try {
use(data);
} finally {
rwl.readLock().unlock();
}
}
}
应用之缓存
1. 缓存更新策略
更新时，是先清缓存还是先更新数据库
先清缓存
先更新数据库
补充一种情况，假设查询线程 A 查询数据时恰好缓存数据由于时间到期失效，或是第一次查询
这种情况的出现几率非常小，见 facebook 论文
2. 读写锁实现一致性缓存
使用读写锁实现一个简单的按需加载缓存
class GenericCachedDao<T> {
// HashMap 作为缓存非线程安全, 需要保护
HashMap<SqlPair, T> map = new HashMap<>();
ReentrantReadWriteLock lock = new ReentrantReadWriteLock();
GenericDao genericDao = new GenericDao();
public int update(String sql, Object... params) {
SqlPair key = new SqlPair(sql, params);
// 加写锁, 防止其它线程对缓存读取和更改
lock.writeLock().lock();
try {
int rows = genericDao.update(sql, params);
map.clear();
return rows;
} finally {
lock.writeLock().unlock();
}
}
public T queryOne(Class<T> beanClass, String sql, Object... params) {
SqlPair key = new SqlPair(sql, params);
// 加读锁, 防止其它线程对缓存更改
lock.readLock().lock();
try {
T value = map.get(key);
if (value != null) {
return value;
}
} finally {
lock.readLock().unlock();
}
// 加写锁, 防止其它线程对缓存读取和更改
lock.writeLock().lock();
try {
// get 方法上面部分是可能多个线程进来的, 可能已经向缓存填充了数据
// 为防止重复查询数据库, 再次验证
T value = map.get(key);
if (value == null) {
// 如果没有, 查询数据库
value = genericDao.queryOne(beanClass, sql, params);
map.put(key, value);
}
return value;
} finally {
lock.writeLock().unlock();
}
}
// 作为 key 保证其是不可变的
class SqlPair {
private String sql;
private Object[] params;
public SqlPair(String sql, Object[] params) {
this.sql = sql;
this.params = params;
}
@Override
public boolean equals(Object o) {
if (this == o) {
return true;
}
if (o == null || getClass() != o.getClass()) {
return false;
}
SqlPair sqlPair = (SqlPair) o;
return sql.equals(sqlPair.sql) &&
Arrays.equals(params, sqlPair.params);
}
@Override
public int hashCode() {
int result = Objects.hash(sql);
result = 31 * result + Arrays.hashCode(params);
return result;
}
}
}
注意
-
以上实现体现的是读写锁的应用，保证缓存和数据库的一致性，但有下面的问题没有考虑
- 适合读多写少，如果写操作比较频繁，以上实现性能低
- 没有考虑缓存容量
- 没有考虑缓存过期
- 只适合单机
- 并发性还是低，目前只会用一把锁
- 更新方法太过简单粗暴，清空了所有 key（考虑按类型分区或重新设计 key）
-
乐观锁实现：用 CAS 去更新
二、Semaphore
1. 基本使用
[ˈsɛməˌfɔr] 信号量，用来限制能同时访问共享资源的线程上限。
public static void main(String[] args) {
// 1. 创建 semaphore 对象
Semaphore semaphore = new Semaphore(3);
// 2. 10个线程同时运行
for (int i = 0; i < 10; i++) {
new Thread(() -> {
// 3. 获取许可
try {
semaphore.acquire();
} catch (InterruptedException e) {
e.printStackTrace();
}
try {
log.debug("running...");
sleep(1);
log.debug("end...");
} finally {
// 4. 释放许可
semaphore.release();
}
}).start();
}
}
输出
07:35:15.485 c.TestSemaphore [Thread-2] - running...
07:35:15.485 c.TestSemaphore [Thread-1] - running...
07:35:15.485 c.TestSemaphore [Thread-0] - running...
07:35:16.490 c.TestSemaphore [Thread-2] - end...
07:35:16.490 c.TestSemaphore [Thread-0] - end...
07:35:16.490 c.TestSemaphore [Thread-1] - end...
07:35:16.490 c.TestSemaphore [Thread-3] - running...
07:35:16.490 c.TestSemaphore [Thread-5] - running...
07:35:16.490 c.TestSemaphore [Thread-4] - running...
07:35:17.490 c.TestSemaphore [Thread-5] - end...
07:35:17.490 c.TestSemaphore [Thread-4] - end...
07:35:17.490 c.TestSemaphore [Thread-3] - end...
07:35:17.490 c.TestSemaphore [Thread-6] - running...
07:35:17.490 c.TestSemaphore [Thread-7] - running...
07:35:17.490 c.TestSemaphore [Thread-9] - running...
07:35:18.491 c.TestSemaphore [Thread-6] - end...
07:35:18.491 c.TestSemaphore [Thread-7] - end...
07:35:18.491 c.TestSemaphore [Thread-9] - end...
07:35:18.491 c.TestSemaphore [Thread-8] - running...
07:35:19.492 c.TestSemaphore [Thread-8] - end...
2. Semaphore 应用 (实现简单连接池)
- 使用 Semaphore 限流，在访问高峰期时，让请求线程阻塞，高峰期过去再释放许可，当然它只适合限制单机线程数量，并且仅是限制线程数，而不是限制资源数（例如连接数，请对比 Tomcat LimitLatch 的实现）
- 用 Semaphore 实现简单连接池，对比『享元模式』下的实现（用wait notify），性能和可读性显然更好,注意下面的实现中线程数和数据库连接数是相等的
@Slf4j(topic = "c.Pool")
class Pool {
// 1. 连接池大小
private final int poolSize;
// 2. 连接对象数组
private Connection[] connections;
// 3. 连接状态数组 0 表示空闲， 1 表示繁忙
private AtomicIntegerArray states;
private Semaphore semaphore;
// 4. 构造方法初始化
public Pool(int poolSize) {
this.poolSize = poolSize;
// 让许可数与资源数一致
this.semaphore = new Semaphore(poolSize);
this.connections = new Connection[poolSize];
this.states = new AtomicIntegerArray(new int[poolSize]);
for (int i = 0; i < poolSize; i++) {
connections[i] = new MockConnection("连接" + (i+1));
}
}
// 5. 借连接
public Connection borrow() {// t1, t2, t3
// 获取许可
try {
semaphore.acquire(); // 没有许可的线程，在此等待
} catch (InterruptedException e) {
e.printStackTrace();
}
for (int i = 0; i < poolSize; i++) {
// 获取空闲连接
if(states.get(i) == 0) {
if (states.compareAndSet(i, 0, 1)) {
log.debug("borrow {}", connections[i]);
return connections[i];
}
}
}
// 不会执行到这里
return null;
}
// 6. 归还连接
public void free(Connection conn) {
for (int i = 0; i < poolSize; i++) {
if (connections[i] == conn) {
states.set(i, 0);
log.debug("free {}", conn);
semaphore.release();
break;
}
}
}
}
3.* Semaphore 原理
3.1 加锁解锁流程
Semaphore 有点像一个停车场，permits 就好像停车位数量，当线程获得了 permits 就像是获得了停车位，然后 停车场显示空余车位减一
刚开始，permits（state）为 3，这时 5 个线程来获取资源
假设其中 Thread-1，Thread-2，Thread-4 cas 竞争成功，而 Thread-0 和 Thread-3 竞争失败，进入 AQS 队列 park 阻塞
这时 Thread-4 释放了 permits，状态如下
接下来 Thread-0 竞争成功，permits 再次设置为 0，设置自己为 head 节点，断开原来的 head 节点，unpark 接下来的 Thread-3 节点，但由于 permits 是 0，因此 Thread-3 在尝试不成功后再次进入 park 状态
3.2 源码分析
static final class NonfairSync extends Sync {
private static final long serialVersionUID = -2694183684443567898L;
NonfairSync(int permits) {
// permits 即 state
super(permits);
}
// Semaphore 方法, 方便阅读, 放在此处
public void acquire() throws InterruptedException {
sync.acquireSharedInterruptibly(1);
}
// AQS 继承过来的方法, 方便阅读, 放在此处
public final void acquireSharedInterruptibly(int arg)
throws InterruptedException {
if (Thread.interrupted())
throw new InterruptedException();
if (tryAcquireShared(arg) < 0)
doAcquireSharedInterruptibly(arg); //当前资源数为0时执行，加入阻塞队列
}
// 尝试获得共享锁
protected int tryAcquireShared(int acquires) {
return nonfairTryAcquireShared(acquires);
}
// Sync 继承过来的方法, 方便阅读, 放在此处
final int nonfairTryAcquireShared(int acquires) { //传入1，我只消耗1个资源数
for (;;) {
int available = getState(); //获取当前可用资源数
int remaining = available - acquires; //剩余资源数
if (
// 如果许可已经用完, 返回负数, 表示获取失败, 进入 doAcquireSharedInterruptibly
remaining < 0 ||
// 如果 cas 重试成功, 返回正数, 表示获取成功
compareAndSetState(available, remaining)
) {
return remaining;
}
}
}
// AQS 继承过来的方法, 方便阅读, 放在此处 (当前资源数为0时执行，加入阻塞队列)
private void doAcquireSharedInterruptibly(int arg) throws InterruptedException {
final Node node = addWaiter(Node.SHARED);
boolean failed = true;
try {
for (;;) {
final Node p = node.predecessor();
if (p == head) {
// 再次尝试获取许可
int r = tryAcquireShared(arg);
if (r >= 0) {
// 成功后本线程出队（AQS）, 所在 Node设置为 head
// 如果 head.waitStatus == Node.SIGNAL ==> 0 成功, 下一个节点 unpark
// 如果 head.waitStatus == 0 ==> Node.PROPAGATE
// r 表示可用资源数, 为 0 则不会继续传播
setHeadAndPropagate(node, r);
p.next = null; // help GC
failed = false;
return;
}
}
// 不成功, 设置上一个节点 waitStatus = Node.SIGNAL, 下轮进入 park 阻塞
if (shouldParkAfterFailedAcquire(p, node) &&
parkAndCheckInterrupt())
throw new InterruptedException();
}
} finally {
if (failed)
cancelAcquire(node);
}
}
// Semaphore 方法, 方便阅读, 放在此处
public void release() {
sync.releaseShared(1);
}
// AQS 继承过来的方法, 方便阅读, 放在此处
public final boolean releaseShared(int arg) {
if (tryReleaseShared(arg)) {
doReleaseShared();
return true;
}
return false;
}
// Sync 继承过来的方法, 方便阅读, 放在此处
protected final boolean tryReleaseShared(int releases) {
for (;;) {
int current = getState();
int next = current + releases;
if (next < current) // overflow
throw new Error("Maximum permit count exceeded");
if (compareAndSetState(current, next))
return true;
}
}
}
三、CountdownLatch
用来进行线程同步协作，等待所有线程完成倒计时。
其中构造参数用来初始化等待计数值，await() 用来等待计数归零，countDown() 用来让计数减一
示例1
public static void main(String[] args) throws InterruptedException {
CountDownLatch latch = new CountDownLatch(3);
new Thread(() -> {
log.debug("begin...");
sleep(1);
latch.countDown();
log.debug("end...{}", latch.getCount());
}).start();
new Thread(() -> {
log.debug("begin...");
sleep(2);
latch.countDown();
log.debug("end...{}", latch.getCount());
}).start();
new Thread(() -> {
log.debug("begin...");
sleep(1.5);
latch.countDown();
log.debug("end...{}", latch.getCount());
}).start();
log.debug("waiting...");
latch.await();
log.debug("wait end...");
}
输出
18:44:00.778 c.TestCountDownLatch [main] - waiting...
18:44:00.778 c.TestCountDownLatch [Thread-2] - begin...
18:44:00.778 c.TestCountDownLatch [Thread-0] - begin...
18:44:00.778 c.TestCountDownLatch [Thread-1] - begin...
18:44:01.782 c.TestCountDownLatch [Thread-0] - end...2
18:44:02.283 c.TestCountDownLatch [Thread-2] - end...1
18:44:02.782 c.TestCountDownLatch [Thread-1] - end...0
18:44:02.782 c.TestCountDownLatch [main] - wait end...
可以配合线程池使用，改进如下
public static void main(String[] args) throws InterruptedException {
CountDownLatch latch = new CountDownLatch(3);
ExecutorService service = Executors.newFixedThreadPool(4);
service.submit(() -> {
log.debug("begin...");
sleep(1);
latch.countDown();
log.debug("end...{}", latch.getCount());
});
service.submit(() -> {
log.debug("begin...");
sleep(1.5);
latch.countDown();
log.debug("end...{}", latch.getCount());
});
service.submit(() -> {
log.debug("begin...");
sleep(2);
latch.countDown();
log.debug("end...{}", latch.getCount());
});
service.submit(()->{
try {
log.debug("waiting...");
latch.await();
log.debug("wait end...");
} catch (InterruptedException e) {
e.printStackTrace();
}
});
}
输出
18:52:25.831 c.TestCountDownLatch [pool-1-thread-3] - begin...
18:52:25.831 c.TestCountDownLatch [pool-1-thread-1] - begin...
18:52:25.831 c.TestCountDownLatch [pool-1-thread-2] - begin...
18:52:25.831 c.TestCountDownLatch [pool-1-thread-4] - waiting...
18:52:26.835 c.TestCountDownLatch [pool-1-thread-1] - end...2
18:52:27.335 c.TestCountDownLatch [pool-1-thread-2] - end...1
18:52:27.835 c.TestCountDownLatch [pool-1-thread-3] - end...0
18:52:27.835 c.TestCountDownLatch [pool-1-thread-4] - wait end...
* 应用之同步等待多线程准备完毕
AtomicInteger num = new AtomicInteger(0);
ExecutorService service = Executors.newFixedThreadPool(10, (r) -> {
return new Thread(r, "t" + num.getAndIncrement());
});
CountDownLatch latch = new CountDownLatch(10);
String[] all = new String[10];
Random r = new Random();
for (int j = 0; j < 10; j++) {
int x = j;
service.submit(() -> {
for (int i = 0; i <= 100; i++) {
try {
Thread.sleep(r.nextInt(100));
} catch (InterruptedException e) {
}
all[x] = Thread.currentThread().getName() + "(" + (i + "%") + ")";
System.out.print("\r" + Arrays.toString(all));
}
latch.countDown();
});
}
latch.await();
System.out.println("\n游戏开始...");
service.shutdown();
中间输出
[t0(52%), t1(47%), t2(51%), t3(40%), t4(49%), t5(44%), t6(49%), t7(52%), t8(46%), t9(46%)]
最后输出
[t0(100%), t1(100%), t2(100%), t3(100%), t4(100%), t5(100%), t6(100%), t7(100%), t8(100%), t9(100%)]
游戏开始...
* 应用之同步等待多个远程调用结束
@RestController
public class TestCountDownlatchController {
@GetMapping("/order/{id}")
public Map<String, Object> order(@PathVariable int id) {
HashMap<String, Object> map = new HashMap<>();
map.put("id", id);
map.put("total", "2300.00");
sleep(2000);
return map;
}
@GetMapping("/product/{id}")
public Map<String, Object> product(@PathVariable int id) {
HashMap<String, Object> map = new HashMap<>();
if (id == 1) {
map.put("name", "小爱音箱");
map.put("price", 300);
} else if (id == 2) {
map.put("name", "小米手机");
map.put("price", 2000);
}
map.put("id", id);
sleep(1000);
return map;
}
@GetMapping("/logistics/{id}")
public Map<String, Object> logistics(@PathVariable int id) {
HashMap<String, Object> map = new HashMap<>();
map.put("id", id);
map.put("name", "中通快递");
sleep(2500);
return map;
}
private void sleep(int millis) {
try {
Thread.sleep(millis);
} catch (InterruptedException e) {
e.printStackTrace();
}
}
}
rest 远程调用
RestTemplate restTemplate = new RestTemplate();
log.debug("begin");
ExecutorService service = Executors.newCachedThreadPool();
CountDownLatch latch = new CountDownLatch(4);
Future<Map<String,Object>> f1 = service.submit(() -> {
Map<String, Object> r =
restTemplate.getForObject("http://localhost:8080/order/{1}", Map.class, 1);
latch.countDown();
return r;
});
Future<Map<String, Object>> f2 = service.submit(() -> {
Map<String, Object> r =
restTemplate.getForObject("http://localhost:8080/product/{1}", Map.class, 1);
latch.countDown();
return r;
});
Future<Map<String, Object>> f3 = service.submit(() -> {
Map<String, Object> r =
restTemplate.getForObject("http://localhost:8080/product/{1}", Map.class, 2);
latch.countDown();
return r;
});
Future<Map<String, Object>> f4 = service.submit(() -> {
Map<String, Object> r =
restTemplate.getForObject("http://localhost:8080/logistics/{1}", Map.class, 1);
latch.countDown();
return r;
});
System.out.println(f1.get());
System.out.println(f2.get());
System.out.println(f3.get());
System.out.println(f4.get());
latch.await();
log.debug("执行完毕");
service.shutdown();
执行结果
19:51:39.711 c.TestCountDownLatch [main] - begin
{total=2300.00, id=1}
{price=300, name=小爱音箱, id=1}
{price=2000, name=小米手机, id=2}
{name=中通快递, id=1}
19:51:42.407 c.TestCountDownLatch [main] - 执行完毕
四、CyclicBarrier
[ˈsaɪklɪk ˈbæriɚ] 循环栅栏，用来进行线程协作，等待线程满足某个计数。构造时设置『计数个数』，每个线程执行到某个需要“同步”的时刻调用 await() 方法进行等待，当等待的线程数满足『计数个数』时，继续执行.
CyclicBarrier 线程都到await后，这些线程再一起向=往下执行，线程数不够时，阻塞等待
CountDownLatch不可重入，当构造函数设2时，两个线程执行完毕后，无法在循环中重新使用该对象，而CyclicBarrier可以重新使用
CyclicBarrier cb = new CyclicBarrier(2); // 个数为2时才会继续执行
new Thread(()->{
System.out.println("线程1开始.."+new Date());
try {
cb.await(); // 当个数不足时，等待 （此时只有1个线程等待，而CyclicBarrier构造函数中传入的参数为2）
} catch (InterruptedException | BrokenBarrierException e) {
e.printStackTrace();
}
System.out.println("线程1继续向下运行..."+new Date());
}).start();
new Thread(()->{
System.out.println("线程2开始.."+new Date());
try {
Thread.sleep(2000);
} catch (InterruptedException e) {
}
try {
cb.await(); // 2 秒后，线程个数够2，继续运行
} catch (InterruptedException | BrokenBarrierException e) {
e.printStackTrace();
}
System.out.println("线程2继续向下运行..."+new Date());
}).start();
注意 ： CyclicBarrier 与 CountDownLatch 的主要区别在于 CyclicBarrier 是可以重用的。 CyclicBarrier 可以被比喻为『人满发车』
