Java生产者消费者模型实践五

回顾前面做一些实验，你会发现，实现一的并发性能高于实现二、三。暂且不关心BlockingQueue的具体实现，来分析看如何优化实现三（与实现二的思路相同，性能相当）的性能。

分析实现三的瓶颈

最好的查证方法是记录方法执行时间，这样可以直接定位到真正的瓶颈。但此问题较简单，我们直接用“瞪眼法”分析。

实现三的并发瓶颈很明显，因为在锁 BUFFER_LOCK 看来，任何消费者线程与生产者线程都是一样的。换句话说，同一时刻，最多只允许有一个线程（生产者或消费者，二选一）操作缓冲区 buffer。

而实际上，如果缓冲区是一个队列的话，“生产者将产品入队”与“消费者将产品出队”两个操作之间没有同步关系，可以在队首出队的同时，在队尾入队。理想性能可提升至实现三的两倍。

去掉这个瓶颈

那么思路就简单了：需要两个锁 CONSUME_LOCK与PRODUCE_LOCK，CONSUME_LOCK控制消费者线程并发出队，PRODUCE_LOCK控制生产者线程并发入队；相应需要两个条件变量NOT_EMPTY与NOT_FULL，NOT_EMPTY负责控制消费者线程的状态（阻塞、运行），NOT_FULL负责控制生产者线程的状态（阻塞、运行）。以此让优化消费者与消费者（或生产者与生产者）之间是串行的；消费者与生产者之间是并行的。

package com.github.xuchengen.concurrent.impl;

import com.github.xuchengen.concurrent.AbsConsumer;
import com.github.xuchengen.concurrent.AbsProducer;
import com.github.xuchengen.concurrent.Model;
import com.github.xuchengen.concurrent.Task;

import java.util.concurrent.atomic.AtomicInteger;
import java.util.concurrent.locks.Condition;
import java.util.concurrent.locks.Lock;
import java.util.concurrent.locks.ReentrantLock;

/**
 * 基于锁条件实现
 * 作者：徐承恩
 * 邮箱：xuchengen@gmail.com
 * 日期：2019/12/12
 */
public class LockConditionModel2 implements Model {

    private final Lock CONSUME_LOCK = new ReentrantLock();

    private final Condition NOT_EMPTY = CONSUME_LOCK.newCondition();

    private final Lock PRODUCE_LOCK = new ReentrantLock();

    private final Condition NOT_FULL = PRODUCE_LOCK.newCondition();

    private final Buffer<Task> buffer = new Buffer<>();

    private AtomicInteger bufLen = new AtomicInteger(0);

    private final int cap;

    private final AtomicInteger increTaskNo = new AtomicInteger(0);

    public LockConditionModel2(int cap) {
        this.cap = cap;
    }

    @Override
    public Runnable newRunnableConsumer() {
        return new ConsumerImpl();
    }

    @Override
    public Runnable newRunnableProducer() {
        return new ProducerImpl();
    }

    private class ConsumerImpl extends AbsConsumer {
        @Override
        public void consume() throws InterruptedException {
            int newBufSize = -1;
            CONSUME_LOCK.lockInterruptibly();
            try {
                while (bufLen.get() == 0) {
                    System.out.println("buffer is empty...");
                    NOT_EMPTY.await();
                }

                Task task = buffer.poll();

                assert task != null;
                // 固定时间范围的消费，模拟相对稳定的服务器处理过程
                Thread.sleep(500 + (long) (Math.random() * 500));
                System.out.println("consume: " + task.no);

                newBufSize = bufLen.decrementAndGet();
                if (newBufSize > 0) {
                    // 非空唤醒继续消费
                    NOT_EMPTY.signalAll();
                }
            } finally {
                CONSUME_LOCK.unlock();
            }
            if (newBufSize < cap) {
                PRODUCE_LOCK.lockInterruptibly();
                try {
                    // 未满唤醒生产者继续生产
                    NOT_FULL.signalAll();
                } finally {
                    PRODUCE_LOCK.unlock();
                }
            }
        }
    }


    private class ProducerImpl extends AbsProducer {
        @Override
        public void produce() throws InterruptedException {
            // 不定期生产，模拟随机的用户请求
            Thread.sleep((long) (Math.random() * 1000));
            int newBufSize = -1;
            PRODUCE_LOCK.lockInterruptibly();
            try {
                while (bufLen.get() == cap) {
                    System.out.println("buffer is full...");
                    NOT_FULL.await();
                }
                Task task = new Task(increTaskNo.getAndIncrement());
                buffer.offer(task);
                newBufSize = bufLen.incrementAndGet();
                System.out.println("produce: " + task.no);
                if (newBufSize < cap) {
                    // 未满唤醒继续消费
                    NOT_FULL.signalAll();
                }
            } finally {
                PRODUCE_LOCK.unlock();
            }
            if (newBufSize > 0) {
                CONSUME_LOCK.lockInterruptibly();
                try {
                    // 非空唤醒消费者继续消费
                    NOT_EMPTY.signalAll();
                } finally {
                    CONSUME_LOCK.unlock();
                }
            }
        }
    }

    private static class Buffer<E> {
        private Node head;
        private Node tail;

        Buffer() {
            // dummy node
            head = tail = new Node(null);
        }

        public void offer(E e) {
            tail.next = new Node(e);
            tail = tail.next;
        }

        public E poll() {
            head = head.next;
            E e = head.item;
            head.item = null;
            return e;
        }

        private class Node {
            E item;
            Node next;

            Node(E item) {
                this.item = item;
            }
        }
    }

    public static void main(String[] args) {
        Model model = new LockConditionModel2(3);
        for (int i = 0; i < 2; i++) {
            new Thread(model.newRunnableConsumer()).start();
        }
        for (int i = 0; i < 5; i++) {
            new Thread(model.newRunnableProducer()).start();
        }
    }

}

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package com.github.xuchengen.concurrent.impl;

import com.github.xuchengen.concurrent.AbsConsumer;

import com.github.xuchengen.concurrent.AbsProducer;

import com.github.xuchengen.concurrent.Model;

import com.github.xuchengen.concurrent.Task;

import java.util.concurrent.atomic.AtomicInteger;

import java.util.concurrent.locks.Condition;

import java.util.concurrent.locks.Lock;

import java.util.concurrent.locks.ReentrantLock;

/**

* 基于锁条件实现

* 作者：徐承恩

* 邮箱：xuchengen@gmail.com

* 日期：2019/12/12

public class LockConditionModel2 implements Model {

private final Lock CONSUME_LOCK = new ReentrantLock();

private final Condition NOT_EMPTY = CONSUME_LOCK.newCondition();

private final Lock PRODUCE_LOCK = new ReentrantLock();

private final Condition NOT_FULL = PRODUCE_LOCK.newCondition();

private final Buffer<Task> buffer = new Buffer<>();

private AtomicInteger bufLen = new AtomicInteger(0);

private final int cap;

private final AtomicInteger increTaskNo = new AtomicInteger(0);

public LockConditionModel2(int cap) {

this.cap = cap;

}

@Override

public Runnable newRunnableConsumer() {

return new ConsumerImpl();

}

@Override

public Runnable newRunnableProducer() {

return new ProducerImpl();

}

private class ConsumerImpl extends AbsConsumer {

@Override

public void consume() throws InterruptedException {

int newBufSize = -1;

CONSUME_LOCK.lockInterruptibly();

try {

while (bufLen.get() == 0) {

System.out.println("buffer is empty...");

NOT_EMPTY.await();

}

Task task = buffer.poll();

assert task != null;

// 固定时间范围的消费，模拟相对稳定的服务器处理过程

Thread.sleep(500 + (long) (Math.random() * 500));

System.out.println("consume: " + task.no);

newBufSize = bufLen.decrementAndGet();

if (newBufSize > 0) {

// 非空唤醒继续消费

NOT_EMPTY.signalAll();

}

} finally {

CONSUME_LOCK.unlock();

}

if (newBufSize < cap) {

PRODUCE_LOCK.lockInterruptibly();

try {

// 未满唤醒生产者继续生产

NOT_FULL.signalAll();

} finally {

PRODUCE_LOCK.unlock();

}

private class ProducerImpl extends AbsProducer {

@Override

public void produce() throws InterruptedException {

// 不定期生产，模拟随机的用户请求

Thread.sleep((long) (Math.random() * 1000));

int newBufSize = -1;

PRODUCE_LOCK.lockInterruptibly();

try {

while (bufLen.get() == cap) {

System.out.println("buffer is full...");

NOT_FULL.await();

}

Task task = new Task(increTaskNo.getAndIncrement());

buffer.offer(task);

newBufSize = bufLen.incrementAndGet();

System.out.println("produce: " + task.no);

if (newBufSize < cap) {

// 未满唤醒继续消费

NOT_FULL.signalAll();

}

} finally {

PRODUCE_LOCK.unlock();

}

if (newBufSize > 0) {

CONSUME_LOCK.lockInterruptibly();

try {

// 非空唤醒消费者继续消费

NOT_EMPTY.signalAll();

} finally {

CONSUME_LOCK.unlock();

}

private static class Buffer<E> {

private Node head;

private Node tail;

Buffer() {

// dummy node

head = tail = new Node(null);

}

public void offer(E e) {

tail.next = new Node(e);

tail = tail.next;

}

public E poll() {

head = head.next;

E e = head.item;

head.item = null;

return e;

}

private class Node {

E item;

Node next;

Node(E item) {

this.item = item;

}

public static void main(String[] args) {

Model model = new LockConditionModel2(3);

for (int i = 0; i < 2; i++) {

new Thread(model.newRunnableConsumer()).start();

}

for (int i = 0; i < 5; i++) {

new Thread(model.newRunnableProducer()).start();

}

需要注意的是，由于需要同时在UnThreadSafe的缓冲区 buffer 上进行消费与生产，我们不能使用实现二、三中使用的队列了，需要自己实现一个简单的缓冲区 Buffer。Buffer要满足以下条件：

在头部出队，尾部入队
在poll()方法中只操作head
在offer()方法中只操作tail

还能进一步优化吗

我们已经优化掉了消费者与生产者之间的瓶颈，还能进一步优化吗？

如果可以，必然是继续优化消费者与消费者（或生产者与生产者）之间的并发性能。然而，消费者与消费者之间必须是串行的，因此，并发模型上已经没有地方可以继续优化了。

仔细分析下，实现四中的signalAll都可以换成signal。这里为了屏蔽复杂性，回避了这个优化。

不过在具体的业务场景中，一般还能够继续优化。如：

并发规模中等，可考虑使用CAS代替重入锁
模型上不能优化，但一个消费行为或许可以进一步拆解、优化，从而降低消费的延迟
一个队列的并发性能达到了极限，可采用“多个队列”（如分布式消息队列等）

这4种写法的本质相同——都是在使用或实现BlockingQueue。实现一直接使用BlockingQueue，实现四实现了简单的BlockingQueue，而实现二、三则实现了退化版的BlockingQueue（性能降低一半）。

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