一、Java集合框架体系与线程安全分析

1.1 基础集合分类图谱

mermaid

graph TD
    A[Collection] --> B[List]
    A --> C[Set]
    A --> D[Queue]
    B --> E[ArrayList]
    B --> F[LinkedList]
    C --> G[HashSet]
    C --> H[TreeSet]
    D --> I[PriorityQueue]
    J[Map] --> K[HashMap]
    J --> L[TreeMap]
    J --> M[LinkedHashMap]

1.2 线程安全风险示例

java

// 典型线程不安全场景
public class UnsafeCollectionDemo {
    private static List<Integer> arrayList = new ArrayList<>();
    
    public static void main(String[] args) throws InterruptedException {
        ExecutorService executor = Executors.newFixedThreadPool(10);
        for (int i = 0; i < 1000; i++) {
            executor.execute(() -> arrayList.add(new Random().nextInt()));
        }
        executor.shutdown();
        executor.awaitTermination(1, TimeUnit.HOURS);
        System.out.println("Final size: " + arrayList.size());
    }
}
// 输出结果可能小于1000，出现数据丢失

二、并发集合实现原理与选型

2.1 并发List实现对比

CopyOnWriteArrayList写时复制机制：

java

public boolean add(E e) {
    final ReentrantLock lock = this.lock;
    lock.lock();
    try {
        Object[] elements = getArray();
        int len = elements.length;
        Object[] newElements = Arrays.copyOf(elements, len + 1);
        newElements[len] = e;
        setArray(newElements);
        return true;
    } finally {
        lock.unlock();
    }
}

2.2 ConcurrentHashMap深度解析

JDK1.7 vs JDK1.8实现对比

mermaid

graph LR
    A[JDK1.7 分段锁] --> B[16个Segment段]
    B --> C[每个Segment独立ReentrantLock]
    D[JDK1.8 CAS+synchronized] --> E[Node链表+红黑树]
    D --> F[锁细化到链表头节点]

关键源码分析（JDK1.8）：

java

final V putVal(K key, V value, boolean onlyIfAbsent) {
    if (key == null || value == null) throw new NullPointerException();
    int hash = spread(key.hashCode());
    int binCount = 0;
    for (Node<K,V>[] tab = table;;) {
        Node<K,V> f; int n, i, fh;
        if (tab == null || (n = tab.length) == 0)
            tab = initTable();
        else if ((f = tabAt(tab, i = (n - 1) & hash)) == null) {
            if (casTabAt(tab, i, null, new Node<K,V>(hash, key, value, null)))
                break;                   // CAS插入新节点
        }
        else if ((fh = f.hash) == MOVED)
            tab = helpTransfer(tab, f);
        else {
            // synchronized锁住链表头节点
            synchronized (f) {
                if (tabAt(tab, i) == f) {
                    // 链表/树节点操作
                }
            }
        }
    }
    addCount(1L, binCount);
    return null;
}

2.3 并发Queue实现方案

三、并发编程核心机制解析

3.1 CAS原理与ABA问题

Compare And Swap操作流程：

+-------------------+     +-------------------+
| 预期值: Expected  |     | 内存值: V         |
+-------------------+     +-------------------+
           | Compare          |
           |----------------->|
           |                  |
           | 相等时更新为新值     |
           |----------------->|
+-------------------+     +-------------------+
| 新值: New Value    |     | 更新后的内存值      |
+-------------------+     +-------------------+

ABA问题解决方案：

java

AtomicStampedReference<Integer> atomicRef = 
    new AtomicStampedReference<>(100, 0);

// 更新时检查版本戳
atomicRef.compareAndSet(100, 200, 0, 1);

3.2 AQS（AbstractQueuedSynchronizer）框架

ReentrantLock实现原理：

mermaid

graph TB
    A[ReentrantLock] --> B[Sync]
    B --> C[NonfairSync]
    B --> D[FairSync]
    C --> E[acquire实现]
    D --> F[hasQueuedPredecessors检查]

AQS核心数据结构：

java

public abstract class AbstractQueuedSynchronizer {
    // CLH队列节点
    static final class Node {
        volatile int waitStatus;
        volatile Node prev;
        volatile Node next;
        volatile Thread thread;
    }
    
    private transient volatile Node head;
    private transient volatile Node tail;
    private volatile int state;
}

四、并发编程实战模式

4.1 生产者-消费者模式

java

public class BoundedBuffer<E> {
    private final ReentrantLock lock = new ReentrantLock();
    private final Condition notFull = lock.newCondition();
    private final Condition notEmpty = lock.newCondition();
    private final E[] items;
    private int putPtr, takePtr, count;

    public BoundedBuffer(int capacity) {
        items = (E[]) new Object[capacity];
    }

    public void put(E x) throws InterruptedException {
        lock.lock();
        try {
            while (count == items.length)
                notFull.await();
            items[putPtr] = x;
            if (++putPtr == items.length) putPtr = 0;
            ++count;
            notEmpty.signal();
        } finally {
            lock.unlock();
        }
    }

    public E take() throws InterruptedException {
        lock.lock();
        try {
            while (count == 0)
                notEmpty.await();
            E x = items[takePtr];
            if (++takePtr == items.length) takePtr = 0;
            --count;
            notFull.signal();
            return x;
        } finally {
            lock.unlock();
        }
    }
}

4.2 Fork/Join框架应用

java

public class FibonacciTask extends RecursiveTask<Long> {
    final int n;
    
    FibonacciTask(int n) { this.n = n; }

    protected Long compute() {
        if (n <= 1)
            return (long) n;
        FibonacciTask f1 = new FibonacciTask(n - 1);
        f1.fork();
        FibonacciTask f2 = new FibonacciTask(n - 2);
        return f2.compute() + f1.join();
    }

    public static void main(String[] args) {
        ForkJoinPool pool = new ForkJoinPool();
        FibonacciTask task = new FibonacciTask(30);
        System.out.println(pool.invoke(task));
    }
}

五、性能调优与最佳实践

5.1 并发集合选择决策树

mermaid

graph TD
    A[需要Map结构?] -->|是| B{写操作频繁?}
    B -->|是| C[ConcurrentHashMap]
    B -->|否| D[ConcurrentSkipListMap]
    A -->|否| E{需要顺序访问?}
    E -->|是| F[CopyOnWriteArrayList]
    E -->|否| G{高吞吐需求?}
    G -->|是| H[LinkedBlockingQueue]
    G -->|否| I[PriorityBlockingQueue]

5.2 锁优化技巧

1. 减少锁粒度：使用细粒度锁（如ConcurrentHashMap的分段锁）
2. 锁分离技术：读写锁分离（ReentrantReadWriteLock）
3. 无锁编程：使用Atomic系列类
4. 锁消除：JVM逃逸分析自动优化
5. 锁粗化：合并连续细粒度锁操作

六、常见问题排查指南

6.1 死锁检测

shell

# 使用jstack检测死锁
jstack -l <pid> | grep -A10 deadlock

6.2 线程池问题诊断

java

// 自定义拒绝策略记录异常
ThreadPoolExecutor executor = new ThreadPoolExecutor(
    4, 8, 60, TimeUnit.SECONDS,
    new LinkedBlockingQueue<>(100),
    new ThreadPoolExecutor.AbortPolicy() {
        public void rejectedExecution(Runnable r, ThreadPoolExecutor e) {
            // 记录任务拒绝日志
            monitor.reportRejectedTask(r);
            throw new RejectedExecutionException();
        }
    });

结语：构建高并发系统的艺术

1. 理解原理：深入掌握JMM内存模型与并发实现机制
2. 合理选型：根据场景选择最优并发工具
3. 预防为主：使用FindBugs等静态分析工具
4. 监控预警：集成APM系统实时监控线程状态
5. 持续优化：定期进行并发压力测试与性能调优

高并发编程如同精密钟表制造，每个零件的协调运作都至关重要。唯有将理论知识与实践验证相结合，方能构建出既稳定又高效的并发系统。