0e46a367c4
Organized 50 interview questions into 12 categories: - 01-分布式系统 (9 files): 分布式事务, 分布式锁, 一致性哈希, CAP理论, etc. - 02-数据库 (2 files): MySQL索引优化, MyBatis核心原理 - 03-缓存 (5 files): Redis数据结构, 缓存问题, LRU算法, etc. - 04-消息队列 (1 file): RocketMQ/Kafka - 05-并发编程 (4 files): 线程池, 设计模式, 限流策略, etc. - 06-JVM (1 file): JVM和垃圾回收 - 07-系统设计 (8 files): 秒杀系统, 短链接, IM, Feed流, etc. - 08-算法与数据结构 (4 files): B+树, 红黑树, 跳表, 时间轮 - 09-网络与安全 (3 files): TCP/IP, 加密安全, 性能优化 - 10-中间件 (4 files): Spring Boot, Nacos, Dubbo, Nginx - 11-运维 (4 files): Kubernetes, CI/CD, Docker, 可观测性 - 12-面试技巧 (1 file): 面试技巧和职业规划 All files renamed to Chinese for better accessibility and organized into categorized folders for easier navigation. Generated with [Claude Code](https://claude.com/claude-code) via [Happy](https://happy.engineering) Co-Authored-By: Claude <noreply@anthropic.com> Co-Authored-By: Happy <yesreply@happy.engineering>
601 lines
14 KiB
Markdown
601 lines
14 KiB
Markdown
# 线程池核心参数详解
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## 问题
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1. 线程池的核心参数有哪些？各自的作用是什么？
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2. 如何合理设置线程池大小？
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3. 线程池的拒绝策略有哪些？如何自定义？
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4. 线程池如何优雅关闭？
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5. 线程池的监控指标有哪些？
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6. 在实际项目中如何使用线程池？
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---
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## 标准答案
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### 1. 线程池核心参数
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#### **ThreadPoolExecutor 构造函数**
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```java
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public ThreadPoolExecutor(
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int corePoolSize, // 核心线程数
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int maximumPoolSize, // 最大线程数
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long keepAliveTime, // 非核心线程空闲存活时间
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TimeUnit unit, // 时间单位
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BlockingQueue<Runnable> workQueue, // 任务队列
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ThreadFactory threadFactory, // 线程工厂
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RejectedExecutionHandler handler // 拒绝策略
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)
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```
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---
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#### **参数详解**
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**1. corePoolSize（核心线程数）**
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- **说明**：即使空闲也保留的线程数
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- **默认值**：创建时无核心线程（任务到达时才创建）
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- **预热**：`prestartAllCoreThreads()` 提前创建核心线程
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```java
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ThreadPoolExecutor executor = new ThreadPoolExecutor(
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10, // 核心线程数
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20, // 最大线程数
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...
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);
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// 预热核心线程
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executor.prestartAllCoreThreads();
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```
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---
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**2. maximumPoolSize（最大线程数）**
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- **说明**：线程池允许的最大线程数
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- **限制**：`maximumPoolSize >= corePoolSize`
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- **动态调整**：运行时可通过 `setMaximumPoolSize()` 调整
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```java
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// 动态调整最大线程数
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executor.setMaximumPoolSize(50);
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```
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---
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**3. keepAliveTime（非核心线程存活时间）**
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- **说明**：非核心线程的空闲存活时间
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- **超时回收**：超过时间后，线程会被回收
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- **允许回收核心线程**：`allowCoreThreadTimeOut(true)`
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```java
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ThreadPoolExecutor executor = new ThreadPoolExecutor(
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10,
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20,
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60, // 存活时间
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TimeUnit.SECONDS,
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new LinkedBlockingQueue<>(100)
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);
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// 允许核心线程超时回收
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executor.allowCoreThreadTimeOut(true);
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```
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---
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**4. workQueue（任务队列）**
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**常见队列**：
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| 队列 | 特性 | 适用场景 |
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|------|------|----------|
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| **SynchronousQueue** | 不存储，直接传递 | 高并发、低延迟 |
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| **LinkedBlockingQueue** | 无界队列（默认 Integer.MAX_VALUE） | 任务提交频繁 |
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| **ArrayBlockingQueue** | 有界队列 | 防止资源耗尽 |
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| **PriorityBlockingQueue** | 优先级队列 | 优先级任务 |
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**示例**：
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```java
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// 1. SynchronousQueue（高并发）
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ExecutorService executor1 = new ThreadPoolExecutor(
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10, 20,
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60L, TimeUnit.SECONDS,
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new SynchronousQueue<Runnable>() // 无队列，直接传递
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);
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// 2. LinkedBlockingQueue（无界）
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ExecutorService executor2 = new ThreadPoolExecutor(
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10, 20,
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60L, TimeUnit.SECONDS,
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new LinkedBlockingQueue<>(1000) // 队列长度 1000
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);
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// 3. PriorityBlockingQueue（优先级）
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ExecutorService executor3 = new ThreadPoolExecutor(
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10, 20,
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60L, TimeUnit.SECONDS,
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new PriorityBlockingQueue<>(100)
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);
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```
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---
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**5. threadFactory（线程工厂）**
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**作用**：
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- 设置线程名称（便于排查）
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- 设置线程优先级
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- 设置是否为守护线程
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**示例**：
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```java
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ThreadFactory namedThreadFactory = new ThreadFactoryBuilder()
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.setNameFormat("order-pool-%d") // 线程名称前缀
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.setDaemon(false) // 非守护线程
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.setPriority(Thread.NORM_PRIORITY)
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.build();
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ThreadPoolExecutor executor = new ThreadPoolExecutor(
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10, 20,
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60L, TimeUnit.SECONDS,
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new LinkedBlockingQueue<>(100),
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namedThreadFactory
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);
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```
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---
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**6. handler（拒绝策略）**
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**内置策略**：
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| 策略 | 说明 |
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|------|------|
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| **AbortPolicy（默认）** | 抛出异常 |
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| **CallerRunsPolicy** | 调用者线程执行 |
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| **DiscardPolicy** | 静默丢弃 |
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| **DiscardOldestPolicy** | 丢弃最旧的任务 |
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```java
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// 自定义拒绝策略
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RejectedExecutionHandler handler = new RejectedExecutionHandler() {
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@Override
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public void rejectedExecution(Runnable r, ThreadPoolExecutor executor) {
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// 记录日志
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log.warn("任务被拒绝: {}", r);
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// 重试（加入队列等待）
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if (!executor.isShutdown()) {
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try {
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executor.getQueue().put(r);
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} catch (InterruptedException e) {
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Thread.currentThread().interrupt();
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}
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}
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}
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};
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ThreadPoolExecutor executor = new ThreadPoolExecutor(
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10, 20,
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60L, TimeUnit.SECONDS,
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new LinkedBlockingQueue<>(100),
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handler
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);
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```
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---
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### 2. 线程池工作流程
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```
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任务提交
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↓
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核心线程数 < corePoolSize？
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├─ 是 → 创建核心线程并执行
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└─ 否 → 继续
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↓
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队列未满？
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├─ 是 → 加入队列
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└─ 否 → 继续
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↓
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线程数 < maximumPoolSize？
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├─ 是 → 创建非核心线程并执行
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└─ 否 → 继续
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↓
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拒绝策略
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```
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---
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### 3. 合理设置线程池大小
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#### **CPU 密集型任务**
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**特点**：主要消耗 CPU 资源（计算、加密）
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**公式**：
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```
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线程数 = CPU 核心数 + 1
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```
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**原因**：
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- CPU 密集型任务不需要太多线程
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- +1 是为了当某线程因页故障等原因暂停时，CPU 不会闲置
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**示例**：
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```java
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int cpuCore = Runtime.getRuntime().availableProcessors(); // 8
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int poolSize = cpuCore + 1; // 9
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ThreadPoolExecutor executor = new ThreadPoolExecutor(
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poolSize, // 核心线程数
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poolSize, // 最大线程数
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0L, TimeUnit.MILLISECONDS,
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new LinkedBlockingQueue<>(100)
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);
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```
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---
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#### **IO 密集型任务**
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**特点**：主要等待 IO（网络、磁盘）
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**公式**：
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```
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线程数 = CPU 核心数 × (1 + IO 耗时 / CPU 耗时)
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```
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**示例**：
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```java
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// IO 耗时 / CPU 耗时 = 2（IO 占 2/3，CPU 占 1/3）
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int cpuCore = Runtime.getRuntime().availableProcessors(); // 8
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int poolSize = cpuCore * (1 + 2); // 24
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ThreadPoolExecutor executor = new ThreadPoolExecutor(
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cpuCore, // 核心线程数 = CPU 核心数
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poolSize, // 最大线程数
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60L, TimeUnit.SECONDS,
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new LinkedBlockingQueue<>(500)
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);
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```
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---
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#### **通用公式**
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```
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线程数 = CPU 核心数 × 目标 CPU 使用率 × (1 + IO 耗时 / CPU 耗时)
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```
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**参数调整**：
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- 目标 CPU 使用率：80% - 90%
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- IO / CPU 比例：通过压测获得
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---
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### 4. 线程池监控
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#### **监控指标**
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| 指标 | 说明 | 获取方法 |
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|------|------|----------|
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| **活跃线程数** | 正在执行任务的线程数 | `getActiveCount()` |
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| **已完成任务数** | 历史完成的任务总数 | `getCompletedTaskCount()` |
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| **总任务数** | 已完成 + 正在执行 | `getTaskCount()` |
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| **队列大小** | 队列中待执行任务数 | `getQueue().size()` |
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| **最大线程数** | 历史最大线程数 | `getLargestPoolSize()` |
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| **线程池是否关闭** | `isShutdown()` | `isShutdown()` |
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---
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#### **监控代码**
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```java
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@Component
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public class ThreadPoolMonitor {
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@Autowired
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private Map<String, ThreadPoolExecutor> executorMap;
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@Scheduled(fixedRate = 60000) // 每分钟
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public void monitor() {
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executorMap.forEach((name, executor) -> {
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ThreadPoolExecutorStats stats = new ThreadPoolExecutorStats();
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stats.setName(name);
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stats.setCorePoolSize(executor.getCorePoolSize());
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stats.setMaximumPoolSize(executor.getMaximumPoolSize());
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stats.setActiveCount(executor.getActiveCount());
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stats.setCompletedTaskCount(executor.getCompletedTaskCount());
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stats.setTaskCount(executor.getTaskCount());
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stats.setQueueSize(executor.getQueue().size());
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stats.setLargestPoolSize(executor.getLargestPoolSize());
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// 上报到监控系统（Prometheus、Grafana）
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Metrics.report(stats);
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// 告警判断
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if (executor.getActiveCount() >= executor.getMaximumPoolSize() * 0.8) {
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alert("线程池 " + name + " 负载过高");
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}
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});
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}
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}
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```
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---
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#### **Actuator 监控（Spring Boot）**
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**依赖**：
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```xml
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<dependency>
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<groupId>org.springframework.boot</groupId>
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<artifactId>spring-boot-starter-actuator</artifactId>
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</dependency>
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```
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**配置**：
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```yaml
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management:
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endpoints:
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web:
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exposure:
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include: health,info,metrics
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metrics:
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export:
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prometheus:
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enabled: true
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```
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**访问**：
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```bash
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curl http://localhost:8080/actuator/metrics
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curl http://localhost:8080/actuator/metrics/executor.pool.size
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```
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---
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### 5. 线程池优雅关闭
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#### **问题**
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不优雅关闭的后果：
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- 已提交的任务可能丢失
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- 正在执行的任务可能被中断
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---
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#### **shutdown()**
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```java
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executor.shutdown();
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try {
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// 等待任务完成
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if (!executor.awaitTermination(60, TimeUnit.SECONDS)) {
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// 超时，强制关闭
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executor.shutdownNow();
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}
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} catch (InterruptedException e) {
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executor.shutdownNow();
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Thread.currentThread().interrupt();
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}
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```
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**特点**：
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- 不再接受新任务
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- 等待已提交的任务完成
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- 超时后可调用 `shutdownNow()` 强制关闭
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---
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#### **shutdownNow()**
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```java
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List<Runnable> unfinishedTasks = executor.shutdownNow();
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```
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**特点**：
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- 不再接受新任务
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- 尝试停止正在执行的任务（通过 `Thread.interrupt()`）
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- 返回未执行的任务列表
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---
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### 6. Spring 线程池配置
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#### **配置类**
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```java
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@Configuration
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public class ThreadPoolConfig {
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@Bean("orderThreadPool")
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public ThreadPoolExecutor orderThreadPool() {
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return new ThreadPoolExecutor(
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10, // 核心线程数
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20, // 最大线程数
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60L, TimeUnit.SECONDS,
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new LinkedBlockingQueue<>(100),
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new ThreadFactoryBuilder()
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.setNameFormat("order-pool-%d")
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.build(),
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new ThreadPoolExecutor.CallerRunsPolicy()
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);
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}
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@Bean("emailThreadPool")
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public ThreadPoolExecutor emailThreadPool() {
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return new ThreadPoolExecutor(
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5,
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10,
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60L, TimeUnit.SECONDS,
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new LinkedBlockingQueue<>(50),
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new ThreadFactoryBuilder()
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.setNameFormat("email-pool-%d")
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.build(),
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new ThreadPoolExecutor.CallerRunsPolicy()
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);
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}
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}
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```
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---
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#### **使用**
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```java
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@Service
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public class OrderService {
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@Autowired
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@Qualifier("orderThreadPool")
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private ThreadPoolExecutor orderThreadPool;
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public void createOrder(Order order) {
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// 异步处理
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orderThreadPool.execute(() -> {
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// 处理订单
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processOrder(order);
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});
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}
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}
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```
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---
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#### **@Async（Spring 异步）**
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**配置**：
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```java
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@Configuration
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@EnableAsync
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public class AsyncConfig {
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@Bean("asyncExecutor")
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public Executor asyncExecutor() {
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ThreadPoolTaskExecutor executor = new ThreadPoolTaskExecutor();
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executor.setCorePoolSize(10);
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executor.setMaxPoolSize(20);
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executor.setQueueCapacity(100);
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executor.setThreadNamePrefix("async-pool-");
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executor.setRejectedExecutionHandler(new ThreadPoolExecutor.CallerRunsPolicy());
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executor.initialize();
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return executor;
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}
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}
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```
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**使用**：
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```java
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@Service
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public class EmailService {
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@Async("asyncExecutor")
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public void sendEmail(String to, String subject, String body) {
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// 异步发送邮件
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mailSender.send(to, subject, body);
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}
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}
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```
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---
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### 7. 实际项目经验
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#### **案例 1：线程池参数调优**
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**问题**：
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- 订单接口响应慢
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- CPU 使用率低（30%），线程池队列满
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**分析**：
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```java
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// 原配置
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corePoolSize = 5
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maximumPoolSize = 10
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queue = LinkedBlockingQueue(100)
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```
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**问题**：
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- 线程数太少，任务堆积在队列
|
||
- 数据库连接池用满，等待连接
|
||
|
||
**优化**：
|
||
```java
|
||
// 优化后配置
|
||
corePoolSize = 20 // 增加
|
||
maximumPoolSize = 50 // 增加
|
||
queue = LinkedBlockingQueue(500) // 增加
|
||
```
|
||
|
||
**结果**：响应时间从 2s 降至 200ms
|
||
|
||
---
|
||
|
||
#### **案例 2：动态线程池**
|
||
|
||
**需求**：根据流量动态调整线程池大小
|
||
|
||
**实现**：
|
||
```java
|
||
@Component
|
||
public class DynamicThreadPoolManager {
|
||
|
||
private final Map<String, ThreadPoolExecutor> executorMap = new ConcurrentHashMap<>();
|
||
|
||
@PostConstruct
|
||
public void init() {
|
||
// 定时调整线程池大小
|
||
ScheduledExecutorService scheduler = Executors.newSingleThreadScheduledExecutor();
|
||
scheduler.scheduleAtFixedRate(this::adjustThreadPoolSize, 1, 1, TimeUnit.MINUTES);
|
||
}
|
||
|
||
private void adjustThreadPoolSize() {
|
||
executorMap.forEach((name, executor) -> {
|
||
// 获取当前负载
|
||
int activeCount = executor.getActiveCount();
|
||
int maximumPoolSize = executor.getMaximumPoolSize();
|
||
|
||
// 负载 > 80%，扩容
|
||
if (activeCount > maximumPoolSize * 0.8) {
|
||
int newSize = Math.min(maximumPoolSize * 2, 100);
|
||
executor.setMaximumPoolSize(newSize);
|
||
log.info("扩容线程池: {} -> {}", name, newSize);
|
||
}
|
||
// 负载 < 20%，缩容
|
||
else if (activeCount < maximumPoolSize * 0.2) {
|
||
int newSize = Math.max(maximumPoolSize / 2, executor.getCorePoolSize());
|
||
executor.setMaximumPoolSize(newSize);
|
||
log.info("缩容线程池: {} -> {}", name, newSize);
|
||
}
|
||
});
|
||
}
|
||
}
|
||
```
|
||
|
||
---
|
||
|
||
### 8. 阿里 P7 加分项
|
||
|
||
**深度理解**：
|
||
- 理解线程池的状态转换（RUNNING、SHUTDOWN、STOP、TIDYING、TERMINATED）
|
||
- 理解 `Worker` 的实现原理（继承 AQS、实现 Runnable）
|
||
- 理解线程池的异常处理机制
|
||
|
||
**实战经验**：
|
||
- 有线程池参数调优的经验
|
||
- 有处理线程池饱和问题的经验
|
||
- 有线程池监控和告警的经验
|
||
|
||
**架构能力**：
|
||
- 能设计动态线程池（根据流量调整）
|
||
- 能设计线程池隔离（不同业务独立线程池）
|
||
- 能设计线程池监控体系
|
||
|
||
**技术选型**：
|
||
- 了解 `ForkJoinPool`（工作窃取线程池）
|
||
- 了解 `ScheduledThreadPoolExecutor`（定时任务线程池）
|
||
- 了解 `Vert.x`、WebFlux 等响应式框架的线程模型
|