Aug 12 2025

Spring Boot 3.5虚拟线程 + LangChain4j：构建百万级并发AI Agent系统

2025年，Java生态系统正在经历一场并发编程的革命。Spring Boot 3.5引入的虚拟线程支持，结合LangChain4j这一强大的AI框架，为构建大规模并发AI Agent系统开辟了全新的可能性。如果你还在为传统线程池的资源限制而苦恼，或者在思考如何优雅地处理数千个并发AI请求，那么这篇文章将为你提供完整的解决方案。

传统的Java应用在处理高并发场景时，往往受限于平台线程的重量级特性——每个线程约占用1MB的栈内存，10,000个线程就需要约10GB的内存开销[1]。而虚拟线程的出现彻底改变了这一局面，轻量级的特性使得应用能够轻松处理百万级的并发任务。

第一章：虚拟线程革命：重新定义Java并发

1.1 Project Loom的技术突破

虚拟线程（Virtual Threads）是Java 21中Project Loom的核心成果，它们是运行在JVM之上的用户模式线程，由Java运行时管理而非操作系统。这一设计带来了根本性的变革：

传统平台线程的局限性：

• 每个线程预分配约1MB的调用栈
• 线程创建和上下文切换开销巨大
• 受限于操作系统线程数量
• 线程池管理复杂且容易成为瓶颈

虚拟线程的革命性优势：

• 每个虚拟线程仅占用约1KB内存
• 创建成本极低，可以按需创建数百万个
• 阻塞时不会占用平台线程资源
• 完全消除了线程池的必要性

1.2 Spring Boot 3.5的集成方式

Spring Boot 3.5通过简洁的配置实现了虚拟线程的无缝集成。启用虚拟线程只需要在application.properties中添加一行配置：

# 启用虚拟线程支持
spring.threads.virtual.enabled=true

一旦启用，Spring Boot会自动重新配置核心组件：

@Configuration
public class VirtualThreadConfig {
    
    @Bean
    @Primary
    public TaskExecutor taskExecutor() {
        return new TaskExecutorAdapter(
            Executors.newVirtualThreadPerTaskExecutor()
        );
    }
    
    @Bean
    public TomcatProtocolHandlerCustomizer<?> protocolHandlerVirtualThreadExecutorCustomizer() {
        return protocolHandler -> {
            protocolHandler.setExecutor(
                Executors.newVirtualThreadPerTaskExecutor()
            );
        };
    }
}

1.3 性能基准测试：虚拟线程 vs 平台线程

基于真实的基准测试数据[2]，虚拟线程在不同场景下展现出显著的性能优势：

内存+CPU密集型任务测试结果（处理1,000,000个整数数组）：

并发数	平台线程耗时(ms)	虚拟线程耗时(ms)	性能提升
10,000	5,059	2,478	2.04x
100,000	34,063	24,385	1.40x
1,000,000	351,020	253,580	1.38x

CPU密集型任务测试结果（回文数检测）：

并发数	平台线程耗时(ms)	虚拟线程耗时(ms)	性能提升
10,000	1,003	124	8.09x
100,000	6,560	805	8.15x
1,000,000	65,596	9,821	6.68x

这些数据清楚地表明，虚拟线程在处理大规模并发任务时具有压倒性的优势，特别是在I/O密集型或阻塞操作场景中。

第二章：LangChain4j深度解析与AI Agent架构

2.1 LangChain4j：Java AI开发的新标杆

LangChain4j是专为Java生态系统设计的AI应用开发框架，它简化了大语言模型（LLM）的集成，提供了统一的API来处理不同的AI模型和服务。

核心架构组件：

1. ChatLanguageModel: 统一的聊天模型接口
2. EmbeddingModel: 向量嵌入模型
3. Tools: 工具调用系统
4. Memory: 对话记忆管理
5. ContentRetriever: RAG检索系统

<!-- Maven依赖配置 -->
<dependency>
    <groupId>dev.langchain4j</groupId>
    <artifactId>langchain4j-spring-boot-starter</artifactId>
    <version>1.0.0-beta1</version>
</dependency>
<dependency>
    <groupId>dev.langchain4j</groupId>
    <artifactId>langchain4j-open-ai-spring-boot-starter</artifactId>
    <version>1.0.0-beta1</version>
</dependency>

2.2 AI Agent架构设计模式

根据Spring AI的最佳实践[3]，现代AI Agent系统可以采用以下几种核心架构模式：

1. 并行化工作流（Parallelization Workflow）

这种模式允许多个LLM任务同时处理并聚合输出，特别适合需要处理大量独立任务的场景：

@Service
public class ParallelAgentService {
    
    @Autowired
    private ChatLanguageModel chatModel;
    
    public CompletableFuture<List<String>> processParallelTasks(List<String> prompts) {
        try (ExecutorService executor = Executors.newVirtualThreadPerTaskExecutor()) {
            List<CompletableFuture<String>> futures = prompts.stream()
                .map(prompt -> CompletableFuture.supplyAsync(() -> 
                    chatModel.generate(prompt), executor))
                .toList();
            
            return CompletableFuture.allOf(futures.toArray(new CompletableFuture[0]))
                .thenApply(v -> futures.stream()
                    .map(CompletableFuture::join)
                    .collect(Collectors.toList()));
        }
    }
}

2. 编排器-工作者模式（Orchestrator-Workers）

中央编排器负责任务分解，专业化的工作者处理具体任务：

@Component
public class OrchestratorAgent {
    
    @Autowired
    private ChatLanguageModel orchestrator;
    
    @Autowired
    private List<WorkerAgent> workers;
    
    public String processComplexTask(String task) {
        // 1. 编排器分析任务
        String decomposition = orchestrator.generate(
            "分解以下任务为子任务：" + task
        );
        
        // 2. 并行执行子任务
        List<String> subtasks = parseSubtasks(decomposition);
        List<CompletableFuture<String>> futures = subtasks.stream()
            .map(subtask -> CompletableFuture.supplyAsync(() -> 
                findBestWorker(subtask).process(subtask)))
            .toList();
        
        // 3. 聚合结果
        List<String> results = futures.stream()
            .map(CompletableFuture::join)
            .toList();
        
        return aggregateResults(results);
    }
    
    private WorkerAgent findBestWorker(String subtask) {
        // 根据任务类型选择最合适的工作者
        return workers.stream()
            .filter(worker -> worker.canHandle(subtask))
            .findFirst()
            .orElse(workers.get(0));
    }
}

2.3 Agent状态管理与持久化

在高并发场景下，Agent状态的管理是一个关键挑战。LangChain4j提供了灵活的内存管理机制：

@Service
public class StatefulAgentService {
    
    private final AiService<AssistantAgent> aiService;
    private final ChatMemoryStore memoryStore;
    
    public StatefulAgentService(ChatLanguageModel model, 
                               ChatMemoryStore memoryStore) {
        this.memoryStore = memoryStore;
        this.aiService = AiServices.builder(AssistantAgent.class)
            .chatLanguageModel(model)
            .chatMemory(memoryId -> MessageWindowChatMemory.builder()
                .chatMemoryStore(memoryStore)
                .maxMessages(20)
                .id(memoryId)
                .build())
            .build();
    }
    
    @Async
    public CompletableFuture<String> processRequest(String userId, String message) {
        return CompletableFuture.supplyAsync(() -> 
            aiService.chat(MemoryId.from(userId), message)
        );
    }
}

interface AssistantAgent {
    String chat(@MemoryId String memoryId, @UserMessage String message);
}

第三章：虚拟线程与AI调用的完美结合

3.1 I/O密集型AI服务的优化

AI服务调用天然是I/O密集型任务——网络请求、模型推理、数据库查询等都涉及阻塞操作。虚拟线程的特性使其成为处理AI工作负载的完美选择：

@RestController
@RequestMapping("/ai")
public class AIAgentController {
    
    @Autowired
    private ChatLanguageModel chatModel;
    
    @Autowired
    private EmbeddingStore embeddingStore;
    
    // 传统方式：受限于线程池大小
    @GetMapping("/traditional/{prompt}")
    public ResponseEntity<String> traditionalChat(@PathVariable String prompt) {
        // 这里会阻塞一个宝贵的平台线程
        String response = chatModel.generate(prompt);
        return ResponseEntity.ok(response);
    }
    
    // 虚拟线程方式：可以处理数百万并发
    @GetMapping("/virtual/{prompt}")
    public CompletableFuture<ResponseEntity<String>> virtualChat(
            @PathVariable String prompt) {
        return CompletableFuture.supplyAsync(() -> {
            String response = chatModel.generate(prompt);
            return ResponseEntity.ok(response);
        }, Executors.newVirtualThreadPerTaskExecutor());
    }
    
    // RAG查询优化
    @PostMapping("/rag/query")
    public CompletableFuture<RagResponse> ragQuery(@RequestBody RagRequest request) {
        return CompletableFuture.supplyAsync(() -> {
            // 1. 向量检索（I/O操作）
            List<EmbeddingMatch<TextSegment>> matches = embeddingStore
                .findRelevant(request.getQuery(), 5);
            
            // 2. 构建上下文
            String context = matches.stream()
                .map(match -> match.embedded().text())
                .collect(Collectors.joining("\n"));
            
            // 3. LLM推理（网络I/O）
            String answer = chatModel.generate(
                buildPrompt(context, request.getQuery())
            );
            
            return new RagResponse(answer, matches.size());
        });
    }
}

3.2 批量处理与流量控制

在处理大规模AI请求时，批量处理和流量控制至关重要。以下是结合虚拟线程的最佳实践：

@Service
public class BatchAIProcessor {
    
    private final ChatLanguageModel chatModel;
    private final Semaphore rateLimiter;
    
    public BatchAIProcessor(ChatLanguageModel chatModel,
                           @Value("${ai.batch.concurrency:300}") int maxConcurrency) {
        this.chatModel = chatModel;
        this.rateLimiter = new Semaphore(maxConcurrency);
    }
    
    public CompletableFuture<List<String>> processBatch(List<String> prompts) {
        return CompletableFuture.supplyAsync(() -> {
            // 分批处理，避免API限流
            List<List<String>> batches = Lists.partition(prompts, 300);
            List<String> allResults = new ArrayList<>();
            
            for (List<String> batch : batches) {
                List<CompletableFuture<String>> futures = batch.stream()
                    .map(this::processWithRateLimit)
                    .toList();
                
                // 等待当前批次完成
                List<String> batchResults = futures.stream()
                    .map(CompletableFuture::join)
                    .filter(Objects::nonNull)
                    .toList();
                
                allResults.addAll(batchResults);
                
                // 批次间延迟，避免API限流
                sleep(1000);
            }
            
            return allResults;
        });
    }
    
    private CompletableFuture<String> processWithRateLimit(String prompt) {
        return CompletableFuture.supplyAsync(() -> {
            try {
                rateLimiter.acquire();
                return chatModel.generate(prompt);
            } catch (Exception e) {
                log.error("AI处理失败: {}", e.getMessage());
                return null;
            } finally {
                rateLimiter.release();
            }
        });
    }
}

3.3 响应式流处理

对于需要流式响应的场景，虚拟线程同样能够提供优秀的性能：

@RestController
public class StreamingAIController {
    
    @Autowired
    private StreamingChatLanguageModel streamingModel;
    
    @GetMapping(value = "/ai/stream/{prompt}", 
                produces = MediaType.TEXT_EVENT_STREAM_VALUE)
    public SseEmitter streamChat(@PathVariable String prompt) {
        SseEmitter emitter = new SseEmitter(60000L);
        
        // 使用虚拟线程处理流式响应
        Thread.ofVirtual().start(() -> {
            try {
                streamingModel.generate(prompt, new StreamingResponseHandler<AiMessage>() {
                    @Override
                    public void onNext(String token) {
                        try {
                            emitter.send(SseEmitter.event()
                                .name("token")
                                .data(token));
                        } catch (IOException e) {
                            emitter.completeWithError(e);
                        }
                    }
                    
                    @Override
                    public void onComplete(Response<AiMessage> response) {
                        emitter.complete();
                    }
                    
                    @Override
                    public void onError(Throwable error) {
                        emitter.completeWithError(error);
                    }
                });
            } catch (Exception e) {
                emitter.completeWithError(e);
            }
        });
        
        return emitter;
    }
}

第四章：高并发架构设计与资源优化

4.1 连接池与资源管理优化

在高并发AI系统中，资源管理是性能的关键因素：

@Configuration
public class ResourcePoolConfig {
    
    @Bean
    @Primary
    public HikariDataSource dataSource(@Value("${spring.datasource.url}") String url) {
        HikariConfig config = new HikariConfig();
        config.setJdbcUrl(url);
        config.setMaximumPoolSize(200); // 虚拟线程环境下可以设置更大的连接池
        config.setMinimumIdle(20);
        config.setConnectionTimeout(30000);
        config.setIdleTimeout(600000);
        return new HikariDataSource(config);
    }
    
    @Bean
    public RedisTemplate<String, Object> redisTemplate(
            LettuceConnectionFactory connectionFactory) {
        // Lettuce客户端天然支持异步操作，与虚拟线程完美配合
        RedisTemplate<String, Object> template = new RedisTemplate<>();
        template.setConnectionFactory(connectionFactory);
        return template;
    }
    
    @Bean
    public HttpClient httpClient() {
        return HttpClient.newBuilder()
            .executor(Executors.newVirtualThreadPerTaskExecutor()) // 使用虚拟线程
            .connectTimeout(Duration.ofSeconds(30))
            .build();
    }
}

4.2 多级缓存策略

在AI应用中，智能的缓存策略可以显著降低成本和延迟：

@Service
@Slf4j
public class CachedAIService {
    
    private final ChatLanguageModel chatModel;
    private final RedisTemplate<String, String> redisTemplate;
    private final Cache<String, String> localCache;
    
    public CachedAIService(ChatLanguageModel chatModel,
                          RedisTemplate<String, String> redisTemplate) {
        this.chatModel = chatModel;
        this.redisTemplate = redisTemplate;
        this.localCache = Caffeine.newBuilder()
            .maximumSize(10000)
            .expireAfterWrite(Duration.ofMinutes(15))
            .build();
    }
    
    @Async
    public CompletableFuture<String> generateWithCache(String prompt) {
        return CompletableFuture.supplyAsync(() -> {
            String cacheKey = generateCacheKey(prompt);
            
            // L1: 本地缓存
            String cached = localCache.getIfPresent(cacheKey);
            if (cached != null) {
                log.debug("本地缓存命中: {}", cacheKey);
                return cached;
            }
            
            // L2: Redis缓存
            cached = redisTemplate.opsForValue().get(cacheKey);
            if (cached != null) {
                log.debug("Redis缓存命中: {}", cacheKey);
                localCache.put(cacheKey, cached);
                return cached;
            }
            
            // L3: AI模型调用
            String result = chatModel.generate(prompt);
            
            // 写入缓存
            localCache.put(cacheKey, result);
            redisTemplate.opsForValue().set(cacheKey, result, 
                Duration.ofHours(1));
            
            return result;
        });
    }
    
    private String generateCacheKey(String prompt) {
        return "ai:prompt:" + DigestUtils.md5DigestAsHex(prompt.getBytes());
    }
}

4.3 限流与熔断机制

基于Resilience4j的限流和熔断配置：

@Configuration
public class ResilienceConfig {
    
    @Bean
    public RateLimiter aiServiceRateLimiter() {
        return RateLimiter.of("ai-service", RateLimiterConfig.custom()
            .limitRefreshPeriod(Duration.ofMinutes(1))
            .limitForPeriod(1000) // 每分钟1000次调用
            .timeoutDuration(Duration.ofSeconds(30))
            .build());
    }
    
    @Bean
    public CircuitBreaker aiServiceCircuitBreaker() {
        return CircuitBreaker.of("ai-service", CircuitBreakerConfig.custom()
            .failureRateThreshold(50)
            .waitDurationInOpenState(Duration.ofSeconds(30))
            .slidingWindowSize(100)
            .minimumNumberOfCalls(10)
            .build());
    }
}

@Service
public class ResilientAIService {
    
    private final ChatLanguageModel chatModel;
    private final RateLimiter rateLimiter;
    private final CircuitBreaker circuitBreaker;
    
    @EventListener
    public void handleCircuitBreakerEvent(CircuitBreakerOnStateTransitionEvent event) {
        log.warn("熔断器状态变化: {} -> {}", 
            event.getStateTransition().getFromState(),
            event.getStateTransition().getToState());
    }
    
    @Async
    public CompletableFuture<String> generateWithResilience(String prompt) {
        Supplier<String> decoratedSupplier = Decorators.ofSupplier(() -> 
                chatModel.generate(prompt))
            .withRateLimiter(rateLimiter)
            .withCircuitBreaker(circuitBreaker)
            .withRetry(Retry.ofDefaults("ai-service"))
            .withFallback(Arrays.asList(
                CallNotPermittedException.class,
                CircuitBreakerOpenException.class),
                throwable -> "服务暂时不可用，请稍后重试");
        
        return CompletableFuture.supplyAsync(decoratedSupplier);
    }
}

第五章：监控与可观测性

5.1 虚拟线程监控指标

虚拟线程的监控需要特殊的指标和工具：

@Component
@Slf4j
public class VirtualThreadMonitor {
    
    private final MeterRegistry meterRegistry;
    private final AtomicLong virtualThreadCount = new AtomicLong(0);
    
    public VirtualThreadMonitor(MeterRegistry meterRegistry) {
        this.meterRegistry = meterRegistry;
        setupMetrics();
    }
    
    private void setupMetrics() {
        // 虚拟线程数量监控
        Gauge.builder("virtual.threads.active")
            .description("当前活跃的虚拟线程数")
            .register(meterRegistry, virtualThreadCount, AtomicLong::get);
        
        // JVM堆内存使用
        Gauge.builder("jvm.memory.virtual_threads.used")
            .description("虚拟线程使用的内存")
            .register(meterRegistry, this, this::getVirtualThreadMemoryUsage);
    }
    
    @EventListener
    public void onThreadEvent(ThreadEvent event) {
        switch (event.getType()) {
            case CREATED -> {
                virtualThreadCount.incrementAndGet();
                meterRegistry.counter("virtual.threads.created").increment();
            }
            case TERMINATED -> {
                virtualThreadCount.decrementAndGet();
                meterRegistry.counter("virtual.threads.terminated").increment();
            }
            case BLOCKED -> {
                meterRegistry.counter("virtual.threads.blocked").increment();
            }
        }
    }
    
    private double getVirtualThreadMemoryUsage() {
        // 估算虚拟线程内存使用（基于活跃线程数 * 每个线程约1KB）
        return virtualThreadCount.get() * 1024.0;
    }
}

5.2 AI服务性能监控

专门针对AI服务的监控指标：

@Aspect
@Component
@Slf4j
public class AIServiceMonitoringAspect {
    
    private final MeterRegistry meterRegistry;
    private final Timer aiRequestTimer;
    private final Counter aiRequestCounter;
    private final Counter aiErrorCounter;
    
    public AIServiceMonitoringAspect(MeterRegistry meterRegistry) {
        this.meterRegistry = meterRegistry;
        this.aiRequestTimer = Timer.builder("ai.requests")
            .description("AI请求处理时间")
            .register(meterRegistry);
        this.aiRequestCounter = Counter.builder("ai.requests.total")
            .description("AI请求总数")
            .register(meterRegistry);
        this.aiErrorCounter = Counter.builder("ai.errors.total")
            .description("AI请求错误总数")
            .register(meterRegistry);
    }
    
    @Around("@annotation(MonitorAI)")
    public Object monitorAICall(ProceedingJoinPoint joinPoint) throws Throwable {
        Timer.Sample sample = Timer.start(meterRegistry);
        aiRequestCounter.increment();
        
        try {
            Object result = joinPoint.proceed();
            sample.stop(aiRequestTimer);
            
            // 记录token使用情况
            if (result instanceof TokenizedResponse response) {
                meterRegistry.counter("ai.tokens.input")
                    .increment(response.getInputTokens());
                meterRegistry.counter("ai.tokens.output")
                    .increment(response.getOutputTokens());
                
                // 计算成本
                double cost = calculateCost(response);
                meterRegistry.counter("ai.cost.total").increment(cost);
            }
            
            return result;
        } catch (Exception e) {
            aiErrorCounter.increment();
            sample.stop(Timer.builder("ai.requests.error")
                .register(meterRegistry));
            throw e;
        }
    }
    
    private double calculateCost(TokenizedResponse response) {
        // 基于token数量计算成本（示例价格）
        double inputCost = response.getInputTokens() * 0.0001; // $0.0001 per 1K tokens
        double outputCost = response.getOutputTokens() * 0.0002; // $0.0002 per 1K tokens
        return inputCost + outputCost;
    }
}

@Target(ElementType.METHOD)
@Retention(RetentionPolicy.RUNTIME)
public @interface MonitorAI {
}

5.3 Grafana监控面板配置

监控面板的关键指标配置：

# prometheus.yml配置
scrape_configs:
  - job_name: 'spring-boot-ai'
    static_configs:
      - targets: ['localhost:8080']
    metrics_path: '/actuator/prometheus'
    scrape_interval: 15s

# Grafana面板查询示例
panels:
  - title: "虚拟线程活跃数"
    targets:
      - expr: virtual_threads_active
        legendFormat: "活跃虚拟线程"
  
  - title: "AI请求QPS"
    targets:
      - expr: rate(ai_requests_total[5m])
        legendFormat: "请求/秒"
  
  - title: "AI请求延迟分布"
    targets:
      - expr: histogram_quantile(0.95, rate(ai_requests_bucket[5m]))
        legendFormat: "P95延迟"
      - expr: histogram_quantile(0.99, rate(ai_requests_bucket[5m]))
        legendFormat: "P99延迟"
  
  - title: "AI成本趋势"
    targets:
      - expr: increase(ai_cost_total[1h])
        legendFormat: "每小时成本($)"
  
  - title: "内存使用情况"
    targets:
      - expr: jvm_memory_used_bytes{area="heap"}
        legendFormat: "堆内存"
      - expr: jvm_memory_virtual_threads_used
        legendFormat: "虚拟线程内存"

第六章：性能调优与生产部署

6.1 JVM参数优化

针对虚拟线程的JVM调优策略：

# 生产环境JVM参数推荐
JAVA_OPTS="
  # 虚拟线程相关
  --enable-preview 
  -Djdk.virtualThreadScheduler.parallelism=16
  -Djdk.virtualThreadScheduler.maxPoolSize=256
  
  # 内存配置
  -Xms4g -Xmx8g
  -XX:MetaspaceSize=256m
  -XX:MaxMetaspaceSize=512m
  -XX:MaxDirectMemorySize=1g
  
  # GC配置（推荐G1GC或ZGC）
  -XX:+UseZGC
  -XX:+UnlockExperimentalVMOptions
  -XX:MaxGCPauseMillis=200
  
  # 性能监控
  -XX:+FlightRecorder
  -XX:+UnlockCommercialFeatures
  -XX:StartFlightRecording=duration=60s,filename=app.jfr
  
  # 虚拟线程调试
  -Djdk.tracePinnedThreads=full
"

6.2 Kubernetes部署配置

完整的Kubernetes部署配置[4]：

# deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: ai-agent-service
  labels:
    app: ai-agent-service
spec:
  replicas: 3
  selector:
    matchLabels:
      app: ai-agent-service
  template:
    metadata:
      labels:
        app: ai-agent-service
      annotations:
        prometheus.io/scrape: "true"
        prometheus.io/port: "8080"
        prometheus.io/path: "/actuator/prometheus"
    spec:
      containers:
      - name: app
        image: ai-agent-service:latest
        ports:
        - containerPort: 8080
        env:
        - name: JAVA_TOOL_OPTIONS
          value: "--enable-preview -Xms2g -Xmx4g -XX:+UseZGC"
        - name: SPRING_PROFILES_ACTIVE
          value: "production"
        - name: THREAD_TYPE
          value: "virtual"
        resources:
          requests:
            memory: "2Gi"
            cpu: "1000m"
          limits:
            memory: "4Gi"
            cpu: "2000m"
        readinessProbe:
          httpGet:
            path: /actuator/health/readiness
            port: 8080
          initialDelaySeconds: 30
          periodSeconds: 10
        livenessProbe:
          httpGet:
            path: /actuator/health/liveness
            port: 8080
          initialDelaySeconds: 60
          periodSeconds: 30

---
apiVersion: v1
kind: Service
metadata:
  name: ai-agent-service
spec:
  selector:
    app: ai-agent-service
  ports:
  - port: 80
    targetPort: 8080
  type: ClusterIP

---
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: ai-agent-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: ai-agent-service
  minReplicas: 3
  maxReplicas: 20
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 70
  - type: Pods
    pods:
      metric:
        name: ai_requests_per_second
      target:
        type: AverageValue
        averageValue: "100"

6.3 Docker优化配置

多阶段构建优化：

# Dockerfile
FROM bellsoft/liberica-openjdk-alpine:21 as builder
WORKDIR /workspace/app

COPY mvnw .
COPY .mvn .mvn
COPY pom.xml .
RUN ./mvnw dependency:go-offline

COPY src src
RUN ./mvnw clean package -DskipTests

FROM bellsoft/liberica-openjdk-alpine:21
RUN apk add --no-cache dumb-init
WORKDIR /app

# 创建非root用户
RUN addgroup -g 1001 -S appgroup && \
    adduser -u 1001 -S appuser -G appgroup

# 复制应用
COPY --from=builder --chown=appuser:appgroup /workspace/app/target/*.jar app.jar

# 设置JVM参数
ENV JAVA_OPTS="\
    --enable-preview \
    -Djava.security.egd=file:/dev/./urandom \
    -Dspring.threads.virtual.enabled=true \
    -Xms2g -Xmx2g \
    -XX:+UseZGC \
    -XX:MaxGCPauseMillis=100"

USER appuser
EXPOSE 8080

ENTRYPOINT ["dumb-init", "--"]
CMD ["sh", "-c", "java $JAVA_OPTS -jar app.jar"]

6.4 性能测试与基准测试

使用JMeter进行压力测试：

<!-- JMeter测试计划 -->
<?xml version="1.0" encoding="UTF-8"?>
<jmeterTestPlan version="1.2">
  <hashTree>
    <TestPlan guiclass="TestPlanGui" testclass="TestPlan" testname="AI Agent性能测试">
      <elementProp name="TestPlan.arguments" elementType="Arguments" guiclass="ArgumentsPanel">
        <collectionProp name="Arguments.arguments"/>
      </elementProp>
      <stringProp name="TestPlan.user_defined_variables"></stringProp>
      <boolProp name="TestPlan.serialize_threadgroups">false</boolProp>
    </TestPlan>
    <hashTree>
      <!-- 线程组配置 -->
      <ThreadGroup guiclass="ThreadGroupGui" testclass="ThreadGroup" testname="Virtual Thread Load Test">
        <intProp name="ThreadGroup.num_threads">1000</intProp>
        <intProp name="ThreadGroup.ramp_time">60</intProp>
        <longProp name="ThreadGroup.duration">300</longProp>
        <boolProp name="ThreadGroup.same_user_on_next_iteration">true</boolProp>
      </ThreadGroup>
      <hashTree>
        <!-- HTTP请求 -->
        <HTTPSamplerProxy guiclass="HttpTestSampleGui" testclass="HTTPSamplerProxy" testname="AI Chat Request">
          <stringProp name="HTTPSampler.domain">localhost</stringProp>
          <stringProp name="HTTPSampler.port">8080</stringProp>
          <stringProp name="HTTPSampler.path">/ai/chat</stringProp>
          <stringProp name="HTTPSampler.method">POST</stringProp>
          <boolProp name="HTTPSampler.use_keepalive">true</boolProp>
          <elementProp name="HTTPsampler.Arguments" elementType="Arguments">
            <collectionProp name="Arguments.arguments">
              <elementProp name="" elementType="HTTPArgument">
                <boolProp name="HTTPArgument.always_encode">false</boolProp>
                <stringProp name="Argument.value">{"prompt": "解释虚拟线程的工作原理"}</stringProp>
                <stringProp name="Argument.metadata">=</stringProp>
              </elementProp>
            </collectionProp>
          </elementProp>
          <stringProp name="HTTPSampler.postBodyRaw">true</stringProp>
        </HTTPSamplerProxy>
        
        <!-- 响应断言 -->
        <ResponseAssertion guiclass="AssertionGui" testclass="ResponseAssertion" testname="成功响应断言">
          <collectionProp name="Asserion.test_strings">
            <stringProp name="49586">200</stringProp>
          </collectionProp>
          <stringProp name="Assertion.test_field">Assertion.response_code</stringProp>
        </ResponseAssertion>
      </hashTree>
    </hashTree>
  </hashTree>
</jmeterTestPlan>

基于真实基准测试的性能对比[4]：

传统线程 vs 虚拟线程性能对比：

配置	内存使用	CPU使用	处理请求数(2分钟)	平均响应时间
标准线程	~900MB	~1.2 core	135k	175ms
虚拟线程	~850MB	~1.1 core	135k	180ms
原生+虚拟线程	~50MB	~1.3 core	100k	240ms

第七章：故障排查与最佳实践

7.1 常见问题与解决方案

问题1：虚拟线程固定（Pinning）

虚拟线程在某些情况下会被”固定”到载体线程上，失去轻量级的优势：

// 问题代码：synchronized块会导致虚拟线程固定
public synchronized String badExample() {
    return chatModel.generate("Hello");
}

// 解决方案：使用ReentrantLock
private final ReentrantLock lock = new ReentrantLock();

public String goodExample() {
    lock.lock();
    try {
        return chatModel.generate("Hello");
    } finally {
        lock.unlock();
    }
}

// 监控固定情况
-Djdk.tracePinnedThreads=full

问题2：内存泄漏监控

@Component
public class MemoryLeakDetector {
    
    private final MeterRegistry meterRegistry;
    
    @Scheduled(fixedRate = 60000)
    public void detectMemoryLeaks() {
        Runtime runtime = Runtime.getRuntime();
        long totalMemory = runtime.totalMemory();
        long freeMemory = runtime.freeMemory();
        long usedMemory = totalMemory - freeMemory;
        
        // 记录内存使用趋势
        meterRegistry.gauge("memory.used.bytes", usedMemory);
        meterRegistry.gauge("memory.free.bytes", freeMemory);
        
        // 检查虚拟线程数量是否异常
        ThreadMXBean threadBean = ManagementFactory.getThreadMXBean();
        int virtualThreadCount = threadBean.getThreadCount();
        
        if (virtualThreadCount > 100000) { // 阈值告警
            log.warn("虚拟线程数量异常: {}", virtualThreadCount);
            meterRegistry.counter("virtual.threads.warning").increment();
        }
    }
}

7.2 生产环境最佳实践

1. 资源限制配置

@Configuration
public class ResourceLimitConfig {
    
    @Bean
    @ConditionalOnProperty(name = "virtual.threads.enabled", havingValue = "true")
    public ThreadPoolTaskExecutor virtualThreadTaskExecutor() {
        ThreadPoolTaskExecutor executor = new ThreadPoolTaskExecutor();
        // 虚拟线程环境下的特殊配置
        executor.setCorePoolSize(1);
        executor.setMaxPoolSize(Integer.MAX_VALUE);
        executor.setQueueCapacity(0);
        executor.setThreadNamePrefix("vthread-");
        executor.setRejectedExecutionHandler(new ThreadPoolExecutor.CallerRunsPolicy());
        return executor;
    }
    
    @Bean
    public Semaphore aiRequestSemaphore(
            @Value("${ai.max.concurrent.requests:10000}") int maxRequests) {
        return new Semaphore(maxRequests);
    }
}

2. 优雅关闭处理

@Component
@Slf4j
public class GracefulShutdownHandler {
    
    private final ExecutorService executorService;
    private volatile boolean shutdown = false;
    
    @PreDestroy
    public void gracefulShutdown() {
        log.info("开始优雅关闭...");
        shutdown = true;
        
        if (executorService != null) {
            executorService.shutdown();
            try {
                if (!executorService.awaitTermination(60, TimeUnit.SECONDS)) {
                    log.warn("强制关闭虚拟线程池");
                    executorService.shutdownNow();
                }
            } catch (InterruptedException e) {
                Thread.currentThread().interrupt();
                executorService.shutdownNow();
            }
        }
        
        log.info("优雅关闭完成");
    }
    
    public boolean isShuttingDown() {
        return shutdown;
    }
}

3. 错误处理与重试机制

@Service
public class RobustAIService {
    
    private final ChatLanguageModel chatModel;
    private final RetryTemplate retryTemplate;
    
    public RobustAIService(ChatLanguageModel chatModel) {
        this.chatModel = chatModel;
        this.retryTemplate = RetryTemplate.builder()
            .maxAttempts(3)
            .exponentialBackoff(1000, 2, 10000)
            .retryOn(Exception.class)
            .build();
    }
    
    @Async
    public CompletableFuture<String> generateWithRetry(String prompt) {
        return CompletableFuture.supplyAsync(() -> {
            return retryTemplate.execute(context -> {
                try {
                    return chatModel.generate(prompt);
                } catch (Exception e) {
                    log.warn("AI调用失败，重试第{}次: {}", 
                        context.getRetryCount() + 1, e.getMessage());
                    
                    // 记录重试指标
                    Metrics.counter("ai.retries", 
                        "attempt", String.valueOf(context.getRetryCount()))
                        .increment();
                    
                    throw e;
                }
            });
        });
    }
}

总结与展望

通过本文的深入探讨，我们展示了Spring Boot 3.5虚拟线程与LangChain4j结合构建大规模AI Agent系统的完整解决方案。虚拟线程的革命性特性——轻量级、高并发、低开销——完美契合了AI应用的I/O密集型特点，使得单个应用实例能够同时处理数百万个AI请求成为可能。

关键收获：

1. 性能突破：虚拟线程在高并发场景下相比传统线程有2-8倍的性能提升
2. 架构简化：消除了复杂的线程池管理，代码更加清晰直观
3. 成本优化：通过缓存、批处理、限流等策略显著降低AI调用成本
4. 生产就绪：完整的监控、部署、调优解决方案确保系统稳定运行

未来发展趋势：

随着Project Loom的持续演进和AI技术的快速发展，我们可以预期：

• 结构化并发：Java将引入更强大的并发控制机制
• AI原生框架：更多专门针对AI工作负载优化的Java框架
• 边缘AI部署：轻量级虚拟线程使得边缘AI部署更加高效

对于企业而言，现在是拥抱这一技术革新的最佳时机。虚拟线程不仅解决了传统并发模型的痛点，更为构建下一代智能应用奠定了坚实基础。

行动建议：

1. 评估现有系统：识别I/O密集型和高并发场景的改造机会
2. 渐进式迁移：从新项目开始，逐步向虚拟线程迁移
3. 性能基准：建立完整的性能监控和基准测试体系
4. 团队培训：提升团队对新并发模型的理解和应用能力

虚拟线程的时代已经到来，结合强大的AI能力，Java开发者正站在一个全新的技术起点上。让我们拥抱这个变革，构建更高效、更智能的应用系统。

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参考资料

[1] Anmol Sehgal. “Java Virtual Threads Explained: How to Handle a Million Concurrent Tasks Easily” - Medium, 2025

[2] Ali Behzadian. “Java Thread Performance Vs. Virtual Threads Part 2” - Medium, 2024

[3] Spring Team. “Building Effective Agents with Spring AI (Part 1)” - Spring.io Blog, 2025

[4] Piotr Mińkowski. “Native Java with GraalVM and Virtual Threads on Kubernetes” - Personal Blog, 2023

[5] Baeldung Team. “Working with Virtual Threads in Spring” - Baeldung, 2024

[6] LangChain4j Team. “Build AI Apps and Agents in Java: Hands-On with LangChain4j” - JavaPro.io, 2025

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本文涵盖了Spring Boot 3.5虚拟线程与LangChain4j集成的完整技术栈，从基础概念到生产部署，为构建大规模AI Agent系统提供了详尽的指导。随着技术的快速发展，建议持续关注官方文档和社区最佳实践的更新。

编程人生

AI, 架构师, Java, Spring, SpringBoot