Spring Boot SSE流式输出技术实战

Spring Boot SSE 流式输出与智能体实时响应技术解析

SSE 技术概述

SSE（Server-Sent Events）是一种基于 HTTP 的服务器推送技术，允许服务器单向向客户端实时推送数据。与 WebSocket 不同，SSE 是单向通信，适用于服务器主动推送、客户端接收的场景。SSE 基于文本协议，默认支持断线重连，兼容性良好。

Spring Boot 实现 SSE 流式输出

在 Spring Boot 中实现 SSE 流式输出需要以下核心组件：

1. SseEmitter 对象：Spring 提供的专门用于 SSE 通信的类，通过 SseEmitter 可以异步发送事件流。
2. 响应类型设置：确保接口的响应类型为 text/event-stream，并禁用缓存。

示例代码：

@GetMapping(path = "/stream", produces = MediaType.TEXT_EVENT_STREAM_VALUE)
public SseEmitter streamData() {
    SseEmitter emitter = new SseEmitter();
    // 异步任务模拟数据推送
    ExecutorService executor = Executors.newSingleThreadExecutor();
    executor.execute(() -> {
        try {
            for (int i = 0; i < 10; i++) {
                emitter.send(SseEmitter.event()
                    .data("Data chunk " + i)
                    .id(String.valueOf(i))
                    .comment("Chunk comment"));
                Thread.sleep(1000);
            }
            emitter.complete();
        } catch (Exception ex) {
            emitter.completeWithError(ex);
        }
    });
    return emitter;
}

智能体实时响应设计

智能体（如 AI 模型）的实时响应通常需要结合以下技术：

1. 异步处理：通过线程池或消息队列（如 Kafka、RabbitMQ）解耦请求与响应。
2. 事件驱动架构：使用 Spring 的 ApplicationEventPublisher 发布事件，由监听器处理并推送至 SSE 流。

示例代码：

@Async
public void handleAIRequest(String prompt, SseEmitter emitter) {
    // 调用 AI 模型获取流式响应
    Flux<String> aiResponseFlux = aiService.streamResponse(prompt);
    aiResponseFlux.subscribe(
        chunk -> emitter.send(chunk),
        error -> emitter.completeWithError(error),
        () -> emitter.complete()
    );
}

性能优化与注意事项

1. 连接管理：维护一个 ConcurrentHashMap 存储活跃的 SseEmitter 实例，避免内存泄漏。
2. 超时设置：配置 SseEmitter 的超时时间（默认 30 秒），根据业务调整：

   SseEmitter emitter = new SseEmitter(60_000L); // 60秒超时
   

3. 错误处理：通过 onCompletion 和 onError 回调清理资源。

前端集成示例

前端通过 EventSource 监听 SSE 流：

const eventSource = new EventSource("/api/stream");
eventSource.onmessage = (event) => {
    console.log.data);
};
eventSource.onerror = () => {
    eventSource.close();
};

应用场景

1. 实时日志推送：监控系统状态或任务执行进度。
2. AI 对话流：逐字输出大语言模型（LLM）的生成结果。
3. 金融数据看板：实时更新股票价格或交易数据。

通过上述方法，Spring Boot 可高效实现 SSE 流式输出，结合智能体的异步处理能力，构建低延迟、高可用的实时交互系统。

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