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Spring Boot分布式项目异常处理实战：从崩溃边缘到优雅恢复

article 2025/3/24 20:16:25

当单体应用拆分成分布式系统，异常就像被打开的潘多拉魔盒：RPC调用超时、分布式事务雪崩、第三方接口突然罢工…在最近的电商大促中，我们的系统就经历了这样的至暗时刻。本文将用真实代码示例，展示如何构建分布式异常处理体系。

一、全局异常拦截：最后的防线

@RestControllerAdvice
public class GlobalExceptionHandler {
    
    // 处理业务异常
    @ExceptionHandler(BizException.class)
    public Result handleBizException(BizException e) {
        log.error("业务异常: {}", e.getErrorMsg());
        return Result.fail(e.getErrorCode(), e.getErrorMsg());
    }

    // 处理Feign调用异常
    @ExceptionHandler(FeignException.class)
    public Result handleFeignException(FeignException e) {
        log.error("服务调用异常: {}", e.contentUTF8());
        return Result.fail(ErrorCode.SERVICE_UNAVAILABLE);
    }

    // 兜底异常处理
    @ExceptionHandler(Exception.class)
    public Result handleException(Exception e) {
        log.error("系统异常: {}", e.getMessage());
        return Result.fail(ErrorCode.SYSTEM_ERROR);
    }
}

关键点：通过@ControllerAdvice实现三层防护，特别注意对Feign异常的单独处理，保留原始错误信息

二、服务间调用异常处理

Feign+Sentinel双保险配置：

feign:
  client:
    config:
      default:
        connectTimeout: 3000
        readTimeout: 5000

sentinel:
  scg:
    fallback:
      mode: response
      response-body: '{"code":503,"msg":"服务降级"}'

自定义FallbackFactory：

@Component
public class OrderServiceFallbackFactory implements FallbackFactory<OrderServiceClient> {
    @Override
    public OrderServiceClient create(Throwable cause) {
        return new OrderServiceClient() {
            @Override
            public Result<OrderDTO> getOrder(String orderId) {
                if(cause instanceof BizException){
                    return Result.fail(((BizException) cause).getErrorCode(), "订单服务异常");
                }
                return Result.fail(ErrorCode.SERVICE_DEGRADE);
            }
        };
    }
}

实战经验：在双十一大促中，这种组合策略帮助我们拦截了70%以上的级联故障

三、分布式事务异常处理

使用Seata的TCC模式示例：

@LocalTCC
public interface OrderTccAction {

    @TwoPhaseBusinessAction(name = "prepareCreateOrder", commitMethod = "commit", rollbackMethod = "rollback")
    boolean prepare(BusinessActionContext actionContext,
                   @BusinessActionContextParameter(paramName = "order") Order order);

    boolean commit(BusinessActionContext actionContext);

    boolean rollback(BusinessActionContext actionContext);
}

补偿策略：

自动重试：对网络抖动等临时性错误
人工干预：对数据不一致等严重问题
事务日志：记录关键操作节点

四、流量洪峰应对策略

Resilience4j熔断配置：

CircuitBreakerConfig config = CircuitBreakerConfig.custom()
    .failureRateThreshold(50)
    .waitDurationInOpenState(Duration.ofMillis(1000))
    .ringBufferSizeInHalfOpenState(2)
    .ringBufferSizeInClosedState(2)
    .build();

自适应限流算法：

@Slf4j
public class AdaptiveLimiter {
    private AtomicInteger currentLimit = new AtomicInteger(100);
    
    public boolean tryAcquire() {
        int current = currentLimit.get();
        if(current <= 0) return false;
        
        // 根据RT和成功率动态调整
        double successRate = getRecentSuccessRate();
        long avgRT = getAvgResponseTime();
        
        if(successRate < 90% || avgRT > 500ms) {
            currentLimit.updateAndGet(x -> Math.max(x/2, 10));
        } else if(successRate > 99% && avgRT < 100ms) {
            currentLimit.updateAndGet(x -> Math.min(x*2, 1000));
        }
        return true;
    }
}

五、异常追踪三板斧

全链路追踪：

@Slf4j
public class TraceInterceptor extends HandlerInterceptorAdapter {
    @Override
    public boolean preHandle(HttpServletRequest request, HttpServletResponse response, Object handler) {
        MDC.put("traceId", UUID.randomUUID().toString());
    }
}

异常画像系统：

@Aspect
@Component
public class ExceptionMonitor {
    @AfterThrowing(pointcut = "execution(* com..*.*(..))", throwing = "ex")
    public void monitorException(Exception ex) {
        ExceptionMetric metric = new ExceptionMetric(
            ex.getClass().getSimpleName(),
            Thread.currentThread().getName(),
            System.currentTimeMillis()
        );
        KafkaTemplate.send("exception_metrics", metric);
    }
}