基于Flink的模型实时预测监控平台

基于Flink的模型实时预测监控平台

核心监控指标配置

1. 预测延迟监控

metrics:
  latency:
    threshold: 500ms
    percentiles: [0.5, 0.9, 0.99]
  throughput:
    min_rate: 1000 req/s

2. 模型准确性指标

# Flink作业中添加自定义指标
latency_metric = metrics.gauge("prediction_latency", lambda: get_current_latency())
accuracy_metric = metrics.gauge("model_accuracy", lambda: calculate_accuracy())

告警配置方案

Flink告警规则：

• 预测延迟超过500ms持续30秒触发告警
• 每分钟预测准确率低于85%时告警
• 系统CPU使用率超过90%时触发

实施步骤

1. 在Flink作业中集成Micrometer监控
2. 配置Prometheus抓取指标
3. 设置Grafana仪表盘展示关键指标
4. 通过Alertmanager配置告警规则

# alert.rules.yml
- alert: HighPredictionLatency
  expr: prediction_latency > 500
  for: 30s
  labels:
    severity: page
  annotations:
    summary: "预测延迟过高"

关键代码示例

public class ModelMonitoringFunction implements RichMapFunction<InferenceRequest, InferenceResponse> {
    private transient Counter requestCounter;
    private transient Histogram latencyHistogram;
    
    @Override
    public void open(Configuration parameters) {
        requestCounter = getRuntimeContext()
            .getMetricGroup().counter("request_count");
        latencyHistogram = getRuntimeContext()
            .getMetricGroup().histogram("latency", new DescriptiveStatisticsHistogram(1000));
    }
}