A robust monitoring system for 1000 web servers should focus on collecting key health metrics and performance data, detecting anomalies or failures, providing real-time visualization of system status, and enabling periodic analysis. Key non-functional requirements include minimizing the load on monitored servers, ensuring low latency in data collection and alerting, and maintaining high scalability to accommodate future growth.
Data Collection Strategy:
- Push Model: Agents on each server collect and periodically push metrics (e.g., CPU, memory, network, application-specific stats) to a central aggregation point, potentially using batching and low-priority threads to minimize impact.
- Pull Model: A central system periodically polls servers (e.g., via health checks or heartbeats) to detect offline or unresponsive machines.
Data Processing and Storage:
- Real-time Stream Processing: Technologies like Kafka or Kinesis can ingest the high volume of incoming metrics, which are then processed in real-time using frameworks like Flink for immediate anomaly detection and alerting.
- Time-Series Database: Store processed metrics in a time-series database (e.g., Prometheus, InfluxDB) optimized for querying and analyzing time-stamped data, enabling historical analysis and trend identification.
Alerting and Visualization:
- Alerting Engine: Define rules based on thresholds or anomaly detection algorithms to trigger alerts via various channels (email, Slack, PagerDuty).
- Dashboarding: Utilize tools like Grafana or Kibana to create real-time dashboards visualizing key metrics, system health, and alert status.
Amazon
Google