What is a metric collection system, and how does it work in cloud-native observability?

A metrics collection system is a tool that collects, processes, and stores metric data (such as CPU usage, request latency) from applications or infrastructure. It is crucial in cloud-native observability, providing real-time insights into system performance to support monitoring, troubleshooting, and optimization decisions. Application scenarios include Kubernetes cluster resource management, service health monitoring, etc., to improve system reliability and efficiency.

Its core components include collectors (e.g., Prometheus exporters), transport agents (e.g., Fluentd), and time-series databases (e.g., InfluxDB), with features covering real-time data aggregation and tag-based filtering. The working principle is based on pull or push models to obtain metrics, which are then processed for analysis and visualization. In cloud-native environments, it integrates with logging and tracing systems to achieve comprehensive observability, significantly impacting operational domains such as reducing Mean Time to Repair (MTTR) and optimizing resource utilization.

Application values include supporting automated alerting, elastic scaling decisions, and capacity planning, thereby enhancing system reliability, reducing operational costs, and promoting proactive performance optimization.