Add Metrics

Overview

The Metrics type is used to collect system performance and other indicator data. It can output in Prometheus format, serving as a data provider through the /metrics (curl localhost:<port>/metrics) .

• Type：Metrics collection

• Function：Collects performance metrics from various subsystems

• Characteristics：

  • Metrics are primarily used to collect system performance metrics such as CPU usage, memory usage, network statistics, etc. They are suitable for monitoring system performance and support real-time analysis and long-term trend observation.
  • Metrics can come from regular procfs/sysfs collection or be generated from tracing types (autotracing, event).
  • Outputs in Prometheus format for seamless integration into the Prometheus observability ecosystem.

• Already Integrated：

  • cpu (sys, usr, util, load, nr_running…)
  • memory（vmstat, memory_stat, directreclaim, asyncreclaim…）
  • IO (d2c, q2c, freeze, flush…)
  • Network（arp, socket mem, qdisc, netstat, netdev, socketstat…）

How to Add Statistical Metrics

Simply implement the Collector interface and complete registration to add metrics to the system.

type Collector interface {
    // Get new metrics and expose them via prometheus registry.
    Update() ([]*Data, error)
}

1. Create a Structure

Create a structure that implements the Collector interface in the core/metrics directory:

type exampleMetric struct{
}

2. Register Callback Function

func init() {
    tracing.RegisterEventTracing("example", newExample)
}

func newExample() (*tracing.EventTracingAttr, error) {
    return &tracing.EventTracingAttr{
        TracingData: &exampleMetric{},
        Flag: tracing.FlagMetric, // Mark as Metric type
    }, nil
}

3. Implement the Update Method

func (c *exampleMetric) Update() ([]*metric.Data, error) {
    // do something
    ...
	return []*metric.Data{
		metric.NewGaugeData("example", value, "description of example", nil),
	}, nil

}

The core/metrics directory in the project has integrated various practical Metrics examples, along with rich underlying interfaces provided by the framework, including BPF program and map data interaction, container information, etc. For more details, refer to the corresponding code implementations.