Have you developed a system for collecting and analyzing metrics on your own?

Yes, when developing a metrics collection and analysis system, it is important to consider several key aspects:

• Data collection: integration with the application or service to obtain necessary metrics (e.g., response time, request count, errors).
• Storage: choosing an appropriate storage (databases, time-series DB) for efficient saving and subsequent analysis.
• Processing: aggregation, filtering, and calculation of statistics on collected data.
• Visualization and alerts: creating dashboards and setting up notifications when thresholds are reached.

An example in Go — a simple request counter using built-in structures:

package main

import (
    "fmt"
    "sync"
)

type Metrics struct {
    mu      sync.Mutex
    counters map[string]int
}

func NewMetrics() *Metrics {
    return &Metrics{counters: make(map[string]int)}
}

func (m *Metrics) Inc(key string) {
    m.mu.Lock()
    defer m.mu.Unlock()
    m.counters[key]++
}

func (m *Metrics) Get(key string) int {
    m.mu.Lock()
    defer m.mu.Unlock()
    return m.counters[key]
}

func main() {
    metrics := NewMetrics()
    metrics.Inc("requests")
    fmt.Println("Requests count:", metrics.Get("requests"))
}

This is a basic example; in real projects, specialized libraries and systems (Prometheus, Grafana, etc.) are used.