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monitoring-observability
GitHub提供生产系统监控与可观测性最佳实践，涵盖指标、日志、链路追踪三大支柱。包含Prometheus等工具使用、结构化日志示例及APM配置指南，助力构建高效监控策略。
Trigger Scenarios
set up monitoring
structured logging
distributed tracing
metrics collection
observability
APM setup
log aggregation
metrics dashboards
error tracking
performance monitoring
Install
npx skills add NeverSight/learn-skills.dev --skill monitoring-observability -g -y
SKILL.md
Frontmatter
{
"name": "monitoring-observability",
"description": "Observability patterns and monitoring best practices. Use when user asks to \"set up monitoring\", \"structured logging\", \"distributed tracing\", \"metrics collection\", \"observability\", \"APM setup\", \"log aggregation\", \"metrics dashboards\", \"error tracking\", \"performance monitoring\", or mentions observability stack and monitoring strategy."
}
Monitoring & Observability
Comprehensive monitoring, observability, and alerting strategies for production systems.
Three Pillars of Observability
Metrics
- Quantitative measurements (counters, gauges, histograms)
- Time-series data (Prometheus, InfluxDB, Datadog)
- Examples: request latency, error rate, CPU usage
Logs
- Structured event records
- Searchable and filterable
- Examples: application logs, access logs, error logs
Traces
- Request flow through system
- Distributed tracing (Jaeger, Zipkin)
- Shows dependencies and bottlenecks
Implementation Approaches
Metrics Collection
from prometheus_client import Counter, Histogram
request_count = Counter('http_requests_total', 'Total requests')
latency = Histogram('http_request_duration_seconds', 'Request latency')
@app.route('/api/users')
def get_users():
request_count.inc()
with latency.time():
return fetch_users()
Structured Logging
{
"timestamp": "2025-02-07T10:30:00Z",
"level": "ERROR",
"service": "user-service",
"request_id": "req_12345",
"user_id": "user_789",
"error_code": "DB_CONNECTION_FAILED",
"message": "Failed to connect to database",
"duration_ms": 1500
}
Distributed Tracing
- Instrument application code
- Propagate trace IDs across services
- Collect traces centrally (Jaeger, Zipkin)
- Visualize service dependencies
Popular Tools
| Category | Tools |
|---|---|
| Metrics | Prometheus, Grafana, Datadog, New Relic |
| Logging | ELK Stack, Splunk, CloudWatch, Loki |
| Tracing | Jaeger, Zipkin, DataDog APM |
| APM | New Relic, DataDog, Dynatrace |
Best Practices
- Structured Logging - JSON format with context
- Contextual Data - Request IDs, user IDs, service names
- Sampling - Don't log everything to save costs
- Retention Policy - Balance cost and retention needs
- Alerts - On error rates, latency, resource usage
- Dashboards - Visualize key metrics
- Runbooks - Document how to respond to alerts
Key Metrics to Monitor
- Request rate and latency (p50, p95, p99)
- Error rate and error types
- Resource usage (CPU, memory, disk)
- Database query performance
- Cache hit rates
- Queue depths
- User session counts
References
- Prometheus Monitoring Best Practices
- Observability Engineering (O'Reilly)
- Google SRE Book
- ELK Stack Documentation
- OpenTelemetry Project
Version History
- e0220ca Current 2026-07-05 20:44
