Monitoring

Overview Have you ever encountered this situation: users report “the system is slow,” you open Grafana and check a bunch of dashboards—CPU is fine, memory is fine, network is fine—but users insist it’s slow. What you need at this point is not more metric dashboards, but a complete request trace—from the moment the user clicks a button to when the database returns results, showing exactly how long each hop took and where it got stuck....

Overview Picture this: it’s 3 AM, you get woken up by an alert, you drag yourself to open Grafana, and you flip through dozens of dashboards—none of them tell you anything useful. You do have millions of metrics, sure. But they’re all garbage. This is not an isolated case. I’ve seen too many teams deploy Prometheus and then forget about it. Metrics pile up, alerting rules multiply, and eventually the monitoring system itself crashes first: Prometheus OOMs on memory, queries time out after 30 seconds, alert evaluation lags by 5+ minutes....

Overview AIOps Anomaly Detection: From Static Thresholds to Intelligent Alerting is an essential skill in SRE operations. In production environments, mastering these techniques can significantly improve system stability and operational efficiency. Why AIOps Anomaly Detection Matters As systems grow in scale and complexity, traditional operations approaches struggle to meet the demands of modern distributed systems. AIOps Anomaly Detection helps operations teams: Rapid Problem Resolution: Systematic tools and methods reduce troubleshooting time Improved System Visibility: Establish comprehensive monitoring and observability Proactive Fault Prevention: Identify and fix potential risks before they cause outages Resource Optimization: Allocate and schedule resources efficiently Core Concepts and Principles Basic Concepts The core of AIOps Anomaly Detection lies in establishing standardized processes and automated toolchains....

Overview Monitoring as Code: Managing Alert Rules with Terraform and YAML is an essential skill in SRE operations. In production environments, mastering these techniques can significantly improve system stability and operational efficiency. Why Monitoring as Code Matters As systems grow in scale and complexity, traditional operations approaches struggle to meet the demands of modern distributed systems. Monitoring as Code helps operations teams: Rapid Problem Resolution: Systematic tools and methods reduce troubleshooting time Improved System Visibility: Establish comprehensive monitoring and observability Proactive Fault Prevention: Identify and fix potential risks before they cause outages Resource Optimization: Allocate and schedule resources efficiently Core Concepts and Principles Basic Concepts The core of Monitoring as Code lies in establishing standardized processes and automated toolchains....

Overview Monitoring systems themselves need to be monitored. Prometheus collects metrics from your entire infrastructure, but if Prometheus’s own configuration has issues, a target goes down, an SSL certificate is about to expire, or an alerting rule has a syntax error — who discovers these problems? The answer: an automated inspection script set. This article builds a complete inspection toolkit around the Prometheus ecosystem, covering “probing → certificate checking → config auditing → rule validation → alert simulation → report generation....

Overview Prometheus is the de facto standard in the cloud-native monitoring space, but it has notable shortcomings in long-term data storage and global querying: local storage retains only 15 days of data by default, single instances cannot aggregate queries across clusters, and high-availability solutions are relatively complex. Thanos, a CNCF incubating project, achieves unlimited-capacity long-term storage by uploading Prometheus data to object storage (such as S3, GCS, MinIO) and provides a cross-cluster global view through distributed query components....

Overview As the de facto standard for cloud-native monitoring, Prometheus has become the default choice for microservice and Kubernetes monitoring. However, as business scale grows, Prometheus’s local storage architecture increasingly exposes significant bottlenecks: limited single-machine storage capacity (default 15-day retention), no native horizontal scaling, memory spikes under high-cardinality scenarios, and difficulty querying historical data. Many teams find themselves facing the “triple dilemma” of disk IO pressure, storage cost inflation, and query latency growth once time series exceed the million-level mark....

Overview Capacity planning is a core SRE responsibility. The Google SRE Book treats capacity planning as “proactive work,” emphasizing data-driven forecasting over experience-based guessing. A team without capacity planning either gets crushed by traffic during peaks or wastes significant resource costs during valleys. This article systematically covers capacity planning engineering practices across four dimensions: metric collection, data modeling, Kubernetes elastic scaling configuration, and pitfall avoidance. For a systematic methodology on capacity planning, see Google SRE Book - Capacity Planning and its discussion of capacity planning and cascading failures....

Overview 3 AM. Your phone buzzes. You get out of bed, open your laptop, SSH in, and find a service with CPU spiking. Kill the process, restart the service, 12 minutes done — but you’re wide awake now. 4:30 AM, another alert: disk usage exceeds 85%. Get up again, du -sh to locate, delete old logs, 15 minutes. This is the daily reality for countless operations engineers. Monitoring is set up, alerts are configured, scripts are written — but the final step still relies on humans....

Architecture Exporter → Prometheus (storage) → Grafana (visualization) ↓ Alertmanager (alert routing) Docker Compose Deployment version: '3.8' services: prometheus: image: prom/prometheus:v2.52.0 ports: ["9090:9090"] volumes: - ./prometheus.yml:/etc/prometheus/prometheus.yml - ./rules:/etc/prometheus/rules - prom_data:/prometheus command: - '--storage.tsdb.retention.time=30d' restart: unless-stopped grafana: image: grafana/grafana:10.4.2 ports: ["3000:3000"] volumes: [grafana_data:/var/lib/grafana] restart: unless-stopped alertmanager: image: prom/alertmanager:v0.27.0 ports: ["9093:9093"] volumes: [./alertmanager.yml:/etc/alertmanager/config.yml] restart: unless-stopped node-exporter: image: prom/node-exporter:v1.8.1 ports: ["9100:9100"] restart: unless-stopped volumes: prom_data: grafana_data: Core Configuration # prometheus.yml global: scrape_interval: 15s evaluation_interval: 15s rule_files: - "rules/*....