Docker

Overview You’ve probably seen this release workflow: a developer builds a Docker image, tags it as dev, and pushes it to the dev registry. After testing passes, they re-tag it as staging and push it again. Finally, for production, they tag it as prod and push a third time. Same image, built once, pushed three times with three different tags. This seems fine — until one day you discover that the image validated in staging and the image running in prod are not the same artifact at all....

Overview You have probably experienced this scenario: a developer commits one line of code, CI runs for 40 minutes, CD deployment takes another 20. You come back after two cups of coffee only to find the build failed—time to start over. By the end of the day, three hours gone just waiting on the pipeline. This is not an isolated case. Among the teams I have worked with, the majority have pipelines exceeding 30 minutes....

The Hidden Costs of Oversized Images Many teams overlook image size during the early stages of containerization. A Spring Boot image often weighs 800MB–1.2GB, and even a Go application image can easily exceed 700MB. The problems caused by oversized images go far beyond “wasting a little disk space”: Slow pulls and deployment latency: In CI/CD pipelines or elastic scaling scenarios, nodes must pull the image before starting the container. A 1GB image takes over 80 seconds to download on a 100 Mbps internal network, while a 50MB image takes just 4 seconds....

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/*....

Overview Many teams perceive Docker Compose as merely a “local development orchestration tool.” In reality, for small to medium production scenarios (single node or a handful of nodes), Compose remains a highly cost-effective solution. Its syntax is simple, the learning curve is low, and it doesn’t require a full K8s cluster operations team—yet it handles multi-service orchestration, dependency management, health checks, and resource limits effectively. This article skips basic Compose syntax and focuses on real production pain points: how to manage service dependencies without cascading startup failures, how to write reliable health checks, how to avoid hardcoding secrets in compose files, how to prevent logs from filling up disk space, and when to migrate from Compose to K8s....