This comprehensive guide examines the strengths, weaknesses, costs, performance, and security of Jenkins, GitLab CI, and GitHub Actions, offering real‑world case studies and a decision matrix to help small‑to‑medium development teams choose the most suitable CI/CD platform within 30 minutes.
Learn how to set up a robust GitLab CI pipeline for Symfony projects, covering preparation, .gitlab-ci.yml creation, defining stages such as test, lint, security, build, and deploy, Docker image configuration, job optimization, environment variables, database services, and pipeline execution.
This article compares two GitLab CI strategies—installing tools on‑the‑fly in scripts versus using a pre‑built Docker image—detailing their implementations, pros and cons, and concluding that pre‑built images provide superior speed, consistency, and security for long‑term projects.
This guide explains how GitLab CI hidden jobs work, why they’re essential for DRY pipelines, and provides step‑by‑step examples of using the extends keyword for single and multiple inheritance, configuration merging, and visualizing job relationships.
This guide explains YAML anchors, aliases, and merge keys, showing how they reduce duplication, improve readability, and simplify maintenance in Docker Compose and GitLab CI configurations through clear examples and practical exercises.
This article explains how to build dynamic GitLab CI pipelines that can create an arbitrary number of environments, trigger downstream pipelines across multiple projects, and pass artifacts between jobs using strategies like depend, needs, and dependencies to achieve flexible, scalable DevOps workflows.
This guide walks through the challenges of manual Go releases, compares two naive deployment approaches, and presents a streamlined solution using GitLab CI pipelines, Makefile automation, and Jenkins to decouple development from operations while ensuring reliable versioned deployments.
This article explores how mature testing teams can leverage large language models for automated code review, outlining the advantages, challenges, and a practical implementation using FastGPT and GitLab CI to build a low‑cost, AI‑enhanced review system that improves efficiency and feedback quality.
The article details how the Mooncake platform tackled outdated, manually‑maintained API docs by introducing naming conventions, a one‑click IntelliJ plugin, GitLab MR auto‑parsing, and a metadata center that supports debugging, mocking, and downstream consumption, saving developers hundreds of hours per release.
This article explains how to use GitLab CI and Kubernetes namespaces to automate building, testing, and deploying containerized services, ensuring environment isolation, resource control, traceability, and low operational overhead without introducing additional platforms.
This guide explains how to integrate GitLab CI/CD with SonarQube, configure runners, set up SonarQube projects, define quality gates, and embed the pipeline into the development workflow to enforce code‑quality standards automatically.
The SmartIDE v1.0 release introduces a fully cloud‑native IDE with one‑command CLI deployment, complete Kubernetes mode, ARM processor support, team resource sharing, hybrid VSCode/JetBrains remote connections, WebTerminal, and GitLab CI pipeline integration, providing developers across Windows, macOS, and Linux with a versatile development environment.
This tutorial demonstrates how to set up automated Docker image vulnerability scanning with Trivy, embed the scan into GitLab CI/CD pipelines, handle severity thresholds, schedule recurring scans, and remediate findings by adjusting the Dockerfile, providing a practical DevOps security workflow.
This article explains what Trivy is, how to install and use it for container vulnerability scanning, demonstrates saving results in JSON, and provides a step‑by‑step guide to integrating Trivy into a GitLab CI/CD pipeline with example configuration and troubleshooting tips.
This comprehensive guide explains what GitLab CI is, how to configure shared or custom runners with Docker, register them, create and write a .gitlab-ci.yml file using YAML syntax, and demonstrates basic and advanced pipeline demos with full code examples and images.
This article outlines a modern icon delivery workflow that moves from static image resources to a Figma‑driven process, using a custom Figma plugin and GitLab CI to fetch, optimize with SVGO, and automatically generate React/Vue SVG component libraries, publish npm packages, and deploy documentation.
This guide explains how to persist Docker layer caches in GitLab CI pipelines on Kubernetes by deploying a standalone Docker‑in‑Docker service with a Local Persistent Volume, configuring a Service for access, updating CI jobs, and adding a CronJob to prune old images.
This article demonstrates how to set up a GitLab CI pipeline trigger token, use curl or Postman to invoke the pipeline via API, embed the trigger in .gitlab-ci.yml, and pass custom variables to the job, with example scripts and best‑practice tips.
This article walks through deploying a Flask web service with GitLab CI/CD, covering Docker image creation, a comprehensive .gitlab-ci.yml pipeline, Kubernetes resource definitions, runner variable configuration, and how branch, tag, and manual review jobs orchestrate continuous integration, delivery, and deployment.
This article walks through setting up GitLab CI to automatically build a Spring Boot JAR, create Docker images for Java 8 and Java 11, push them to a private registry, and deploy them to a server, covering required Dockerfiles, .gitlab-ci.yml configuration, environment files, and necessary GitLab variables.
This guide explains the fundamentals of continuous integration, continuous delivery, and continuous deployment, walks through a complete CI/CD workflow, and provides practical examples using GitLab CI, Docker, and GitLab Runner to automate building, testing, and deploying applications.
This article explains how Ctrip’s NFES framework adopts a GitDev CI/CD pipeline—detailing the Install, Verify, and SonarAndImage stages, their constituent steps, and the .gitlab-ci.yml configuration—to streamline web development, improve code quality, and accelerate delivery of front‑end projects.
This guide details a complete GitLab‑based CI/CD workflow that starts with issue and milestone creation, defines feature‑branch pipelines, builds and tests Java applications, performs code analysis, creates Docker images, and deploys to Kubernetes with controlled pipeline triggers and rollback strategies.
This article describes a Laravel‑based static analysis tool that automatically scans PHP files to detect missing namespace imports, unresolved class references, and undefined class constants, using regex and token_get_all, and integrates the checks into GitLab CI for early error prevention.
This guide explains how to use GitLab CI to automatically build, push, and run Docker images for a Spring Boot application, covering Dockerfile creation, Java version considerations, environment variable management, and deployment steps across development, testing, and production environments.
This tutorial demonstrates how to integrate Trivy image security scanning into a GitLab CI/CD pipeline, covering tool selection, Dockerfile creation, pipeline configuration, scheduled scans, handling vulnerability reports, and strategies for failing builds based on severity levels.
This article provides a step‑by‑step guide to implementing a GitOps workflow on a Kubernetes cluster using Argo CD and GitLab CI, covering Argo CD installation, Helm deployment, application configuration, CI/CD pipeline definition, and deployment to dev and prod environments.
This guide explains how to configure a Docker‑in‑Docker GitLab Runner to pull images from a private container registry by setting the DOCKER_AUTH_CONFIG variable, covering both job‑level and runner‑level methods, credential generation, and example runner registration commands.
This comprehensive guide walks frontend developers through the concepts, benefits, and step‑by‑step implementation of automated deployment using GitLab CI, covering pipeline mechanics, runner configuration, YAML syntax, practical .gitlab-ci.yml examples, common pitfalls, and advanced features to streamline continuous integration and delivery.
This guide walks you through setting up a GitLab CI environment, configuring a Docker‑enabled runner, writing Dockerfile and .gitlab-ci.yml files, and automating code compilation, container image creation, and deployment of a Go application, complete with command examples and troubleshooting tips.
This article demonstrates a complete GitOps workflow on Kubernetes using Argo CD and GitLab CI, covering installation, configuration of Argo CD, GitLab project setup, Helm deployment, and a multi‑stage CI/CD pipeline that automatically builds, publishes Docker images, and synchronizes applications in dev and prod environments.
This guide walks you through creating a Python Flask project, manually building and running its Docker image, configuring GitLab runners, writing a .gitlab-ci.yml pipeline for style checks, tests, deployment, and release, and managing a private Docker registry for versioned releases.
This guide demonstrates how to create a complete GitLab CI pipeline for a Python Flask application, covering dependency installation, code compilation, testing, static analysis, Docker image building, and Kubernetes deployment, with detailed configuration snippets and a sample Dockerfile.
This guide explains how to register a Docker‑based GitLab Runner, view its configuration, and use image, services, environment, and inheritance settings in .gitlab-ci.yml files, as well as how to organize reusable pipeline templates for efficient DevOps workflows.
This tutorial explains how to use GitLab CI's parallel needs, control artifact downloads, incorporate local, file, template, and remote includes, apply extends for job inheritance, and configure multi‑project, parent‑child, and downstream pipeline triggers with practical YAML examples.
This guide explains how to configure GitLab CI artifacts—including specifying paths, exposing artifacts in merge requests, customizing artifact names, controlling upload conditions, setting expiration times, and collecting test reports—while providing practical code examples and important usage notes.
This article explains GitLab CI cache configuration, including cache paths, keys, policies, and examples of global and job-specific caching, demonstrates pipeline execution and runner cache behavior, and provides best‑practice conclusions for effective CI/CD caching.
This tutorial explains how to use GitLab CI's only/except parameters and the newer rules syntax—including if, changes, exists, and allow_failure clauses—to control job execution, configure branch restrictions, and define workflow-level pipeline creation, illustrated with comprehensive YAML examples.
This tutorial explains how to use GitLab CI keywords such as tags, allow_failure, when, manual, delayed, retry, timeout, and parallel to control runner selection, job execution conditions, failure handling, scheduling, retry policies, time limits, and concurrent job instances within CI/CD pipelines.
This article explains the drawbacks of the traditional software release process, introduces continuous integration, delivery, and deployment concepts, compares GitLab CI/CD with Jenkins, and outlines the architecture, configuration files, and advantages of using GitLab’s built‑in CI/CD platform.
This article details VPGAME's end‑to‑end migration of its esports platform services to a Kubernetes‑based architecture, covering CI/CD tool selection, Docker and GitLab Runner setup, automated Docker image builds, and Kubernetes Deployment and Service templating with health checks, resource limits, logging and monitoring configurations.
This article walks through creating a complete CI/CD workflow for a Python service using GitLab CI, Docker‑based gitlab‑runner, unit testing, static code analysis with SonarQube, service containers for Redis and MongoDB, and deployment steps, providing all necessary configuration files and commands.
This article explains the concept of CI/CD, its benefits for agile development, and provides a detailed comparison of seven popular CI/CD tools—including Jenkins, Travis CI, Circle CI, TeamCity, Codeship, GitLab CI, and Bamboo—covering their features, pricing, and ideal use cases.
