Overview

Our organization successfully migrated hundreds of GitLab repositories—along with their issues, pipelines, and user permissions—to GitHub. This large-scale operation required designing a strategy that minimized manual steps, ensuring a smooth cutover and continuous developer productivity. Below is an overview of how we planned, executed, and validated the move, while keeping manual intervention to a minimum.

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1. Planning & Requirements

1. Inventory of All Repositories
   • We first produced a full list (or database) of every GitLab project.
   • Labeled them by priority (active, archived, legacy) to focus immediate efforts on high-traffic repos.
2. Automation-First Mindset
   • Our biggest requirement was minimal manual work—scripts and automated tools were crucial to handle hundreds of repositories.
   • Ensured any one-off tasks (like verifying user roles) were also streamlined wherever possible.
3. CI/CD & Issue Migration
   • Since many of our pipelines were in GitLab CI, we needed a reliable way to convert .gitlab-ci.yml to GitHub Actions with minimal rewriting.
   • We sought tools or scripts that could mass-migrate issues, labels, and merge requests to GitHub Issues / Pull Requests.
4. Downtime Constraints
   • Multiple teams relied on these repos around the clock, so extended downtime was not acceptable.
   • We planned a short read-only window on GitLab for final sync, but everything else needed to stay accessible.

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2. Tools & Approaches

1. Scripted Repository Transfer
   • GitHub’s Import API: Provided a straightforward way to migrate each repo’s commit history.
   • Batch Scripts: We developed a custom script (using GitLab and GitHub APIs) to iterate over our repository list and automate the push process.
   • Parallelization: Where possible, we ran multiple import jobs concurrently, reducing overall migration time.
2. Automated Issue & Merge Request Migration
   • Third-Party Tools / Custom Scripts:
     • Some open-source tools can batch-export and import GitLab issues into GitHub.
     • For more nuanced data (labels, attachments, comments), we wrote scripts to call both GitLab and GitHub APIs, mapping old references (#123) to new links on GitHub.
   • ID Mapping & Conflict Resolution: Our scripts generated a mapping file that kept track of old vs. new issue IDs, ensuring we didn’t lose references.
3. CI/CD Pipeline Conversion
   • We created a tool that scanned .gitlab-ci.yml files, then produced template .github/workflows/*.yml pipelines.
   • Complex pipelines or environment variables still required some manual checks, but this automation covered most typical build/test patterns.
   • Environment Variables & Secrets: A script used GitHub’s API to create the same secrets that were configured in GitLab.
4. User Permissions & Roles
   • Role Mapping Spreadsheet: We mapped GitLab roles (Guest, Reporter, Developer, Maintainer, Owner) to GitHub’s roles (Read, Triage, Write, Maintain, Admin).
   • Bulk Invites: A script triggered GitHub invites for all relevant users, verified emails, and assigned roles automatically.

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3. Implementation Steps

1. Pre-Migration Preparation
   • Repository Freeze Planning: Scheduled a brief read-only window for the final sync.
   • Data Validation: Confirmed every repo in GitLab was healthy (no unmerged branches, broken refs, etc.).
   • Pilot Migration: Tested the end-to-end process on a small subset of repositories to refine scripts.
2. Batch Repository Transfer
   • Clone & Push: For each repo, our script cloned from GitLab, created a corresponding GitHub repo, and pushed all branches and tags.
   • API Validation: After each push, we used GitHub’s API to confirm the commit count, branches, and tags matched the source.
3. Issues & Pull Requests
   • Incremental Sync: We first imported open/active issues to confirm correct references, then pulled in closed issues.
   • Comments & Attachments: Iterated through each issue’s comment thread, uploading attachments to GitHub if necessary.
   • Label Migration: Mapped GitLab labels to GitHub labels, preserving color codes and naming conventions.
4. CI/CD Migration
   • Automated Conversion: Ran our .gitlab-ci.yml → .github/workflows/* converter.
   • Manual Edge Cases: Some advanced or environment-specific jobs needed a review by DevOps engineers.
5. Cutover & Verification
   • Read-Only Window: Marked GitLab projects read-only, triggered a final data sync for issues and PRs to catch any last-minute changes.
   • DNS & Documentation Updates: Updated all references (internal docs, Confluence pages, etc.) to point to the new GitHub repos.
   • Post-Migration Checks: Confirmed that commit history, branches, issues, and CI/CD pipelines worked as expected in GitHub.

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4. Challenges & How We Addressed Them

Challenge	Description	Resolution
1. Handling Hundreds of Repos	Manually importing each repository would be too time-consuming.	– Wrote batch scripts to automate cloning, pushing, and verifying repos via Git APIs. – Employed parallel imports for speed.
2. Issue & PR References	Issues in commits and wikis referenced GitLab IDs, which could break once in GitHub.	– Created a mapping file that updated references on import. – Added a final check script to fix or flag any unresolved references.
3. CI/CD Conversion Complexity	Some pipelines had advanced features not directly supported by GitHub Actions.	– Automatically converted common patterns. – Documented best practices for advanced environment or service dependencies.
4. User Permissions & Bulk Invites	Assigning roles for hundreds of users could become messy and prone to human error.	– Used a script that cross-referenced user emails between GitLab and GitHub. – Mapped roles in a “one-to-many” approach.
5. Maintaining Minimal Downtime	Active development teams needed near-zero disruption.	– Final sync happened during a short, well-communicated read-only window. – Coordinated with team leads to freeze merges briefly.

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5. Results & Best Practices

1. Minimal Manual Effort
   • 90%+ of the repositories were migrated purely via automated scripts, saving countless hours.
   • Manual checks were limited to pipeline edge cases and advanced user permission scenarios.
2. Seamless Cutover
   • A short read-only window in GitLab was enough to finalize the import; developers quickly resumed work in GitHub.
   • Clear communication kept stakeholders prepared, and no data was lost in the transition.
3. Enhanced Collaboration
   • Teams can now leverage GitHub’s robust ecosystem, including Pull Requests, Actions, and large user community.
   • External contributors or open-source collaborations became easier via GitHub’s familiar interface.

Best Practices:

• Create a Comprehensive Migration Script: Centralize your logic for cloning, pushing, verifying, and translating issues—this is crucial for large-scale moves.
• Use Pilot Projects: Test your automation pipeline end-to-end on a small group of repos to refine processes before tackling the entire portfolio.
• Maintain a Mapping File: Track old-to-new references for issues, attachments, and pipelines to avoid broken links or missing data.
• Automated Role Assignments: If user permissions are complex, a script referencing a role mapping spreadsheet can drastically reduce manual overhead.
• Monitor & Iterate: Run automated checks post-migration to catch any anomalies—like missing attachments or incorrectly mapped references.

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6. Conclusion

Migrating hundreds of repositories from GitLab to GitHub can be accomplished with minimal manual interventionby leveraging a well-designed suite of automated scripts and a robust planning process. By batching repositories, pre-mapping user roles, converting CI/CD definitions en masse, and allocating a short read-only window for final sync, we ensured high fidelity of data and a smooth cutover for developers.

Next Steps

• Continue refining GitHub Actions to optimize build and deployment processes.
• Periodically revisit and improve your bulk migration scripts for future expansions or platform changes.
• Ensure new GitHub features (e.g., Advanced Security, Codespaces) are integrated to maximize your DevOps environment’s potential.

Through careful preparation and scripting, even large, complex migrations can be achieved with minimal disruptions—ensuring teams remain productive and confident in their new platform.