Christian Ledermann: Scratching the Itch, Paying the Debt: How Community Keeps Legacy Open Source Pr
Introduction
Every developer has that one project that started as a personal solution and unexpectedly found a life of its own. For me, that wasFastKML, a library I built in 2012 to “scratch my own itch.” I needed to embed maps into a website, and at the time,KMLwas the de facto standard for visualizing geospatial data on the web.GeoJSONexisted but was still in its infancy and unsupported byOpenLayers, which was then the best tool for embedding maps.
Other Python libraries for KML existed, but most were either limited in scope, lacked Python 3 support, or didn’t meet my performance needs. Performance was crucial, so I builtFastKMLusinglxmlinstead of the slower XML DOM used by many contemporaries.
As FastKML evolved, it depended onShapelyfor geometry handling, an excellent library, but one that required C extensions and added installation complexity. That led to the birth ofPyGeoIf, a pure Python implementation of basic geospatial objects. PyGeoIf aimed to serve as a lightweight, dependency-free substitute for Shapely when users didn’t need all of its advanced geometry operations. The API mirrored Shapely’s closely, making migration as simple as replacing
with
Over the years, both projects aged gracefully, but not without technical debt. They bore the marks of an earlier Python era: Python 2/3 compatibility hacks (at the very beginning Python 2.6 was still in use), missing type hints, and occasionally ambiguous function signatures.
Still, they worked. The test coverage exceeded 95%, bugs were rare, and they continued solving real problems for users long after I had moved on to other roles outside GIS. To my surprise, the packages remained popular; downloads were steady, and employers still asked about them. But I knew the code looked dated, and if I had to review it today, it wouldn’t pass.
Fast forward to 2020. The geospatial landscape had changed;GeoJSONhad overtaken KML, Python’s ecosystem had matured, and I had learned a great deal about clean code and maintainability. It was time to modernize these legacy projects for the new decade.
The Need for Change
Modernization wasn’t just a matter of adding type hints or updating syntax, it was about bringing two long-lived projects in line with modern development practices. The original codebases had served well for years, but they were increasingly difficult to extend. Function signatures were ambiguous, internal logic was tangled, and adding new features often causedshotgun surgery, requiring edits across multiple unrelated files.
TheShapelyAPI had evolved too, fully embracingPEP 8naming conventions and adopting more expressive methods. To remain compatible, PyGeoIf needed to evolve alongside it. Meanwhile, Python itself had transformed: type hints, static analysis, and property-based testing were now standard practice rather than novelty.
Drivers of Change
The single most important motivator was theintroduction of type hintsin Python. Type annotations have revolutionized how Python code is written, reviewed, and maintained, enhancing readability and catching subtle bugs early through tools likemypy.
The first step was static analysis with tools like mypy, which immediately flagged legacy Python 2 compatibility hacks, ambiguous function signatures, and missing type hints. Extending the tests in tandem ensured that each refactor preserved correctness.
Beyond that, the desire for clearer APIs, more maintainable structures, and modern testing techniques pushed the modernization effort forward. I wanted code that not only worked but wasreadable, testable, and future-proof.
A Tale of Two Refactors
ForPyGeoIf, version 0.7 had been released in 2017. Four years later, in September 2021, I published the first beta of the 1.0 series: fully type-annotated, statically checked withmypy, and tested usingproperty-based testingwithHypothesisand improved tests withmutation testingwithMutMut. By September 2022, version 1.0 was stable, and by October 2025, it had matured to version 1.5.
ForFastKML, after a long silence since version 0.11 in 2015, I released version 0.12 in September 2021, incorporating long-neglected pull requests and minor improvements. A month later cameFastKML 1.0 alpha 1on PyPI. What I thought would be a quick release became an 18-iteration journey spanning three years, culminating inversion 1.0in November 2024; finally the library I had envisioned years earlier.
Reflecting on Contributions and Community Support
Over the past few years of developingPyGeoIfandFastKML, the journey has been shaped not only by personal effort but also by the support and engagement of the open-source community. One striking example of this has beenHacktoberfest contributionswhich consistently provided motivation and tangible progress.
These contributions may seem small individually, but collectively they havekept the momentum going. Seeing community members engage with the projects during Hacktoberfest has been acontinuing source of encouragement, reminding me that every bit of contribution helps make the software more robust, maintainable, and welcoming to others.
The positive impact goes beyond the specific changes. Hacktoberfest contributions have:
- Encouraged ongoing improvementby motivating incremental updates.
- Highlighted the value of community participationin maintaining and modernizing open-source projects.
- Reinforced a sense of shared purpose, showing that even small efforts can collectively advance a project.
This ongoing collaboration has made the development processmore rewarding and sustainable, reinforcing a simple but powerful lesson: in open-source, community engagement isn’t just about code, it’s about inspiration and momentum.
Hacktoberfest contributions aren’t just code, they’reencouragement. They spark incremental improvements, highlight the value of shared effort, and inspire continued development. Seeing others invest their time and ideas in these projects has been aconstant source of motivationto keep improving, testing, and refining.
Hacktoberfest and the Power of Community
DevelopingPyGeoIfandFastKMLhas been a journey of learning, coding, and refining, but it’s thecommunity contributions, especially during Hacktoberfest, that have truly kept the momentum alive.
October has consistently brought a wave of engagement: pre-commit hooks, bug fixes, minor enhancements, and automated improvements. Each contribution, no matter how small,reinforced the sense of progressand reminded me that open-source thrives on collaboration.
Looking forward, this collaborative energy continues tofuel future features and refinements. Hacktoberfest has proven that even small contributions can make a big difference, both in the code and in the spirit of the community.
