Xcited to share another something I've been tinkering with: xs_h3, a pure Rust implementation of Uber's H3 geospatial indexing system. If you haven't bumped into H3 before, you can learn a lot more at its official site: h3geo.org. In a nutshell, it's this super smart way of gridding the entire globe into mostly hexagons (plus a few pentagons to make it all fit). It’s the kind of thing that powers a lot of cool location-based features out there – you've probably interacted with it without even knowing!
My first encounter with H3 was during my time at Uber. Honestly, when I first saw it, I thought it was straight-up alien technology. The math, the way it used an icosahedron to map the sphere, how it handled indexing across the whole planet – it was pretty mind-bending. And even now, after porting a good chunk of its logic into Rust, a lot of the really deep, intricate parts still have that aura of "wow, that's clever." What's crystal clear, though, is how incredibly efficient and powerful it is. Uber Technologies, Inc. really did contribute something special to the open-source world with H3, and the knowledge and the "let's build cool, hard stuff" spirit I picked up there? That’s something I’ll never forget. It just becomes part of how you think.
So, why try to wrestle this into Rust? For me, Rust's promise of performance and memory safety felt like the perfect fit for something as core and critical as a geospatial index. The goal was to build a library, xs_h3, that fellow Rustaceans could easily pull into their projects, getting all that H3 goodness without FFI headaches. The "xs" is just a little nod – maybe "Cross-Safe" or "Extra Safe" – to those Rust guarantees.
So, what can you actually do with xs_h3 (and H3)?
H3 is incredibly versatile. Think about any app or system that deals with locations:
Gaming: Niantic famously uses H3 for games like Pokémon GO to define areas for gameplay, distribute game elements, and manage real-world interactions.
Logistics and Ride-Sharing: Optimizing routes, determining ETAs, pricing zones, and matching supply with demand.
Data Visualization & Analytics: Aggregating vast amounts of point data (like IoT sensor readings or user activity) into visually digestible heatmaps or for regional analysis.
Marketplace Dynamics: Analyzing local supply and demand, defining service areas, or understanding competitive landscapes.
Environmental Modeling: Tracking weather patterns, species distribution, or resource management across geographical areas.
With xs_h3, you can tap into this power in your Rust projects:
Gridify the World: Quickly figure out which H3 cell any latitude/longitude point belongs to.
// Quick peek at the API feel: let location_rads = LatLng { lat: to_radians(37.77), lng: to_radians(-122.41) }; let cell_index: H3Index = lat_lng_to_cell(&location_rads, 9)?; // Boom, H3Index.Understand Proximity: Easily find a cell's neighbors, or all cells within a certain number of steps (a "k-ring").
let nearby_cells: Vec<H3Index> = grid_disk(cell_index, 2)?;IGNORE_WHEN_COPYING_STARTIGNORE_WHEN_COPYING_ENDZoom In, Zoom Out: H3 cells have a neat parent-child hierarchy. You can effortlessly move from a coarse view to a super detailed one.
let coarser_parent: H3Index = cell_to_parent(cell_index, 8)?; let finer_children: Vec<H3Index> = cell_to_children(coarser_parent, 9)?;IGNORE_WHEN_COPYING_STARTIGNORE_WHEN_COPYING_ENDCover Areas (Polyfill): Figure out which H3 cells you need to represent an arbitrary shape, like a city boundary or a delivery zone.
let some_area_polygon: GeoPolygon = ...; let cells_covering_area: Vec<H3Index> = polygon_to_cells(&some_area_polygon, 10)?;IGNORE_WHEN_COPYING_STARTIGNORE_WHEN_COPYING_END
Personally, the bit that really gets me going is how xs_h3 can plug into other systems. I've been deep in the weeds building a strongly consistent, leaderless key-value store, and the thought of using H3 for efficient, location-based sharding and querying – all with Rust's safety – is incredibly appealing. It’s tools like H3 that don't just solve existing problems but open the door for entirely new kinds of innovation and smoother user experiences.
Where xs_h3 is At
Right now, xs_h3 is definitely in its early days – version 0.1.0. It's experimental. That means the API might wiggle around a bit, not every single H3 feature is fully baked, and there's a whole lot more testing and optimization on the to-do list. I've tried to get the core indexing, hierarchy, and a good chunk of the traversal logic in place, aiming to mirror how the C H3 library works. More complex stuff like filling polygons (polyfill) and turning sets of cells back into nice polygon shapes is still pretty basic.
Why Share Now?
Even at this stage, I figured xs_h3 could be handy for anyone wanting to play with H3 in a pure Rust setting. And maybe, just maybe, it can help a few more cool projects sprout up in the Rust ecosystem that can benefit from this kind of geospatial power.
If you're into geospatial tech, love Rust, or are just intrigued by this "alien technology," feel free to poke around the xs_h3 GitHub or find it on crates.io. Feedback, bug reports, ideas, contributions – all super welcome, especially now. And don't forget to check out h3geo.org to learn more about the H3 system itself!
It's been quite the dive trying to understand and re-implement parts of H3. Sometimes, the best way to appreciate complex tech is to try and build it yourself. Other times, you just end up with an even deeper respect for the original "aliens" who conjured it up!