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docs about pydeck vs lonboard differences #118

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Expand Up @@ -12,6 +12,16 @@ The downside of ipyleaflet is that it doesn't support large datasets as well. It

Lonboard does not try to cover deck.gl's _full_ API, but rather has an opinionated approach that nudges users to the fastest rendering for many common use cases.

### Why not contribute back to pydeck?

Pydeck and lonboard have very different goals.

A stated goal of pydeck is to be non-opinionated and to allow users with various data sources (GeoJSON strings, URLs to arbitrary data sources, etc.) to render. It makes sense for "official" bindings to be non-opinionated, but lonboard takes an opposite tack. By forcing users to use Arrow, we can get reliably fast performance the very common use case of rendering `GeoDataFrame`s. A downside here is that an Arrow-based implementation has required dependencies that pydeck wouldn't want. `pyarrow` on the Python side is 90MB on disk. Arrow JS on the JS side is ~200kb, and the default `parquet-wasm` build is ~1MB.

Pydeck is tightly tied into the deck.gl [JSON renderer](https://deck.gl/docs/api-reference/json/overview), which allows describing a map state fully in JSON. It's not clear how this would work with the JavaScript GeoArrow layers.

Aside from this, pydeck and lonboard use different widget architectures. Pydeck is built on the historical ipywidget layout, using the [widget cookiecutter as inspiration](https://github.com/jupyter-widgets/widget-ts-cookiecutter) and having a separate Jupyter Widget package published to NPM. Lonboard takes a newer approach (unavailable at the time pydeck was created) that uses [Anywidget](https://anywidget.dev/), vastly simplifying the widget process.

## Lonboard vs datashader

[Datashader](https://datashader.org/) is a truly _scalable_ rendering library. Datashader will re-render your data from scratch when panning around in a map. This allows datashader to _aggregate_ the source data before rendering. Datashader _minimizes the amount of data being rendered_ and thus, in theory, Datashader should perform well for datasets as large as your computer's memory.
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