pcodec VS gdal

Context: I've been iterating on my side project pcodec (a codec for columns of numerical data) and have gradually improved decompression speed from ~150MB/s to ~1GB/s. Not everything here is novel or Rust-specific, but here's what I've learned in the process:

For context: I'm creating an experimental successor to my library Quantile Compression, which does good compression for numerical sequences and has several users. I have a variable number of symbols which may be as high as 212 in some cases, but is ~26 in most cases. The data is typically 216 to 224 tokens long.

I'm not a member, but you can use the CLI to try it out pretty easily: https://github.com/mwlon/quantile-compression/tree/main/q_compress_cli . Let me know how it does

You can try it out very easily with the CLI which works on CSV and Parquet columns now, e.g. cargo run --release compress --csv my.csv --col-name my_column out.qco

Right, please don't try to use it for general files. It looks like zpaq is kinda hard to set up except on windows, so I'm probably not going to, but I encourage you to try it out! There's an example you can use to generate a bunch of random numerical distributions, outputting binary files, .qco, and other formats.

Here's how you can generate benchmark data, including binary files: https://github.com/mwlon/quantile-compression/blob/main/q_compress/examples/primary.md

I made a simple CLI for compressing and inspecting .qco files. Not available on package managers yet, but it's still pretty easy to try out: https://github.com/mwlon/quantile-compression/blob/main/CLI.md

TiTiler is a dynamic tile server built on FastAPI and Rasterio/GDAL. Its main features include support for Cloud Optimized GeoTIFF(COG), multiple projection methods, various output formats (JPEG, JP2, PNG, WEBP, GTIFF, NumpyTile), WMTS, and virtual mosaic. It also provides Lambda and ECS deployment environments using AWS CDK.

GDAL is the real heart, the python aspect is mostly wrappers around that I'm fairly sure. I love python for the record, the only reason I bring it up, is cause python haters accuse it of being slow, but QGIS drops down to C++ when speed is necessary, like most modern packages do.

gdal 0.15 (repo, docs), a set of Rust bindings for the GDAL library, used for access to geo-spatial data formats, is now out!

GDAL (Geospatial Data Abstraction Library) is a the geospatial data processing library. It handles a lot of Raster/Vector analysis and alteration. gdal_contour and gdal_rasterize which would be used to create isolines (contour lines) . There's more complex processing and analyses than that. More common is reprojecting multiple layers, and some that being as Vector files, into different coordiatne systems.

Access: GDAL

As an example we use GDAL heavily through its C# binds. We do all the additional data processing, that isn't done by the C++ GDAL, in C#. The final results are both Data (held in memory or temp exported to a file), and a normalized Raster Texture that we can display on a TextureRect. Most of the C# data processing scripts aren't even Inheriting from any Godot Class.

I think this would be the best way to handle this. GDAL is what you should look into for this project.