Xarray-Beam is a Python library for building Apache Beam pipelines with Xarray datasets.

The project aims to facilitate data transformations and analysis on large-scale multi-dimensional labeled arrays, such as:

• Ad-hoc computation on Xarray data, by dividing a xarray.Dataset into many smaller pieces ("chunks").
• Adjusting array chunks, using the Rechunker algorithm.
• Ingesting large, multi-dimensional array datasets into an analysis-ready, cloud-optimized format, namely Zarr (see also Pangeo Forge).
• Calculating statistics (e.g., "climatology") across distributed datasets with arbitrary groups.

For more about our approach and how to get started, read the documentation!

Warning: Xarray-Beam is a sharp tool 🔪

Xarray-Beam is relatively new, and focused on expert users:

• We use it extensively at Google for processing large-scale weather datasets, but there is not yet a vibrant external community.
• It provides low-level abstractions that facilitate writing very large scale data pipelines (e.g., 100+ TB), but by design it requires explicitly thinking about how every operation is parallelized.

Xarray-Beam requires recent versions of immutabledict, Xarray, Dask, Rechunker, Zarr, and Apache Beam. For best performance when writing Zarr files, use Xarray 0.19.0 or later.

Xarray-Beam is an experiment that we are sharing with the outside world in the hope that it will be useful. It is not a supported Google product. We welcome feedback, bug reports and code contributions, but cannot guarantee they will be addressed.

See the "Contribution guidelines" for more.

Contributors:

• Stephan Hoyer
• Jason Hickey
• Cenk Gazen
• Alex Merose