Virtual Zarr Cookbook (Kerchunk and VirtualiZarr)

This Project Pythia Cookbook covers using the Kerchunk, VirtualiZarr, and Zarr-Python libraries to access archival data formats as if they were ARCO (Analysis-Ready-Cloud-Optimized) data.

Motivation

The Kerchunk library pioneered the access of chunked and compressed data formats (such as NetCDF3. HDF5, GRIB2, TIFF & FITS), many of which are the primary data formats for many data archives, as if they were in ARCO formats such as Zarr which allows for parallel, chunk-specific access. Instead of creating a new copy of the dataset in the Zarr spec/format, Kerchunk reads through the data archive and extracts the byte range and compression information of each chunk, then writes that information to a "virtual Zarr store" using a JSON or Parquet "reference file". The VirtualiZarr library provides a simple way to create these "virtual stores" using familiary xarray syntax. Lastly, the icechunk provides a new way to store and re-use these references.

These virtual Zarr stores can be re-used and read via Zarr and Xarray.

For more details on how this process works please see this page on the Kerchunk docs).

Authors

Raphael Hagen

Much of the content of this cookbook was inspired by Martin Durant, the creator of Kerchunk and the Kerchunk documentation.

Contributors

Structure

This cookbook is broken up into two sections, Foundations and Example Notebooks.

Section 1 - Foundations

In the Foundations section we will demonstrate how to use Kerchunk and VirtualiZarr to create reference files from single file sources, as well as to create multi-file virtual Zarr stores from collections of files.

Section 2 - Generating Virtual Zarr Stores

The notebooks in the Generating Virtual Zarr Stores section demonstrates how to use Kerchunk and VirtualiZarr to create datasets for all the supported file formats. These libraries currently support virtualizing NetCDF3, NetCDF4/HDF5, GRIB2, TIFF (including COG).

Section 3 - Using Virtual Zarr Stores

The Using Virtual Zarr Stores section contains notebooks demonstrating how to load existing references into Xarray, generating coordinates for GeoTiffs using xrefcoord, and plotting using Hvplot Datashader.

Running the Notebooks

You can either run the notebook using or on your local machine.

Running on Binder

The simplest way to interact with a Jupyter Notebook is through Binder, which enables the execution of a Jupyter Book in the cloud. The details of how this works are not important for now. All you need to know is how to launch a Pythia Cookbooks chapter via Binder. Simply navigate your mouse to the top right corner of the book chapter you are viewing and click on the rocket ship icon and be sure to select “launch Binder”. After a moment you should be presented with a notebook that you can interact with. You’ll be able to execute and even change the example programs. The code cells have no output at first, until you execute them by pressing {kbd}Shift+{kbd}Enter. Complete details on how to interact with a live Jupyter notebook are described in Getting Started with Jupyter.

Running on Your Own Machine

If you are interested in running this material locally on your computer, you will need to follow this workflow:

1. Install mambaforge/mamba

2. Clone the https://github.com/ProjectPythia/kerchunk-cookbook repository:

bash git clone https://github.com/ProjectPythia/kerchunk-cookbook.git

1. Move into the kerchunk-cookbook directory bash cd kerchunk-cookbook
2. Create and activate your conda environment from the environment.yml file. Note: In the environment.yml file, Kerchunk` is currently being installed from source as development is happening rapidly.

bash mamba env create -f environment.yml mamba activate kerchunk-cookbook

1. Move into the notebooks directory and start up Jupyterlab bash cd notebooks/ jupyter lab