kerchunk's Introduction

kerchunk

Cloud-friendly access to archival data

Kerchunk is a library that provides a unified way to represent a variety of chunked, compressed data formats (e.g. NetCDF, HDF5, GRIB), allowing efficient access to the data from traditional file systems or cloud object storage. It also provides a flexible way to create virtual datasets from multiple files. It does this by extracting the byte ranges, compression information and other information about the data and storing this metadata in a new, separate object. This means that you can create a virtual aggregate dataset over potentially many source files, for efficient, parallel and cloud-friendly in-situ access without having to copy or translate the originals. It is a gateway to in-the-cloud massive data processing while the data providers still insist on using legacy formats for archival storage.

Why Kerchunk:

We provide the following things:

completely serverless architecture
metadata consolidation, so you can understand a many-file dataset (metadata plus physical storage) in a single read
read from all of the storage backends supported by fsspec, including object storage (s3, gcs, abfs, alibaba), http, cloud user storage (dropbox, gdrive) and network protocols (ftp, ssh, hdfs, smb...)
loading of various file types (currently netcdf4/HDF, grib2, tiff, fits, zarr), potentially heterogeneous within a single dataset, without a need to go via the specific driver (e.g., no need for h5py)
asynchronous concurrent fetch of many data chunks in one go, amortizing the cost of latency
parallel access with a library like zarr without any locks
logical datasets viewing many (>~millions) data files, and direct access/subselection to them via coordinate indexing across an arbitrary number of dimensions