Kerchunk is a library that provides a unified way to represent a variety of chunked, compressed data formats (e.g. NetCDF/HDF5, GRIB2, TIFF, …), 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.
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
This library was created to solve the problem of reading existing scientific datatypes as efficiently as possible in the cloud. The amount of observed and simulated data is now too large to be handled effectively via download and local processing. In the cloud era, the answer is to move compute to the data in the Cloud, process in parallel, and access the data in situ, and only as much as needed to solve a problem or use case.
Datasets are stored in many different file formats, and often as collections of files. Many of these formats are designed for on-premises filesystems and are hard or inefficient to use seamlessly in the cloud.
For binary storage of array data, essentially all formats involve taking blocks of in-memory C buffers and encoding/compressing them to disc, with some additional metadata describing the details of that buffer plus any other attributes. This description can be applied to a very wide variety of data formats.
The primary purpose of
kerchunk is to find where these binary blocks are, and how to decode them,
so that blocks from one or more files can be arranged into aggregate datasets accessed via the
zarr library and the power of fsspec. To understand how this works, please read
Detailed description. Or consider the PyData talk: All You Need Is Zarr.
- Quick Start
- Detailed description
- Case studies
- References specification
- Beyond Python
- Non-zarr uses
- API Reference
- Contributing to kerchunk
- Advanced Topics