Package: python3-kerchunk (0.2.9-2)
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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.
Features:
* 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
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Download python3-kerchunk
| Architecture | Package Size | Installed Size | Files |
|---|---|---|---|
| all | 456.1 kB | 3,972.0 kB | [list of files] |
