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added kerchunk as backend documentation (#9163)
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* added kerchunk as backend documentation

* Update io.rst

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* updated the io.rst file

* updated io.rst

* modified the combined.json file

* Apply suggestions from code review

* added new references

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* fixed some typos

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Justus Magin <[email protected]>
Co-authored-by: Deepak Cherian <[email protected]>
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1 change: 1 addition & 0 deletions ci/requirements/doc.yml
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Expand Up @@ -8,6 +8,7 @@ dependencies:
- bottleneck
- cartopy
- cfgrib
- kerchunk
- dask-core>=2022.1
- dask-expr
- hypothesis>=6.75.8
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30 changes: 30 additions & 0 deletions doc/combined.json
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{
"version": 1,
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}
53 changes: 53 additions & 0 deletions doc/user-guide/io.rst
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Expand Up @@ -1060,6 +1060,59 @@ reads. Because this fall-back option is so much slower, xarray issues a
instead of falling back to try reading non-consolidated metadata.


.. _io.kerchunk:

Kerchunk
--------

`Kerchunk <https://fsspec.github.io/kerchunk/index.html>`_ is a Python library
that allows you to access chunked and compressed data formats (such as NetCDF3, NetCDF4, HDF5, GRIB2, TIFF & FITS),
many of which are primary data formats for many data archives, by viewing the
whole archive as an ephemeral `Zarr`_ dataset which allows for parallel, chunk-specific access.

Instead of creating a new copy of the dataset in the Zarr spec/format or
downloading the files locally, Kerchunk reads through the data archive and extracts the
byte range and compression information of each chunk and saves as a ``reference``.
These references are then saved as ``json`` files or ``parquet`` (more efficient)
for later use. You can view some of these stored in the `references`
directory `here <https://github.com/pydata/xarray-data>`_.


.. note::
These references follow this `specification <https://fsspec.github.io/kerchunk/spec.html>`_.
Packages like `kerchunk`_ and `virtualizarr <https://github.com/zarr-developers/VirtualiZarr>`_
help in creating and reading these references.


Reading these data archives becomes really easy with ``kerchunk`` in combination
with ``xarray``, especially when these archives are large in size. A single combined
reference can refer to thousands of the original data files present in these archives.
You can view the whole dataset with from this `combined reference` using the above packages.

The following example shows opening a combined references generated from a ``.hdf`` file stored locally.

.. ipython:: python
storage_options = {
"target_protocol": "file",
}
# add the `remote_protocol` key in `storage_options` if you're accessing a file remotely
ds1 = xr.open_dataset(
"./combined.json",
engine="kerchunk",
storage_options=storage_options,
)
ds1
.. note::

You can refer to the `project pythia kerchunk cookbook <https://projectpythia.org/kerchunk-cookbook/README.html>`_
and the `pangeo guide on kerchunk <https://guide.cloudnativegeo.org/kerchunk/intro.html>`_ for more information.


.. _io.iris:

Iris
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