Document Xarray zarr encoding conventions

doc/internals.rst

@@ -138,3 +138,49 @@ To help users keep things straight, please `let us know
 <https://github.com/pydata/xarray/issues>`_ if you plan to write a new accessor
 for an open source library. In the future, we will maintain a list of accessors
 and the libraries that implement them on this page.
+
+.. _zarr_encoding:
+
+Zarr Encoding Specification
+---------------------------
+
+In implementing support for the `Zarr <https://zarr.readthedocs.io/>`_ storage
+format, Xarray developers made some *ad hoc* choices about how to store
+NetCDF data in Zarr.
+
+First, Xarray can only read and write Zarr groups. There is currently no support
+for reading / writting individual Zarr arrays. Zarr groups are mapped to
+Xarray ``Dataset`` objects.
+
+Second, from Xarray's point of view, the key difference between
+NetCDF and Zarr is that all NetCDF arrays have *dimension names* while Zarr
+arrays do not. Therefore, in order to store NetCDF data in Zarr, Xarray must
+somehow encode and decode the name of each array's dimensions.
+
+To accomplish this, Xarray developers decided to define a special Zarr array
+attribute: ``_ARRAY_DIMENSIONS``. The value of this attribute is a list of
+dimension names (strings), for example ``["time", "lon", "lat"]``. When writing
+data to Zarr, Xarray sets this attribute on all variables based on the variable
+dimensions. When reading a Zarr group, Xarray looks for this attribute on all
+arrays, raising an error if it can't be found. The attribute is used to define
+the variable dimension names and then removed from the attributes dictionary
+returned to the user.
+
+Because of these choices, Xarray cannot read arbitrary array data, but only
+Zarr data with valid ``_ARRAY_DIMENSIONS`` attributes on each array.
+
+After decoding the ``_ARRAY_DIMENSIONS`` attribute and assigning the variable
+dimensions, Xarray proceeds to [optionally] decode each variable using its
+standard CF decoding machinery used for NetCDF data (see :py:func:`decode_cf`).
+
+As a concrete example, here we write a tutorial dataset to Zarr and then
+re-open it directly with Zarr
+
+.. ipython:: python
+
+    ds = xr.tutorial.load_dataset('rasm')
+    ds.to_zarr('rasm.zarr', mode='w')
+    import zarr
+    zgroup = zarr.open('rasm.zarr')
+    print(zgroup.tree())
+    dict(zgroup['Tair'].attrs)

doc/io.rst

@@ -463,7 +463,7 @@ This is not CF-compliant but again facilitates roundtripping of xarray datasets.
 Invalid netCDF files
 ~~~~~~~~~~~~~~~~~~~~
 
-The library ``h5netcdf`` allows writing some dtypes (booleans, complex, ...) that aren't 
+The library ``h5netcdf`` allows writing some dtypes (booleans, complex, ...) that aren't
 allowed in netCDF4 (see
 `h5netcdf documentation <https://github.com/shoyer/h5netcdf#invalid-netcdf-files>`_).
 This feature is availabe through :py:meth:`DataArray.to_netcdf` and
@@ -837,7 +837,9 @@ Xarray's Zarr backend allows xarray to leverage these capabilities.
 Xarray can't open just any zarr dataset, because xarray requires special
 metadata (attributes) describing the dataset dimensions and coordinates.
 At this time, xarray can only open zarr datasets that have been written by
-xarray. To write a dataset with zarr, we use the :py:attr:`Dataset.to_zarr` method.
+xarray. For implementation details, see :ref:`zarr_encoding`.
+
+To write a dataset with zarr, we use the :py:attr:`Dataset.to_zarr` method.
 To write to a local directory, we pass a path to a directory
 
 .. ipython:: python