Zarr Encoding Specification¶
In implementing support for the Zarr storage format, Xarray developers made some ad hoc choices about how to store NetCDF data in Zarr. Future versions of the Zarr spec will likely include a more formal convention for the storage of the NetCDF data model in Zarr; see Zarr spec repo for ongoing discussion.
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 decode_cf()
).
As a concrete example, here we write a tutorial dataset to Zarr and then re-open it directly with Zarr:
In [1]: import xarray as xr
In [2]: import zarr
In [3]: ds = xr.tutorial.load_dataset("rasm")
In [4]: ds.to_zarr("rasm.zarr", mode="w")
Out[4]: <xarray.backends.zarr.ZarrStore at 0x7f87c6d70a00>
In [5]: zgroup = zarr.open("rasm.zarr")
In [6]: print(zgroup.tree())
/
├── Tair (36, 205, 275) float64
├── time (36,) float64
├── xc (205, 275) float64
└── yc (205, 275) float64
In [7]: dict(zgroup["Tair"].attrs)
Out[7]:
{'_ARRAY_DIMENSIONS': ['time', 'y', 'x'],
'coordinates': 'yc xc',
'long_name': 'Surface air temperature',
'time_rep': 'instantaneous',
'type_preferred': 'double',
'units': 'C'}