I want to store an array with 1364688000 boolean values in zarr. I will have to read this array many times, so I am trying to do it as efficiently as possible.

I have noticed that, if we try to write boolean data to zarr from xarray, zarr stores it as i8. This means we are using 8x more memory than we actually need.
In researching this, I actually learned that numpy bools use a full byte of memory 😲!
However, we could still improve performance (albeit very marginally) by skipping the unnecessary dtype encoding that happens here.

Example

import xarray as xr
import zarr
for dtype in ['f8', 'i4', 'bool']:
    ds = xr.DataArray([1, 0]).astype(dtype).to_dataset('foo')
    store = {}
    ds.to_zarr(store)
    za = zarr.open(store)['foo']
    print(dtype, za.dtype, za.attrs.get('dtype'))

gives

f8 float64 None
i4 int32 None
bool int8 bool

So it seems like, during serialization of bool data, xarray is converting the data to int8 and then adding a {'dtype': 'bool'} to the attributes as encoding. When the data is read back, this gets decoded and the data is coerced back to bool.

Problem description

Since zarr is fully capable of storing bool data directly, we should not need to encode the data as i8.

I think this happens in encode_cf_variable:

which calls maybe_encode_bools:

So maybe we make the boolean encoding optional?

Output of xr.show_versions()