I cannot reproduce this on main. What version are you running

``` (xarray-tests) 17:55:11 [cgdm-caguas] {~/python/xarray/devel} ──────> python lazy-nbytes.py 8582842640 Filename: /Users/dcherian/work/python/xarray/devel/lazy-nbytes.py

Line # Mem usage Increment Occurrences Line Contents

 4    101.5 MiB    101.5 MiB           1   @profile
 5                                         def get_dataset_size() :
 6    175.9 MiB     74.4 MiB           1       dataset =     xa.open_dataset("test_1.nc")
 7    175.9 MiB      0.0 MiB           1       print(dataset.nbytes)

```

The BackendArray types define shape and dtype so we can calculate size without loading the data.

that's a numpy array with sparse data. What @TomNicholas was talking about is a array of type sparse.COO (from the sparse package).

And as far as I can tell, our wrapper class (which is the reason why you don't get the memory error on open) does not define nbytes, so at the moment there's no way to do that. You could try using dask, though, which does allow working with bigger-than-memory data.

Thanks for raising this @dabhicusp !

So why have that if block at line 396?

Because xarray can wrap many different type of numpy-like arrays, and for some of those types then the self.size * self.dtype.itemsize approach may not return the correct size. Think of a sparse matrix for example - its size in memory is designed to be much smaller than the size of the matrix would suggest. That's why in general we defer to the underlying array itself to tell us its size if it can (i.e. if it has a .nbytes attribute).

But you're not using an unusual type of array, you're just opening a netCDF file as a numpy array, in theory lazily. The memory usage you're seeing is not desired, so something weird must be happening in the .nbytes call. Going deeper into the stack at that point would be helpful.