issue_comments: 1517649648

The first point that you mentioned does not seem to be correct. Please see the below code (we took the sparse matrix ) and output: ``` import xarray as xa import numpy as np

def get_data(): lat_dim = 7210 lon_dim = 7440

lat = [0] * lat_dim
lon = [0] * lon_dim 
time = [0] * 5

nlats = lat_dim; nlons = lon_dim; ntimes = 5

var_1 = np.empty((ntimes,nlats,nlons))
var_2 = np.empty((ntimes,nlats,nlons))
var_3 = np.empty((ntimes,nlats,nlons))
var_4 = np.empty((ntimes,nlats,nlons))

data_arr = np.random.uniform(low=0,high=0,size=(ntimes,nlats,nlons))
data_arr[:,0,:] = 1
data_arr[:,:,1] = 1

var_1[:,:,:] = data_arr 
var_2[:,:,:] = data_arr 
var_3[:,:,:] = data_arr 
var_4[:,:,:] = data_arr

dataset = xa.Dataset( 
        data_vars = {
            'var_1': (('time','lat','lon'), var_1),
            'var_2': (('time','lat','lon'), var_2),
            'var_3': (('time','lat','lon'), var_3),
            'var_4': (('time','lat','lon'), var_4)},
        coords = {
            'lat': lat,
            'lon': lon,
            'time':time})

print(sum(v.size * v.dtype.itemsize for v in dataset.variables.values()))
print(dataset.nbytes)

if name == "main": get_data() ```

8582901240 8582901240 As we can observe here both nbytes and self.size * self.dtype.itemsize gives the same size.

And for the 2nd point can you share any solution for the nbytes for the netCDF or grib file as it takes too much memory and killed the process?