issue_comments

python %%prun -s cumulative for _ in range(10000): ds.isel(x=[0]) Output (uncached): ncalls tottime percall cumtime percall filename:lineno(function) 10000 0.148 0.000 3.317 0.000 dataset.py:1854(isel) [...] 60000 0.092 0.000 0.122 0.000 indexing.py:535(shape)

I prefer pandas.utils.cache_readonly to the Python 3.8 cachedproperty. Besides not needing a dict, it is also written in cython, which should make a substantial difference.

max-sixty could you post your benchmark where you measure 150us? I tried caching that property with @cache_readonly and I only get a boost of 7us.

```python import xarray ds = xarray.Dataset({'d': ('x', [1, 2]), 'x': [10, 20]}) ds ds.to_netcdf("foo.nc") ds.close() ds = xarray.open_dataset("foo.nc")

%timeit ds.isel(x=[0]) ` With@property: 187us With@cache_readonly``: 180us

@max-sixty afraid so. But as I said it should be straightforward to use a variant that instead of self.__dict__[self.name] uses getattr/setattr.

By reading the implementation of cachedproperty, it needs a __dict__. It should be straightforward to write a variant that uses slots though.

Is there a way of doing that which doesn't nullify the benefits of slots? i.e. if we add a _cache_dict to the class, is that the same overhead as having a __dict__?

By reading the implementation of cachedproperty, it needs a __dict__. It should be straightforward to write a variant that uses slots though.

Great, I didn't know about that, thanks

It does remind me, though, that I'm not sure it's possible given we're using __slots__, at least without adding an entry to __slots__... https://github.com/python/cpython/blob/d360346640e19231032b072216195484fa2450b4/Lib/functools.py#L954-L958

I think this would be totally fine to add. A variant on cache_readonly appears in Python 3.8 standard library as functools.cached_property. I think we could backport that pretty easily: https://github.com/python/cpython/blob/d360346640e19231032b072216195484fa2450b4/Lib/functools.py#L924