Should we cache some small properties?

I was doing some profiling on isel, and see there are some properties that (I think) never change, but are called frequently. Should we cache these on their object?

Pandas uses cache_readonly for these cases.

Here’s a case: we call LazilyOuterIndexedArray.shape frequently when doing a simple indexing operation. Each call takes ~150µs. An attribute lookup on a python object takes ~50ns (i.e. 3000x faster). IIUC the result on that property should never change.

I don’t think this is the solution to performance issues, and there’s some additional complexity. Could they be easy & small wins, though?

[crusaderky]

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

[crusaderky]

%%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)