Slow performance of isel

Hi,

I get a very slow performance of Dataset.isel or DataArray.isel in comparison with the native numpy approach. Do you know where this comes from?

ds = xr.Dataset(
    {
        "a": ("time", np.arange(55_000_000))
    }, coords={
        "time": np.arange(55_000_000)
    }
)
time_filter = ds.time > 50_000

Select some values with DataArray.isel:

%timeit ds.a.isel(time=time_filter)

2.22 s ± 375 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

Use the native numpy approach:

%timeit ds.a.values[time_filter]

163 ms ± 12.1 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

Reproduce the comparison between Dataset.isel or DataArray.isel and the native ds.a.values[time_filter] approach using the provided 55-million-element example. Trace the isel entry point and benchmark the relevant indexing path; done means the source of the slowdown is identified and a measurable performance improvement is demonstrated.