issue_comments

@dschwoerer are you sure that you are actually calculating the same thing in both cases? What exactly do the values of slc[d] look like? I would test thing on smaller inputs to verify. My guess is that you are inadvertently calculating something different, recalling that Xarray's broadcasting rules differ slightly from NumPy's.

Yes, align checks index.equals(other) first, which has a shortcut for the same object.

The real mystery here is why time_filter.indexes['time'] and ds.indexes['time'] are not the same object. I guess this is likely due to lazy initialization of indexes, and should be fixed eventually by the explicit indexes refactor.

https://github.com/pydata/xarray/pull/3319 gives us about a 2x performance boost. It could likely be much faster, but at least this fixes the regression.

Yes, I'm seeing similar numbers, about 10x slower indexing in a DataArray. This seems to have gotten slower over time. It would be good to track this down and add a benchmark!

@WeatherGod does adding something like da = da.chunk({'time': 1}) reproduce this with your example?

My measurements: ```

%timeit ds.a.isel(time=time_filter) 1 loop, best of 3: 906 ms per loop %timeit ds.a.isel(time=time_filter.values) 1 loop, best of 3: 447 ms per loop %timeit ds.a.values[time_filter] 10 loops, best of 3: 169 ms per loop ```

Given the size of this gap, I suspect this could be improved with some investigation and profiling, but there is certainly an upper-limit on the possible performance gain.

One simple example is that indexing the dataset needs to index both 'a' and 'time', so it's going to be at least twice as slow as only indexing 'a'. So the second indexing expression ds.a.isel(time=time_filter.values) is only 447/(169*2) = 1.32 times slower than the best case scenario.