saving on memory can cost both memory and performance
xref: https://github.com/pydata/pandas/pull/8676#issuecomment-66888559

**memory cost**:

with `int16` labels:

```
$ python -m memory_profiler mem-profile.py 
Filename: mem-profile.py

Line #    Mem usage    Increment   Line Contents
================================================
     4   80.156 MiB    0.000 MiB   @profile
     5                             def ix(obj):
     6   87.809 MiB    7.652 MiB       obj.ix[999]
```

with `int64` labels:

```
$ python -m memory_profiler mem-profile.py 
Filename: mem-profile.py

Line #    Mem usage    Increment   Line Contents
================================================
     4   79.387 MiB    0.000 MiB   @profile
     5                             def ix(obj):
     6   79.387 MiB    0.000 MiB       obj.ix[999]
```

**performance cost**:

```
-------------------------------------------------------------------------------
Test name                                    | head[ms] | base[ms] |  ratio   |
-------------------------------------------------------------------------------
frame_xs_mi_ix                               |   8.5303 |   0.6206 |  13.7452 |
series_xs_mi_ix                              |   8.0659 |   0.5600 |  14.4023 |
-------------------------------------------------------------------------------
Test name                                    | head[ms] | base[ms] |  ratio   |
-------------------------------------------------------------------------------

Ratio < 1.0 means the target commit is faster then the baseline.
Seed used: 1234

Target [c11e75c] : PERF: set multiindex labels with coerced dtype (GH8456)
Base   [6bbb39e] : Merge pull request #8675 from pydata/setitem
```

the `mem-profile.py` used for memory profiling:

```
@profile
def ix(obj):
    obj.ix[999]

if __name__ == '__main__':
    import numpy as np
    from pandas import MultiIndex, Series
    mi = MultiIndex.from_tuples([(x,y) for x in range(1000) for y in range(1000)])
    ts =  Series(np.random.randn(1000000), index=mi)
    ix(ts)
```
