issues: 484499801

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id node_id number title user state locked assignee milestone comments created_at updated_at closed_at author_association active_lock_reason draft pull_request body reactions performed_via_github_app state_reason repo type
484499801 MDExOlB1bGxSZXF1ZXN0MzEwMzYxOTMz 3250 __slots__ 6213168 closed 0     10 2019-08-23T12:16:44Z 2019-08-30T12:13:28Z 2019-08-29T17:14:20Z MEMBER   0 pydata/xarray/pulls/3250

What changes: - Most classes now define __slots__ - removed _initialized property - Enforced checks that all subclasses must also define __slots__. For third-party subclasses, this is for now a DeprecationWarning and should be changed into a hard crash later on. - 22% reduction in RAM usage - 5% performance speedup for a DataArray method that performs a _to_temp_dataset roundtrip

DISCUSS: support for third party subclasses is very poor at the moment (#1097). Should we skip the deprecation altogether?

Performance benchmark: ```python import timeit import psutil import xarray

a = xarray.DataArray([1, 2], dims=['x'], coords={'x': [10, 20]}) RUNS = 10000 t = timeit.timeit("a.roll(x=1, roll_coords=True)", globals=globals(), number=RUNS) print("{:.0f} us".format(t / RUNS * 1e6))

p = psutil.Process() N = 100000 rss0 = p.memory_info().rss x = [ xarray.DataArray([1, 2], dims=['x'], coords={'x': [10, 20]}) for _ in range(N) ] rss1 = p.memory_info().rss print("{:.0f} bytes".format((rss1 - rss0) / N)) ``` Output:

| test | env | master | slots | |:-------------:|:---:|:----------:| ----------:| | DataArray.roll | py35-min | 332 us | 360 us | | DataArray.roll | py37 | 354 us | 337 us | | RAM usage of a DataArray | py35-min | 2755 bytes | 2074 bytes | | RAM usage of a DataArray | py37 | 1970 bytes | 1532 bytes |

The performance degradation on Python 3.5 is caused by the deprecation mechanism - see changes to common.py.

I honestly never realised that xarray objects are measured in kilobytes (vs. 32 bytes of underlying buffers!)

 
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