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Fix distributed.Client.compute applied to DataArray #3173

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Aug 1, 2019
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Fix distributed.Client.compute applied to DataArray #3173

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Fix distributed.Client.compute applied to DataArray
Aug 1, 2019
5299aa7
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Aug 1, 2019
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5 changes: 4 additions & 1 deletion doc/whats-new.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -55,8 +55,11 @@ Bug fixes
due to a ``datetime`` issue in NumPy (:issue:`2334`).
By `Graham Inggs <https://github.com/ginggs>`_.
- Fixed bug in ``combine_by_coords()`` causing a `ValueError` if the input had
an unused dimension with coordinates which were not monotonic (:issue`3150`).
an unused dimension with coordinates which were not monotonic (:issue:`3150`).
By `Tom Nicholas <http://github.com/TomNicholas>`_.
- Fixed crash when applying ``distributed.Client.compute()`` to a DataArray
(:issue:`3171`). By `Guido Imperiale <https://github.com/crusaderky>`_.


.. _whats-new.0.12.3:

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6 changes: 3 additions & 3 deletions xarray/core/dataarray.py
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Original file line number Diff line number Diff line change
Expand Up @@ -300,7 +300,7 @@ def _replace(
self,
variable: Variable = None,
coords=None,
name: Union[Hashable, None, ReprObject] = __default,
name: Optional[Hashable] = __default,
) -> 'DataArray':
if variable is None:
variable = self.variable
Expand All @@ -313,7 +313,7 @@ def _replace(
def _replace_maybe_drop_dims(
self,
variable: Variable,
name: Union[str, None, utils.ReprObject] = __default
name: Optional[Hashable] = __default
) -> 'DataArray':
if variable.dims == self.dims and variable.shape == self.shape:
coords = self._coords.copy()
Expand Down Expand Up @@ -356,7 +356,7 @@ def _to_temp_dataset(self) -> Dataset:
def _from_temp_dataset(
self,
dataset: Dataset,
name: Union[Hashable, ReprObject] = __default
name: Hashable = __default
) -> 'DataArray':
variable = dataset._variables.pop(_THIS_ARRAY)
coords = dataset._variables
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10 changes: 10 additions & 0 deletions xarray/core/utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -462,12 +462,22 @@ def __repr__(self: Any) -> str:
class ReprObject:
"""Object that prints as the given value, for use with sentinel values.
"""
__slots__ = ('_value', )

def __init__(self, value: str):
self._value = value

def __repr__(self) -> str:
return self._value

def __eq__(self, other) -> bool:
if isinstance(other, ReprObject):
return self._value == other._value
return False

def __hash__(self) -> int:
return hash((ReprObject, self._value))
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I don't think this will be stable across processes - I just tried this in two different processes:

In [5]: hash(ReprObject)
Out[5]: 8774331846827

(new process)


In [2]: hash(ReprObject)
Out[2]: 8795055479397

Does that matter here? If so, could we change to hash(('ReprObject', self._value))?

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Ah, the hash is never stable across processes, I just tried the same for 'hello'.

Does dask rely on the hash, or an equality (or something else)?

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I think it's here: --> 708 variable = ds._variables.pop(_THIS_ARRAY) from #3171

So then both, so this looks good - doesn't need to be consistent across processes

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xarray (actually not dask) relies on the fact that a deep copy of a key object that has gone through a round-trip on the network produces the same hash as the original. The fact that the hash changes (by design; it's an anti-DoS measure) every time you restart the interpteter is irrelevant because the __hash__ method is always invoked locally. It would be, on the other hand, a grave mistake to cache it and then allow the cache to be serialised.

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@crusaderky crusaderky Aug 1, 2019 •
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To elaborate:

BAD - the hash defaults to the id, which will break as soon as the object is shallow-copied

class C:
    pass

GOOD:

class C:
    def __hash__(self):
        return 123  # skip: actual calculation

BAD - it works as long as you do everything locally, but it will break as soon as you pickle the object and unpickle it in a different interpreter. Worth noting that a typical unit test c2 = pickle.loads(pickle.dumps(c)) will NOT spot the issue as the pickling and unpickling happen in the same interpreter.

class C:
    _hash_cache = None

    def __hash__(self):
        if self._hash_cache is None:
            self._hash_cache = 123  # skip: actual, CPU-intensive, calculation
        return self._hash_cache

GOOD AGAIN:

class C:
    _hash_cache = None

    def __hash__(self):
        if self._hash_cache is None:
            self._hash_cache = 123  # skip: actual, CPU-intensive, calculation
        return self._hash_cache

    def __getstate__(self):
        state = self.__dict__.copy()
        state.pop("_hash_cache", None)
        return state

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Thanks for the explanation! Out of interest, why is this the case:

BAD - it works as long as you do everything locally, but it will break as soon as you pickle the object and unpickle it in a different interpreter.

...assuming...

The fact that the hash changes every time you restart the interpteter is irrelevant because the hash method is always invoked locally.

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I'm a little surprised that these default values end up in pickles, but I guess it can happen.

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I'm doubtful the default values would - an attribute on the class wouldn't be in the instance dict until it's assigned to



@contextlib.contextmanager
def close_on_error(f):
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16 changes: 16 additions & 0 deletions xarray/tests/test_utils.py
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Original file line number Diff line number Diff line change
@@ -1,5 +1,6 @@
from collections import OrderedDict
from datetime import datetime
from typing import Hashable

import numpy as np
import pandas as pd
Expand Down Expand Up @@ -179,6 +180,21 @@ def test_sorted_keys_dict(self):
def test_repr_object():
obj = utils.ReprObject('foo')
assert repr(obj) == 'foo'
assert isinstance(obj, Hashable)
assert not isinstance(obj, str)


def test_repr_object_magic_methods():
o1 = utils.ReprObject('foo')
o2 = utils.ReprObject('foo')
o3 = utils.ReprObject('bar')
o4 = 'foo'
assert o1 == o2
assert o1 != o3
assert o1 != o4
assert hash(o1) == hash(o2)
assert hash(o1) != hash(o3)
assert hash(o1) != hash(o4)


def test_is_remote_uri():
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