Josh Rosenberg <[email protected]> added the comment:
Sorry, I think I need examples to grok this in the general case. ChainMap
unioned with dict makes sense to me (it's equivalent to update or
copy-and-update on the top level dict in the ChainMap). But ChainMap unioned
with another ChainMap is less clear. Could you give examples of what the
expected end result is for:
d1 = {'a': 1, 'b': 2}
d2 = {'b': 3, 'c': 4}
d3 = {'a': 5, 'd': 6}
d4 = {'d': 7, 'e': 8}
cm1 = ChainMap(d1, d2)
cm2 = ChainMap{d3, d4)
followed by either:
cm3 = cm1 | cm2
or
cm1 |= cm2
? As in, what is the precise state of the ChainMap cm3 or the mutated cm1,
referencing d1, d2, d3 and d4 when they are still incorporated by references in
the chain?
My impression from what you said is that the plan would be for the updated cm1
to preserve references to d1 and d2 only, with the contents of cm2 (d3 and d4)
effectively flattened and applied as an in-place update to d1, with an end
result equivalent to having done:
cm1 = ChainMap(d1, d2)
d1 |= d4
d1 |= d3
(except the key ordering would actually follow d3 first, and d4 second), while
cm3 would effectively be equivalent to having done (note ordering):
cm3 = ChainMap(d1 | d4 | d3, d2)
though again, key ordering would be based on d1, then d3, then d4, not quite
matching the union behavior. And a reference to d2 would be preserved in the
final result, but not any other original dict. Is that correct? If so, it seems
like it's wasting ChainMap's key feature (lazy accumulation of maps), where:
cm1 |= cm2
could be equivalent to either:
cm1.maps += cm2.maps
though that means cm1 wins overlaps, where normal union would have cm2 win, or
to hew closer to normal union behavior, make it equivalent to:
cm1.map[:0] = cm2.maps
prepending all of cm2's maps to have the same duplicate handling rules as
regular dicts (right side wins) at the expense of changing which map cm1 uses
as the target for writes and deletes. In either case it would hew to the spirit
of ChainMap, making dict "union"-ing an essentially free operation, in exchange
for increasing the costs of lookups that don't hit the top dict.
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Python tracker <[email protected]>
<https://bugs.python.org/issue36144>
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