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Returns a list of commands/delta to go from one tree of objects to another

Project description

Take 2 sets of containers and provide a (deep) delta between them

This module is used by the author to diff yaml files and on disk file trees and best work out how to transition from one state to another (mainly for work with containers).


At the time this module was started, no modules existed that had this functionality. After poking the difflib module there was code that could be reused to achieve part of what i wanted. functionality above and beyond this was trivial to implement (diffing unsorted items)

There are a number of libraries out there that provide diffing functionality however a quick review of these has indicated they are mainly for display output and do not provide an easy way for a program to diff 2 things and then react based on the results (ie they require you to split lots of strings to get at the information you need)


Objdiff uses difflib built into python for lists and tuples (basically sorted things) and implements its own comparison code for dictionaries. User types are detected via the type and instance comparison and are treated as dictionaries (ie unsorted item => value mappings)

What does this look like?

>>> import objdiff
>>> a = {'a': 1, 'b':[1,2,3], 'c':None}
>>> b = {'a': 1, 'b':[1,4], 'c':'hello'}
>>> objdiff.obj_diff(a, b)
<generator object obj_diff at 0xb6a3da80>

We return an iterator and make use of yield from so you can process large trees of objects efficiently and incremental

>>> from pprint import pprint
>>> pprint(list(objdiff.obj_diff(a, b)))
[modified(path=['c'], old=None, new='hello'),
 modified(path=['b'], old=[1, 2, 3], new=[1, 4]),
 equal(path=['a'], old=1, new=1)]

Expanding out the generator we get back a bunch of tuples containing the command value, key path and before and after value

>>> c = {'a':{1: None, 2: 2, 3: 3}, 'b': None}
>>> d = {'a':{1: 1, 2: 2}, 'b': {'1':{}, '2':{'2':2}}}
>>> pprint(list(objdiff.obj_diff(c, d)))
[modified(path=['b'], old=None, new={'1': {}, '2': {'2': 2}}),
 modified(path=['a', 1], old=None, new=1),
 equal(path=['a', 2], old=2, new=2),
 deleted(path=['a', 3], val=3)]

Note in the above how you get a full list of keys to the destined object after the command value.

In total there are 4 types of command, as listed below with one internal type that can be ignored.

  • added
  • deleted
  • modified
  • equal (internal, scalar values are equal)


Path navigation is one that comes up a lot when given a list of keys. to quickly and efficiently get a value given a path try using the following pattern

>>> from functools import reduce
>>> from operator import getitem
>>> path = ['a', 1]
>>> d = {'a':{1: 1, 2: 2}, 'b': {'1':{}, '2':{'2':2}}}
>>> reduce(getitem, path, d)

If you have 2 dictionaries where one has to be applied over the top of another. eg one is a base config and the other is a profile that updates that base config. Then this library may be of use.

The Naive way of updating the old values based on the new values does not work for deeply nested data, ie

>>> base = {'a': {1:1, 2:2, 3:3}, 'b': None}
>>> updates = {'a': {1:None}}
>>> base.update(updates)
>>> base
{'a': {1: None}, 'b': None}

In the above we only wanted to update obj[‘a’][1] to be None, however the other keys where overwritten. with objdiff we can instead get the paths of the updated objects and update in a ‘deep’ fashion (deep in the sense that the copy module defines it, ie recursively).

>>> base = {'a': {1:1, 2:2, 3:3}, 'b': None}
>>> updates = {'a': {1:None}}
>>> commands = objdiff.obj_diff(base, updates)
>>> pprint(list(commands))
[modified(path=['a', 1], old=1, new=None),
 deleted(path=['a', 2], val=2),
 deleted(path=['a', 3], val=3),
 deleted(path=['b'], val=None)]
>>> for cmd in commands:
...     if isinstance(cmd, objdiff.modified):
...         ptr = base
...         # we need the last key for making the update
...         for key in cmd.path[:-1]:
...             ptr = ptr[key]
...         ptr[cmd.path[-1]] =
>>> base
{'a': {1:None, 2:2, 3:3}, 'b': None}

In the above you can see we ignore all commands except add and modify, deleted is discarded as the updated dictionary is only a subset of the original data and has holes in it to be filled by the base data structure.

There we have it, a structure that has been updated deeply with a replacement strategy of ‘replace all’, with a bit of tuning strategies such as ‘append to list’ are possible

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