Textually diffing JSON

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挽巷
挽巷 2020-12-13 15:52

As part of my release processes, I have to compare some JSON configuration data used by my application. As a first attempt, I just pretty-printed the JSON and diff\'ed them

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  • 2020-12-13 16:06

    Eclipse might do better. Open the two files in an eclipse project, select them both, and right click --> compare --> with each other.

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  • 2020-12-13 16:08

    If any of your tool has the option, Patience Diff could work a lot better for you. I'll try to find a tool with it (other tha Git and Bazaar) and report back.

    Edit: It seems that the implementation in Bazaar is usable as a standalone tool with minimal changes.

    Edit2: WTH, why not paste the source of the new cool diff script you made me hack? Here it is, no copyright claim on my side, it's just Bram/Canonical's code re-arranged.

    #!/usr/bin/env python
    # Copyright (C) 2005, 2006, 2007 Canonical Ltd
    # Copyright (C) 2005 Bram Cohen, Copyright (C) 2005, 2006 Canonical Ltd
    #
    # This program is free software; you can redistribute it and/or modify
    # it under the terms of the GNU General Public License as published by
    # the Free Software Foundation; either version 2 of the License, or
    # (at your option) any later version.
    #
    # This program is distributed in the hope that it will be useful,
    # but WITHOUT ANY WARRANTY; without even the implied warranty of
    # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
    # GNU General Public License for more details.
    #
    # You should have received a copy of the GNU General Public License
    # along with this program; if not, write to the Free Software
    # Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
    
    
    import os
    import sys
    import time
    import difflib
    from bisect import bisect
    
    __all__ = ['PatienceSequenceMatcher', 'unified_diff', 'unified_diff_files']
    
    py3k = False
    try:
        xrange
    except NameError:
        py3k = True
        xrange = range
    
    # This is a version of unified_diff which only adds a factory parameter
    # so that you can override the default SequenceMatcher
    # this has been submitted as a patch to python
    def unified_diff(a, b, fromfile='', tofile='', fromfiledate='',
                     tofiledate='', n=3, lineterm='\n',
                     sequencematcher=None):
        r"""
        Compare two sequences of lines; generate the delta as a unified diff.
    
        Unified diffs are a compact way of showing line changes and a few
        lines of context.  The number of context lines is set by 'n' which
        defaults to three.
    
        By default, the diff control lines (those with ---, +++, or @@) are
        created with a trailing newline.  This is helpful so that inputs
        created from file.readlines() result in diffs that are suitable for
        file.writelines() since both the inputs and outputs have trailing
        newlines.
    
        For inputs that do not have trailing newlines, set the lineterm
        argument to "" so that the output will be uniformly newline free.
    
        The unidiff format normally has a header for filenames and modification
        times.  Any or all of these may be specified using strings for
        'fromfile', 'tofile', 'fromfiledate', and 'tofiledate'.  The modification
        times are normally expressed in the format returned by time.ctime().
    
        Example:
    
        >>> for line in unified_diff('one two three four'.split(),
        ...             'zero one tree four'.split(), 'Original', 'Current',
        ...             'Sat Jan 26 23:30:50 1991', 'Fri Jun 06 10:20:52 2003',
        ...             lineterm=''):
        ...     print line
        --- Original Sat Jan 26 23:30:50 1991
        +++ Current Fri Jun 06 10:20:52 2003
        @@ -1,4 +1,4 @@
        +zero
         one
        -two
        -three
        +tree
         four
        """
        if sequencematcher is None:
            import difflib
            sequencematcher = difflib.SequenceMatcher
    
        if fromfiledate:
            fromfiledate = '\t' + str(fromfiledate)
        if tofiledate:
            tofiledate = '\t' + str(tofiledate)
    
        started = False
        for group in sequencematcher(None,a,b).get_grouped_opcodes(n):
            if not started:
                yield '--- %s%s%s' % (fromfile, fromfiledate, lineterm)
                yield '+++ %s%s%s' % (tofile, tofiledate, lineterm)
                started = True
            i1, i2, j1, j2 = group[0][3], group[-1][4], group[0][5], group[-1][6]
            yield "@@ -%d,%d +%d,%d @@%s" % (i1+1, i2-i1, j1+1, j2-j1, lineterm)
            for tag, i1, i2, j1, j2 in group:
                if tag == 'equal':
                    for line in a[i1:i2]:
                        yield ' ' + line
                    continue
                if tag == 'replace' or tag == 'delete':
                    for line in a[i1:i2]:
                        yield '-' + line
                if tag == 'replace' or tag == 'insert':
                    for line in b[j1:j2]:
                        yield '+' + line
    
    
    def unified_diff_files(a, b, sequencematcher=None):
        """Generate the diff for two files.
        """
        mode = 'rb'
        if py3k: mode = 'r'
        # Should this actually be an error?
        if a == b:
            return []
        if a == '-':
            file_a = sys.stdin
            time_a = time.time()
        else:
            file_a = open(a, mode)
            time_a = os.stat(a).st_mtime
    
        if b == '-':
            file_b = sys.stdin
            time_b = time.time()
        else:
            file_b = open(b, mode)
            time_b = os.stat(b).st_mtime
    
        # TODO: Include fromfiledate and tofiledate
        return unified_diff(file_a.readlines(), file_b.readlines(),
                            fromfile=a, tofile=b,
                            sequencematcher=sequencematcher)
    
    
    def unique_lcs_py(a, b):
        """Find the longest common subset for unique lines.
    
        :param a: An indexable object (such as string or list of strings)
        :param b: Another indexable object (such as string or list of strings)
        :return: A list of tuples, one for each line which is matched.
                [(line_in_a, line_in_b), ...]
    
        This only matches lines which are unique on both sides.
        This helps prevent common lines from over influencing match
        results.
        The longest common subset uses the Patience Sorting algorithm:
        http://en.wikipedia.org/wiki/Patience_sorting
        """
        # set index[line in a] = position of line in a unless
        # a is a duplicate, in which case it's set to None
        index = {}
        for i in xrange(len(a)):
            line = a[i]
            if line in index:
                index[line] = None
            else:
                index[line]= i
        # make btoa[i] = position of line i in a, unless
        # that line doesn't occur exactly once in both,
        # in which case it's set to None
        btoa = [None] * len(b)
        index2 = {}
        for pos, line in enumerate(b):
            next = index.get(line)
            if next is not None:
                if line in index2:
                    # unset the previous mapping, which we now know to
                    # be invalid because the line isn't unique
                    btoa[index2[line]] = None
                    del index[line]
                else:
                    index2[line] = pos
                    btoa[pos] = next
        # this is the Patience sorting algorithm
        # see http://en.wikipedia.org/wiki/Patience_sorting
        backpointers = [None] * len(b)
        stacks = []
        lasts = []
        k = 0
        for bpos, apos in enumerate(btoa):
            if apos is None:
                continue
            # as an optimization, check if the next line comes at the end,
            # because it usually does
            if stacks and stacks[-1] < apos:
                k = len(stacks)
            # as an optimization, check if the next line comes right after
            # the previous line, because usually it does
            elif stacks and stacks[k] < apos and (k == len(stacks) - 1 or
                                                  stacks[k+1] > apos):
                k += 1
            else:
                k = bisect(stacks, apos)
            if k > 0:
                backpointers[bpos] = lasts[k-1]
            if k < len(stacks):
                stacks[k] = apos
                lasts[k] = bpos
            else:
                stacks.append(apos)
                lasts.append(bpos)
        if len(lasts) == 0:
            return []
        result = []
        k = lasts[-1]
        while k is not None:
            result.append((btoa[k], k))
            k = backpointers[k]
        result.reverse()
        return result
    
    
    def recurse_matches_py(a, b, alo, blo, ahi, bhi, answer, maxrecursion):
        """Find all of the matching text in the lines of a and b.
    
        :param a: A sequence
        :param b: Another sequence
        :param alo: The start location of a to check, typically 0
        :param ahi: The start location of b to check, typically 0
        :param ahi: The maximum length of a to check, typically len(a)
        :param bhi: The maximum length of b to check, typically len(b)
        :param answer: The return array. Will be filled with tuples
                       indicating [(line_in_a, line_in_b)]
        :param maxrecursion: The maximum depth to recurse.
                             Must be a positive integer.
        :return: None, the return value is in the parameter answer, which
                 should be a list
    
        """
        if maxrecursion < 0:
            print('max recursion depth reached')
            # this will never happen normally, this check is to prevent DOS attacks
            return
        oldlength = len(answer)
        if alo == ahi or blo == bhi:
            return
        last_a_pos = alo-1
        last_b_pos = blo-1
        for apos, bpos in unique_lcs_py(a[alo:ahi], b[blo:bhi]):
            # recurse between lines which are unique in each file and match
            apos += alo
            bpos += blo
            # Most of the time, you will have a sequence of similar entries
            if last_a_pos+1 != apos or last_b_pos+1 != bpos:
                recurse_matches_py(a, b, last_a_pos+1, last_b_pos+1,
                    apos, bpos, answer, maxrecursion - 1)
            last_a_pos = apos
            last_b_pos = bpos
            answer.append((apos, bpos))
        if len(answer) > oldlength:
            # find matches between the last match and the end
            recurse_matches_py(a, b, last_a_pos+1, last_b_pos+1,
                               ahi, bhi, answer, maxrecursion - 1)
        elif a[alo] == b[blo]:
            # find matching lines at the very beginning
            while alo < ahi and blo < bhi and a[alo] == b[blo]:
                answer.append((alo, blo))
                alo += 1
                blo += 1
            recurse_matches_py(a, b, alo, blo,
                               ahi, bhi, answer, maxrecursion - 1)
        elif a[ahi - 1] == b[bhi - 1]:
            # find matching lines at the very end
            nahi = ahi - 1
            nbhi = bhi - 1
            while nahi > alo and nbhi > blo and a[nahi - 1] == b[nbhi - 1]:
                nahi -= 1
                nbhi -= 1
            recurse_matches_py(a, b, last_a_pos+1, last_b_pos+1,
                               nahi, nbhi, answer, maxrecursion - 1)
            for i in xrange(ahi - nahi):
                answer.append((nahi + i, nbhi + i))
    
    
    def _collapse_sequences(matches):
        """Find sequences of lines.
    
        Given a sequence of [(line_in_a, line_in_b),]
        find regions where they both increment at the same time
        """
        answer = []
        start_a = start_b = None
        length = 0
        for i_a, i_b in matches:
            if (start_a is not None
                and (i_a == start_a + length)
                and (i_b == start_b + length)):
                length += 1
            else:
                if start_a is not None:
                    answer.append((start_a, start_b, length))
                start_a = i_a
                start_b = i_b
                length = 1
    
        if length != 0:
            answer.append((start_a, start_b, length))
    
        return answer
    
    
    def _check_consistency(answer):
        # For consistency sake, make sure all matches are only increasing
        next_a = -1
        next_b = -1
        for (a, b, match_len) in answer:
            if a < next_a:
                raise ValueError('Non increasing matches for a')
            if b < next_b:
                raise ValueError('Non increasing matches for b')
            next_a = a + match_len
            next_b = b + match_len
    
    
    class PatienceSequenceMatcher_py(difflib.SequenceMatcher):
        """Compare a pair of sequences using longest common subset."""
    
        _do_check_consistency = True
    
        def __init__(self, isjunk=None, a='', b=''):
            if isjunk is not None:
                raise NotImplementedError('Currently we do not support'
                                          ' isjunk for sequence matching')
            difflib.SequenceMatcher.__init__(self, isjunk, a, b)
    
        def get_matching_blocks(self):
            """Return list of triples describing matching subsequences.
    
            Each triple is of the form (i, j, n), and means that
            a[i:i+n] == b[j:j+n].  The triples are monotonically increasing in
            i and in j.
    
            The last triple is a dummy, (len(a), len(b), 0), and is the only
            triple with n==0.
    
            >>> s = PatienceSequenceMatcher(None, "abxcd", "abcd")
            >>> s.get_matching_blocks()
            [(0, 0, 2), (3, 2, 2), (5, 4, 0)]
            """
            # jam 20060525 This is the python 2.4.1 difflib get_matching_blocks
            # implementation which uses __helper. 2.4.3 got rid of helper for
            # doing it inline with a queue.
            # We should consider doing the same for recurse_matches
    
            if self.matching_blocks is not None:
                return self.matching_blocks
    
            matches = []
            recurse_matches_py(self.a, self.b, 0, 0,
                               len(self.a), len(self.b), matches, 10)
            # Matches now has individual line pairs of
            # line A matches line B, at the given offsets
            self.matching_blocks = _collapse_sequences(matches)
            self.matching_blocks.append( (len(self.a), len(self.b), 0) )
            if PatienceSequenceMatcher_py._do_check_consistency:
                if __debug__:
                    _check_consistency(self.matching_blocks)
    
            return self.matching_blocks
    
    
    unique_lcs = unique_lcs_py
    recurse_matches = recurse_matches_py
    PatienceSequenceMatcher = PatienceSequenceMatcher_py
    
    
    def main(args):
        import optparse
        p = optparse.OptionParser(usage='%prog [options] file_a file_b'
                                        '\nFiles can be "-" to read from stdin')
        p.add_option('--patience', dest='matcher', action='store_const', const='patience',
                     default='patience', help='Use the patience difference algorithm')
        p.add_option('--difflib', dest='matcher', action='store_const', const='difflib',
                     default='patience', help='Use python\'s difflib algorithm')
    
        algorithms = {'patience':PatienceSequenceMatcher, 'difflib':difflib.SequenceMatcher}
    
        (opts, args) = p.parse_args(args)
        matcher = algorithms[opts.matcher]
    
        if len(args) != 2:
            print('You must supply 2 filenames to diff')
            return -1
    
        for line in unified_diff_files(args[0], args[1], sequencematcher=matcher):
            sys.stdout.write(line)
    
    
    if __name__ == '__main__':
        sys.exit(main(sys.argv[1:]))
    

    Edit 3: I've also made a minimally standalone version of Neil Fraser's Diff Match and Patch, I'd be very interested in a comparison of results for your use case. Again, I claim no copyrights.

    Edit 4: I just found DataDiff, which might be another tool to try.

    DataDiff is a library to provide human-readable diffs of python data structures. It can handle sequence types (lists, tuples, etc), sets, and dictionaries.

    Dictionaries and sequences will be diffed recursively, when applicable.

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  • 2020-12-13 16:11

    Beyond formatting changes, diffing tool should also order JSON object properties in a stable manner (alphabetically, for example), since the order of properties is semantically meaningless. That is, reordering of properties should not change the meaning of contents.

    Other than this, parsing and pretty-printing in a way that puts at most one entry on a single line might allow use of textual diff. If not, any diff algorithm that works on trees (which is used for xml diffing) should work better.

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  • 2020-12-13 16:12

    So, I wrote a tool to do unified diffs of JSON files a while ago that might be of some interest.

    https://github.com/jclulow/jsondiff

    Some examples of input and output for the tool appear on the github page.

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  • 2020-12-13 16:28

    You should checkout difflet from substack. It's both a node.js module and command-line utility that does exactly this:

    https://github.com/substack/difflet

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  • 2020-12-13 16:29

    I know this is a pretty old question, but the python module "JSON Tools" provides another solution for diffing json files:

    https://pypi.python.org/pypi/json_tools https://bitbucket.org/vadim_semenov/json_tools/src/75cc15381188c760badbd5b66aef9941a42c93fa?at=default

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