Fastest way to compare and replace key value pairs in Python

According to http://pravin.paratey.com/posts/super-quick-find-replace, regex is the fastest way to go for Python. (Building a Trie data structure would be fastest for C++) :

import sys, re, time, hashlib

class Regex:

    # Regex implementation of find/replace for a massive word list.

    def __init__(self, mappings):
        self._mappings = mappings

    def replace_func(self, matchObj):
        key = matchObj.group(0)
        if self._mappings.has_key(key):
            return self._mappings[key]
        else:
            return key

    def replace_all(self, filename):
        text = ''
        with open(filename, 'r+') as fp
            text = fp.read()
        text = re.sub("[a-zA-Z]+", self.replace_func, text)
        fp = with open(filename, "w") as fp:
            fp.write(text)

# mapping dictionary of (find, replace) tuples defined 
mappings = {'original-1': 'replace-1', 'original-2': 'replace-2'}

# initialize regex class with mapping tuple dictionary
r = Regex(mappings)

# replace file
r.replace_all( 'file' )

The slow part of this is the searching, not the replacing. (Even if I'm wrong, you can easily speed up the replacing part by first searching for all the indices, then splitting and replacing from the end; it's only the searching part that needs to be clever.)

Any naive mass string search algorithm is obviously going to be O(NM) for an N-length string and M substrings (and maybe even worse, if the substrings are long enough to matter). An algorithm that searched M times at each position, instead of M times over the whole string, might be offer some cache/paging benefits, but it'll be a lot more complicated for probably only a small benefit.

So, you're not going to do much better than cjrh's implementation if you stick with a naive algorithm. (You could try compiling it as Cython or running it in PyPy to see if it helps, but I doubt it'll help much—as he explains, all the inner loops are already in C.)

The way to speed it up is to somehow look for many substrings at a time. The standard way to do that is to build a prefix tree (or suffix tree), so, e.g, "original-1" and "original-2" are both branches off the same subtree "original-", so they don't need to be handled separately until the very last character.

The standard implementation of a prefix tree is a trie. However, as Efficient String Matching: An Aid to Bibliographic Search and the Wikipedia article Aho-Corasick string matching algorithm explain, you can optimize further for this use case by using a custom data structure with extra links for fallbacks. (IIRC, this improves the average case by logM.)

Aho and Corasick further optimize things by compiling a finite state machine out of the fallback trie, which isn't appropriate to every problem, but sounds like it would be for yours. (You're reusing the same mappings dict 50 times.)

There are a number of variant algorithms with additional benefits, so it might be worth a bit of further research. (Common use cases are things like virus scanners and package filters, which might help your search.) But I think Aho-Corasick, or even just a plain trie, is probably good enough.

Building any of these structures in pure Python might add so much overhead that, at M~60000, the extra cost will defeat the M/logM algorithmic improvement. But fortunately, you don't have to. There are many C-optimized trie implementations, and at least one Aho-Corasick implementation, on PyPI. It also might be worth looking at something like SuffixTree instead of using a generic trie library upside-down if you think suffix matching will work better with your data.

Unfortunately, without your data set, it's hard for anyone else to do a useful performance test. If you want, I can write test code that uses a few different modules, that you can then run against you data. But here's a simple example using ahocorasick for the search and a dumb replace-from-the-end implementation for the replace:

tree = ahocorasick.KeywordTree()
for key in mappings:
    tree.add(key)
tree.make()    
for start, end in reversed(list(tree.findall(target))):
    target = target[:start] + mappings[target[start:end]] + target[end:]

This use a with block to prevent leaking file descriptors. The string replace function will ensure all instances of key get replaced within the text.

mappings = {'original-1': 'replace-1', 'original-2': 'replace-2'}

# Open file for substitution
with open('file', 'r+') as fd:

    # read in all the data
    text = fd.read()

    # seek to the start of the file and truncate so file will be edited inline
    fd.seek(0)
    fd.truncate()

    for key in mappings.keys():
        text = text.replace(key, mappings[key])

    fd.write(text)