Is there a way to filter a django queryset based on string similarity (a la python difflib)?

Question

I have a need to match cold leads against a database of our clients.

The leads come from a third party provider in bulk (thousands of records) and sales is asking us to (in their words) "filter out our clients" so they don't try to sell our service to a established client.

Obviously, there are misspellings in the leads. Charles becomes Charlie, Joseph becomes Joe, etc. So I can't really just do a filter comparing lead_first_name to client_first_name, etc.

I need to use some sort of string similarity mechanism.

Right now I'm using the lovely difflib to compare the leads' first and last names to a list generated with Client.objects.all(). It works, but because of the number of clients it tends to be slow.

I know that most sql databases have soundex and difference functions. See my test of it in the update below - it doesn't work as well as difflib.

Is there another solution? Is there a better solution?

Edit:

Soundex, at least in my db, doesn't behave as well as difflib.

Here is a simple test - look for "Joe Lopes" in a table containing "Joseph Lopes":

with temp (first_name, last_name) as (
select 'Joseph', 'Lopes'
union
select 'Joe', 'Satriani'
union
select 'CZ', 'Lopes'
union
select 'Blah', 'Lopes'
union
select 'Antonio', 'Lopes'
union
select 'Carlos', 'Lopes'
)
select first_name, last_name
  from temp
 where difference(first_name+' '+last_name, 'Joe Lopes') >= 3
 order by difference(first_name+' '+last_name, 'Joe Lopes')

The above returns "Joe Satriani" as the only match. Even reducing the similarity threshold to 2 doesn't return "Joseph Lopes" as a potential match.

But difflib does a much better job:

difflib.get_close_matches('Joe Lopes', ['Joseph Lopes', 'Joe Satriani', 'CZ Lopes', 'Blah Lopes', 'Antonio Lopes', 'Carlos Lopes'])
['Joseph Lopes', 'CZ Lopes', 'Carlos Lopes']

Edit after gruszczy's response:

Before writing my own, I looked for and found a T-SQL implementation of Levenshtein Distance in the repository of all knowledge.

In testing it, it still won't do a better matching job than difflib.

Which led me to research what algorithm is behind difflib. It seems to be a modified version of the Ratcliff-Obershelp algorithm.

Unhappily I can't seem to find some other kind soul who has already created a T-SQL implementation based on difflib's... I'll try my hand at it when I can.

If nobody else comes up with a better answer in the next few days, I'll grant it to gruszczy. Thanks, kind sir.

Answer 1

soundex won't help you, because it's a phonetic algorithm. Joe and Joseph aren't similar phonetically, so soundex won't mark them as similar.

You can try Levenshtein distance, which is implemented in PostgreSQL. Maybe in your database too and if not, you should be able to write a stored procedure, which will calculate the distance between two strings and use it in your computation.