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Posted by / 01-Jan-2020 02:07

Dating database example

Profile related information can go in its own table.

This will be an infinitely expandable table with a key = val nature.

That way you can make informed decisions about adapting the schema to resolve specific performance problems, rather than guessing. This will significantly reduce the amount of storage, code complexity, and the effort to changing the system to add new attributes.

Assuming that each attribute can be represented by an Ordinal number, and that you're only looking for symmetrical matches (i.e.

My indexes are now greatly smaller, and searches are easier. Either having lookup tables for attribute values or have them What you could do is split the user data accross two tables.

In general, you shouldn't sacrifice database integrity for performance.

Below is part of a slightly edited great dating profile example written by a woman.

I found this profile to stand out from most others, making the writer sound interesting and attractive as a person, rather than ordinary and cliche, like the vast majority of dating profiles seem to appear.

Effectively you are looking for nodes within the same proximity in a N-dimensional space, unfortunately most relational databases aren't really setup for this kind of operation (I believe Postgre SQL has support for this).

So most people would probably start with something like: $current_user AND candidate.id=candidate_attrs.user_id AND candidate_attrs.attr_type=current_user.attr_type AND candidate_attrs.attr_value=current_user.attr_value GROUP BY ORDER BY COUNT(*) DESC; $current_user AND candidate.id=candidate_attrs.user_id AND candidate_attrs.attr_type=current_user.attr_type AND candidate_attrs.attr_value BETWEEN current_user.attr_value $tolerance AND current_user.attr_value-$tolerance GROUP BY ORDER BY COUNT(*) DESC; SELECT candidate.id, SUM(1/1 ((candidate_attrs.attr_value - current_user.attr_value) *(candidate_attrs.attr_value - current_user.attr_value)) ) as match_score FROM users candidate, attributes candidate_attrs, attributes current_user_attrs WHERE current_user_attrs.user_id=$current_user AND candidate.user_id$current_user AND candidate.id=candidate_attrs.user_id AND candidate_attrs.attr_type=current_user.attr_type AND candidate_attrs.attr_value BETWEEN current_user.attr_value $tolerance AND current_user.attr_value-$tolerance GROUP BY ORDER BY COUNT(*) DESC; SELECT candidate.id, SUM(1/1 ((candidate_attrs.attr_value - current_user.attr_value) *(candidate_attrs.attr_value - current_user.attr_value)) ) as match_score FROM users candidate, attributes candidate_attrs, attributes current_user_attrs, attribute_subsets s WHERE current_user_attrs.user_id=$current_user AND candidate.user_id$current_user AND candidate.id=candidate_attrs.user_id AND candidate_attrs.attr_type=current_user.attr_type AND candidate_attrs.attr_value AND s.subset_name=$required_subset AND s.attr_type=current_user.attr_type BETWEEN current_user.attr_value $tolerance AND current_user.attr_value-$tolerance GROUP BY ORDER BY COUNT(*) DESC; Obviously this does not accomodate non-ordinal data (e.g. Without knowing a lot more about te structure of the existing data, its rather hard to say exactly how effective this will be.

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So, what do you think it's the best way to go about it?

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