Counting shared tags (or other commonalities) with a SQL view

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Occasionally I surprise myself and end up feeling a desire to write about it and toot my own horn a little bit. What better place to do that than on a professional blog at least part of the purpose of which is to show prospective employers or clients that I’m good at stuff?

I’m pretty good, I guess

note: personal background jabber, skip this section at will

I’m largely self-taught in the area of databases and SQL. The only course I ever took on the subject was a quarter-length database class, circa 1999, at Hamilton College (since bought up by Kaplan, I think) as part of their two-year IT degree program. It used Microsoft Access and was very beginner-level and I think I might have been out sick on joins day. Later when pursuing my Computer Science degree I avoided the databases course out of dislike for the professor who taught it; the alternative course to meet the same requirement had more to do with text indexing, information theory – search-engine kind of stuff – and oddly enough, the course taught and used an open-source multi-dimensional hierarchical database and MUMPS compiler developed by the course’s professor (multi-dimensional databases are quite good at storing and comparing things like, vectors of the occurrences of hundreds of different words in a bunch of textual articles). So, yes, I learned MUMPS in college instead of SQL. Actually, you can download and make-install the C++ code for the MUMPS compiler we used yourself, which compiles MUMPS into C++, if you ever get a wild urge to do such a thing. In fact, I’d recommend it to my fellow programming language nerds, especially those interested in old, obscure, or just plain weird languages. At the very least you’ll have a little fun with it; and I believe MUMPS is even still in use in some corners of the health care industry, so you’d be picking up a skill that’s in some demand yet increasingly difficult to hire for. (While you’re at it, check out Dr. O’Kane’s MUMPS book and his rollicking, action-packed novel.)

At my first real programming job, I started out coding in Actionscript 2.0 but when a particular developer left the company, someone was needed to take over server-side development in PHP, so I took it upon myself to learn PHP, and, as it turned out, also ended up needing to learn SQL and relational databases. I read a PHP book or two and a whole lot of blogs, but mostly just dove right in to the existing code and gradually made sense out of it. Eventually I was working back and forth between Actionscript and PHP pretty regularly. That kind of pick-it-up-as-needed approach is pretty much how I roll, though it’s hard to explain this kind of adaptability to recruiters who are looking to basically keyword-match your experience against a job description, which can be a real drag if you’re the type of person who craves new experiences. When at UNI I had been the kind of student that made a point of taking the more theoretical computer-sciencey courses, on the rationale that things like programming languages are certain to change in the future, but they will most likely continue to build on the same underlying theory dating at last as far back as good ol’ Alan Turing. I would say that approach has paid off well for me in the years since. My first boss described me in a LinkedIn endorsement as being capable of working in multiple programming languages simultaneously, “something which drives most of us insane.”

But I digress (often). Like I said starting out this post, sometimes I still surprise myself. When I pull off something new or just more complex than I’m used to, it feels good, and I like to share it, not just to strut about, but also because I am sure others are out there trying to solve similar problems, and also to give credit to others whose work I drew on to arrive at my solution. And like I said, my SQL skills are largely the product of a few old blog posts and experience so I was pretty stoked at what I pulled off this week.

The assignment

I was given the task of populating a “related articles” part of a page on a news website. Naturally the first thing I thought we needed to hash out was how the system should conclude that two articles are related. After some discussion we arrived at this idea: we would score two articles’ relatedness based on:

  • The number of keyword tags they have in common (this was the same site using acts_as_taggable_on from which I drew this recent post)
  • The number of retailers they have in common (Article HABTM Retailer)
  • How close or far apart their published_at timestamps are (in months)

How this turns out to be slightly difficult

This sounds perfectly reasonable, even like it would be pretty easy to express in an OO/procedural kind of way in Ruby or any other mainstream programming language. But once this site gets a long history of articles, it’s likely that looping or #maping through all of them to work this out is going to get way too time and memory intensive to keep the site running responsively.

Another alternative is to store relatedness scores in a database table and update them only when they need to change; we could hook in to Rails’s lifecycle callbacks like after_save so that when an article is created or saved, we insert or update a record for its relatedness to every other article. That still sounds intensive but we could at least kick off a background worker to handle it. However, I got the feeling that there was potential for errors caused by overlooking some event that would warrant recalculating this table, or missing some pairs.

And there was still another wrinkle to work out: the relatedness scores pertain to pairs of articles, and those pairs should be considered un-ordered: the concept of article A’s relatedness to article B is identical to B’s relatedness to A. I don’t know if any databases have an unordered tuple data type and even if they did whether ActiveRecord would know how to use it. It seems wasteful and error-prone to maintain redundant records so as to have the pairings both ways around. Googling about for good ways to represent a symmetrical matrix in a SQL database didn’t bear much fruit. So it would probably be best to enforce an ordering (“always put the article with the lower ID first” seems reasonable). But then this means to look up related articles, we need to find the current article’s ID in one of two association columns, rather than just one, and then use the other column to find the related article. I’m pretty sure ActiveRecord doesn’t have a way to express this kind of thing as an association. Which is too bad, because ideally, if possible, we’d like to get the relatedness scores and related articles in the form of a Relation so that we can chain other operations like #limit or #order onto it. (Possibly we could write it as a scope with a lambda and give the model a method that passes self.id to that, but I’m still not sure we would get a Relation rather than an Array. The point at which ActiveRecord’s magic decides to convert from one to the other is something I find myself constantly guessing on, guessing wrong, and getting confused and annoyed trying to come up with a workaround.) But so it goes.

Any way we look at this, it looks like we’re going to be stuck writing some pretty serious SQL “by hand”.

I’m not going to show my whole solution here, but you probably don’t need all of it anyway. I think the most useful bit of it to share is the shared-tags calculation.

Counting shared tags in SQL

acts_as_taggable_on has some methods for matching any (or all) of the tags on a list, and versions of this that are aware of tag contexts (the gem supports giving things different kinds/contexts of tags, which I’m not going into here but it’s a cool feature). So obviously you can call #tagged_with using an Article’s tag list to get Articles that share tags with it, but the documentation doesn’t mention anything about ordering the results according to how many tags are matched, or even finding out that number. Well, here’s the SQL query I arrived at that uses acts_as_taggable_on’s taggings table to build a list of article pairs and counts of their shared tags. One nifty thing about it is that it involves joining a table to itself. To do this, you have to alias the tables so that you can specify which side of the join you mean when specifying columns, otherwise you’ll either get an ambiguous column name error or you’ll just get confused. You’ll see I’ve also added a condition in the join that the “first” id be lower than the “second,” forcing an ordering to the ID pairs so as to eliminate duplicate/reversed-order rows and also eliminate comparing any article with itself, since we don’t care to consider an article related to itself. (Also, the way this is written Article pairings with no shared tags won’t be returned at all. Maybe try a left join if you want that.)

select
  first.taggable_id as first_article_id,
  second.taggable_id as second_article_id,
  count(first.tag_id) as shared_tags
from taggings as first
join taggings as second
on
  first.tag_id = second.tag_id and
  first.taggable_type = second.taggable_type and
  first.taggable_id < second.taggable_id
where first.taggable_type = 'Article'
group by first_article_id, second_article_id

Add a and first_article_id = 23 or second_article_id = 23 to the where clause here and you’ll get just the rows pertaining to article

  1. Add an order by shared_tags desc and the rows will come back with the highest shared-tag-counts, the “most related,” at the top. If you’re looking to know the number of shared acts_as_taggable_on tags among your articles or whatever other model you have, here you are.

Building a leaning tower of SQL

So, for the other two relatedness factors, I did a similar query to this against the articles_retailers table to count shared retailers, and another on articles to compute the number of months apart that pairs of articles were published to the site. Each query used the same “first id less than second id” constraint. Then I pulled the three queries together as subqueries of one larger query, joining them by first_article_id and second_article_id, and added a calculated column whose value was the shared tags count plus the shared retailers count minus the months-apart count and call this their score – a heuristic, arbitrary measure of “how related” each pairing of articles is. (The coalesce function came in mighty handy here. Despite its esoteric-sounding name, all it does is exchange a null value for something else you specify, like you might do with || in Ruby – so coalesce(shared_tags, 0) returns 0 if shared_tags is null, or otherwise returns whatever shared_tags is, for example.)

As you are probably picturing in your head, the resulting master relatedness-score query is huge. It took me a good couple hours at a MySQL command-line prompt composing the subqueries and overall query a little bit at a time. It felt awesome. But still: the result was one seriously big glob of SQL. (Incidentally iTerm2 acted up in a really weird way when I tried pasting these large blocks of code into it, but not when I was SSHed into a remote server; if this rings a bell to you, drop me a line.) I’m going to spare you the eye-bleeding caused by seeing the whole thing. You’re going to drop that big nasty thing in the middle of some ActiveRecord model? Yikes!

Views to the rescue

In a forum thread where I was looking for help on the implementation of all this, Frank Rietta suggested I consider using a database view. To be perfectly honest, I hadn’t used a view in years, if ever. I didn’t even think MySQL had them (yes, I’m using MySQL, don’t judge) – maybe some older version I used in the past didn’t and they’ve been added since? At first I wasn’t sure how this could help me, but then Frank wrote this excellent blog post on the subject. I read it, and the more I thought about it, the better the idea sounded.

Basically, a view acts like a regular database table, at least when it comes to querying it with a select. But underneath it’s based on some query you come up with of other tables and views. You can’t write to it, but it provides you with a different “view” of your data by what I would describe as “abstracting a query.” And because the view can be read from like any other table, it can also act as the table behind an ActiveRecord model (at least, until you try to #save to it). Go read Frank’s post so I don’t have to recap it here. You’ll be glad you did.

The great advantage of using a view to hold the relatedness scoring is that I don’t have to think about writing Ruby code to maintain the table of relatedness scores, I don’t have to think about background jobs or hooking into ActiveRecord lifecycle callbacks to maintain the data or any of that – the database itself keeps this “table” updated. Any time the tables it depends on change, it changes right along with them automatically. Plus it gets the big hairy SQL query out of my Ruby code where it won’t distract or confuse anyone; and it handles the issue of making sure first_article_id is always lower than second_article_id because that’s expressed right in the query it’s based on.

So that settles it, I create a view out of my big relatedness-scoring query and an ActiveRecord model over top of it! Only one problem, and it turned out to be pretty minor, but as I mentioned, my big relatedness query involved a join over three subqueries. Turns out that in MySQL, views can’t have subqueries. Perhaps they can in other database engines, I would not be surprised, but not in MySQL. The workaround for this is to create views for the subqueries and query those views. Honestly that probably makes the SQL read more easily anyway. On the other hand, I ended up creating four views. That was definitely the longest Rails migration I have ever written, by far.

The models and other miscellaneous thoughts

So, now I have a table called article_relations that contains pairs of Article id’s and their relatedness scores, I can give it a model like this:

class ArticleRelation < ActiveRecord::Base
  belongs_to :first_article,  class_name: 'Article'
  belongs_to :second_article, class_name: 'Article'

  def other_article(source)
    [first_article, second_article].find{|a| a != source}
  end

  def readonly?
    true
  end
end

And give the Article model a couple methods like this:

  def article_relations
    ArticleRelation.where(
      'first_article_id = ? or second_article_id = ?', id, id).order('score desc')
  end

  def related_articles
    article_relations.map{|r| r.other_article(self)}
  end

Or something to this effect. You’ll likely want to have your view only contain records where the score is above 0, for instance, or give the above methods an optional parameter to use in a limit so you can limit the number of related articles you show.

Which reminds me, speaking of #limit… as I alluded to before, it would be great if I could do things like @article.related_articles.limit(10) here but I can’t. This bugs me a little bit, because it means that some of my queries to the Article class are going to call #limit and others will have to pass the limit as a parameter, or slice the array like [0..9] or something, so I have code where doing the “same” thing reads completely differently. (I am also unfortunate enough to still be working with Rails 2 regularly, where limit goes in an options hash. It appears if you try that syntax in Rails 3, it just ignores it.) There are other gems like punching_bag where this itches at me a little as well (not to mention, I’d like to be able to give my model a method or scope with a name more appropriate to my domain such as popular or hot and have that delegate to most_hit). I think this might just be a product of the usual leakiness of ORM abstractions and I’ll just have to get over it.

One caveat that should be pointed out is that Rails’s generating of schema.rb doesn’t handle views “properly” and probably can’t be made to when you think about it or depending on what you think the proper thing for it to do would be. Rails will dump the structure of your views out as regular tables, so if you use rake db:schema:load you’ll get tables rather than views with all their cool magic. At this point it’s probably a good idea to uncomment that config.active_record.schema_format = :sql line in your application.rb configuration file, which will make rake db:migrate spit out a structure.sql file instead of schema.rb, and get rid of schema.rb altogether.

Another thing worth considering, depending on the complexity of your view(s), is whether to make them materialized views. This is a view that’s backed by a physical table that gets updated as needed. It’s more efficient to query but a little slower to update so the effects of a change to one of the tables it depends on might not be reflected right away, but this may be a worthwhile trade-off to make.

Join me next time when I talk about technical debt or something like that.

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