Database agnostic != database ignorant

Every now and then I stumble across a blog post that triggers something in me. Lately, this was the case for an article I read over at RubyFleebie about a Rails programmer having trouble with the very basics when it comes to databases. While the resumee of the post was quite satisfying (he thinks that Rails developers should start to care about the database they’re using, despite relying on ActiveRecord), the content itself scared me: This guy, so it seems, doesn’t know anything about foreign keys, indexes and not even differences between outer and inner joins. I don’t mean to insult him, but it really, really scared me.

Back when I started coding, a web developer had to be familiar with all aspects of the whole application: database, HTML/CSS/JavaScript and the code that links it all together. I don’t think that this requirement has changed a lot – or in fact, it probably hasn’t changed at all. But frameworks like Rails make it easy to hide some of the aspects behind their mighty layers of abstraction.

Anyways, I’m not here to complain about that. Instead, I’d like to use this post to give you a quick rundown of the most important database related stuff, that I think is mandatory for every web developer, whether they use Rails or not. While I’m certainly no potential DBA, I know a thing or two about database design and I think, some things might help you along the way.


In databases, joins are used to link multiple tables of a database together so that multidimensional (so to speak) data can be retrieved using just a single query. This is useful in two major aspects:

  • First of all, every database query is a – comparatively – time consuming operation. More often than not, you’ll find that reducing the number of queries in your application will give you a major performance increase. This is basically what Rails’ eager loading is all about:
Person.find(:all, :include => :addresses)

This loads all people including their addresses in just one query.

  • Secondly, it depicts reality a little more accurately. Your data is hardly ever only two-dimensional. You’ll have people that have addresses, orders that consist of items, topics that have posts, etc. Using only one query to fetch all related data is not only more efficient, but also seems logical.

An important thing to know about joins is that a join always connects two tables. You can, of course, chain multiple joins to fetch data from more than one table. But a join, by itself, can only connect two tables at a time. How the tables are connected is decided by the type of join you apply: You can outer or inner join tables.


The probably more common way to join tables is the so-called INNER JOIN. INNER JOIN connects all matching rows of both tables.

Let’s take another look at the people/addresses example I mentioned earlier and assume that there’s two people, George (id = 1) and Alice (id = 2). George has one address (i.e. there is one row in the addresses table with a person_id = 1) and Alice has two addresses. Let’s join them using an INNER JOIN:

SELECT * FROM people INNER JOIN addresses ON addresses.person_id = people.id

This will output a set of three rows – one for George and two for Alice. The set includes all rows where the join condition (ON addresses.person_id = people.id) matches.

Now we add a third person, Bill, who doesn’t have an address. If we run our query again, the output doesn’t change because the INNER JOIN only includes rows with matches in both tables. If we want to have one of the two tables included in the result set, regardless of whether there’s a corresponding row in the second table or not, we have to use an OUTER JOIN.

Note that the order in which you’re joining the tables doesn’t make any difference with inner joins. You could write the following and the result would be the same:

SELECT * FROM addresses INNER JOIN people ON addresses.person_id = people.id


An OUTER JOIN includes one of the two joined tabled tables completely, even if it doesn’t find a matching row in the other table. Which table is going to be complete (i.e. not all fields are NULL values) and which may be incomplete is decided by adding kind of “direction” to the join and make it a LEFT OUTER JOIN or RIGHT OUTER JOIN. But let’s take a look at an example that makes this easier to understand.

Let’s revisit George, Alice and Bob. If we change our INNER JOIN to a LEFT OUTER JOIN, Bill is included in the result set and all fields of the addresses table are set to NULL.

So, in short, the added keyword LEFT/RIGHT decides, which of the joined tables will get fully included in the result set. Note that, contrary to the INNER JOIN, with the LEFT/RIGHT OUTER JOIN the order of the tables does actually matter – if you swap the tables, your result set will usually change.

When to use what

When to use which kind of join largely depends on what you’re trying to achieve. A general rule of thumb would be something like this:

  • If you can be sure that you only need records with corresponding rows in both tables or if you somehow enforce (at database or application level) that each record in one table must have a corresponding row in the other table, an INNER JOIN is the way to go, because it’s usually fast and doesn’t yield any NULL values you have to deal with.
  • If what you’re trying to achieved doesn’t fall in the category mentioned above, use an OUTER JOIN. Using LEFT/RIGHT OUTER JOIN is mostly a matter of personal preference (I prefer to LEFT OUTER JOIN), unless you’re chaining lots of tables in one big query – then you might need to have some LEFT/RIGHT OUTER JOIN alternating.


Indexes (aka keys) are used to index records (who would have thought that?!). Basically, using indexes can result in an enormous performance boost if the indexes are applied correctly, because they help the database server to find the desired results more efficiently. This can be achieved because without an index, databases usually perform what is called a full table scan. Consider the following query:

SELECT * FROM people WHERE last_name = "Smith"

If there is no index on the last_name field, the database will go through the whole table and filter every record with a last_name value of “Smith”. While this is not a problem with only a few hundred records, it quickly gets inefficient if you have multiple thousand records stored in the table. An index, in this case, will build a kind of virtual table that is ordered by last_name. When the database is queried like above, it will find go in an search till it finds the first occurrence of “Smith” and selects all records till it finds the first record where last_name doesn’t equal “Smith”. Obviously, this can save a lot of time.

When and where to use indexes is a science by itself (contrary to what people might tell you, DBA is actually not redundant job for all those tech guys born before 1960), especially when it comes to combined indexes (i.e. indexes across multiple fields). Nevertheless, here’s some indicators when an index might be useful:

  • Any field of a table that gets mentioned in WHERE clauses may make a good index. Let’s assume that you have a table containing blog posts and you want to filter all posts by a certain user, using something like
    SELECT * FROM posts WHERE author = 'Clemens'
    In this case, author would be a potential index. Same goes for fields used in ORDER BY clauses – they’re usually more efficient when indexes, especially with large amounts of data.
  • Any field that is part of a join condition is definitely a good choice for an index. In other words: Index your foreign keys. Always! Example:
    SELECT * FROM people LEFT OUTER JOIN addresses ON addresses.person_id = people.id
    In this example, addresses.person_id should definitely be indexed. (Note: people.id is, hopefully, indexed as primary key already.)
  • Any field that is used by an aggregate function like SUM, COUNT, etc. may be a good candidate as well. If you don’t write your own SQL and solely rely on ActiveRecord, you will hardly ever (if at all) use aggregate functions.

With most database engines, there are multiple types of indexes. For example, in MySQL there are primary keys, unique indexes and “standard” indexes. I think, this is really straight forward, but I’ll explain it in short anyway.

  • There is only one primary key per table (hence the name) and is unique for the table (i.e. there is only one record with a certain value in this field). In Rails, this field will usually be called “id” and be an integer of some sort, that is auto incremented if a new record is inserted. Don’t change Rails’ behavior here – it’s a real pain!
  • The “standard” index is the most common index. You’ll use it for most foreign keys and if you speed up your WHERE statements by indexing the fields that are part of the clause.
  • A unique index can be used to make a second field (in addition to the primary key) unique for a table. A good example would be if you want to make sure that the users table in your application makes sure that a given e-mail address can only be used once – just make it unique. Of course, you always need some application logic (preferably in the model layer) to handle the unique violation if a second record with an already existing value is inserted.
  • Combined indexes are strictly speaking not a separate type of indexes – you can have combined primary keys, combined uniques and combined “standard” indexes. Since combined indexes are – I think – a very difficult topic and could easily make a full-blown article, I decided to put them out of scope.

Summing up

ActiveRecord and similar ORMs take a lot of pain and time consuming tasks away from the developer. That doesn’t mean, though, that it exempts the developer from learning and knowing what’s going on behind the covers. I hope, my article gave some people an insight in the works of a database. Maybe, some day I’ll write another article on how to optimize your database.

Comments are much appreciated!

UPDATE: pjm suggested to write a little paragraph explaining why an index may be helpful. I added a short section to the article – I hope it’s clear enough. Further information can be found in the MySQL manual about how MySQL uses indexes – most of the stuff that is mentioned there isn’t MySQL-specific but can be applied to other popular database engines as well.