# Relational algebra

## :confetti_ball:

In this article I will give a brief description of Edgar F. Codd’s relational algebra, which might help the reader get a better understanding of modern relational databases. It’s intended to be light-hearted and I expect the reader to have a working knowledge of SQL. This article should provide a theoretical underpinning for that working knowledge.

The terminology around relational algebra has slight differences from typical database terminology; that which might generally be referred to as a table is here referred to as a relation [1], similarly rows are referred to as tuples and columns as attributes. That’s not too bad, most programmers are happy(ish) to have interchangeable names for common concepts e.g. in a.b, the .b part would probably be called an “attribute” by a Python girl, but a JavaScript chap would probably say “property”.

As might be expected, relational algebra is founded in set theory. We borrow three familiar operations, namely; union, relative complement and Cartesian product. The definition of Cartesian product is slightly modified, but I’ll come to that. With Cantor’s foundation only three new operations are required to produce an algebra that describes the basic[2] functions of modern relational databases:

• projection, written (say “pi”)
• selection, written (say “sigma”)
• rename, written (say “rho”)

# Basic Definitions

Remember that we are going to refer to columns as attributes. In the same way that columns be said to apply to both tables and rows, attributes apply to both relations and the tuples that they contain — both tupes and relations “have” attributes.

It’s also worth pointing out (before we get into the definitions) that there is an odd difference between how an operation on a relation is defined here and how we would normally define a function in a programming language. To describe things from a programmer’s point of view; there are two ways to pass arguments in to an operation — only those arguments that are relations are considered when describing an operation as unary or binary, other “arguments” are considered part of the definition of the operation.

For example, take the following notation for a projection operation

If this where to be written in Python the function defintion would probably look like this:

```def project(relation, attrs):
# do the things
return
```

And the call to that function corresponding to our earlier notation would look like this:

```project(R, ['a', 'b', 'c'])
```

We would most likely describe the Python function as being binary[3] (or variadic[4] if we used *attrs instead of passing a list), but we describe our algebraic operation as unary[5] — the non-relation arguments are considered part of the definition of the operation, not as true arguments. You may wish to refer back to these comments as you read on.

## Projection

Written , projection is a unary operation (see above for discussion on terminology) that takes a single relation as its argument and results in a new relation that has a subset of the attributes of the original relation. The projection operation can be thought of a “column filter”. The equivalent in SQL would be the part of the query where the programmer picks the columns to be included in the result.

```SELECT name, house_number FROM addresses;
--     ^^^^^^^^^^^^^^^^^^
```

The projection part of the query is emphasised with ^ above.

Let be a relation with the set of attribute names , then we would write a projection operation that picked only the attributes called as follows.

The resultant relation would have the same cardinality (number of tuples) but each tuple would only have the attributes called .

Projection is simple, but it will be important in our definition of more interesting operations later.

## Selection

Written , selection is also a unary operation on a relation. It results in a relation with a cardinality than the cardinality of the original relation. Selection is equivalent to a WHERE clause in an SQL query. I find the notation similar to _.filter, and the result of the operation is the same.

Let be a relation and let be a “propositional formula” (using our lodash example, is the function that is passed as the second argument to _.filter), then a selection according to on is written

The relation resulting from a selection operation will always have a cardinality to the operand, since it only contains tuples that satisfy .

## Rename

The final privitive operation is rename, written . The rename operation is unary, taking as its single argument a relation. It simply changes the names of all attributes in the passed relation.

Where should be renamed to the specification for a rename operation is written . Yes, I got it right way round ... the rename is applied . When we write a rename operation (in this case renaming and on a relation ) it looks like this

# Further Definitions

The definitions we have established here, along with the handful of set theoretic operations we are going to borrow are sufficient to mathematically define common database queries. Let’s recap on notation:

• Union
• Relative complement
• Cartesian product (with slight modifications — stay tuned)
• Projection
• Selection
• Rename

With this set of operators and the concept of a set, tuple and attributes forming a relation, we are equipped to define the following:

• Natural join
• Semijoin
• Antijoin
• Division

Lets do it!

## Cartesian product in relational algebra

But before we do (as alluded to above), we have to quickly look at the perhaps subtle, but certainly critical difference between a set theory Cartesian product and a linear algebra Cartesian product.

Let there be two sets, and , defined as follows

Now, the set theoretic Cartesian product of the two sets is defined as follows

In relational algebra, however, their Cartesian product is defined like this instead

That is, instead of pairs of 2-tuples being nested in an outer 2-tup, the pair of 2-tups was flattened into a 4-tup. This is intuitive if you’ve ever executed a join before, but it’s worth pointing out the difference. What might not be so intuitive about this definition of the Cartesian product is the following; the operation is only defined if the relations have disjoint sets of attribute names. That is, if shares an attribute name with the Cartesian product is not defined.

When we refer to the Cartesian product we will use the “flattening” and “no common attributes” version persented above.

We can write this more generally in set builder notation as follows. Let and be relations with no common attributes

Now we really do have everything we need to define us some joins, let’s really do it this time!

## Natural join

This is where things may become familar to readers who have used SQL databases. It’s also where we will start to make use of the primitives we have defined above. First an English language definition.

A natural join is a binary operation where both operands are relations, resulting in a third relation containing the flattened tuples from each relation where the values all common attributes are equal.

No let’s describe the natural join more formally, let and be relations with some common attributes, then

where is a function that evaluates to true if all common attributes are equal — deciding if the merged tuple is in the result set.

Ok, that’s cool. It will be a bog standard thing if you’re at all familiar with to relational databases ... but how would we define this operation using our algebraic primitives?

The Cartesian product seems like it would be a good place to start, since it will have all possible combinations of tuples from each relation. But wait, since part of our definition of the operation is that our relations should have some common attributes — the Cartesian product is undefined! Blast. What to do?

Here’s where it gets nice.

Let’s define some sets of attributes for our relations and .

Let be attribute names unique to . Similarly, let be attribute names unique to . Now, by definition there are also attribute names that are common to and , so let be attribute names common to both and . Finally, let be attribute names that exist in neither relation.

Take note that the number of these “unused” attribute names is equal to the number of common attribute names. Why will become clear very soon. Now let’s break out some relational primitives ...

... the first thing to do is to get rid of the common attribute names that were blocking our Cartesian product. We can do that with a crafty rename.

That is, all attribute names on that also appear in should be given names that aren’t used by either. Now, our Cartesian product is defined and we can use to create a relation that has everything we need to know to select tuples that should appear in our natural join.

Remember that are the common attributes. This select operation will result in a relation that has the tuples that fulfil our predicate and all that remains is to drop the duplicate attributes used for comparison in the previous step.

Pretty swish!

## Semijoin

Having defined the natural join, defining the semijoin is a breeze. If the natural join is half-way commutative operation; in that the resulting relation contains the same values regardless of the “handedness” of the operands, then the semijoin is non-commutative — the clue is in the notation used for each. We write semijoin and , now remember that we write natural join .

The result of a left semijoin on relations and (written ) is the set of all tuples for which all common attributes of some tuple are equal.

This operation can be built from our algebraic primitives as well. We have done most of the work in defining our natural join, all we need to do is carry out a natural join and then project the left[6] operand’s attributes on to the result.

So, where are all attribute names on , the semijoin can be written

and since we were able to construct the natural join using our primitives, we could do the same for the semijoin.

## Antijoin

The antijoin follows nicely from the semijoin, and can be loosely defined as a semijoin where the condition of its “internal” natural join is inverted. Let’s do this simply:

This of course can be expanded using earlier definitions.

## Division

This one I leave as an excercise.[7]