Dr Administrator

This is a roundup post for a rather specialist set of posts I wrote on SQL (Structured Query Language), a computer language for creating and querying databases. Basically the posts are my notes on the language which I’m learning because a couple of programming projects I have in mind will need it. The main source for these notes is the Head First SQL book. I’ve used a another book in this series (Head First Design Patterns) – I quite like the presentational style. The code in the posts is formatted for display using this SQL to HTML formatter.

Topics covered:
Some notes on SQL: 1 – Creation
Some notes on SQL: 2 – Basic SELECT
Some notes on SQL: 3 – Changing a table
Some notes on SQL: 4 – Advanced SELECT
Some notes on SQL: 5 – Database design
Some notes on SQL: 6 – Multi-table operations
Some notes on SQL: 7 – Subqueries and views

Of course you can find SQL cheatsheets elsewhere.

The Head First SQL book also has material on transactions and security, if I get a renewed bout of enthusiasm I will add a post on these items.

I used MySQL via its command line client to do the exercises, because it’s about as straightforward as you can get. Notepad++ recognises SQL as a language and will do syntax highlighting, so I type my commands into it and copy them into the MySQL command line client. MySQL is pretty straightforward to install. I also installed Microsoft SQL Server Express 2008, which turned out to be a bit more of a struggle but on the plus side integration the C# .NET, which is what I normally program in, looks better than for MySQL.

I’ve been using with the SQL Server via SQL Management Studio (a graphical interface to databases) on the general election data compiled by The Guardian. First contact with actual data, as opposed to learning exercises has proved interesting! A lot of things that are fiddly to do in a spreadsheet are straightforward using  SQL.

SQL was designed in the early 1970’s, with commercial implementations appearing towards the end of the decade. It’s influence visible is visible in more modern languages, such as the LINQ extensions to C# (this influence is pretty explicitly stated). Some of the ideas of database design (normalisation) seem relevant to object-oriented programming.

It’s been an interesting learning experience, my scientific background in programming has me stuffing pretty much any sort of data into an array in the first instance. SQL and a database look like a much better solution for many situations.

This is the sixth in a series of blog posts on SQL, the first covered creating a database, the second selecting information from a database, the third commands to modify the structure and contents of an existing database, the fourth, advanced selection. The fifth post covered database design. This post covers multi-table database operations. No claim of authority is made for these posts, they are mainly intended as my notes on the topic.

Good database design leads us to separate information into separate tables, the information we require from a SELECT statement may reside in multiple tables. There are keywords and methods in SQL to help with extracting data from multiple tables. To assist with clarity aliases, indicated using the AS keyword, allow tables to be given shorter, or clearer, names temporarily. Various JOIN keywords enable lookups between tables, as with other aspects of SQL there are multiple ways of achieving the same results – in this case ‘subqueries’.

The AS keyword can be used to populate a new table with the results of a SELECT statement, or it can be used to alias a table name. In it’s aliasing guise it can be dropped, in shorthand. This is AS being used in table creation:
CREATE TABLE profession
(
id         INT(11) NOT NULL AUTO_INCREMENT PRIMARY KEY,
profession VARCHAR(20)
) AS
SELECT profession
FROM   my_contacts
GROUP  BY profession
ORDER  BY profession;
The following two forms are equivalent, the first uses the AS to alias, the second uses an implicit alias:
SELECT profession AS mc_prof
FROM   my_contacts AS mc
GROUP  BY mc_prof
ORDER  BY mc_prof;

SELECT profession mc_prof
FROM   my_contacts mc
GROUP  BY mc_prof
ORDER  BY mc_prof;


The following examples use two tables boys which is a three column table {boy_id, boy, toy_id} and toys a two column table {toy_id, toy}.

Cross, cartesian, comma join are all names for the same, relatively little used operation which returns every row from one table crossed with every row from a second table, that’s to say two 6 row tables will produce a result with 36 rows. Although see here for an application.
SELECT t.toy,
b.boy
FROM   toys AS t
CROSS JOIN boys AS b;
Notice the use of the period and aliases to reference columns, this query will produce a 20 row table.

Inner join combines the rows from two tables using comparison operators in a condition, an equijoin returns rows which are the same, a non-equijoin returns rows that are different. These are carried out with the same keywords, the condition is different. This is an equijoin:
SELECT boys.boy,
toys.toy
FROM   boys
INNER JOIN toys
ON boys.toy_id = toys.toy_id;

The ON and WHERE keywords can be used interchangeable; in this instance we do not use aliases furthermore since the columns in the two tables (toys and boys) have the same name we could use a natural join:
SELECT boys.boy,
toys.toy
FROM   boys
NATURAL JOIN toys;


Natural join is a straightforward lookup operation, a key from one table is used to extract a matching row from a second table, where the key column has the same name in each table. Both of these versions produce the following table:

A non-equijoin looks like this:
SELECT boys.boy,
toys.toy
FROM   boys
INNER JOIN toys
ON boys.toy_id<>toys.toy_id
ORDER  BY boys.boy;

the resultant in this instance is four rows for each boy containing the four toys he does not have.

Outer joins are quite similar to inner joins, with the exception that they can return rows when no match is found, inserting a null value. The following query
SELECT b.boy,
t.toy
FROM   boys b
LEFT OUTER JOIN toys t
ON b.toy_id = t.toy_id;

produces this result

That’s to say each row of the toys table is taken and matched to the boys table, where there is no match (for toy_id=4, harmonica) a null value is inserted in the boy column. Both LEFT OUTER JOIN and RIGHT OUTER JOIN are available but the same effect can be achieved by swapping the order in which tables are used in the query.


In some instances a table contains a self-referencing foreign key which is the primary key of the table. An example might be a three column table, clown_info, of “clowns” {id, name, boss_id} where each id refers to a clown name and the bosses identified by boss_id are simply other clowns in the same table. To resolve this type of key a self-join is required this uses two aliases of the same table.
SELECT c1.name,
c2.name AS boss
FROM   clown_info c1
INNER JOIN clown_info c2
ON c1.boss_id = c2.id; 


Notice both c1 and c2 alias to clown_info.

Keywords: AS, ON, INNER JOIN, NATURAL JOIN, LEFT OUTER JOIN, RIGHT OUTER JOIN, UNION, INTERSECT, EXCEPT