String Aggregation in PostgreSQL, SQL Server, and MySQL

Question: You have a table of people and a table that specifies the activities each person is involved in. You want to return a result that has one record per person and a column that has a listing of activities for each person separated by semicolons and alphabetically sorted by activity. You also want the whole set alphabetically sorted by person's name.

This is a question we are always asked and since we mentor on various flavors of databases, we need to be able to switch gears and provide an answer that works on the client's database. Most often the additional requirement is that you can't install new functions in the database. This means that for PostgreSQL/SQL Server that both support defining custom aggregates, that is out as an option.

Normally we try to come up with an answer that works in most databases, but sadly the only solution that works in most is to push the problem off to the client front end and throw up your hands and proclaim -- "This ain't something that should be done in the database and is a reporting problem." That is in fact what many database purists do, and all I can say to them is wake up and smell the coffee before you are out of a job. We feel that data transformation is an important function of a database, and if your database is incapable of massaging the data into a format your various client apps can easily digest, WELL THAT's A PROBLEM.

We shall now document this answer rather than trying to answer for the nteenth time. For starter's PostgreSQL has a lot of answers to this question, probably more so than any other, though some are easier to execute than others and many depend on the version of PostgreSQL you are using. SQL Server has 2 classes of answers neither of which is terribly appealing, but we'll go over the ones that don't require you to be able to install .NET stored functions in your database since we said that is often a requirement. MySQL has a fairly simple, elegant and very portable way that it has had for a really long time.