Joins

Few problems use a single set of data. In many cases, there are easy ways to obviate the need to try and process numerous discrete yet related data sets within the MapReduce framework.

The analogy here is, of course, to the concept of join in a relational database. It is very natural to segment data into numerous tables and then use SQL statements that join tables together to retrieve data from multiple sources. The canonical example is where a main table has only ID numbers for particular facts, and joins against other tables are used to extract data about the information referred to by the unique ID.

When this is a bad idea

It is possible to implement joins in MapReduce. Indeed, as we'll see, the problem is less about the ability to do it and more the choice of which of many potential strategies to employ.

However, MapReduce joins are often difficult to write and easy to make inefficient. Work with Hadoop for any length of time, and you will come across a situation where you need to...