Now that we are running on a cluster, we need to modify our driver script a little bit. We'll look at the movie similarity sample again and figure out how we can scale that up to actually use a million movie ratings. To do so, you can't just run it as is and hope for the best, you wouldn't succeed if you were to do that. Instead, we have to think about things such as how is this data going to be partitioned? It's not hard, but it is something you need to address in your script. In this section we'll cover partitioning and how to use it in your Spark script.

Let's get on with actually running our movie-similarities script on a cluster. This time we're going to talk about throwing a million ratings at it instead of a hundred thousand ratings. Now, if we were to just modify our script to use the 1 million rating dataset from grouplens.org, it's not going to run on your desktop obviously. The main reason is that when we use self-join to generate every possible combination of movie...