So let's start talking about what we do when things go wrong with our Spark job. It has a web-based console that we can look at in some circumstances, so let's start by talking about that.
Troubleshooting Spark jobs on a cluster is a bit of a dark art. If it's not immediately obvious what is going on from the output of the Spark driver script, a lot of times what you end up doing is throwing more machines at it and throwing more memory at it, like we looked at with the executor memory option. But if you're running on your own cluster or one that you have within your own network, Spark does offer a console UI that runs by default on port 4040. It does give you a little bit more of a graphical, in-depth look as to what's going on and a way to access the logs and see which executor is doing what. This can be helpful in understanding what's happening. Unfortunately, in Elastic MapReduce, it's pretty much next to impossible to connect to Spark's UI console from...
