We need to migrate all current RDD functionality over to DataFrames. This means porting all matchbox UDFs over to DF UDFs.

There are two possible ways to do this - we can simply take matchbox UDFs and wrap them, or rewrite them from scratch. I suggest we revisit one by one, which will give us an opportunity to refine the UDF we actually want.

For example, the current RDD matchbox ExtractDomain is implemented a bit differently than the DF version we've been playing with... it, for example, strips the prefix www, whereas the RDD impl doesn't. I like the newer implementation better, but open to discussion.

Also, this is an issue we'll come across sooner or later:
https://stackoverflow.com/questions/33664991/spark-udf-initialization

I have a general question we need to look into from the performance perspective: what's the lifecycle status of a Spark DF UDF? In particular, if there's initialization like compiling regexp, we don't want to do that over and over again... we want to have an init stage?

@TitusAn let's start developing in parallel the DF versions of the apps in #222 and try and work this out?