upq upq will give you the first few entries in that rdd if you just want to get little peek into what' in there for debugging purposes sfevdf the more powerful action is sfevdf which will actually let you combine values together for the same common key value you can also use rdds in the context of key-value data the sfevdf function lets you define way of combining together all the values for given key it is very much similar in spirit to mapreduce sfevdf is basically the analogous operation to sfevdfs in mapreduceand nbq is analogous to nbqqfs soit' often very straightforward to actually take mapreduce job and convert it to spark by using these functions remembertoothat nothing actually happens in spark until you call an action once you call one of those action methodsthat' when spark goes out and does its magic with directed acyclic graphsand actually computes the optimal way to get the answer you want but remembernothing really occurs until that action happens sothat can sometimes trip you up when you're writing spark scriptsbecause you might have little print statement in thereand you might expect to get an answerbut it doesn' actually appear until the action is actually performed that is spark in nutshell those are the basics you need for spark programming basicallywhat is an rdd and what are the things you can do to an rdd once you get those conceptsthen you can write some spark code let' change tack now and talk about mlliband some specific features in spark that let you do machine learning algorithms using spark introducing mllib fortunatelyyou don' have to do things the hard way in spark when you're doing machine learning it has built-in component called mllib that lives on top of spark coreand this makes it very easy to perform complex machine learning algorithms using massive datasetsand distributing that processing across an entire cluster of computers sovery exciting stuff let' learn more about what it can do