I sat at Berkeley CS faculty lunch this past week with Brian Harvey and Dan Garcia, two guys who think hard about teaching computing to undergraduates. I was waxing philosophical about how we need to get data-centric thinking embedded deep into the initial CS courses—not just as an application of traditional programming, but as a key frame of reference for how to think about computing per se.
Dan pointed out that he and Brian and others took steps in this direction years ago at Berkeley, by introducing MapReduce and Hadoop in our initial 61A course. I have argued elsewhere that this is a Good Thing, because it gets people used to the kind of disorderly thinking needed for scaling distributed and data-centric systems.
But as a matter of both pedagogy and system design, I have begun to think that Google’s MapReduce model is not healthy for beginning students. The basic issue is that Google’s narrow MapReduce API conflates logical semantics (define a function over all items in a collection) with an expensive physical implementation (utilize a parallel barrier). As it happens, many common cluster-wide operations over a collection of items do not require a barrier even though they may require all-to-all communication. But there’s no way to tell the API whether a particular Reduce method has that property, so the runtime always does the most expensive thing imaginable in distributed coordination: global synchronization.
From an architectural point of view, a good language for parallelism should expose pipelining, and MapReduce hides it. Brian suggested I expand on this point somewhere so people could talk about it. So here we go.
