Why Spark Works - Secrets of its Parallel Processing Powers

A Spark progam consists of a driver program that runs the user's main function and runs several parallel operations on a cluster.   It is a parallel processing engine for data. The data structure that enables this is the RDD abstraction.

RDDs are resilient distributed datasets (perhaps a better acronym could be RDDS?) - a collection of elements partitioned across nodes of a cluster that can be operated on in parallel.  

Clearly, this definition speaks to the distributed dataset aspect, but what about the resilient aspect?  Can we say it is implicit in the "can be operated on in parallel" dimension? We need to probe what that actually means, to uncover the "secret" of RDDs.

RDDs are born as files in the Hadoop system (or any Hadoop-supported file system) or an existing Scale collection. RDDs can be persisted in memory for efficient processing and they are resilient against node failures (this bit needs to be understood better - how is this achieved - redundancy of storage??).

Once you probe deeper you will start to see ideas percolating from older frameworks like MPI in C++.