MapReduce programming and cost-based optimization?: crossing this chasm with starfish

AbstractMapReduce has emerged as a viable competitor to database systems in big data analytics. MapReduce programs are being written for a wide variety of application domains including business data processing, text analysis, natural language processing, Web graph and social network analysis, and computational science. However, MapReduce systems lack a feature that has been key to the historical success of database systems, namely, cost-based optimization. A major challenge here is that, to the MapReduce system, a program consists of black-box map and reduce functions written in some programming language like C++, Java, Python, or Ruby.Starfish is a self-tuning system for big data analytics that includes, to our knowledge, the first Cost-based Optimizer for simple to arbitrarily complex MapReduce programs. Starfish also includes a Profiler to collect detailed statistical information from unmodified MapReduce programs, and a What-if Engine for fine-grained cost estimation. This demonstration will present the profiling, what-if analysis, and cost-based optimization of MapReduce programs in Starfish. We will show how (nonexpert) users can employ the Starfish Visualizer to (a) get a deep understanding of a MapReduce program's behavior during execution, (b) ask hypothetical questions on how the program's behavior will change when parameter settings, cluster resources, or input data properties change, and (c) ultimately optimize the program.