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Parallel dataflow systems have become a standard technology for large-scaledata analytics. Complex data analysis programs in areas such as machinelearning and graph analytics often involve control flow, i.e., iterations andbranching. Therefore, systems for advanced analytics should include controlflow constructs that are efficient and easy to use. A natural approach is toprovide imperative control flow constructs similar to those of mainstreamprogramming languages: while-loops, if-statements, and mutable variables, whosevalues can change between iteration steps.  However, current parallel dataflow systems execute programs written usingimperative control flow constructs by launching a separate dataflow job afterevery control flow decision (e.g., for every step of a loop). The performanceof this approach is suboptimal, because (a) launching a dataflow job incursscheduling overhead; and (b) it prevents certain optimizations across iterationsteps.  In this paper, we introduce Labyrinth, a method to compile programs writtenusing imperative control flow constructs to a single dataflow job, whichexecutes the whole program, including all iteration steps. This way, we achieveboth efficiency and ease of use. We also conduct an experimental evaluation,which shows that Labyrinth has orders of magnitude smaller per-iteration-stepoverhead than launching new dataflow jobs, and also allows for significantoptimizations across iteration steps.

Labyrinth: Compiling Imperative Control Flow to Parallel Dataflows