Parallel dataflow systems are a central part of most analytic pipelines for
big data. The iterative nature of many analysis and machine learning
algorithms, however, is still a challenge for current systems. While certain
types of bulk iterative algorithms are supported by novel dataflow frameworks,
these systems cannot exploit computational dependencies present in many
algorithms, such as graph algorithms. As a result, these algorithms are
inefficiently executed and have led to specialized systems based on other
paradigms, such as message passing or shared memory. We propose a method to
integrate incremental iterations, a form of workset iterations, with parallel
dataflows. After showing how to integrate bulk iterations into a dataflow
system and its optimizer, we present an extension to the programming model for
incremental iterations. The extension alleviates for the lack of mutable state
in dataflows and allows for exploiting the sparse computational dependencies
inherent in many iterative algorithms. The evaluation of a prototypical
implementation shows that those aspects lead to up to two orders of magnitude
speedup in algorithm runtime, when exploited. In our experiments, the improved
dataflow system is highly competitive with specialized systems while
maintaining a transparent and unified dataflow abstraction.Comment: VLDB201