We are happy to announce that our paper with the title Efficient Control Flow in Dataflow Systems: When Ease-of-Use Meets High Performance has won the ICDE 2021 Best Paper Award. The paper was created in collaboration with colleagues from TU Berlin and Snowflake.
Abstract: Modern data analysis tasks often involve control flow statements, such as iterations. Common examples are PageRank and K-means. To achieve scalability, developers usually imple-ment data analysis tasks in distributed dataflow systems, such as Spark and Flink. However, for tasks with control flow statements, these systems still either suffer from poor performance or are hard to use. For example, while Flink supports iterations and Spark provides ease-of-use, Flink is hard to use and Spark has poor performance for iterative tasks. As a result, developers typically have to implement different workarounds to run their jobs with control flow statements in an easy and efficient way.
We propose Mitos, a system that achieves the best of both worlds: it achieves both high performance and ease-of-use. Mitos uses an intermediate representation that abstracts away specific control flow statements and is able to represent any imperative control flow. This facilitates building the dataflow graph and coordinating the distributed execution of control flow in a way that is not tied to specific control flow constructs. Our experimental evaluation shows that the performance of Mitos is more than one order of magnitude better than systems that launch new dataflow jobs for every iteration step. Remarkably, it is also up to 10.5 times faster than Flink, which has native iteration support, while matching the ease-of-use of Spark. Index Terms - Iterative dataflow, Loop pipelining, Loop-invariant hoisting.