Modern data analysis tasks often involve control flow statements, such as the iterations in PageRank and K-means. To achieve scalability, developers usually implement these tasks in distributed dataflow systems, such as Spark and Flink. Designers of such systems have to choose between providing imperative or functional control flow constructs to users. Imperative constructs are easier to use, but functional constructs are easier to compile to an efficient dataflow job. We propose Mitos, a system where control flow is both easy to use and efficient. Mitos relies on an intermediate representation based on the static single assignment form. This allows us to abstract away from specific control flow constructs and treat any imperative control flow uniformly both when building the dataflow job and when coordinating the distributed execution.

现代数据分析任务通常涉及控制流语句，例如 PageRank 和 K-means 中的迭代。为了实现可扩展性，开发人员通常在分布式数据流系统中实现这些任务，例如 Spark 和 Flink。此类系统的设计者必须在向用户提供命令式或功能性控制流构造之间做出选择。命令式构造更容易使用，但函数式构造更容易编译为高效的数据流作业。我们提出了 Mitos，一个控制流既易于使用又高效的系统。Mitos 依赖于基于静态单一赋值形式的中间表示。这允许我们在构建数据流作业和协调分布式执行时从特定的控制流构造中抽象出来并统一处理任何命令式控制流。