Auto-Pipeline: Synthesizing Complex Data Pipelines By-Target Using Reinforcement Learning and Search - Yang, Junwen; He, Yeye; Chaudhuri, Surajit (VLDB 2021, 2021)
Recent work has made significant progress in helping users to automate single data preparation steps, such as string-transformations and table-manipulation operators (e.g., Join, GroupBy, Pivot, etc.). We in this work propose to automate multiple such steps end-to-end, by synthesizing complex data-pipelines with both stringtransformations and table-manipulation operators. We propose a novel by-target paradigm that allows users to easily specify the desired pipeline, which is a significant departure from the traditional by-example paradigm. Using by-target, users would provide input tables (e.g., csv or json files), and point us to a “target table” (e.g., an existing database table or BI dashboard) to demonstrate how the output from the desired pipeline would schematically “look like”. While the problem is seemingly underspecified, our unique insight is that implicit table constraints such as FDs and keys can be exploited to significantly constrain the space to make the problem tractable. Inspired by game-playing agents such as AlphaGo and Atari, we develop an Auto-Pipeline system that uses ''self-play'' to learn to synthesize pipelines using reinforcement learning and search. Experiments on large numbers of real pipelines crawled from GitHub and commercial vendors suggest that Auto-Pipeline can successfully synthesize 60-70% of these complex pipelines with up to 10 steps. The benchmark datasets used in this work have been released here: https://gitlab.com/jwjwyoung/autopipeline-benchmarks