Auto-Pipeline: Synthesizing Complex Data Pipelines By-Target Using Reinforcement Learning and Search

TL;DR

This paper introduces Auto-Pipeline, a system that automates the synthesis of complex data processing pipelines by leveraging a novel by-target paradigm, reinforcement learning, and search, enabling end-to-end automation of multiple data transformation steps.

Contribution

It presents a new by-target paradigm for pipeline specification and a reinforcement learning-based approach to synthesize complex data pipelines, incorporating implicit table constraints for tractability.

Findings

Successfully synthesizes 60-70% of real-world pipelines from GitHub

Handles pipelines with up to 10 steps

Demonstrates effectiveness over traditional methods

Abstract

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 string transformations 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. We develop an Auto-Pipeline system that learns to synthesize pipelines using reinforcement learning and search. Experiments on large numbers of real pipelines crawled from GitHub suggest that Auto-Pipeline can successfully synthesize 60-70% of these complex pipelines with up to 10 steps.