From Batch to Stream: Automatic Generation of Online Algorithms

Abstract

Online streaming algorithms, tailored for continuous data processing, offer substantial benefits but are often more intricate to design than their offline counterparts. This paper introduces a novel approach for automatically synthesizing online streaming algorithms from their offline versions. In particular, we propose a novel methodology, based on the notion of relational function signature (RFS), for deriving an online algorithm given its offline version. Then, we propose a concrete synthesis algorithm that is an instantiation of the proposed methodology. Our algorithm uses the RFS to decompose the synthesis problem into a set of independent subtasks and uses a combination of symbolic reasoning and search to solve each subproblem. We implement the proposed technique in a new tool called Opera and evaluate it on over 50 tasks spanning two domains: statistical computations and online auctions. Our results show that Opera can automatically derive the online version of the original algorithm for 98% of the tasks. Our experiments also demonstrate that Opera significantly outperforms alternative approaches, including adaptations of SyGuS solvers to this problem as well as two of Opera's own ablations.