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.

@article{WangPPWD24,
  title = {From Batch to Stream: Automatic Generation of Online Algorithms},
  author = {Ziteng Wang and Shankara Pailoor and Aaryan Prakash and Yuepeng Wang and Işıl Dillig},
  journal = {Proceedings of the ACM on Programming Languages},
  volume = {8},
  number = {PLDI},
  year = {2024},
  doi = {10.1145/3656418}
}