@inproceedings{56b1bbb28d9a4498b5fbda60c63927e6,
title = "Verifying equivalence of spark programs",
abstract = "Apache Spark is a popular framework for writing large scale data processing applications. Our long term goal is to develop automatic tools for reasoning about Spark programs. This is challenging because Spark programs combine database-like relational algebraic operations and aggregate operations, corresponding to (nested) loops, with User Defined Functions (UDF s). In this paper, we present a novel SMT-based technique for verifying the equivalence of Spark programs. We model Spark as a programming language whose semantics imitates Relational Algebra queries (with aggregations) over bags (multisets) and allows for UDFs expressible in Presburger Arithmetics. We prove that the problem of checking equivalence is undecidable even for programs which use a single aggregation operator. Thus, we present sound techniques for verifying the equivalence of interesting classes of Spark programs, and show that it is complete under certain restrictions. We implemented our technique, and applied it to a few small, but intricate, test cases.",
author = "Shelly Grossman and Sara Cohen and Shachar Itzhaky and Noam Rinetzky and Mooly Sagiv",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017; 29th International Conference on Computer Aided Verification, CAV 2017 ; Conference date: 24-07-2017 Through 28-07-2017",
year = "2017",
doi = "10.1007/978-3-319-63390-9\_15",
language = "الإنجليزيّة",
isbn = "9783319633893",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "282--300",
editor = "Viktor Kuncak and Rupak Majumdar",
booktitle = "Computer Aided Verification - 29th International Conference, CAV 2017, Proceedings",
address = "ألمانيا",
}