Relational data stores are widely used because they provide persistence, scalability, and fault tolerance with a simple interface. However, most data store applications configure the data store to use weak isolation for scalable performance, permitting sporadic unserializable executions that produce incorrect results or failures. Prior work uses dynamic predictive analysis to infer violations from execution traces, but existing techniques cannot handle relational (i.e., SQL) queries with complex predicates, and they predict executions that do not violate View Serializability, leading to false negatives and false positives.

This paper introduces Augur, the first dynamic predictive program analysis that (1) supports data store applications with complex relational queries and (2) reports only executions that violate View Serializability. The evaluation demonstrates that Augur finds feasible, unserializable executions in OLTP-Bench programs and in the widely used e-commerce application Spree.