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We consider methods for learning vector representations of SQL queries tosupport generalized workload analytics tasks, including workload summarizationfor index selection and predicting queries that will trigger memory errors. Weconsider vector representations of both raw SQL text and optimized query plans,and evaluate these methods on synthetic and real SQL workloads. We find thatgeneral algorithms based on vector representations can outperform existingapproaches that rely on specialized features. For index recommendation, wecluster the vector representations to compress large workloads with no loss inperformance from the recommended index. For error prediction, we train aclassifier over learned vectors that can automatically relate subtle syntacticpatterns with specific errors raised during query execution. Surprisingly, wealso find that these methods enable transfer learning, where a model trained onone SQL corpus can be applied to an unrelated corpus and still enable goodperformance. We find that these general approaches, when trained on a largecorpus of SQL queries, provides a robust foundation for a variety of workloadanalysis tasks and database features, without requiring application-specificfeature engineering.

Query2Vec: An Evaluation of NLP Techniques for Generalized Workload Analytics