Query2Vec: NLP Meets Databases for Generalized Workload Analytics
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We propose methods for learning vector representations of SQL workloads to support a variety of administration tasks and application features, including query recommendation, workload summarization, index selection, identifying expensive queries, and predicting query reuse. We consider vector representations of both raw SQL text and optimized query plans under various assumptions and pre-processing strategies, and evaluate these methods on multiple real SQL workloads by comparing with results of task and application feature metrics in the literature. We find that simple algorithms based on these generic vector representations compete favorably with previous approaches that require a number of assumptions and task-specific heuristics. We then present a new embedding strategy specialized for queries based on tree-structured Long Short Term Memory (LSTM) network architectures that improves on the text-oriented embeddings for some tasks. We find that the general approach, when trained on a large corpus of SQL queries, provides a robust foundation for a variety of workload analysis tasks. We conclude by considering how workload embeddings can be deployed as a core database system feature to support database maintenance and novel applications.
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