Abstract: Techniques are described herein for generating, editing, and optimizing queries using neural networks. In some embodiments, the techniques include training a neural network using a set of performant database queries to automatically learn patterns between different sequences of tokens in performant queries. Once trained, the neural network may receive an incomplete query as input, where the incomplete query includes one or more query tokens. The trained neural network may then perform next token prediction to project a set of one or more additional query tokens that may follow the one or more query tokens in the incomplete query to form a completed, performant query.

Abstract: Techniques are described herein for leveraging recurrent neural networks for query processing. In some embodiments, a query analytic system determines a sequence of tokens for at least a portion of a query and determines a vector representation for each token. The query analytic system further generates, using a neural network based on the sequence of tokens, a performance prediction associated with executing at least the portion of the query, wherein the neural network assigns at least a first weight for at least a first token in the sequence of tokens based at least in part on at least a second token that preceded the token in the sequence. The query analytic system further triggers a responsive action, such as triggering an alert and/or tuning the query, based at least in part on the performance prediction.

Abstract: Techniques are described herein for generating, editing, and optimizing queries using neural networks. In some embodiments, the techniques include training a neural network using a set of performant database queries to automatically learn patterns between different sequences of tokens in performant queries. Once trained, the neural network may receive an incomplete query as input, where the incomplete query includes one or more query tokens. The trained neural network may then perform next token prediction to project a set of one or more additional query tokens that may follow the one or more query tokens in the incomplete query to form a completed, performant query.

Abstract: Techniques are described herein for leveraging recurrent neural networks for query processing. In some embodiments, a query analytic system determines a sequence of tokens for at least a portion of a query and determines a vector representation for each token. The query analytic system further generates, using a neural network based on the sequence of tokens, a performance prediction associated with executing at least the portion of the query, wherein the neural network assigns at least a first weight for at least a first token in the sequence of tokens based at least in part on at least a second token that preceded the token in the sequence. The query analytic system further triggers a responsive action, such as triggering an alert and/or tuning the query, based at least in part on the performance prediction.