Query2Vec: An Evaluation of NLP Techniques for Generalized Workload Analytics
Table 3: Performance of classifier using query embeddings generated by LSTM based autoencoder for different error types (-1 signifies no error).
['Error Code', 'precision', 'recall', 'f1-score', '# queries']
The classifier performs well for the errors that occur sufficiently frequently, suggesting that the syntax alone can indicate queries that will generate errors. This mechanism can be used in an online fashion to route queries to specific resources with monitoring and debugging enabled to diagnose the problem. Offline, query error classification can be used for forensics; it is this use case that was our original motivation. Although individual bugs are not difficult to diagnose, there is a long tail of relatively rare errors; manual inspection and diagnosis of these cases is prohibitively expensive. With automated classification, the patterns can be presented in bulk.