Query2Vec: An Evaluation of NLP Techniques for Generalized Workload Analytics
Table 4: Classifier performance predicting OOM errors for various baselines and Query2Vec based models. LSTM autoencoder based models significantly outperform all the other methods.
['Method', 'precision', 'recall', 'f1-score']
We find that our method significantly outperforms the baseline heuristics, without requiring any domain knowledge or custom feature extractors. We do find that the presence of heavy joins and window functions in the queries are good indicators of OOM errors (specially if they occur together) given the precision of these baselines, however, the low recall suggests that such hard-coded heuristics would miss a other causes of OOM errors. Query2Vec obviates the need for such hard-coded heuristics. As with any errors, this mechanism can be used to route potentially problematic queries to clusters instrumented with debugging or monitoring harnesses, or potentially clusters with larger available main memories. We see Query2Vec as a component of a comprehensive scheduling and workload management solution; these experiments show the potential of the approach.