Recent advancements in learning-based query performance prediction models
have demonstrated remarkable efficacy. However, these models are predominantly
validated using synthetic datasets focused on cardinality or latency
estimations. This paper explores the application of these models to LinkedIn’s
complex real-world OLAP queries executed on Trino, addressing four primary
research questions: (1) How do these models perform on real-world industrial
data with limited information? (2) Can these models generalize to new tasks,
such as CPU time prediction and classification? (3) What additional information
available from the query plan could be utilized by these models to enhance
their performance? (4) What are the theoretical performance limits of these
models given the available data? To address these questions, we evaluate
several models-including TLSTM, TCNN, QueryFormer, and XGBoost, against the
industrial query workload at LinkedIn, and extend our analysis to CPU time
regression and classification tasks. We also propose a multi-task learning
approach to incorporate underutilized operator-level metrics that could enhance
model understanding. Additionally, we empirically analyze the inherent upper
bound that can be achieved from the models.

Este artículo explora los viajes en el tiempo y sus implicaciones.

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