An Empirical Analysis of Deep Learning for Cardinality Estimation

Understand

We implement and evaluate deep learning for cardinality estimation by studying the accuracy, space and time trade-offs across several architectures.

• We find that simple deep learning models can learn cardinality estimations across a variety of datasets (reducing the error by 72% - 98% on average compared to PostgreSQL).
• In addition, we empirically evaluate the impact of injecting cardinality estimates produced by deep learning models into the PostgreSQL optimizer.
• In many cases, the estimates from these models lead to better query plans across all datasets, reducing the runtimes by up to 49% on select-project-join workloads.