  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. As promising as these models are, we also
discuss and address some of the challenges of using them in practice.
