  Cardinality estimation is a fundamental problem in database systems. To
capture the rich joint data distributions of a relational table, most of the
existing work either uses data as unsupervised information or uses query
workload as supervised information. Very little work has been done to use both
types of information, and cannot fully make use of both types of information to
learn the joint data distribution. In this work, we aim to close the gap
between data-driven and query-driven methods by proposing a new unified deep
autoregressive model, UAE, that learns the joint data distribution from both
the data and query workload. First, to enable using the supervised query
information in the deep autoregressive model, we develop differentiable
progressive sampling using the Gumbel-Softmax trick. Second, UAE is able to
utilize both types of information to learn the joint data distribution in a
single model. Comprehensive experimental results demonstrate that UAE achieves
single-digit multiplicative error at tail, better accuracies over
state-of-the-art methods, and is both space and time efficient.
