  For efficient query processing, DBMS query optimizers have for decades relied
on delicate cardinality estimation methods. In this work, we propose an
Attention-based LEarned Cardinality Estimator (ALECE for short) for SPJ
queries. The core idea is to discover the implicit relationships between
queries and underlying dynamic data using attention mechanisms in ALECE's two
modules that are built on top of carefully designed featurizations for data and
queries. In particular, from all attributes in the database, the data-encoder
module obtains organic and learnable aggregations which implicitly represent
correlations among the attributes, whereas the query-analyzer module builds a
bridge between the query featurizations and the data aggregations to predict
the query's cardinality. We experimentally evaluate ALECE on multiple dynamic
workloads. The results show that ALECE enables PostgreSQL's optimizer to
achieve nearly optimal performance, clearly outperforming its built-in
cardinality estimator and other alternatives.
