  Cardinality estimation (CardEst) is essential for optimizing query execution
plans. Recent ML-based CardEst methods achieve high accuracy but face
deployment challenges due to high preparation costs and lack of transferability
across databases. In this paper, we propose PRICE, a PRetrained multI-table
CardEst model, which addresses these limitations. PRICE takes low-level but
transferable features w.r.t. data distributions and query information and
elegantly applies self-attention models to learn meta-knowledge to compute
cardinality in any database. It is generally applicable to any unseen new
database to attain high estimation accuracy, while its preparation cost is as
little as the basic one-dimensional histogram-based CardEst methods. Moreover,
PRICE can be finetuned to further enhance its performance on any specific
database.
  We pretrained PRICE using 30 diverse datasets, completing the process in
about 5 hours with a resulting model size of only about 40MB. Evaluations show
that PRICE consistently outperforms existing methods, achieving the highest
estimation accuracy on several unseen databases and generating faster execution
plans with lower overhead. After finetuning with a small volume of
databasespecific queries, PRICE could even find plans very close to the optimal
ones. Meanwhile, PRICE is generally applicable to different settings such as
data updates, data scaling, and query workload shifts. We have made all of our
data and codes publicly available at https://github.com/StCarmen/PRICE.
