  In this paper, we introduce zero-shot cost models which enable learned cost
estimation that generalizes to unseen databases. In contrast to
state-of-the-art workload-driven approaches which require to execute a large
set of training queries on every new database, zero-shot cost models thus allow
to instantiate a learned cost model out-of-the-box without expensive training
data collection. To enable such zero-shot cost models, we suggest a new
learning paradigm based on pre-trained cost models. As core contributions to
support the transfer of such a pre-trained cost model to unseen databases, we
introduce a new model architecture and representation technique for encoding
query workloads as input to those models. As we will show in our evaluation,
zero-shot cost estimation can provide more accurate cost estimates than
state-of-the-art models for a wide range of (real-world) databases without
requiring any query executions on unseen databases. Furthermore, we show that
zero-shot cost models can be used in a few-shot mode that further improves
their quality by retraining them just with a small number of additional
training queries on the unseen database.
