Publication
GRACEFUL: A Learned Cost Estimator for UDFs
Johannes Wehrstein; Tiemo Bang; Roman Heinrich; Carsten Binnig
In: 41st IEEE International Conference on Data Engineering, ICDE 2025, Hong Kong, May 19-23, 2025. IEEE International Conference on Data Engineering (ICDE), Pages 2450-2463, IEEE, 2025.
Abstract
User-Defined-Functions (UDFs) are a pivotal feature
in modern DBMS, enabling the extension of native DBMS
functionality with custom logic. However, the integration of UDFs
into query optimization processes poses significant challenges,
primarily due to the difficulty of estimating UDF execution
costs. Consequently, existing cost models in DBMS optimizers
largely ignore UDFs or rely on static assumptions, resulting
in suboptimal performance for queries involving UDFs. In this
paper, we introduce GRACEFUL, a novel learned cost model
to make accurate cost predictions of query plans with UDFs
enabling optimization decisions for UDFs in DBMS. For example,
as we show in our evaluation, using our cost model, we can
achieve 50× speedups through informed pull-up/push-down filter
decisions of the UDF compared to the standard case where always
a filter push-down is applied. Additionally, we release a synthetic
dataset of over 90,000 UDF queries to promote further research
in this area.