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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 50x 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.
Comment: The paper has been accepted by ICDE 2025 |
