The traditional cost-based query optimizer approach enumerates different execution plans for each individual query, assesses each plan with costs, and selects the plan that promises the lowest costs for execution. However, - as we all know - the optimal execution plan is not always selected. To steer the optimizer in the right direction, many database systems provide optimizer hints. These hints can be set for workloads, individual queries or even for query fragments.
Within this talk, we first show the potential of optimizer hinting by presenting the results of a comprehensive and in-depth evaluation using three benchmarks and two different versions of the open-source database system PostgreSQL. Subsequently, we highlight that query optimizer hinting is a nontrivial challenge and show two potential solutions: On the one hand, we propose TONIC, a novel cardinality estimation-free extension for generic SPJ query optimizers. TONIC follows a learning-based approach and revises operator decisions for arbitrary join paths based on learned query feedback. To continuously capture and reuse optimal operator selections, we introduce a lightweight yet powerful Query Execution Plan Synopsis (QEP-S). On the other hand, we provide insights into FASTgres, a context-aware classification strategy for a more holistic hint set prediction. Both strategies show in the context of end-to-end evaluations significant reductions of benchmark runtimes.
