Issue #103: Google creates new models benchmark for cardinality estimation
Introducing CardBench
Welcome to Issue #103 of One Minute AI, your daily AI news companion. This issue discusses a recent announcement from Google.
What is CardBench?
Cardinality estimation (CE) is a critical component in optimizing query performance in relational databases, as it predicts the number of intermediate results a query will produce. Accurate cardinality estimates enable query optimizers to select the most efficient execution plans, significantly impacting overall database performance. However, traditional CE methods rely on heuristics and simplified models, which often lead to inaccurate predictions, especially for complex queries involving multiple tables. Although learned CE models offer improved accuracy by using data-driven approaches, they face challenges such as high training overheads and the need for large datasets, limiting their practical adoption.
To address these challenges, researchers from Google AI introduced CardBench, a comprehensive benchmark designed to evaluate learned CE models systematically. CardBench includes thousands of queries across 20 distinct real-world databases, allowing for robust assessments under diverse conditions. The benchmark supports three setups: instance-based models trained on a single dataset, zero-shot models tested on unseen datasets after pre-training on multiple datasets, and fine-tuned models that achieve high accuracy with minimal training data. CardBench's design, which includes tools for generating SQL queries and creating annotated query graphs, provides a significant resource for developing and testing new CE models, promoting innovation and practical solutions in database management.
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