Cardinality estimation is crucial for optimizing database query performance. It predicts the number of results a query will return, influencing execution plans and join methods. Accurate estimates lead to efficient query execution, while inaccurate ones can slow down performance. Traditional techniques struggle with complex queries, but learned models offer better accuracy. Google’s CardBench provides a benchmark with thousands of queries across real-world databases, making it easier to evaluate learned CE models. CardBench supports three key setups: instance-based models, zero-shot models, and fine-tuned models, allowing for thorough evaluation under various conditions. It includes tools for generating realistic SQL queries and provides training data for single table and binary join queries. Performance evaluations using CardBench show promising results, especially for fine-tuned models. These models achieve comparable accuracy to instance-based methods with less training data, making them practical for real-world applications. CardBench represents a significant advancement in learned cardinality estimation, offering a practical solution for evaluating and comparing different CE models. It lowers the barrier for researchers interested in developing and testing new CE models, fostering further innovation in this critical area. To revolutionize learned cardinality estimation and evolve your company with AI, connect with us at hello@itinai.com for AI KPI management advice. Follow us on Telegram or Twitter for continuous insights into leveraging AI. Useful Links: - AI Lab in Telegram @itinai – free consultation - Twitter – @itinaicom
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