Spider 2.0: Evaluating Language Models on Real-World Enterprise Text-to-SQL Workflows
Fangyu Lei Jixuan Chen Yuxiao Ye Ruisheng Cao Dongchan Shin Hongjin SU ZHAOQING SUO Hongcheng Gao Wenjing Hu Pengcheng Yin Victor Zhong Caiming Xiong Ruoxi Sun Qian Liu Sida Wang Tao Yu
Real-world enterprise text-to-SQL workflows often involve complex cloud or local data across various database systems, multiple SQL queries in various dialects, and diverse operations from data transformation to analytics.
We introduce Spider 2.0, an evaluation framework comprising $632$ real-world text-to-SQL workflow problems derived from enterprise-level database use cases. 
The databases in Spider 2.0 are sourced from real data applications, often containing over 1,000 columns and stored in local or cloud database systems such as BigQuery and Snowflake.
We show that solving problems in Spider 2.0 frequently requires understanding and searching through database metadata, dialect documentation, and even project-level codebases. 
This challenge calls for models to interact with complex SQL workflow environments, process extremely long contexts, perform intricate reasoning, and generate multiple SQL queries with diverse operations, often exceeding $100$ lines, which goes far beyond traditional text-to-SQL challenges.
Our evaluations indicate that based on o1-preview, our code agent framework successfully solves only 21.3\% of the tasks, compared with 91.2\% on Spider 1.0 and 73.0\% on BIRD.
Our results on Spider 2.0 show that while language models have demonstrated remarkable performance in code generation --- especially in prior text-to-SQL benchmarks --- they require significant improvement in order to achieve adequate performance for real-world enterprise usage.
Progress on Spider 2.0 represents crucial steps towards developing intelligent, autonomous, code agents for real-world enterprise settings.
Our code, baseline models, and data are available at [spider2-sql.github.io](spider2-sql.github.io) .
LLM Benchmark, Data Science and Engineering, Code Generation, Text-to-SQL, LLM Agent
A benchmark for enterprise-level Text-to-SQL involving complex databases, challenging tasks, and real-world scenarios.