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Demonstrating SQLBarber: Leveraging Large Language Models to Generate Customized and Realistic SQL Workloads

Summary: SQLBarber is an LLM-driven system that generates customized, realistic SQL workloads without prebuilt templates, guided by natural-language constraints. It derives template specifications and cost distributions from Redshift/Snowflake stats, scales to large workloads, and enables exploring cost-realism trade-offs under user constraints. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
7151
Venue
SIGMOD
Year
2025
Pagerank
4.407457e-05
Overall Rank
9,005 | 37.42%
DOI
10.1145/3722212.3725101

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Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank Citing Paper Year Venue Pagerank
9,980 Survivorship Bias in Industrial Database Workloads 2026 CIDR 4.1905499e-05
10,212 SQLBarber: A System Leveraging Large Language Models to Generate Customized and Realistic SQL Workloads 2026 SIGMOD 4.1905499e-05
10,282 Toward Drift-Aware Database Benchmarking 2026 VLDB 4.1905499e-05
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

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