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SQLBarber: A System Leveraging Large Language Models to Generate Customized and Realistic SQL Workloads
Summary: SQLBarber uses LLMs to synthesize realistic, customizable SQL workloads from natural-language constraints, avoiding hand-written templates. It couples self-correcting template generation with Bayesian cost targeting to match production-derived cardinality/plan-cost distributions and scales to large benchmark synthesis.
(summarized by gpt-5-mini on Apr 11 2026)
- Paper ID
- 7525
- Venue
- SIGMOD
- Year
- 2026
- Pagerank
- 4.1905499e-05
- Overall Rank
- 10,212 | 29.03%
- DOI
-
10.1145/3786699
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Incoming Citations (Sorted by Pagerank)
Showing 0 of 0 citing papers.
| Rank |
Citing Paper |
Year |
Venue |
Pagerank |
Outgoing Citations (Sorted by Pagerank)
Showing 20 of 20 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank |
Cited Paper |
Year |
Venue |
Pagerank |
| 71 |
How Good Are Query Optimizers, Really? |
2016 |
VLDB |
0.00059446482 |
| 997 |
CAESURA: Language Models as Multi-Modal Query Planners |
2024 |
CIDR |
0.00014726927 |
| 998 |
CodeS: Towards Building Open-source Language Models for Text-to-SQL |
2024 |
SIGMOD |
0.00014726344 |
| 1,404 |
DB-BERT: A Database Tuning Tool that "Reads the Manual" |
2022 |
SIGMOD |
0.00012179714 |
| 1,631 |
CodexDB: Synthesizing Code for Query Processing from Natural Language Instructions using GPT-3 Codex |
2022 |
VLDB |
0.00011071662 |
| 2,013 |
Palimpzest: Optimizing AI-Powered Analytics with Declarative Query Processing |
2025 |
CIDR |
9.7986166e-05 |
| 2,280 |
Generating Targeted Queries for Database Testing |
2008 |
SIGMOD |
9.1247368e-05 |
| 3,105 |
GPTuner: A Manual-Reading Database Tuning System via GPT-Guided Bayesian Optimization |
2024 |
VLDB |
7.5567226e-05 |
| 3,131 |
Why TPC Is Not Enough: An Analysis of the Amazon Redshift Fleet |
2024 |
VLDB |
7.5054309e-05 |
| 3,435 |
Real-time Workload Pattern Analysis for Large-scale Cloud Databases |
2023 |
VLDB |
7.0946114e-05 |
| 3,465 |
LLM-R2: A Large Language Model Enhanced Rule-based Rewrite System for Boosting Query Efficiency |
2025 |
VLDB |
7.0668293e-05 |
| 4,681 |
Cloud Analytics Benchmark |
2023 |
VLDB |
5.9953512e-05 |
| 5,206 |
ThalamusDB: Approximate Query Processing on Multi-Modal Data |
2024 |
SIGMOD |
5.625641e-05 |
| 5,289 |
LearnedSQLGen: Constraint-aware SQL Generation using Reinforcement Learning |
2022 |
SIGMOD |
5.5790771e-05 |
| 5,944 |
SAM: Database Generation from Query Workloads with Supervised Autoregressive Models |
2022 |
SIGMOD |
5.2583712e-05 |
| 7,676 |
E2ETune: End-to-End Knob Tuning via Fine-tuned Generative Language Model |
2025 |
VLDB |
4.6770108e-05 |
| 7,887 |
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2025 |
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4.6218382e-05 |
| 7,994 |
Blueprinting the Cloud: Unifying and Automatically Optimizing Cloud Data Infrastructures with BRAD |
2024 |
VLDB |
4.607322e-05 |
| 9,005 |
Demonstrating SQLBarber: Leveraging Large Language Models to Generate Customized and Realistic SQL Workloads |
2025 |
SIGMOD |
4.407457e-05 |
| 10,277 |
SemBench: A Benchmark for Semantic Query Processing Engines |
2026 |
VLDB |
4.1905499e-05 |
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