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Balsa: Learning a Query Optimizer Without Expert Demonstrations

Summary: Balsa uses deep RL to learn a query optimizer without expert demonstrations, from a simple simulator to safe real-execution fine-tuning. On Join Order Benchmark, it matches two expert optimizers after ~2 hours and beats them up to 2.8x with training. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6337
Venue
SIGMOD
Year
2022
Pagerank
9.5017232e-05
Overall Rank
2,121 | 85.25%
DOI
10.1145/3514221.3517885

Incoming Non-self Citations Over Time

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Incoming Citations (Sorted by Pagerank)

Showing 50 of 52 citing papers.

Rank Citing Paper Year Venue Pagerank
3,348 Lero: A Learning-to-Rank Query Optimizer 2023 VLDB 7.1904529e-05
4,462 LOGER: A Learned Optimizer towards Generating Efficient and Robust Query Execution Plans 2023 VLDB 6.1611784e-05
4,593 Auto-WLM: Machine Learning Enhanced Workload Management in Amazon Redshift 2023 SIGMOD 6.0606891e-05
5,334 LEON: A New Framework for ML-Aided Query Optimization 2023 VLDB 5.5649836e-05
5,423 Kepler: Robust Learning for Faster Parametric Query Optimization 2023 SIGMOD 5.5130233e-05
5,640 AutoSteer: Learned Query Optimization for Any SQL Database 2023 VLDB 5.3933314e-05
5,832 Stage: Query Execution Time Prediction in Amazon Redshift 2024 SIGMOD 5.3111109e-05
5,930 FASTgres: Making Learned Query Optimizer Hinting Effective 2023 VLDB 5.2682075e-05
5,952 Eraser: Eliminating Performance Regression on Learned Query Optimizer 2024 VLDB 5.2591691e-05
6,383 Sample-Efficient Cardinality Estimation Using Geometric Deep Learning 2024 VLDB 5.0884322e-05
6,685 How Good are Learned Cost Models, Really? Insights from Query Optimization Tasks 2025 SIGMOD 4.9627485e-05
6,862 Join Order Selection with Deep Reinforcement Learning: Fundamentals, Techniques, and Challenges 2023 VLDB 4.9051979e-05
6,885 PilotScope: Steering Databases with Machine Learning Drivers 2024 VLDB 4.895386e-05
7,008 Is Your Learned Query Optimizer Behaving As You Expect? A Machine Learning Perspective 2024 VLDB 4.8643538e-05
7,011 Simple Adaptive Query Processing vs. Learned Query Optimizers: Observations and Analysis 2023 VLDB 4.8629458e-05
7,221 Speeding Up End-to-end Query Execution via Learning-based Progressive Cardinality Estimation 2023 SIGMOD 4.797194e-05
7,330 Lemo: A Cache-Enhanced Learned Optimizer for Concurrent Queries 2023 SIGMOD 4.7609373e-05
7,753 Rethinking Learned Cost Models: Why Start from Scratch? 2023 SIGMOD 4.660151e-05
8,020 The Holon Approach for Simultaneously Tuning Multiple Components in a Self-Driving Database Management System with Machine Learning via Synthesized Proto-Actions 2024 VLDB 4.6040862e-05
8,186 E2ETune: End-to-End Knob Tuning via Fine-tuned Generative Language Model 2025 VLDB 4.5651684e-05
8,345 SlabCity: Whole-Query Optimization using Program Synthesis 2023 VLDB 4.5426916e-05
8,488 Can Large Language Models Be Query Optimizer for Relational Databases? 2026 SIGMOD 4.4998609e-05
8,659 Learned Offline Query Planning via Bayesian Optimization 2025 SIGMOD 4.4722928e-05
8,956 T3: Accurate and Fast Performance Prediction for Relational Database Systems With Compiled Decision Trees 2025 SIGMOD 4.4214154e-05
9,006 Hit the Gym: Accelerating Query Execution to Efficiently Bootstrap Behavior Models for Self-Driving Database Management Systems 2024 VLDB 4.4101482e-05
9,108 BASE: Bridging the Gap between Cost and Latency for Query Optimization 2023 VLDB 4.3950066e-05
9,326 BladeDISC: Optimizing Dynamic Shape Machine Learning Workloads via Compiler Approach 2023 SIGMOD 4.3556432e-05
9,345 LIMAO: A Framework for Lifelong Modular Learned Query Optimization 2025 VLDB 4.3536343e-05
9,587 Low Rank Learning for Offline Query Optimization 2025 SIGMOD 4.3215645e-05
9,628 Approximate Sketches 2024 SIGMOD 4.3143499e-05
9,825 Athena: An Effective Learning-based Framework for Query Optimizer Performance Improvement 2025 SIGMOD 4.2751057e-05
9,960 An Elephant Under The Microscope: Analyzing The Interaction Of Optimizer Components In PostgreSQL 2025 SIGMOD 4.2294678e-05
10,018 GenJoin: Conditional Generative Plan-to-Plan Query Optimizer that Learns from Subplan Hints 2026 SIGMOD 4.1945683e-05
10,032 Rainbow: Risk-aware Index Benefit Estimation Facing Out Of Distribution Workloads 2026 SIGMOD 4.1945683e-05
10,050 APQO: An Adaptive Framework for Parametric Query Optimization 2026 SIGMOD 4.1945683e-05
10,096 NeuSO: Neural Optimizer for Subgraph Queries 2026 SIGMOD 4.1945683e-05
10,112 SEFRQO: A Self-Evolving Fine-Tuned RAG-Based Query Optimizer 2026 SIGMOD 4.1945683e-05
10,156 Divo: Learning a Stable and Effective Query Optimizer with a Diverse Workload 2026 SIGMOD 4.1945683e-05
10,219 Practical Parameterized Query Optimization via Efficient Plan Reuse and List-wise Ranking 2026 SIGMOD 4.1945683e-05
10,225 LIO: A lightweight and interpretable query optimizer based on an evolutionary forest 2026 VLDB 4.1945683e-05
10,227 Sample-based Distinct Cardinality Estimation for Multiple Attributes in Multi-Dataset Queries 2026 VLDB 4.1945683e-05
10,271 OBELISK: Efficient Offline Query Planning with Bayesian Optimization-Informed Language Model Reasoning 2026 VLDB 4.1945683e-05
10,288 TATA: An Efficient Framework for Task Transfer in Query Plan Representation 2026 VLDB 4.1945683e-05
10,627 Robust Plan Evaluation based on Approximate Probabilistic Machine Learning 2025 VLDB 4.1945683e-05
10,630 Conformal Prediction for Verifiable Learned Query Optimization 2025 VLDB 4.1945683e-05
10,726 Improving DBMS Scheduling Decisions with Accurate Performance Prediction on Concurrent Queries 2025 VLDB 4.1945683e-05
10,772 veDB-HTAP: a Highly Integrated, Efficient and Adaptive HTAP System 2025 VLDB 4.1945683e-05
10,840 Learned Cost Models for Query Optimization: From Batch to Streaming Systems 2025 VLDB 4.1945683e-05
10,859 Graph Transformers for Query Plan Representation: Potentials and Challenges 2025 VLDB 4.1945683e-05
10,868 LEAP: A Low-cost Spark SQL Query Optimizer using Pairwise Comparison 2025 VLDB 4.1945683e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 16 of 16 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

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