mlsanityData to 7 Oct 2026

Neo: A Learned Query Optimizer

arXiv 1904.03711 ↗First posted 7 Apr 2019cs.DBcited by 80

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Abstract (arXiv)

Query optimization is one of the most challenging problems in database systems. Despite the progress made over the past decades, query optimizers remain extremely complex components that require a great deal of hand-tuning for specific workloads and datasets. Motivated by this shortcoming and inspired by recent advances in applying machine learning to data management challenges, we introduce Neo (Neural Optimizer), a novel learning-based query optimizer that relies on deep neural networks to generate query executions plans. Neo bootstraps its query optimization model from existing optimizers and continues to learn from incoming queries, building upon its successes and learning from its failures. Furthermore, Neo naturally adapts to underlying data patterns and is robust to estimation errors. Experimental results demonstrate that Neo, even when bootstrapped from a simple optimizer like PostgreSQL, can learn a model that offers similar performance to state-of-the-art commercial optimizers, and in some cases even surpass them.

Key facts. Cited by 80 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Apr 2019.

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Citing papers per year: 2021: 15; 2022: 11; 2023: 7; 2024: 12; 2025: 22; 2026: 13.

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  1. 2021Federated Learning on Non-IID Data Silos: An Experimental Studycited by 566
  2. 2025Barbarians at the Gate: How AI is Upending Systems Researchcited by 48
  3. 2023Lero: A Learning-to-Rank Query Optimizercited by 26
  4. 2021DB-BERT: a Database Tuning Tool that "Reads the Manual"cited by 19
  5. 2022Balsa: Learning a Query Optimizer Without Expert Demonstrationscited by 18
  6. 2022Zero-Shot Cost Models for Out-of-the-box Learned Cost Predictioncited by 16
  7. 2021A Survey on Advancing the DBMS Query Optimizer: Cardinality Estimation, Cost Model, and Plan Enumerationcited by 14
  8. 2025Glia: A Human-Inspired AI for Automated Systems Design and Optimizationcited by 14
  9. 2022FactorJoin: A New Cardinality Estimation Framework for Join Queriescited by 13
  10. 2025Cortex AISQL: A Production SQL Engine for Unstructured Datacited by 11
  11. 2021A Unified Transferable Model for ML-Enhanced DBMScited by 10
  12. 2021One Model to Rule them All: Towards Zero-Shot Learning for Databasescited by 9
38 more
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  2. 2022Detect, Distill and Update: Learned DB Systems Facing Out of Distribution Datacited by 6
  3. 2023Online Reinforcement Learning in Non-Stationary Context-Driven Environmentscited by 6
  4. 2025Graph Neural Networks for Databases: A Surveycited by 6
  5. 2025QUITE: A Query Rewrite System Beyond Rules with LLM Agentscited by 6
  6. 2025Batch Query Processing and Optimization for Agentic Workflowscited by 6
  7. 2022CodexDB: Generating Code for Processing SQL Queries using GPT-3 Codexcited by 5
  8. 2024NeurDB: An AI-powered Autonomous Data Systemcited by 5
  9. 2021Efficient Deep Learning Pipelines for Accurate Cost Estimations Over Large Scale Query Workloadcited by 4
  10. 2021Learned Sorted Table Search and Static Indexes in Small Model Spacecited by 4
  11. 2022Learning to Improve Code Efficiencycited by 4
  12. 2023Real-time Workload Pattern Analysis for Large-scale Cloud Databasescited by 4
  13. 2024Sibyl: Forecasting Time-Evolving Query Workloadscited by 4
  14. 2024A Survey of Learned Indexes for the Multi-dimensional Spacecited by 4
  15. 2025RCRank: Multimodal Ranking of Root Causes of Slow Queries in Cloud Database Systemscited by 4
  16. 2023Kepler: Robust Learning for Faster Parametric Query Optimizationcited by 3
  17. 2024AnnotatedTables: A Large Tabular Dataset with Language Model Annotationscited by 3
  18. 2024Learned Graph Rewriting with Equality Saturation: A New Paradigm in Relational Query Rewrite and Beyondcited by 3
  19. 2024HERO: Hint-Based Efficient and Reliable Query Optimizercited by 3
  20. 2021Can Deep Neural Networks Predict Data Correlations from Column Names?cited by 2
  21. 2022Demystifying Reinforcement Learning in Time-Varying Systemscited by 2
  22. 2022Deploying a Steered Query Optimizer in Production at Microsoftcited by 2
  23. 2022Learned-Database Systems Securitycited by 2
  24. 2023ML-Powered Index Tuning: An Overview of Recent Progress and Open Challengescited by 2
  25. 2025Towards Agentic OS: An LLM Agent Framework for Linux Schedulerscited by 2
  26. 2025Supporting Dynamic Agentic Workloads: How Data and Agents Interactcited by 2
  27. 2025Let the Barbarians In: How AI Can Accelerate Systems Performance Researchcited by 2
  28. 2025Cost Trade-offs of Reasoning and Non-Reasoning Large Language Models in Text-to-SQLcited by 2
  29. 2026AI-Driven Research for Databasescited by 2
  30. 2021A Pluggable Learned Index Method via Sampling and Gap Insertioncited by 1
  31. 2021TENSILE: A Tensor granularity dynamic GPU memory scheduling method toward multiple dynamic workloads systemcited by 1
  32. 2021Phoebe: A Learning-based Checkpoint Optimizercited by 1
  33. 2023JoinGym: An Efficient Query Optimization Environment for Reinforcement Learningcited by 1
  34. 2023FOSS: A Self-Learned Doctor for Query Optimizercited by 1
  35. 2024GEqO: ML-Accelerated Semantic Equivalence Detectioncited by 1
  36. 2024Robust Plan Evaluation based on Approximate Probabilistic Machine Learningcited by 1
  37. 2024A Novel Technique for Query Plan Representation Based on Graph Neural Netscited by 1
  38. 2024Hype or Heuristic? Quantum Reinforcement Learning for Join Order Optimisationcited by 1

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