mlsanityData to 7 Oct 2026

Apache Calcite: A Foundational Framework for Optimized Query Processing Over Heterogeneous Data Sources

arXiv 1802.10233 ↗First posted 28 Feb 2018cs.DBcited by 24

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Abstract (arXiv)

Apache Calcite is a foundational software framework that provides query processing, optimization, and query language support to many popular open-source data processing systems such as Apache Hive, Apache Storm, Apache Flink, Druid, and MapD. Calcite's architecture consists of a modular and extensible query optimizer with hundreds of built-in optimization rules, a query processor capable of processing a variety of query languages, an adapter architecture designed for extensibility, and support for heterogeneous data models and stores (relational, semi-structured, streaming, and geospatial). This flexible, embeddable, and extensible architecture is what makes Calcite an attractive choice for adoption in big-data frameworks. It is an active project that continues to introduce support for the new types of data sources, query languages, and approaches to query processing and optimization.

Key facts. Cited by 24 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 Feb 2018.

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Citing papers per year: 2022: 2; 2023: 4; 2024: 6; 2025: 7; 2026: 5.

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  1. 2023D-Bot: Database Diagnosis System using Large Language Modelscited by 28
  2. 2024LLM-R2: A Large Language Model Enhanced Rule-based Rewrite System for Boosting Query Efficiencycited by 16
  3. 2024R-Bot: An LLM-based Query Rewrite Systemcited by 16
  4. 2022Query Processing on Tensor Computation Runtimescited by 12
  5. 2023LLM-SQL-Solver: Can LLMs Determine SQL Equivalence?cited by 10
  6. 2024LLM-Enhanced Data Managementcited by 7
  7. 2025Graph-Reward-SQL: Execution-Free Reinforcement Learning for Text-to-SQL via Graph Matching and Stepwise Rewardcited by 7
  8. 2025QUITE: A Query Rewrite System Beyond Rules with LLM Agentscited by 6
  9. 2022Analytical Engines With Context-Rich Processing: Towards Efficient Next-Generation Analyticscited by 4
  10. 2023A Unified and Efficient Coordinating Framework for Autonomous DBMS Tuningcited by 4
  11. 2024Sibyl: Forecasting Time-Evolving Query Workloadscited by 4
  12. 2024Learned Graph Rewriting with Equality Saturation: A New Paradigm in Relational Query Rewrite and Beyondcited by 3
12 more
  1. 2025OpenMLDB: A Real-Time Relational Data Feature Computation System for Online MLcited by 3
  2. 2023ML-Powered Index Tuning: An Overview of Recent Progress and Open Challengescited by 2
  3. 2025A Learned Cost Model-based Cross-engine Optimizer for SQL Workloadscited by 2
  4. 2026AI-Driven Research for Databasescited by 2
  5. 2024FuncEvalGMN: Evaluating Functional Correctness of SQL via Graph Matching Networkcited by 1
  6. 2025Low Rank Learning for Offline Query Optimizationcited by 0
  7. 2025InferF: Declarative Factorization of AI/ML Inferences over Joinscited by 0
  8. 2025Bridging Global Intent with Local Details: A Hierarchical Representation Approach for Semantic Validation in Text-to-SQLcited by 0
  9. 2026Beyond Text-to-SQL: Can LLMs Really Debug Enterprise ETL SQL?cited by 0
  10. 2026Health+: Empowering Individuals via Unifying Health Datacited by 0
  11. 2026RedParrot: Accelerating NL-to-DSL for Business Analytics via Query Semantic Cachingcited by 0
  12. 2026Closing the Cross-Dialect Gap: Query Plans as a Portable Interface in Text-to-SQLcited by 0