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

arXiv: https://arxiv.org/abs/1802.10233 · HTML: https://mlsanity.com/paper/1802.10233/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Feb 2018.

## Built on by (most cited first, 25 of 25)

- [D-Bot: Database Diagnosis System using Large Language Models](https://mlsanity.com/paper/2312.01454/) (2023)
- [LLM-R2: A Large Language Model Enhanced Rule-based Rewrite System for Boosting Query Efficiency](https://mlsanity.com/paper/2404.12872/) (2024)
- [R-Bot: An LLM-based Query Rewrite System](https://mlsanity.com/paper/2412.01661/) (2024)
- [Query Processing on Tensor Computation Runtimes](https://mlsanity.com/paper/2203.01877/) (2022)
- [LLM-SQL-Solver: Can LLMs Determine SQL Equivalence?](https://mlsanity.com/paper/2312.10321/) (2023)
- [QUITE: A Query Rewrite System Beyond Rules with LLM Agents](https://mlsanity.com/paper/2506.07675/) (2025)
- [LLM-Enhanced Data Management](https://mlsanity.com/paper/2402.02643/) (2024)
- [Graph-Reward-SQL: Execution-Free Reinforcement Learning for Text-to-SQL via Graph Matching and Stepwise Reward](https://mlsanity.com/paper/2505.12380/) (2025)
- [Analytical Engines With Context-Rich Processing: Towards Efficient Next-Generation Analytics](https://mlsanity.com/paper/2212.07517/) (2022)
- [A Unified and Efficient Coordinating Framework for Autonomous DBMS Tuning](https://mlsanity.com/paper/2303.05710/) (2023)
- [Sibyl: Forecasting Time-Evolving Query Workloads](https://mlsanity.com/paper/2401.03723/) (2024)
- [Learned Graph Rewriting with Equality Saturation: A New Paradigm in Relational Query Rewrite and Beyond](https://mlsanity.com/paper/2407.12794/) (2024)
- [OpenMLDB: A Real-Time Relational Data Feature Computation System for Online ML](https://mlsanity.com/paper/2501.08591/) (2025)
- [ML-Powered Index Tuning: An Overview of Recent Progress and Open Challenges](https://mlsanity.com/paper/2308.13641/) (2023)
- [A Learned Cost Model-based Cross-engine Optimizer for SQL Workloads](https://mlsanity.com/paper/2506.02802/) (2025)
- [AI-Driven Research for Databases](https://mlsanity.com/paper/2604.06566/) (2026)
- [FuncEvalGMN: Evaluating Functional Correctness of SQL via Graph Matching Network](https://mlsanity.com/paper/2407.14530/) (2024)
- [Low Rank Learning for Offline Query Optimization](https://mlsanity.com/paper/2504.06399/) (2025)
- [InferF: Declarative Factorization of AI/ML Inferences over Joins](https://mlsanity.com/paper/2511.20489/) (2025)
- [Bridging Global Intent with Local Details: A Hierarchical Representation Approach for Semantic Validation in Text-to-SQL](https://mlsanity.com/paper/2512.22744/) (2025)
- [Beyond Text-to-SQL: Can LLMs Really Debug Enterprise ETL SQL?](https://mlsanity.com/paper/2601.18119/) (2026)
- [Health+: Empowering Individuals via Unifying Health Data](https://mlsanity.com/paper/2602.19319/) (2026)
- [RedParrot: Accelerating NL-to-DSL for Business Analytics via Query Semantic Caching](https://mlsanity.com/paper/2604.22758/) (2026)
- [Closing the Cross-Dialect Gap: Query Plans as a Portable Interface in Text-to-SQL](https://mlsanity.com/paper/2609.33670/) (2026)
- [DSABench: Benchmarking Source Discovery, Coordination, and Execution for Multi-Database Agents](https://mlsanity.com/paper/2610.14700/) (2026)

## 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.