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How to Architect a Query Compiler

Summary: Proposes a multi-DSL, multi-stage lowering architecture for query compilers to replace monolithic template expanders. Leverages PL/compiler ideas to build modular DSL stacks with staged lowering, enabling richer optimizations and easier maintenance; demonstrated by recreating a classic query compiler. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
hcb4a928e001a1cd3
Venue
SIGMOD
Year
2016
Pagerank
9.7378418e-05
Overall Rank
1,742 | 88.29%
DOI
10.1145/2882903.2915244

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{shaikhha_sigmod16,
        title = {{How to Architect a Query Compiler}},
        author = {Shaikhha, Amir and Klonatos, Yannis and Parreaux, Lionel and Brown, Lewis and Dashti, Mohammad and Koch, Christoph},
        series = {{SIGMOD} '16},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/2882903.2915244},
        url = {https://dl.acm.org/doi/10.1145/2882903.2915244},
        year = {2016}
}

Incoming Citations (Sorted by Pagerank)

Showing 29 of 29 citing papers.

Rank Citing Paper Year Venue Pagerank
605 Everything You Always Wanted to Know About Compiled and Vectorized Queries But Were Afraid to Ask 2018 VLDB 0.00015647561
1,786 Procedural Extensions of SQL: Understanding their usage in the wild 2021 VLDB 9.6326265e-05
1,803 Tuplex: Data Science in Python at Native Code Speed 2021 SIGMOD 9.6068397e-05
2,249 Evaluating End-to-End Optimization for Data Analytics Applications in Weld 2018 VLDB 8.7549752e-05
2,419 How to Architect a Query Compiler, Revisited 2018 SIGMOD 8.4923505e-05
2,460 Query Processing on Tensor Computation Runtimes 2022 VLDB 8.4348335e-05
2,663 A Layered Aggregate Engine for Analytics Workloads 2019 SIGMOD 8.1581558e-05
3,101 On Optimizing Operator Fusion Plans for Large-Scale Machine Learning in SystemML 2018 VLDB 7.649219e-05
3,330 SPOOF: Sum-Product Optimization and Operator Fusion for Large-Scale Machine Learning 2017 CIDR 7.4173693e-05
3,505 Fast Queries Over Heterogeneous Data Through Engine Customization 2016 VLDB 7.2508161e-05
3,935 The Case For Heterogeneous HTAP 2017 CIDR 6.9166006e-05
3,985 Designing an Open Framework for Query Optimization and Compilation 2022 VLDB 6.8730085e-05
4,161 The Relational Data Borg is Learning 2020 VLDB 6.7700593e-05
4,918 LightSaber: Efficient Window Aggregation on Multi-core Processors 2020 SIGMOD 6.3548575e-05
5,230 Babelfish: Efficient Execution of Polyglot Queries 2022 VLDB 6.2189001e-05
5,543 Charting the Design Space of Query Execution using VOILA 2021 VLDB 6.0889645e-05
6,088 Iterative Query Processing based on Unified Optimization Techniques 2019 SIGMOD 5.8908778e-05
6,296 Declarative Sub-Operators for Universal Data Processing 2023 VLDB 5.8195258e-05
6,575 WeBridge: Synthesizing Stored Procedures for Large-Scale Real-World Web Applications 2024 SIGMOD 5.7453775e-05
7,580 Bringing Compiling Databases to RISC Architectures 2023 VLDB 5.491668e-05
8,207 Architecting a Query Compiler for Spatial Workloads 2020 SIGMOD 5.3778259e-05
8,356 Optimizing Nested Recursive Queries 2024 SIGMOD 5.3481891e-05
8,482 An Intermediate Representation for Hybrid Database and Machine Learning Workloads 2021 VLDB 5.3327903e-05
9,472 Query Compilation Without Regrets 2024 SIGMOD 5.1711207e-05
9,794 Raqlet: Cross-Paradigm Compilation for Recursive Queries 2026 CIDR 5.1257999e-05
10,889 Rhyme Native: Efficient Code Generation for Structured and Semi-Structured Workloads 2026 VLDB 4.9793485e-05
11,344 Towards Designing Future-Proof Data Processing Systems 2025 VLDB 4.9793485e-05
11,765 Asymptotically Better Query Optimization Using Indexed Algebra 2023 VLDB 4.9793485e-05
11,779 Big Data Analytic Toolkit: A general-purpose, modular, and heterogeneous acceleration toolkit for data analytical engines 2023 VLDB 4.9793485e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 14 of 14 cited papers.

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

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