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: 5333
Venue: SIGMOD
Year: 2016
Pagerank: 9.7368925e-05
Overall Rank: 1,797 | 87.68%
DOI: 10.1145/2882903.2915244

Incoming Non-self Citations Over Time

Authors

1. Amir Shaikhha (EPFL)
2. Yannis Klonatos (EPFL)
3. Lionel Parreaux (EPFL)
4. Lewis Brown (EPFL)
5. Mohammad Dashti (EPFL)
6. Christoph Koch (EPFL)

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 28 of 28 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
649	Everything You Always Wanted to Know About Compiled and Vectorized Queries But Were Afraid to Ask	2018	VLDB	0.00015320656
1,768	Tuplex: Data Science in Python at Native Code Speed	2021	SIGMOD	9.8041636e-05
1,892	Procedural Extensions of SQL: Understanding their usage in the wild	2021	VLDB	9.5277793e-05
2,316	Evaluating End-to-End Optimization for Data Analytics Applications in Weld	2018	VLDB	8.7596739e-05
2,488	How to Architect a Query Compiler, Revisited	2018	SIGMOD	8.5091578e-05
2,769	A Layered Aggregate Engine for Analytics Workloads	2019	SIGMOD	8.1465406e-05
2,823	Query Processing on Tensor Computation Runtimes	2022	VLDB	8.0893814e-05
3,205	On Optimizing Operator Fusion Plans for Large-Scale Machine Learning in SystemML	2018	VLDB	7.6386536e-05
3,284	SPOOF: Sum-Product Optimization and Operator Fusion for Large-Scale Machine Learning	2017	CIDR	7.5663058e-05
3,638	Fast Queries Over Heterogeneous Data Through Engine Customization	2016	VLDB	7.2338361e-05
3,895	The Case For Heterogeneous HTAP	2017	CIDR	7.0416054e-05
4,128	The Relational Data Borg is Learning	2020	VLDB	6.8850804e-05
4,215	Designing an Open Framework for Query Optimization and Compilation	2022	VLDB	6.8275676e-05
4,820	LightSaber: Efficient Window Aggregation on Multi-core Processors	2020	SIGMOD	6.493623e-05
5,301	Babelfish: Efficient Execution of Polyglot Queries	2022	VLDB	6.2750553e-05
5,765	Charting the Design Space of Query Execution using VOILA	2021	VLDB	6.0953705e-05
5,991	Iterative Query Processing based on Unified Optimization Techniques	2019	SIGMOD	6.0171458e-05
6,253	Declarative Sub-Operators for Universal Data Processing	2023	VLDB	5.940599e-05
6,475	WeBridge: Synthesizing Stored Procedures for Large-Scale Real-World Web Applications	2024	SIGMOD	5.8687157e-05
7,461	Bringing Compiling Databases to RISC Architectures	2023	VLDB	5.611858e-05
8,057	Architecting a Query Compiler for Spatial Workloads	2020	SIGMOD	5.4982831e-05
8,960	An Intermediate Representation for Hybrid Database and Machine Learning Workloads	2021	VLDB	5.3444589e-05
9,960	Optimizing Nested Recursive Queries	2024	SIGMOD	5.1879626e-05
10,072	Query Compilation Without Regrets	2024	SIGMOD	5.1624689e-05
10,143	Raqlet: Cross-Paradigm Compilation for Recursive Queries	2026	CIDR	5.093636e-05
10,957	Towards Designing Future-Proof Data Processing Systems	2025	VLDB	5.093636e-05
11,453	Asymptotically Better Query Optimization Using Indexed Algebra	2023	VLDB	5.093636e-05
11,468	Big Data Analytic Toolkit: A general-purpose, modular, and heterogeneous acceleration toolkit for data analytical engines	2023	VLDB	5.093636e-05

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.

Rank	Cited Paper	Year	Venue	Pagerank
12	C-Store: A Column-oriented DBMS	2005	VLDB	0.00069513174
23	Efficiently Compiling Efficient Query Plans for Modern Hardware	2011	VLDB	0.00054886415
24	Spark SQL: Relational Data Processing in Spark	2015	SIGMOD	0.00054865648
241	Morsel-Driven Parallelism: A NUMA-Aware Query Evaluation Framework for the Many-Core Age	2014	SIGMOD	0.00023654664
292	LINQ: Reconciling Objects, Relations and XML in the .NET Framework	2006	SIGMOD	0.00022259549
438	DBToaster: Higher-order Delta Processing for Dynamic, Frequently Fresh Views	2012	VLDB	0.00018471721
632	Adaptive Aggregation on Chip Multiprocessors	2007	VLDB	0.00015575286
925	Dictionary-based Order-preserving String Compression for Main Memory Column Stores	2009	SIGMOD	0.00013182044
960	Incremental Query Evaluation in a Ring of Databases	2010	PODS	0.00012945163
1,291	Managing Intra-operator Parallelism in Parallel Database Systems	1995	VLDB	0.00011307625
2,094	Tupleware: "Big" Data, Big Analytics, Small Clusters	2015	CIDR	9.1819738e-05
3,867	DBToaster: A SQL Compiler for High-Performance Delta Processing in Main-Memory Databases	2009	VLDB	7.0617513e-05
5,356	Code generation for efficient query processing in managed runtimes	2014	VLDB	6.2498397e-05
7,653	Abstraction without regret in data management systems	2013	CIDR	5.575416e-05

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