Efficiently Compiling Efficient Query Plans for Modern Hardware

By Thomas Neumann. Proceedings of the VLDB Endowment, 2011

627 citations recorded by the source corpus.

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Morsel-driven parallelism: a NUMA-aware query evaluation framework for the many-core age (2014, methodology): “[8] P. Boncz, T. Neumann, and O. Erling.”
In-Memory Big Data Management and Processing: A Survey (2015, background): “There have been some criticisms on the traditional iterator-style query processing mechanisms for in-memory databases recently as it usually results in poor code and data locality [36], [112], [167].”
Cypher: An Evolving Query Language for Property Graphs (2018, methodology): “The final query compilation uses either a simple tuple-at-a-time iterator-based execution model, or compiles the query to Java bytecode with a push-based execution model based on [46].”
DuckDB: an Embeddable Analytical Database (2019, methodology): “This approach was chosen over Just-in-Time compilation (JIT) of SQL queries [8] for portability reasons.”
The adaptive radix tree: ARTful indexing for main-memory databases (2013, methodology): “Transactions are implemented in a scripting language which is compiled to assembler-like LLVM instructions [31].”
The Snowflake Elastic Data Warehouse (2016, neutral): “control flow logic from tight loops [41].”
Hekaton: SQL server's memory-optimized OLTP engine (2013, background): “Using functions and inlining with multiple outer joins, there is a risk of an exponential growth in code size [14].”