What question did this study set out to answer?

This research aims to enhance the sorting capabilities of EmbedDB for embedded systems with limited memory.

May 8, 2026Open Access

Translating research algorithms into production systems : Adding sorting to EmbedDB

Key Points

This research aims to enhance the sorting capabilities of EmbedDB for embedded systems with limited memory.
Improved interfaces and memory management were developed for EmbedDB.
Robust testing was conducted to ensure functionality and performance.
Benchmarks were compared between EmbedDB and SQLite across various devices.
SQLite demonstrated faster sorting speeds across all tested devices.
EmbedDB showed improved relative performance on less capable hardware.
Sorting enhancements allow for GROUP BY functionality on any column.

Abstract

Embedded systems with constrained random access memory require external sorting algorithms that carefully manage memory across buffer allocation and SD file read/write operations. This thesis advances the EmbedDB sorting implementation to a production-ready state by introducing improved interfaces, robust memory management, and comprehensive testing. The sorting features also allow the implementation of GROUP BY on any column that can be used in conjunction with the existing aggregate methods and queries. Benchmarks between EmbedDB and SQLite sorting on different devices is performed, showing faster speeds for SQLite but improved relative performance for EmbedDB on less performant hardware.

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Cite This Study

Alexander Argatoff (Thu,) studied this question.

synapsesocial.com/papers/69fd7f0dbfa21ec5bbf0760d https://doi.org/https://doi.org/10.14288/1.0452106

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