Graph Theory and Algorithms
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Analysis of PostgreSQL and Neo4j database engines’ efficiency compares them across different sizes of genealogical datasets and types of operations on artificially generated data to show that an optimized SQL approach is generally faster and uses less memory than the graph-based counterpart, while requiring more disk space.
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The modern digital age led to the creation of a field called digital genealogy. To this day there seems to be little consensus on which database approach is most suited for such environment; genealogical data remains overlooked in database research. This study examines whether the popular relational SQL database systems prove themselves to be more efficient than graph databases when operating on graph-like genealogical structures. The article analyses PostgreSQL and Neo4j database engines’ efficiency, comparing them across different sizes of genealogical datasets and types of operations on artificially generated data. Results show that an optimized SQL approach is generally faster and uses less memory than the graph-based counterpart, while requiring more disk space, in all tested cases.
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@article{Muzyka2026Comparative,
title = {Comparative analysis of the performance of PostgreSQL and Neo4j databases in the context of genealogical queries},
author = {Michał Muzyka and Mateusz Niedźwiedź and Marek Miłosz},
journal = {Journal of Computer Sciences Institute},
year = {2026},
doi = {10.35784/jcsi.9866},
url = {https://doi.org/10.35784/jcsi.9866}
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