Investigation of Key-Value Model Performance in PostgreSQL and Redis: A Comparative Analysis of Read/Write Operations and Parallel Load Impact

Ratmir Voloshin; Anatoly Khomonenko; Stepan Yudnikov

doi:10.20295/2413-2527-2026-347-44-55

VAK Russia 2.3.5 UDC 004.65 VAK Russia 2.9.8

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INVESTIGATION OF KEY-VALUE MODEL PERFORMANCE IN POSTGRESQL AND REDIS: A COMPARATIVE ANALYSIS OF READ/WRITE OPERATIONS AND PARALLEL LOAD IMPACT Investigation of Key-Value Model Performance in PostgreSQL and Redis: A Comparative Analysis of Read/Write Operations and Parallel Load Impact

АА Аа

Published in Intellectual Technologies on Transport · Issue 3, 2026 · Pages 44–55 · Rubric: MATHEMATICAL AND SOFTWARE SUPPORT FOR COMPUTER COMPLEXES AND NETWORKS

DOI: https://doi.org/10.20295/2413-2527-2026-347-44-55 · EDN: QCMWWH

Received: 09.08.2026 Accepted: 26.08.2026 Published: 01.10.2026

Authors

Ratmir Voloshin

Anatoly Khomonenko

Stepan Yudnikov

Ratmir Voloshin

Emperor Alexander I St. Petersburg State Transport University Russian Federation

Anatoly Khomonenko

Academic degree: doctor of technical sciences

Science rang: professor

Emperor Alexander I St. Petersburg State Transport University Professor · “Information and Computing Systems” Department · Russian Federation · Saint Petersburg

Stepan Yudnikov

Emperor Alexander I St. Petersburg State Transport University “Information and Computing Systems” Department · Russian Federation · Saint Petersburg

ABSTRACT

An experimentally substantiated approach to comparing the performance of the key-value model implementation in PostgreSQL and Redis is proposed. Purpose: to evaluate the latency and throughput of read and write operations under varying degrees of parallel load. Methods: analysis of PostgreSQL architecture (MVCC, WAL, B-tree and GIN indexes, jsonb and hstore types) and Redis architecture (in-memory storage, strings and hashes, TTL, pipelining); creation of an experimental testbed; generation of a test table with 1,000,000 records; load testing using pgbench and redis-benchmark with parallelism from 1 to 128 clients; measurement of throughput, average and tail latencies (p95, p99). Results: Redis provides 5–10 times lower read latency and 3–5 times higher throughput compared to PostgreSQL for point GET/SET operations; the use of pipelining additionally increases throughput by 2–3 times; PostgreSQL retains advantages in versatility, supporting transactions, complex queries, and long-term storage. Practical significance: the developed testing methodology and obtained quantitative estimates are applicable when choosing a storage architecture for highload information systems, including monitoring and tracking systems in transport.

KEYWORDS

PostgreSQL, Redis, key-value model, latency, throughput, high-load transactions, jsonb, hstore, in-memory storage

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1. Almeida D., et al. Performance Comparison of Redis, Memcached, MySQL, and PostgreSQL: A Study on Key-Value and Relational Databases, Proceedings of the 2023 Second International Conference on Smart Technologies for Smart Nation (SmartTechCon), Singapore, Singapore, August 18–19, 2023. Institute of Electrical and Electronics Engineers, 2023, pp. 902–907. DOI:https://doi.org/10.1109/SmartTechCon57526.2023.10391649

2. Khomonenko A. D., Tsygankov V. M., Maltsev M. G. Bazy dannykh: uchebnik dlya vuzov [Databases: A Textbook for Universities]. Saint Petersburg, Korona-Vek Publishing House, 2009, 736 p. (In Russian)

3. Connolly T. M., Begg C. E. Bazy dannykh: proektirovanie, realizatsiya i soprovozhdenie: teoriya i praktika. Tretye izdanie [Database Systems: A Practical Approach to Design, Implementation, and Management. Third Edition]. Moscow, Williams Publishing House, 2017, 1440 p. (In Russian)

4. Kleppmann M. Vysokonagruzhennye prilozheniya. Programmirovanie, masshtabirovanie, podderzhka [Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems]. Saint Petersburg, Piter Publishing House, 2018, 640 p. (In Russian)

5. Gembalczyk D., Schuhknecht F. M., Dittrich J. An Experimental Analysis of Different Key-Value Stores and Relational Databases. In: Mitschang B., et al. (eds) Datenbanksysteme für Business, Technologie und Web (BTW 2017): Proceedings 17 Fachtagung des GI-Fachbereichs “Datenbanken und Informationssysteme” (DBIS), Stuttgart, Germany, March 06–10, 2017. Lecture Notes in Informatics, vol. 265. Bonn, Gesellschaft für Informatik, 2017, pp. 351–360.

6. Salunke S. V., Ouda A. A Performance Benchmark for the PostgreSQL and MySQL Databases, Future Internet, 2024, vol. 16, iss. 10, art. no. 382, 22 p. DOI:https://doi.org/10.3390/fi16100382 EDN: https://elibrary.ru/XWZWQP

7. Dean J., Barroso L. A. The Tail at Scale, Communications of the ACM, 2013, vol. 56, no. 2, pp. 74–80. DOI:https://doi.org/10.1145/2408776.2408794

8. Sikh A., Axén J. High-Performing Cloud Native SW Using Key-Value Storage or Database for Externalized States. Bachelor’s Thesis. Department of Computer and Information Science, Linköping University. Linköping, 2023, 18 p. Available at: http://liu.diva-portal.org/smash/get/diva2:1768552/ FULLTEXT01.pdf (accessed: August 03, 2026).

9. Imam Sakti M. D., et al. Optimizing Asynchronous Performance in Node.js with Express and PostgreSQL, Procedia Computer Science, 2025, vol. 269, pp. 172–181. DOI:https://doi.org/10.1016/j.procs.2025.08.270 EDN: https://elibrary.ru/SOMZTT

10. Khanum S., et al. High-Throughput Real-Time Leaderboard a Comparative Performance Analysis of PostgreSQL and Redis. In: Nhu N. G., et al. (eds) Proceedings of International Conference on Computational Technologies for Research in Data Analytics (ICCTRDA 2025), Da Nang, Vietnam, 21–22 November 2025. Volume 3. Lecture Notes in Networks and Systems, vol. 1988. Cham, Springer, 2026, pp. 114–126. DOI:https://doi.org/10.1007/978-3-032-26898-3_11

11. Abbasi M., et al. Adaptive and Scalable Database Management with Machine Learning Integration: A PostgreSQL Case Study, Information, 2024, vol. 15, iss. 9, art. no. 574, 25 p. DOI:https://doi.org/10.3390/info15090574 EDN: https://elibrary.ru/BUCNED

12. Malygin D. S. Problemy proizvoditelnosti relyatsionnykh baz dannykh v raspredelennykh arkhitekturakh i strategii ikh resheniya [Performance Issues of Relational Databases in Distributed Architectures and Strategies for Resolution], Sovremennye naukoemkie tekhnologii [Modern High Technologies], 2024, no. 10, pp. 61–71. DOI:https://doi.org/10.17513/snt.40173 (In Russian) EDN: https://elibrary.ru/JKCOIB

13. Privalov M. V., Stupina M. V. Improving Web-Oriented Information Systems Efficiency Using Redis Caching Mechanisms, Indonesian Journal of Electrical Engineering and Computer Science, 2024, vol. 33, no. 3, pp. 1667–1675. DOI:https://doi.org/10.11591/ijeecs.v33.i3.pp1667-1675 EDN: https://elibrary.ru/RVEVAB

14. Cherkasov S. D., Khomonenko A. D. Issledovanie vliyaniya fragmentatsii dannykh na proizvoditelnost chteniya/zapisi i metody ee ustraneniya v MySQL (InnoDB) [Investigation of Data Fragmentation Impact on Read/Write Performance and Methods for Its Elimination in MySQL (InnoDB)], Intellektualnye tekhnologii na transporte [Intellectual Technologies on Transport], 2026, no. 2 (46), pp. 64–74. DOI:https://doi.org/10.20295/2413-2527-2026-246-64-74 (In Russian) EDN: https://elibrary.ru/EIFGWS

15. Zulfa M. I., Fadli A., Wardhana A. W. Application Caching Strategy Based on In-Memory Using Redis Server to Accelerate Relational Data Access, Jurnal Teknologi dan Sistem Komputer, 2020, vol. 8, no. 2, pp. 157–163. DOI:https://doi.org/10.14710/jtsiskom.8.2.2020.157-163 EDN: https://elibrary.ru/DWVXLS

16. Bianchi G., et al. Implementation of an AI-Based Predictive Structural Health Monitoring Strategy for Bonded Insulated Rail Joints Using Digital Twins Under Varied Bolt Conditions, Railway Engineering Science, 2025, vol. 33, no. 4, pp. 703–720. DOI:https://doi.org/10.1007/s40534-024-00371-3 EDN: https://elibrary.ru/ULMQJJ

17. Ariyachandra M. R. M. F., Wen Y., Yu J. Advancing Rail Infrastructure: Integrating Digital Twins and Cyber-Physical Systems for Predictive Maintenance. In: Petrova E., et al. (eds) Proceedings of the 2025 European Conference on Computing in Construction and 42nd International CIB W78 Conference on Information Technology in Construction, Porto, Portugal, July 14–17, 2025. European Council on Computing in Construction, 2025, pp. 1266–1273. DOI:https://doi.org/10.35490/EC3.2025.272

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Editor-in-Chief

Anatoly Khomonenko

khomon@mail.ru

Deputy Editor-in-Chief

Lesya Bozhko

Principal Contact

Maksim Orzhevsky

206@it-pgups.ru

Editorial Staff

Staff Editorial

itt-pgups@yandex.ru +7(812)457-86-06

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GOST

Voloshin R. A., Khomonenko A. D., Yudnikov S. S. Investigation of Key-Value Model Performance in PostgreSQL and Redis: A Comparative Analysis of Read/Write Operations and Parallel Load Impact // Intellectual Technologies on Transport. 2026. no. 3. pp. 44-55. DOI: https://doi.org/10.20295/2413-2527-2026-347-44-55. EDN: QCMWWH. (Date of access 03.11.2026).

MLA

Ratmir Voloshin, Khomonenko A. D., Yudnikov S. S.. "Investigation of Key-Value Model Performance in PostgreSQL and Redis: A Comparative Analysis of Read/Write Operations and Parallel Load Impact." Intellectual Technologies on Transport.3 (2026): 44-55. DOI: https://doi.org/10.20295/2413-2527-2026-347-44-55. EDN: QCMWWH.

APA

Voloshin R. A., Khomonenko A. D., Yudnikov S. S. (2026). Investigation of Key-Value Model Performance in PostgreSQL and Redis: A Comparative Analysis of Read/Write Operations and Parallel Load Impact. Intellectual Technologies on Transport(3), 44-55. https://doi.org/10.20295/2413-2527-2026-347-44-55 EDN: QCMWWH.

Chicago

Ratmir Voloshin, Khomonenko A. D., Yudnikov S. S.. "Investigation of Key-Value Model Performance in PostgreSQL and Redis: A Comparative Analysis of Read/Write Operations and Parallel Load Impact." Intellectual Technologies on Transport, no. 3 (2026): 44-55. https://doi.org/10.20295/2413-2527-2026-347-44-55. EDN: QCMWWH.

WIKI

{{cite journal | author = Voloshin R. A., Khomonenko A. D., Yudnikov S. S. | title = Investigation of Key-Value Model Performance in PostgreSQL and Redis: A Comparative Analysis of Read/Write Operations and Parallel Load Impact | journal = Intellectual Technologies on Transport | year = 2026 | issue = 3 | pages = 44-55 | doi = 10.20295/2413-2527-2026-347-44-55 }} EDN: QCMWWH

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INVESTIGATION OF KEY-VALUE MODEL PERFORMANCE IN POSTGRESQL AND REDIS: A COMPARATIVE ANALYSIS OF READ/WRITE OPERATIONS AND PARALLEL LOAD IMPACT Investigation of Key-Value Model Performance in PostgreSQL and Redis: A Comparative Analysis of Read/Write Operations and Parallel Load Impact

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