Systematic Approach to NoSQL Indexing: Validating the ESR (Equality, Sort, Range) Heuristic for Query Performance

DOI: https://doi.org/10.52549/ijeei.v14i2.7670
Published: 24-06-2026 • Issue: Vol. 14 No. 2: June 2026 • Section: Regular paper

Abstract

In this paper we describe the ESR heuristic, a simple and repeatable way to build compound indexes in MongoDB. The method prioritizes the query requirements in order of equality, sort, and range. The ESR heuristic can boost the query performance without the need for a complicated machine learning model. The experiment was conducted on four real-world datasets and two kinds of mixed queries on Equality, Sort, and Range (ESR). The results show that the systematic technique for compound indexing based on the ESR principle reduces the mean query latency in most cases. For example, the Online Retail dataset had a clear improvement with p-values less than 0.01 in both Welch’s t-test and the Mann-Whitney U-test. For the NYC Taxi dataset, the non-parametric test indicated a significant change in the latency distribution (p=0.032), implying that many queries have decreased tail latency. The Abalone dataset indicated a favorable trend, but it was not very significant statistically . The filter condition had low selectivity, hence only a little improvement was observed in the Bank Marketing dataset. The experiment also highlighted the COLLSCAN contradiction where MongoDB still scanned the entire collection with a proper compound index. However, the ESR heuristic decreased variance and tail latency in most circumstances.

References

A. O. Thakare, O. W. Tembhurne, A. R. Thakare, and S. N. Reddy, “NoSQL Databases: Modern Data Systems for Big Data Analytics - Features, Categorization and Comparison,†International Journal of Electrical and Computer Engineering Systems, vol. 14, no. 2, pp. 207-216, 2023.

B. Jose and S. Abraham, “Performance analysis of NoSQL and relational databases with MongoDB and MySQL,†Materials Today: Proceedings, vol. 24, no. 3, pp. 2036-2043, 2020.

S. Ramzan, I. S. Bajwa, R. Kazmi, and Amna, “Challenges in NoSQL-Based Distributed Data Storage: A Systematic Literature Review,†Electronics, vol. 8, no. 5, p. 488, 2019.

M. A. Qader, S. Cheng, and V. Hristidis, “A Comparative Study of Secondary Indexing Techniques in LSM-based NoSQL Databases,†in Proceedings of the 2018 International Conference on Management of Data (SIGMOD'18), Houston, TX, USA, 2018, pp. 551-566.

D. Preuveneers and W. Joosen, “Automated Configuration of NoSQL Performance and Scalability Tactics for Data-Intensive Applications,†Informatics, vol. 7, no. 3, p. 29, 2020.

D. Van Aken, A. Pavlo, G. J. Gordon, and B. Zhang, “Automatic Database Management System Tuning Through Large-scale Machine Learning,†in Proceedings of the 2017 ACM International Conference on Management of Data (SIGMOD’17), Chicago, IL, USA, 2017, pp. 1009-1024.

M. R. Hasanzadeh, F. Keynia, and M. Hashemipour, “A New Index-Based Hyper-Heuristic Algorithm for Global Optimisation Problems,†IET Software, vol. 16, no. 5, pp. 493–515, 2022.

Y. Kwon, S. Lee, Y. Nam, J. C. Na, K. Park, S. K. Cha, and B. Moon, “DB+-tree: A new variant of B+-tree for main-memory database systems,†Information Systems, vol. 119, p. 102287, 2023.

M. Diogo, B. Cabral, and J. Bernardino, “Consistency Models of NoSQL Databases,†Future Internet, vol. 11, no. 2, p. 43, 2019.

Y. Yan, S. Yao, H. Wang, and M. Gao, “Index Selection for NoSQL Database with Deep Reinforcement Learning,†Information Sciences, vol. 561, pp. 20–30, 2021.

F. Z. Belkadi and R. Esbai, “A Model-Driven Engineering: From Relational Database to Document-oriented Database in Big Data Context,†in Proceedings of the 16th International Conference on Software Technologies (ICSOFT 2021), 2021, pp. 653–659.

A. Rafique, D. Van Landuyt, B. Lagaisse, and W. Joosen, “On the Performance Impact of Data Access Middleware for NoSQL Data Stores: A Study of the Trade-Off between Performance and Migration Cost,†IEEE Transactions on Cloud Computing, vol. 6, no. 3, pp. 843–858, 2018.

X. Yao, J. Li, Y. Tao, and S. Ji, “Relational Database Query Optimization Strategy Based on Industrial Internet Situation Awareness System,†in Proceedings of the 7th International Conference on Computer and Communication Systems (ICCCS), Wuhan, China, 2022, pp. 152–155.

M. Abbasi, M. V. Bernardo, P. Váz, J. Silva, and P. Martins, “Optimizing Database Performance in Complex Event Processing through Indexing Strategies,†Data, vol. 9, no. 8, pp. 1–24, 2024.

A. Karras, C. Karras, D. Samoladas, K. C. Giotopoulos, and S. Sioutas, “Query Optimization in NoSQL Databases Using an Enhanced Localized R-tree Index,†in Proceedings of the 24th International Conference on Information Integration and Web Intelligence (iiWAS 2022), Springer LNCS 13635, 2022, pp. 391–398.

N. El Moukhi, I. El Azami, and S. Hajbi, “Towards a New Hybrid Approach for Building Document-Oriented Data Warehouses,†International Journal of Electrical and Computer Engineering, vol. 12, no. 6, pp. 6423–6431, 2022.

C. Delimitrou and C. Kozyrakis, “Amdahl's Law for Tail Latency,†Communications of the ACM, vol. 61, no. 8, pp. 65-73, 2018.

H. He, et al., “Benchmarking Distributed Transactional Database Systems,†in Proceedings of the 2024 International Conference on Benchmarking, Measuring, and Optimizing (Bench 2024), LNCS 15519, 2025, pp. 37-53.

T. Siddiqui and W. Wu, “ML-powered Index Tuning: An Overview of Recent Progress and Open Challenges,†SIGMOD Record, vol. 52, no. 4, pp. 19-30, 2024.

A. E. Topcu, et al., “Evaluating the Performance of NoSQL Databases for Big Data in Cloud Computing Environments,†HighTech and Innovation Journal, vol. 6, no. 3, pp. 808-830, 2025.

Z. Fan, et al., “Understanding the Long Tail Latency of TCP in Large-scale Cloud Networks,†in Proceedings of the 2025 Asia-Pacific Workshop on Networking (APNET 2025), Shanghai, China, 2025, pp. 1-8.

M. Aytimur and A. Çakmak, “Estimating the Selectivity of LIKE Queries Using Pattern-based Histograms,†Turkish Journal of Electrical Engineering & Computer Sciences, vol. 26, pp. 3319-3334, 2018.

How to Cite

[1]
N. Butploy, K. Khiewwan, J. Ongate, T. Kueathaweekun, and P. Maneechot, “Systematic Approach to NoSQL Indexing: Validating the ESR (Equality, Sort, Range) Heuristic for Query Performance”, IJEEI, vol. 14, no. 2, pp. 563–570, Jun. 2026, doi: 10.52549/ijeei.v14i2.7670.