IRO Journals
Journal of Soft Computing Paradigm

Search System Optimization Using Elasticsearch with Redis-Based Caching

Open Access
Published: 13 October, 2026
Pages: 359-372
  • Sharmila Jeyarani S. , Sharmila Jeyarani S.
    Assistant Professor, Department of Computer Science and Engineering, NPR College of Engineering and Technology, Natham, Dindigul, India
    Assistant Professor, Department of Computer Science and Engineering, NPR College of Engineering and Technology, Natham, Dindigul, India
  • Anwarsha K. , Anwarsha K.
    Student, Department of Computer Science and Engineering, NPR College of Engineering and Technology, Natham, Dindigul, India
    Student, Department of Computer Science and Engineering, NPR College of Engineering and Technology, Natham, Dindigul, India
  • Akash S. , Akash S.
    Student, Department of Computer Science and Engineering, NPR College of Engineering and Technology, Natham, Dindigul, India
    Student, Department of Computer Science and Engineering, NPR College of Engineering and Technology, Natham, Dindigul, India
  • Mani Kandan R. Mani Kandan R.
    Student, Department of Computer Science and Engineering, NPR College of Engineering and Technology, Natham, Dindigul, India
    Student, Department of Computer Science and Engineering, NPR College of Engineering and Technology, Natham, Dindigul, India
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How to Cite

S., Sharmila Jeyarani, Anwarsha K., Akash S., and Mani Kandan R. 2026. “Search System Optimization Using Elasticsearch With Redis-Based Caching”. Journal of Soft Computing Paradigm 8 (4): 359-72. https://doi.org/10.36548/jscp.2026.4.003.
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Keywords

Elasticsearch
Redis
Search Optimization
Cache-Aside
Query Caching
Inverted Index
Response Time
Information Retrieval

Abstract

Large-scale search system requires low latency and efficient management of requests in the face of large-scale data. In this research, a cache-aware search system is introduced through the use of Elasticsearch with Redis to minimize duplicate search processes. Elasticsearch is adopted as the main tool for full-text retrieval, while Redis acts as an in-memory cache through the cache-aside approach. On each request received, the backend first checks the cache via Redis. When there is a cache hit, the result already cached from previous searches is returned without the need to call Elasticsearch. The performance of the proposed system was measured on the 500,000+ US Homes Data for Sale (Properties Dataset) data set, which comprises around 600,000 house entries. Three types of queries were tested: both single and multiple result search. The average execution time was reduced from 175.39 milliseconds without caching to 0.85 milliseconds when using Redis, which makes up 99.52% decrease. The decrease in time per individual query ranged from 98.96% to 99.73%. Comparison with a similar search scheme with the implementation of PostgreSQL proves the superiority of Elasticsearch – Redis in terms of lower latency of queries.

References

  1. Y. Zhu, T. Xia, T. Zhu, Z. Zhao, K. Li, and X. Hu, “RAPO: An Automated Performance Optimization Tool for Redis Clusters in Distributed Storage Metadata Management,” IEEE Access, vol. 13, pp. 58060–58071, 2025, doi: 10.1109/ACCESS.2025.3556240
  2. N. Kathare, O. V. Reddy, and V. Prabhu, “A Comprehensive Study of Elasticsearch,” Journal of Research in Science and Engineering (JRSE), Nov. 2022, vol. 4, no. 11, 34-38
  3. L. Kaptosv, “Using Redis for Caching Optimization in High-Traffic Web Applications,” International Journal of Advanced Multidisciplinary Research and Studies 2025, vol. 5, no. 4, pp. 1714–1722.
  4. Elasticsearch B.V., “Elasticsearch Documentation,” [Online]. Available: https://www.elastic.co
  5. Redis Labs, “Redis Documentation,” [Online]. Available: https://redis.io.
  6. ReEF. Scholer, H. E. Williams, J. Yiannis, and J. Zobel, “Compression of Inverted Indexes for Fast Query Evaluation,” Proc. ACM SIGIR, 2002.
  7. PolarTech, “500,000+ US Homes Data for Sale (Properties Dataset),” Kaggle,[Online]. Available:https://www.kaggle.com/datasets/polartech/500000-us-homes-data-for-sale-properties. Accessed: Apr. 2026.
  8. E. P. Markatos, “On Caching Search Engine Query Results,” Computer Communications 2001, vol. 24, no. 2, pp. 137–143.
  9. T. Fagni, R. Perego, F. Silvestri, and S. Orlando, “Boosting The Performance of Web Search Engines: Caching and Prefetching Query Results by Exploiting Historical Usage Data,” ACM Transactions on Information Systems 2006, vol. 24, no. 1, pp. 51–78.
  10. R. Lempel and S. Moran, “Predictive Caching and Prefetching of Query Results in Search Engines,” in Proc. 12th International Conference on World Wide Web (WWW), 2003, pp. 19–28.
  11. X. Long and T. Suel, “Three-Level Caching for Efficient Query Processing in Large Web Search Engines,” World Wide Web 2006, vol. 9, no. 4, pp. 369–395.
  12. R. Ozcan, I. S. Altingovde, B. B. Cambazoglu, F. P. Junqueira, and Ö. Ulusoy, “A Five-Level Static Cache Architecture for Web Search Engines,” Information Processing Management 2012, vol. 48, no. 5, pp. 828–840.