Optimizing Graph-Based Search Algorithms for Large-Scale SaaS Applications

Authors

Ritesh Kumar

Abstract

Graph-based search algorithms offer significant advantages in optimizing query performance, scalability, and search relevance in large-scale, multi-tenant Software-as-a-Service (SaaS) applications. Traditional search methodologies often struggle with high query volumes, dynamic indexing, and tenant isolation, leading to increased latency and inefficient resource utilization. This paper explores the role of graph traversal techniques, including Dijkstra’s Algorithm, A* Search, Breadth-First Search (BFS), and PageRank-inspired methods, in enhancing search efficiency. Various indexing strategies, data partitioning models, and caching mechanisms are analyzed to optimize search response times while ensuring scalability. Additionally, the study examines distributed graph processing frameworks and parallelization techniques to improve performance in cloud-native SaaS architectures. Experimental results demonstrate how graph-based search optimizations reduce query latency, enhance recommendation accuracy, and improve overall search system efficiency in large-scale SaaS platforms. The findings provide practical insights for designing robust, high-performance search architectures tailored for modern enterprise SaaS environments.

Keywords

Graph-Based Search, Multi-Tenant SaaS, Large-Scale Search Optimization, Dijkstra’s Algorithm, A* Search, Breadth-First Search (BFS), PageRank, Query Performance, Distributed Graph Processing, Indexing Strategies, Cloud Computing, Search Algorithms, SaaS Scalability, Parallel Computing, Search Personalization

Optimizing Graph-Based Search Algorithms for Large-Scale SaaS Applications. Ritesh Kumar. 2021. IJIRCT, Volume 7, Issue 5. Pages 1-12. https://www.ijirct.org/viewPaper.php?paperId=2503029