Hybrid Struktur Radix Tree Dan B-Tree Pada In-Memory Database Untuk Aplikasi Fintech
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Abstract
The financial technology (fintech) sector requires a database system that can handle high transaction volumes with very low latency. In-Memory Database (IMDB) is the primary choice due to its speed, but the efficiency of the index data structure within it is crucial. This article proposes and analyzes the potential of a hybrid data structure that combines Radix Tree and B-Tree in IMDB, specifically for fintech applications. Radix Tree is known to be efficient for fast prefix and string searches (Goyal & Bajaj, 2022), while B-Tree excels in range operations and large ordered data management (Anderson & Johnson, 2022). We explore how the combination of these two structures can optimize the performance of IMDB, especially for scenarios such as real-time fraud detection, high-frequency trading, and portfolio management. The methods used include an in-depth literature review of IMDB index optimization (Sedgewick & Wayne, 2021) and a conceptual case study of a hybrid implementation. The results show that the hybrid approach has the potential to reduce search latency, improve storage efficiency, and support the diverse workloads typical of fintech applications. This article contributes to the development of high-performance database architectures for the ever-growing fintech ecosystem.
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