Optimizing Database Image Retrieval with In-Memory Key-Value Store, Image Compression, and Smart Cache Updater Algorithm

Image retrieval from databases traditionally relies on storing images as Binary Large Objects (BLOBs) alongside data compression techniques. However, handling high volumes of image queries in advanced multimedia web applications often incurs significant costs related to indexing and decompression. This paper presents an optimized image file system retrieval framework designed to accelerate the retrieval of large volumes of image files from databases. The proposed approach leverages Redis, an in-memory data structure store, and Sharp, a high-performance image compression library, to enhance speed and efficiency. Our proposed approach involves caching images in Redis with customizable time-to-live (TTL) settings, cautious image compression to balance load times and image quality, and a smart cache updater to synchronize cache with the database. The study explores the system's design, implementation, and performance evaluation through extensive experimentation, showcasing remarkable improvements in image retrieval speed compared to traditional database methods. The results demonstrate that our proposed technique is efficient and practical for the ever-growing demands of images in web applications.