Learning in horizontal scaling MongoDB data sharding on windows

Abstract

MongoDB is a multi-storage NoSQL database. With the fast growth of technology, the number of large databases has exponentially increased, and relational databases cannot fulfill the need for managing such large amounts of data. To address this issue, intensive research was made on the NoSQL (Not Only SQL) data management model, specifically focusing on MongoDB, and a novel approach to manage large-scale remote sensing data is proposed. Social networking sites generate massive amounts of data, and Structured Query Language (SQL) and NoSQL are predominantly used for data storage. NoSQL proves to be a superior choice for web applications due to its ability to handle large data volumes, unlike Relational Database Management Systems (RDBMS). While various data partitioning techniques such as hash and round-robin exist, they are not efficient for small transactions involving only a few tuples. The results demonstrate that the proposed method of different segmentation overcomes the limitations of conventional approaches, facilitating horizontal expansion of the database and making it more suitable for managing extensive stored data. This research provides an essential technical support for effectively managing large amounts of stored data.