Experimental performance analysis of data consistency levels in NoSQL databases

Objective: NoSQL database management systems (DBMSs) are designed to handle large-scale data for modern applications. These systems often operate in a distributed manner, allowing data to be spread across multiple nodes to ensure replication, reduce data loss, and facilitate recovery. The consistency level in these DBMSs determines how synchronized data is across nodes, influencing the trade-off among consistency, availability, and system performance. Given that different applications have unique requirements, understanding the impact of various consistency levels is essential. This study conducts an in-depth analysis of how consistency level choices affect the performance of three leading NoSQL DBMSs: Cassandra, MongoDB, and Redis.

Methods: These systems were evaluated under different consistency configurations, user loads, and workloads, with performance metrics including average response time and operations per second.

Results: Our results show that Cassandra and Redis handle write operations faster than MongoDB, though Cassandra experiences significant slowdowns of up to 200% when switching to strong consistency. This performance degradation is observed for both read and write operations, making Cassandra the most affected DBMS when opting for strong consistency.

Conclusion: The detailed findings offer valuable insights into the trade-offs between performance and consistency in these DBMSs, providing guidance for database engineers in selecting appropriate consistency levels based on their application needs.