Benchmarking consistency levels of cloud-distributed NoSQL databases using YCSB

Distributed database systems offer scalability and fault tolerance by replicating databases across geographically dispersed nodes. This redundancy aims to ensure data availability even during failures and allows for backups in case of disasters. However, maintaining strong data consistency, where all nodes reflect the latest data simultaneously, becomes a challenge in such geographically distributed setups since database systems need to prioritize availability or performance over strong data consistency. This study examines how different data consistency configurations affect the performance of popular NoSQL (Not only SQL) databases, namely Cassandra, MongoDB, and Redis, in a multi-region cloud environment. We adopt the Yahoo! Cloud Serving Benchmark (YCSB) tool to simulate various workloads, measure performance metrics, and compare the results. Our findings reveal significant performance degradation associated with strong data consistency configurations. For instance, in Cassandra, the number of writing/reading operations processed per second can decrease by up to 95% for specific workloads. Similarly, enforcing strong data consistency in Redis can result in execution times that are over 20 times slower on writing/reading operations.