This study presents a systematic empirical evaluation of monolithic and distributed database architectures deployed in Kubernetes environments. As containerized and cloud-native infrastructures become increasingly prevalent, understanding the performance implications of running stateful data systems under orchestration platforms has become critical. We evaluate five widely used database systems—PostgreSQL, MySQL, MongoDB, Redis, and Cassandra—using standardized workload generation frameworks, including pgbench, sysbench, YCSB, redis-benchmark, and cassandra-stress. Controlled experiments were conducted across varying concurrency levels and workload types to measure throughput, latency, and scalability in both single-node and distributed deployments. Redis achieves a maximum throughput of 4.2 million operations per second with sub-millisecond latency. In contrast, Cassandra delivers 214,743 distributed read operations per second at ONE consistency, approaching Redis’s non-pipelined baseline throughput (257,732–262,467 ops/sec) within a Kubernetes cluster. The write throughput of Cassandra decreases by 45.2% when the consistency level is elevated to QUORUM, accompanied by an elevenfold increase in run-to-run variability (CV from 7.1% to 84.7%), indicating that the consistency level is the primary performance determinant in distributed systems. PostgreSQL experiences a 72% decrease in write throughput in Kubernetes (74,072 → 20,805 TPS). In contrast, MySQL PXC anomalously attains a 37.3% increase in write throughput in Kubernetes compared to its monolithic deployment—the sole reversal noted among the five systems. These findings underscore a critical trade-off between vertical efficiency and horizontal scalability, illustrating that hybrid database architecture can be an effective solution for contemporary cloud-native applications compared to either paradigm independently.
Redžepagić et al. (Wed,) studied this question.