Benchmarking SQL and NoSQL Persistence in Microservices Under Variable Workloads

This paper presents a controlled comparative evaluation of SQL and NoSQL persistence mechanisms in containerized microservice architectures under variable workload conditions. Three persistence configurations—SQL with indexing, SQL without indexing, and a document-oriented NoSQL database, including supplementary hybrid SQL variants used for robustness analysis—are assessed across read-dominant, write-dominant, and mixed workloads, with concurrency levels ranging from low to high contention. The experimental setup is fully containerized and executed in a single-node environment to isolate persistence-layer behavior and ensure reproducibility. System performance is evaluated using multiple metrics, including percentile-based latency (p95), throughput, CPU utilization, and memory consumption. The results reveal distinct performance trade-offs among the evaluated configurations, highlighting the sensitivity of persistence mechanisms to workload composition and concurrency intensity. In particular, indexing strategies significantly affect read-heavy scenarios, while document-oriented persistence demonstrates advantages under write-intensive workloads. The findings emphasize the importance of workload-aware persistence selection in microservice-based systems and support the adoption of polyglot persistence strategies. Rather than providing absolute performance benchmarks, the study focuses on comparative behavioral trends that can inform architectural decision-making in practical microservice deployments.

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Publication Details

Journal
Future Internet
Published
2026-01-15
DOI
https://doi.org/10.3390/fi18010053
Primary Topic
Software System Performance and Reliability
Type
article
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article

Benchmarking SQL and NoSQL Persistence in Microservices Under Variable Workloads

Nenad Pantelić, Milan Erić, Miladin Stefanovic, Aleksandar Djordjevic et al.
Future Internet
Software System Performance and Reliability
article

Benchmarking SQL and NoSQL Persistence in Microservices Under Variable Workloads

Nenad Pantelić, Milan Erić, Miladin Stefanovic, Aleksandar Djordjevic, Stefan Eric, Lazar Jakovljevic, Ljiljana Matic
article en

Abstract

This paper presents a controlled comparative evaluation of SQL and NoSQL persistence mechanisms in containerized microservice architectures under variable workload conditions. Three persistence configurations—SQL with indexing, SQL without indexing, and a document-oriented NoSQL database, including supplementary hybrid SQL variants used for robustness analysis—are assessed across read-dominant, write-dominant, and mixed workloads, with concurrency levels ranging from low to high contention. The experimental setup is fully containerized and executed in a single-node environment to isolate persistence-layer behavior and ensure reproducibility. System performance is evaluated using multiple metrics, including percentile-based latency (p95), throughput, CPU utilization, and memory consumption. The results reveal distinct performance trade-offs among the evaluated configurations, highlighting the sensitivity of persistence mechanisms to workload composition and concurrency intensity. In particular, indexing strategies significantly affect read-heavy scenarios, while document-oriented persistence demonstrates advantages under write-intensive workloads. The findings emphasize the importance of workload-aware persistence selection in microservice-based systems and support the adoption of polyglot persistence strategies. Rather than providing absolute performance benchmarks, the study focuses on comparative behavioral trends that can inform architectural decision-making in practical microservice deployments.

Future InternetVol. 18(1)
University of Kragujevac (RS)
Peace, Justice and strong institutions
Openalex Percentile: Top 89%
Software System Performance and Reliability
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Benchmarking SQL and NoSQL Persistence in Microservices Under Variable Workloads — Nenad Pantelić, Milan Erić, et al. · Future Internet (2026) | TGRS Research Map | TGRS