Vol. 2 No. 1 (2022): Journal of AI-Assisted Scientific Discovery
Articles

Evaluating etcd Performance in Large-Scale Stateful Kubernetes Applications

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  • Babulal Shaik
Babulal Shaik
Cloud Solutions Architect at Amazon Web Services, USA
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Published 16-02-2022

Keywords

  • etcd,
  • Kubernetes
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

How to Cite

[1]
Babulal Shaik, “Evaluating etcd Performance in Large-Scale Stateful Kubernetes Applications ”, Journal of AI-Assisted Scientific Discovery, vol. 2, no. 1, pp. 543–561, Feb. 2022, Accessed: Dec. 29, 2024. [Online]. Available: https://scienceacadpress.com/index.php/jaasd/article/view/255

Abstract

The etcd key-value store is the backbone of Kubernetes, acting as the cluster's central database and ensuring consistency and integrity across all operations. Its performance directly impacts the reliability and efficiency of Kubernetes, especially in large-scale stateful applications where accurate state management is vital. As Kubernetes deployments grow in size and complexity, the demands on etcd intensify, presenting challenges such as increased latency, resource contention, and potential bottlenecks that can compromise the entire cluster. This article explores the architecture of etcd and its critical role in Kubernetes. It examines how it handles cluster state management and ensures high availability through features like leader election & consensus protocols. Key challenges in large-scale deployments are discussed, including the effects of high workloads, the need for optimized resource usage, and strategies to safeguard fault tolerance. By focusing on real-world scenarios, the discussion highlights best practices for tuning, etc, to handle heavy loads, from configuring storage and network resources to optimizing cluster topology. It also addresses techniques for achieving scalability and durability, such as leveraging snapshots, implementing efficient backup mechanisms, and deploying multiple etcd instances for redundancy. Additionally, we explore the importance of monitoring tools and proactive maintenance in minimizing disruptions. We provide recommendations to help developers and operators refine the configuration of etcd, ensuring it meets the rigorous demands of stateful Kubernetes environments while maintaining robust performance. This comprehensive evaluation offers actionable insights for those managing large-scale clusters, empowering them to optimize etcd's functionality and ensure their Kubernetes deployments remain resilient, efficient, and scalable in dynamic application landscapes.

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