TDengine-K8S

TDengine-K8S provides a streamlined method for deploying TDengine, a high-performance time-series database, as a clustered service on Kubernetes. It simplifies the process of running TDengine in containerized environments, enabling scalable and manageable deployments for time-series data workloads.

Key Features

• Kubernetes Deployment — Uses YAML manifests to define and deploy TDengine clusters within Kubernetes namespaces.
• Docker Image Support — Includes a Dockerfile for building custom TDengine images, allowing version-specific deployments.
• Cluster Management — Supports multi-node TDengine clusters with odd-numbered node configurations for high availability.
• Local Storage Setup — Provides guidance for preparing local disk storage volumes to persist TDengine data.
• Status Monitoring — Offers commands to check cluster status via kubectl exec and RESTful API endpoints.

Philosophy

The project focuses on practical, infrastructure-as-code approaches to deploy TDengine in modern cloud-native environments, emphasizing simplicity and reproducibility.