How do I achieve zero-downtime deployments for Kubernetes cost optimization?

Zero-downtime deployments in Kubernetes are essential for maintaining high availability and customer satisfaction. To achieve this, several strategies can be employed, including blue-green deployments, rolling updates, and canary releases. These methods not only ensure minimal service disruption but also optimize costs by efficiently utilizing resources during the deployment process.

Here is an example of a Kubernetes deployment using a rolling update strategy, which allows for zero-downtime by incrementally updating the application without affecting the users:

apiVersion: apps/v1 kind: Deployment metadata: name: my-app spec: replicas: 3 selector: matchLabels: app: my-app template: metadata: labels: app: my-app spec: containers: - name: my-app-container image: my-app-image:v2 ports: - containerPort: 80 strategy: type: RollingUpdate rollingUpdate: maxSurge: 1 maxUnavailable: 1

zero-downtime deployments Kubernetes cost optimization blue-green deployments rolling updates canary releases high availability application deployment

Related Questions

How do I avoid rehashing overhead with std::set in multithreaded code?

How do I find elements with custom comparators with std::set for embedded targets?

How do I erase elements while iterating with std::set for embedded targets?

How do I provide stable iteration order with std::unordered_map for large datasets?

How do I reserve capacity ahead of time with std::unordered_map for large datasets?

How do I erase elements while iterating with std::unordered_map in multithreaded code?

How do I provide stable iteration order with std::map for embedded targets?

How do I provide stable iteration order with std::map in multithreaded code?

How do I avoid rehashing overhead with std::map in performance-sensitive code?

How do I merge two containers efficiently with std::map for embedded targets?