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Anti-Design Pattern Series - Why Deployment Metrics Matter in Kubernetes - Blog #14

Updated: Apr 12






Introduction:


Metrics play a crucial role in detecting and predicting incidents in Kubernetes deployments. While logs and tracing are helpful for post-incident analysis, metrics enable real-time incident detection and capacity adjustments. In this blog post, we'll discuss why metrics are essential in Kubernetes deployments and how automation can help incorporate metrics into the deployment process.




Content:

Kubernetes deployments are highly distributed, making metrics more important than ever before. With ephemeral applications, understanding how they respond to changes in traffic volume is critical. By quickly obtaining vital information, you can pinpoint bottlenecks and adjust resource capacity for optimal performance.



However, the most crucial use case for metrics is knowing whether or not your deployment was successful. Just because a container is online doesn't mean the application is running or accepting requests. Incorporating metrics into every step of the deployment process is crucial, and automation is key.



Automated rollbacks and rollouts can be triggered based on metric analysis, and the deployment can be designated as complete or reverted after comparison to a baseline. Human intervention is not required in these processes, making metrics an invaluable tool for efficient and effective Kubernetes deployments.



In conclusion, metrics are not something you should look at once in a while. They should be incorporated into every step of your deployment process to ensure optimal performance and incident detection.






Continuous Blog Series :

Blog #1 : Kubernetes Design Pattern Series - An Overview

Blog #2 : K8s Design Pattern Series - Fundamental Pattern

Blog #3 : K8s Design Pattern Series - Structural Pattern

Blog #4: K8s Design Pattern Series: Behavioral Patterns

Blog #5: K8s Design Pattern Series: Higher Level Patterns

Blog #6: K8s Design Pattern Series: Summary

Blog #7: K8s Anti-Design Pattern Series

Blog #8: K8s Anti-Design Pattern Series - Putting the Configuration into the Images of the Containers

Blog #9: K8s Anti-Design Pattern Series - Connecting Applications to Kubernetes Features/Services without Justification

Blog #10: K8s Anti-Design Pattern Series - Mixing Infrastructure and Application Deployment

Blog #11: K8s Anti-Design Pattern Series - Deploying without Memory and CPU Limits

Blog #12: K8s Anti-Design Pattern Series - Understanding Health Probes In Kubernetes

Blog #13: K8s Anti-Design Pattern Series - The Pitfall of ignoring Helm in Kubernetes Package Management

Blog #14: K8s Anti-Design Pattern Series - Why Deployment Metrics matter in Kubernetes

Blog #15: K8s Anti-Design Pattern Series - To Kubernetes or not to Kubernetes weighing Pros and Cons

Blog #16:K8s Anti-Design Pattern Series - Connecting Applications to Kubernetes Features/Services

Blog #17: K8s Anti-Design Pattern Series - Manual Kubectl Edit/Patch Deployments

Blog #18: K8s Anti-Design Pattern Series - Kubernetes Deployments with Latest-Tagged Containers

Blog #19: K8s Anti-Design Pattern Series - Kubectl Debugging

Blog #20: K8s Anti-Design Pattern Series - Misunderstanding Kubernetes Network Concepts

Blog #21: K8s Anti-Design Pattern Series - Dynamic Environments in Kubernetes why Fixed Staging is an Anti-Design

Blog #22: K8s Anti-Design Pattern Series - Combining Clusters of Production and Non-Production


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