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Kubernetes Static Vs Dynamic Config Maps
Today’s Agenda :
Kubernetes Static Vs Dynamic Config Maps
Convert Cloud Infrastructure to Terraform Code
Best practices for streamlining log centralization with Cloud Logging
AWS CodePipeline introduces stage level conditions
Azure announces general availability of Container Storage for Kubernetes
DigitalOcean Load Balancer: Scaling to 1,000,000+ Connections
Read Time: 4 minutes
Kubernetes Static Vs Dynamic Config Maps
Namaste 🙏 TechOps Soldiers!
Monday Blues are out and we are into productive Tuesday 🖖
Today's use case is all about Static vs. Dynamic Configurations in Kubernetes with a real-world example to show you how to keep your deployments up-to-date with the latest changes.
To begin with, what is a ConfigMap?
It's an API object in Kubernetes used to store non-confidential configuration data as key-value pairs, enabling the decoupling of configuration artifacts from image content for better application portability.
When distinguishing between static and dynamic ConfigMaps in Kubernetes, it's all about management and updates.
Static ConfigMaps are created and updated manually, requiring users to reapply changes and possibly restart pods to apply the new configurations.
Dynamic ConfigMaps are more hands-off, updating automatically from external sources or through processes that refresh the running pods without the need for restarts. This allows for smoother and more efficient configuration management in dynamic environments.
As we see in the below example:
Static configurations are simple and easy to implement but come with significant limitations.
No Real-time Updates: Any change in the configuration map requires a manual redeployment of the pods to pick up the changes.
Downtime: Manually updating configurations can lead to downtime or inconsistent states during the deployment process.
Scalability: Managing configurations manually does not scale well, especially in large and dynamic environments.
A static configuration looks like this, watch out of ‘configMapKeyRef’ :
apiVersion: apps/v1
kind: Deployment
metadata:
name: node-app
spec:
replicas: 2
selector:
matchLabels:
app: node-app
template:
metadata:
labels:
app: node-app
spec:
containers:
- name: node-app
image: node-app:latest
ports:
- containerPort: 3000
env:
- name: DATABASE_URL
valueFrom:
configMapKeyRef:
name: static-db-config
key: DATABASE_URL
To overcome these limitations, dynamic configurations allow your deployments to pick up changes in real-time without requiring a manual redeployment.
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Let's get back to the context,
A dynamic configuration looks like this with a CONFIG_RELOAD_INTERVAL :
apiVersion: apps/v1
kind: Deployment
metadata:
name: node-app
spec:
replicas: 2
selector:
matchLabels:
app: node-app
template:
metadata:
labels:
app: node-app
spec:
containers:
- name: node-app
image: node-app:latest
ports:
- containerPort: 3000
env:
- name: DATABASE_URL
valueFrom:
configMapKeyRef:
name: dynamic-db-config
key: DATABASE_URL
- name: CONFIG_RELOAD_INTERVAL
value: 30 # Reload config every 30 seconds
Points to Consider Before Implementing Dynamic Configs:
Application Compatibility: Ensure your application can handle configuration changes at runtime without requiring a restart.
Security: Dynamic updates might introduce security risks if sensitive configurations are exposed or not properly validated.
Performance Overhead: Continuously checking for updates might introduce some performance overhead; evaluate the impact on your application.
Testing: Thoroughly test dynamic configurations in a staging environment to catch potential issues before rolling them out to production.
Logging and Monitoring: Implement robust logging and monitoring to track configuration changes and quickly identify any issues caused by updates.
Remember, Kubernetes and peace of mind usually don't go hand in hand, but with dynamic configurations, at least we try not to lose a day of sleep on upgrade days :)
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📖 Resources & Tutorials
Terracognita by Cycloid automates the conversion of existing cloud infrastructure into Terraform code, streamlining the transition to Infrastructure as Code (IaC). Supporting AWS, GCP, Azure, and VMware.
Centralizing log management with Cloud Logging enhances system visibility, security, and efficiency. By aggregating logs, setting up a central observability project, and customizing storage, you streamline operations and reduce costs.
📈 Trends & Updates
AWS CodePipeline now supports stage-level conditions, allowing teams to enforce quality and compliance checks at critical points in the pipeline. Configure conditions to gate pipeline executions based on CloudWatch alarms, time windows, or custom AWS Lambda functions.
Microsoft's Azure Container Storage is now generally available, offering the first platform-managed container-native storage service in the public cloud. This service, integrated with Azure Kubernetes Service (AKS), simplifies managing stateful workloads with high performance and low latency and it supports ephemeral disks and Azure Disks.
DigitalOcean's revamped Load Balancer architecture scales to over 1 million connections, ensuring high availability and consistent performance. By leveraging a passthrough network load balancer (NLB) and BGP/ECMP.
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