Achieving zero-downtime deployments for ConfigMaps in Kubernetes can significantly improve the reliability and user experience of your applications. By leveraging techniques such as rolling updates, you can ensure that your application continues to serve traffic without interruption while updating configuration settings.
To implement zero-downtime deployments with ConfigMaps, consider the following steps:
The following example demonstrates how to update a ConfigMap and deploy it without downtime:
kind: ConfigMap
apiVersion: v1
metadata:
name: my-app-config
data:
KEY_1: "value1"
KEY_2: "value2"
---
kind: Deployment
apiVersion: apps/v1
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:latest
env:
- name: MY_CONFIG_KEY_1
valueFrom:
configMapKeyRef:
name: my-app-config
key: KEY_1
- name: MY_CONFIG_KEY_2
valueFrom:
configMapKeyRef:
name: my-app-config
key: KEY_2
readinessProbe:
httpGet:
path: /health
port: 8080
initialDelaySeconds: 5
periodSeconds: 10
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?