One of these applications is a simple Nginx web server. This server is used as part of a secure backend application, and the company would like it to be configured to use HTTP basic authentication.
This will require an htpasswd file as well as a custom Nginx config file. In order to deploy this Nginx server to the cluster with good configuration practices, you will need to load the custom Nginx configuration from a ConfigMap (this already exists) and use a Secret to store the htpasswd data.
Create a Pod with a container running the nginx:1.19.1 image. Supply a custom Nginx configuration using a ConfigMap, and populate an htpasswd file using a Secret.
% Total % Received % Xferd Average Speed Time Time Time Current
Dload Upload Total Spent Left Speed
100 612 100 612 0 0 48846 0 --:--:-- --:--:-- --:--:-- 51000
<!DOCTYPE html>
<html>
<head>
<title>Welcome to nginx!</title>
<style>
body {
width: 35em;
margin: 0 auto; font-family: Tahoma, Verdana, Arial, sans-serif;
}
</style>
</head>
<body>
<h1>Welcome to nginx!</h1>
<p>If you see this page, the nginx web server is successfully installed and
working. Further configuration is required.</p>
<p>For online documentation and support please refer to
<a href="http://nginx.org/">nginx.org</a>.<br/>
Commercial support is available at
<a href="http://nginx.com/">nginx.com</a>.</p>
<p><em>Thank you for using nginx.</em></p>
</body>
</html>
Backups are an important part of any resilient system. Kubernetes is no exception. In this post , I will show you how to backup/restore kubernetes data.
Back Up the etcd Data
Look up the value for the key cluster.name in the etcd cluster:
Restore the etcd data from the backup (this command spins up a temporary etcd cluster, saving the data from the backup file to a new data directory in the same location where the previous data directory was):
Note:- you should not perform upgrades on all worker nodes at the same time. Make sure enough nodes are available at any given time to provide uninterrupted service.
Worker nodes
Run the following on the control plane node to drain worker node 1:
In the control plane node, create the token and copy the kubeadm join command (NOTE:The join command can also be found in the output from kubeadm init command):
kubeadm token create --print-join-command
Copy the output
Worker node Setup.
from the above command of Kubeadm join run it using sudo command.
In the control plane node, view cluster status (Note: You may have to wait a few moments to allow all nodes to become ready)
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All-at-once deployments instantly shift traffic from the original (old) Lambda function to the updated (new) Lambda function, all at one time. All-at-once deployments can be beneficial when the speed of your deployments matters. In this strategy, the new version of your code is released quickly, and all your users get to access it immediately.
Canary
A linear deployment is similar to canary deployment. In this strategy, you direct a small amount of traffic to
In a canary deployment, you deploy your new version of your application code and shift a small percentage of production traffic to point to that new version. After you have validated that this version is safe and not causing errors, you direct all traffic to the new version of your code.
Linear
A linear deployment is similar to canary deployment. In this strategy, you direct a small amount of traffic to your new version of code at first. After a specified period of time, you automatically increment the amount of traffic that you send to the new version until you’re sending 100% of production traffic.
Comparing deployment strategies
To help you decide which deployment strategy to use for your application, you’ll need to consider each option’s consumer impact, rollback, event model factors, and deployment speed. The comparison table below illustrates these points.
Deployment
Consumer Impact
Rollback
Event Model Factors
Deployment Speed
All-at-once
All at once
Redeploy older version
Any event model at low concurrency rate
Immediate
Canary/ Linear
1-10% typical initial traffic shift, then phased
Revert 100% of traffic to previous deployment
Better for high-concurrency workloads
Minutes to hours
Deployment preferences with AWS SAM
Traffic shifting with aliases is directly integrated into AWS SAM. If you’d like to use all-at-once, canary, or linear deployments with your Lambda functions, you can embed that directly into your AWS SAM templates. You can do this in the deployment preferences section of the template. AWS CodeDeploy uses the deployment preferences section to manage the function rollout as part of the AWS CloudFormation stack update. SAM has several pre-built deployment preferences you can use to deploy your code. See the table below for examples.
Deployment Preferences Type
Description
Canary10Percent30Minutes
Shifts 10 percent of traffic in the first increment. The remaining 90 percent is deployed 30 minutes later.
Canary10Percent5Minutes
Shifts 10 percent of traffic in the first increment. The remaining 90 percent is deployed 5 minutes later.
Canary10Percent10Minutes
Shifts 10 percent of traffic in the first increment. The remaining 90 percent is deployed 10 minutes later.
Canary10Percent15Minutes
Shifts 10 percent of traffic in the first increment. The remaining 90 percent is deployed 15 minutes later.
Linear10PercentEvery10Minutes
Shifts 10 percent of traffic every 10 minutes until all traffic is shifted.
Linear10PercentEvery1Minute
Shifts 10 percent of traffic every minute until all traffic is shifted.
Linear10PercentEvery2Minutes
Shifts 10 percent of traffic every 2 minutes until all traffic is shifted.
Linear10PercentEvery3Minutes
Shifts 10 percent of traffic every 3 minutes until all traffic is shifted.
AllAtOnce
Shifts all traffic to the updated Lambda functions at once.
Creating a deployment pipeline
When you check a piece of code into source control, you don’t want to wait for a human to manually approve it or have each piece of code run through different quality checks. Using a CI/CD pipeline can help automate the steps required to release your software deployment and standardize on a core set of quality checks.
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