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Blue Green Deployment

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OVERVIEW OF BLUE GREEN DEPLOYMEN

Blue Green Deployment

What is meant by blue green deployment?

The old versions can be called the blue environment while the new version can be known as the green environment.

Blue green deployment is an application releases model or solutions that gradually transfer user traffic from a previous version of an app or microservice to a nearly identical new release environment—both of which are running in production.

What is a blue green deployment in AWS?
A blue/green deployment is a deployment strategy in which you create two separate, but identical environments. One environment (blue) is running the current application version and one environment (green) is running the new application version.Blue-green deployments have two identical production environments, one is labeled Blue and the other is labeled Green . Only one of the environments is ever live, and deployments are always done to the inactive environment.

Blue-green deployment is a technique that reduces downtime and risk by running two identical production environments called Blue and Green.At any time, only one of the environments is live, with the live environment serving all production traffic. For this example, Blue is currently live and Green is idle.
As you prepare a new version of your software, deployment and the final stage of testing takes place in the environment that is not live: in this example, Green. Once you have deployed and fully tested the software in Green, you switch the router so all incoming requests now go to green instead of blue. Green is now live, and blue is idle.

What is canary deployment then?

Canary deployments are a pattern for rolling out releases to a subset of users or servers. The idea is to first deploy the change to a small subset of servers, test it, and then roll the change out to the rest of the servers. The canary deployment serves as an early warning indicator with less impact on downtime: if the canary deployment fails, the rest of the servers aren’t impacted.

Blue/green deployments provide near zero-downtime release and rollback capabilities.Blue/green deployment works by shifting traffic between two identical environments that are running different versions of the application. Blue environment represents the current application version serving production traffic.In parallel, the green environment is staged running a different version of your application.After the green environment is ready and tested, production traffic is redirected from blue to green.If any problems are identified, you can roll back by reverting traffic back to the blue environment.

Rolling Deployment

A rolling deployment is a deployment strategy that updates running instances of an application with the new release. All nodes in a target environment are incrementally updated with the service or artifact version in integer N batches.

Pros:
The benefits of a rolling deployment are that it is relatively simple to roll back, less risky than a basic deployment, and the implementation is simple. 

Cons:
Since nodes are updated in batches, rolling deployments require services to support both new and old versions of an artifact. Verification of an application deployment at every incremental change also makes this deployment slow.

How do you automate blue green deployment?
But how can we do a blue green deployment?

DNS switching of load balancer. You can use DNS switching between the two environments.
Load balancer switching. You can switch the target of the load balancer.
Approach using physical / virtualised machines.

What is Helm all about?
Helm is the first application package manager running on top of Kubernetes. It allows you to describe the application structure through convenient helm-charts and to manage it with simple commands.

Why is Helm important?

Because it’s a huge shift in the way the server-side applications are defined, stored, and managed. Adoption of Helm might well be the key to the mass adoption of microservices, as using this package manager simplifies their management greatly.

 

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