With the advent of microservices architecture, we see containers in almost every environment, from on-premises servers to public cloud instances running in AWS, Google Cloud, and Microsoft Azure. Naturally, you won't be running a single container. With the vast number of apps, you can’t maintain containers on your own throughout their lifecycle.
A single, small application is likely to have dozens of containers in the real world. And an enterprise might deploy thousands of containers across its apps and services. More containers, the more time and resources you must spend managing them. A container orchestrator can perform the critical life cycle management task with little human intervention in a fraction of the time.
A container can be orchestrated in several ways based on the tools used by the admin. Container orchestration tools communicate using JSON or YAML files that are generally user-created for outlining the application’s configuration. Additionally, orchestration tools are responsible for automating container deployment in clusters as well as identification of the perfect host for the same. After host allocation, the container orchestration tool is responsible for managing the containers throughout their entire lifecycle using preset requirements.
Deploying an application takes place by following certain steps. Prior to the immense popularity of Kubernetes, every infrastructure leveraged deployment automation technology that uses a procedural approach toward deployment configuration.
Kubernetes changes this scenario with the onset of the declarative model. The requirement of defining stages in order to achieve the desired result is removed by a declarative approach. Instead, what is declared or defined is the ultimate desirable condition. To achieve and maintain the intended state, the Kubernetes platform automatically modifies the configuration. Because it abstracts the complicated steps, the declarative method saves a lot of time. The "what" becomes more important than the "how."
Any container runtime engine like Docker is not only responsible for packing the application and all its dependencies. Rather, many of the common events of a container lifecycle are also handled by the runtime engine, such as -
- Pull an image from a registry
- Creating a container-based image
- Start/stop one or more containers
It is feasible to manage all these tasks with a smaller number of containers. However, in an enterprise situation where there are thousands of containers, attempting to carry out this manually is a tedious process.
Container orchestration platform like Kubernetes allows you to declare what you wish to accomplish rather than coding the intermediary steps. By using such a platform, one can accomplish
- Application scalability
- Improved governance
- Container health monitoring
- Optimal resource allocation
- Container lifecycle management
Consider a situation where you are managing a few microservices running on a single server. You are the person responsible for deploying, scaling, and maintaining all the microservices. If all the microservices are developed using the same technologies, managing them won’t be much of a challenge. But, how will you handle a situation where you need to manage a thousand microservices where each of them is developed differently? You will end up encountering challenges like -
- Identifying under or over-allocated containers
- Knowing whether your application is appropriate load-balanced across multiple servers
- Rolling back applications
- Enforcing security standards across all infrastructure
Using Kubernetes, software development teams can specify the networking state they want for an application before it is deployed. Then, a Pod that hosts numerous containers is assigned a single IP address by Kubernetes. By matching the network identification to the identity of the application, this method reduces the complexity demanded of the container networking layer and made scaling up maintenance easier.
Managing large-scale containers on an enterprise level is a challenge. One needs to rely on automation and container orchestration tools to scale, deploy and configure thousands of containers heavily. Which of the above difficulties did you find the most interesting? Let us know in the comments below.