Top 6 Best practices for Container Orchestration

Containers have become the de facto standard for deployment in the cloud. They simplify application development and maintenance, enabling developers to package applications with all relevant resources so they can be deployed anywhere.

Containers also allow you to isolate your applications from other parts of your infrastructure and speed up deployments. But containers aren’t always easy, especially when it comes to orchestrating them with each other. There are plenty of choices out there for orchestrators — from Kubernetes and Docker Swarm to Amazon ECS and Mesosphere DC/OS — and each has its own unique strengths.

Don’t manage containers by hand.

• A container orchestration tool is a must-have for any organization that wants to deploy its applications in containers. With a good container orchestrator, you can create and manage your entire application infrastructure from code to production in an automated way, speeding up the process of deploying new features and services while reducing errors and improving quality.
• Choose the right orchestrator for your environment. There are many options available when choosing a container orchestration tool — some are open source while others are proprietary, some offer support on multiple platforms while others only work on one particular platform — so it’s important to evaluate which option will best meet your needs before making a decision about which product or service to use with your Docker stack.

Let the container orchestrator decide where to start a container.

The container orchestrator should be able to handle starting and stopping containers, as well as changing the number of containers running on a host. If your application needs a certain number of instances to run smoothly, let the orchestrator decide where to start the container.

If you want to change which host is running a particular container or if you need more resources for your app, let the orchestrator do it for you. You might have one instance running on one server today, but in three months when your traffic increases by tenfold and there’s no room left on that server because all its resources are used up by other applications and services — the last thing I want is for someone in engineering (or worse yet: me!) doing manual workarounds with shell scripts just so we can get some new servers started up ASAP!

Consider long-running tasks with background processing.

If you have tasks that are better suited to be run in a container environment, consider launching these tasks as part of the initial deployment. For example, if your application needs to run complex calculations or perform intensive data processing before returning a result, you can run it as a background process (often called “long-running tasks”) and use container orchestration tools such as Kubernetes or Docker Swarm to manage it.

Long-running jobs can provide benefits such as:

• Stateless nature: The stateless nature of containers means that no matter how many instances of long-running processes are running at any given time, each instance does not need to maintain its own internal information about previous executions or state. As long as there is enough memory available for each container instance, this makes scaling up or down very easy without needing any special consideration for managing state between multiple instances. This enables better utilization across your infrastructure resources by allowing all available CPU power and memory capacity within an application pod (the smallest unit of scheduling in Kubernetes clusters) be used by only one process executing at once rather than being blocked waiting on another task’s execution before continuing with additional work.
• Distributed workloads: Since containers have no notion of where they might be running next (or even if they’ll get moved somewhere else), this allows you greater flexibility with scheduling which resources should be used when needed most based off load patterns observed across different geographic regions — a feature known as cross zone replication/synchronization using cloud provider APIs like AWS CloudHSM and Azure Key Vault).

Keep it stateless.

Keeping your containers stateless makes them easier to scale, debug, migrate and maintain.

Scalable — A container requires less resources than a VM. If a container is stateless, it can be easily scaled horizontally by adding more instances of the same container. For example if you have a web application that needs 100 separate web servers to handle traffic coming from all over the world, but at peak time only 50% of requests come in from different parts of the world then you can deploy 50 containers total (50%) with one inside each server or datacenter as needed. You don’t need to worry about scaling up virtual machines because each one has its own disk space; just add more instances based on demand and you are good to go!

Debugging — When debugging issues with complex applications, having something like Docker Swarm or Kubernetes helps tremendously! With these tools you can pull up logs for individual instances quickly instead having them stored on physical storage devices which may not always have enough space available either due to being filled up already causing new logs not being able to be written onto them without deleting existing ones first since they are only using local storage anyway (which means they cannot be accessed remotely). This also means there won’t be any data loss during those times when someone accidentally deletes files off their laptop by mistake while working remotely via SSH connection without realizing what happened until after they try logging back into their machine again later on today’s date tomorrow morning.

Be prepared for failures when you run containers.

If you are going to run containers, it’s important to be prepared for failures. If a container crashes or is running in an unhealthy state, the host will be impacted.

Container orchestration tools like Kubernetes can automatically detect unhealthy containers and restart them on other hosts in the cluster. This is known as health checking. It’s good practice to regularly check whether your containers are healthy using health checks and if they’re not, then take action such as restarting them or terminating them if they cannot recover after being restarted multiple times.

Other types of monitoring include: log management; security monitoring; vulnerability scanning; vulnerability testing

Use the sidecar pattern for background processing.

• Use the sidecar pattern for background processing. In this pattern, you use a container for background processing and run it outside of your application container. This gives you the advantage of being able to update its version independently from your application.
• Use a sidecar container to run a service. Sidecar containers are useful in situations where an application needs to access resources provided by another element such as databases or message brokers. The sidecar approach allows you to leave these resources running in their own containers while running your scripts on top of them.

Orchestrate your deployments to prevent being overwhelmed by complexity

As we have seen, container orchestration is a powerful tool that can help you manage your containers on cloud-native applications. Before deciding on one of these solutions, however, it’s important to consider the pros and cons of each option:

• Docker Swarm is an open source clustering technology developed by Docker Inc. that allows you to run multiple docker engines in a swarm cluster and then manage them using a single CLI command. This makes it easy for users who are new to Docker containers or are familiar with the command line interface (CLI).
• Kubernetes is an open source platform designed by Google based on its internal Borg system for managing large clusters of virtual machines (VMs). It provides developers with tools for deploying, monitoring and maintaining applications across multiple clusters. Kubernetes enables companies to deploy applications without having deep knowledge about infrastructure details such as network topology or configuration management tasks such as installing updates on individual servers running VMs within a cluster or performing maintenance tasks such as rebooting failed nodes before they become unreachable due to hardware failure or software bugs affecting specific processes at different times during their operation cycle.

Conclusion

We hope this article has helped you understand the importance of container orchestration, as well as some of the best practices for making it work. If you’re ready to start using containers in your organization, but don’t know where to begin, we recommend starting with one of our tutorials. It will walk through setting up a local development environment and deploying a simple application using Docker Compose — the easiest way to get started with Docker!