Source: Google Cloud
With organizations moving towards DevOps, the need to simplify application deployment is pushing businesses to embrace containerization.
While containers are the future (if not the present) of deployment, they’re challenging to manage. As an example, organizations that managed the networking of their containerized applications found automating the deployments, networking, and clustering very challenging.
This challenge is the very reason why container orchestration tools such as Kubernetes took off.
Today, Kubernetes is the preferred choice of most organizations adopting containerization. You can also get managed Kubernetes services from most public cloud providers, especially Google, Microsoft, and Amazon, among others.
However, while Kubernetes solves a lot of problems with deploying containers, the platform itself is complicated. In this post, we’ll look at the top DevOps challenges of Kubernetes projects.
See How to Deploy Kubernetes Correctly the First Time:
The first challenge with deploying Kubernetes concerns your most important objective, to get a working application live on the internet. Yes, you can deploy your application using Kubernetes CLI (kubectl), but if you wanted to automate the process, you must configure a load balancer.
Unless you’re running your application on Google Cloud Platform (GCP), you must configure the load balancer on your own. Your main alternative would be to expose the service (check out one of our earlier blogs to see what we mean by ‘service’) to a port on your host machine (such as a virtual machine/VM). But you can run into the risk of port conflicts and difficulty scaling clusters.
After deploying your load balancer, you’ll need to configure it using another set of tools, such as etcd and confd, among others.
A major benefit of containerization is the ability to efficiently use computing power, especially by directly accessing the RAM or CPU instead of spinning up a new VM. But to use that capability, you need to know how to configure Kubernetes to request resources on each pod.
If you skip this step, you’ll put your application at risk of crashing because its containers failed to source enough memory or processing power. That will leave you with downtime and, potentially, dissatisfied end-users/customers and a loss of revenue.
In Kubernetes, a centralized logging and monitoring system is critical.
At a high-level, you’re now with exponentially more issues, such as dozens (or hundreds) of services, each connected to its own database.
However, more specifically, you’re also dealing with new logistics or workload challenges. As an example, with many services in play, you can’t just log into a server to view log files each time to troubleshoot an issue.
It’s not feasible to do it the old way because you’re now dealing with many servers and data sources. You don’t have that kind of time, so you need to simplify it.
To achieve this, you’ll need to consider external tools such as Kafka for logging and Graylog for indexing. Again, you’ll need to configure them to work with your Kubernetes cluster and nodes.
One benefit of Kubernetes is that it recovers from crashes well. If for whatever reason the pods crash, Kubernetes will automatically restart them. This capability is great for end-users, but you still need a way to monitor these issues and, ideally, prevent them in the future.
You can look into open-source tools, such as Heapster and Grafana (among others) to acquire real-time monitoring of your Kubernetes nodes. But as with a lot of Kubernetes, this requires a lot of additional work in terms of configuration and testing.
The challenges we discussed above can apply to any DevOps team, be it within small or large organizations. However, from a business standpoint, large organizations may find the following issues challenges in terms of controlling cost and generating ROI.
The challenge with securing Kubernetes is that it’s complex and easily vulnerable to hackers.
According to TechBeacon, “it’s easy for attackers to identify Kubernetes clusters since they usually listen on a range of well-defined and somewhat distinctive ports.” For example, etcd,
one of the tools we covered above, uses port 2379/TCP, which is easy to find.
When moving from a legacy monolithic application to a containerized microservices one, even large organizations find security a major challenge.
Source: The New Stack
After all, each Kubernetes API (which enable you to use its tools) acts as a “front door”, so to speak, into your Kubernetes cluster.
For example, in 2018, Tesla suffered a breach through its Kubernetes administrative console; the hackers used Tesla’s cloud resources on Amazon Web Services for mining cryptocurrency.
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The lesson here is that you can’t treat Kubernetes, containers, or microservices the same way you had your legacy application and environment. To secure your new assets, you will need to identify the new vulnerabilities and close them accordingly. Don’t take the past for granted.
Not surprisingly, most of the deployment and security challenges with Kubernetes tend to be more common among large organizations.
Source: The New Stack
It doesn’t matter if you’re a start-up or an enterprise giant, the keys to a successful transition to Kubernetes are planning and having enough developer resources to configure correctly.
With those in play, you can eliminate the costs and time involved with Day 2 operations (i.e., the DevOps challenges we looked at above), and generate ROI from Kubernetes.
Slash your time-to-market and Kubernetes deployment costs by configuring it right the first time. Contact us today to get started (and skip Day 2 operational struggles).