5 Best Practices to Monitor Containerized Deployments on AWS

AWS

Monitoring and observability a key consideration when choosing an AWS container service. There is no denying that containerized environments are highly dynamic and ephemeral in nature. This makes it hard to monitor, especially with traditional infrastructure monitoring or APM solutions. There are thousands and more components that are spun every day, every hour, every batch that monitoring solutions find it challenging to keep up with the churn. And in some cases, it fails to see the new components, thereby not offering correct multi-dimensional real-time analysis.

However, there are a few streaming analytics services that can keep up with such a dynamic nature of containerized environments. And can do so without compromising performance. Let’s look at some of the best practices to monitor containerized deployments on AWS with analytical services.

5 Best practices to monitor containerized deployments on AWS

1. Create your monitoring plan for your specific container environment.

It is vital that you create a monitoring plan. This plan should address the specifics related to your container environment. It can include elements like:

  • Monitoring goals
  • List of resource types and services that need to be monitored
  • Requirements of tools and integrations
  • Monitoring frequency
  • Roles and responsibilities
  • Processes to notify and handle alerts
  • Handling of other levels of automation.

By creating such a plan, you can closely monitor AWS container deployment in real-time. It can also help auto-discover services and provide in-depth metrics about your containers. Such actionable insight can also divulge you into cost-saving opportunities, identify usage patterns, and set categories specific to your set up to achieve your goals.

2. Dynamic cluster map

With Kubernetes navigator, you can quickly view the performance of your entire Kubernetes environment through intuitive and hierarchical navigation. You can also select, filter, search for a specific entity, node, container level within seconds. By discovering these components and containerized services, you can monitor your entire deployment. This also enables you to understand dynamic Kubernetes components and make adjustments to fix performance issues.

3. Drill-Downs

Through drill-downs, you can view the entire Kubernetes environment at a glance and monitor how your overall system is performing. It is additionally vital to have granular and detailed insight into individual components so that you can narrow it down to the source of the problem. Drill downs let you gain insight into everything from nodes to pods to containers and workloads.

4. In-context investigation

Another great way to monitor all aspects of your container deployment is by pivoting your data to logs. This will give you a granular view of your application, Kubernetes, and all container logs. You can then correlate performances across the entire environment, thereby monitoring achievements. What’s more, is that it also helps you understand and maintain your security and compliance postures.

5. Kubernetes analyzer

One of the most important aspects of monitoring performance is to understand the ‘why’ of an anomaly. The Kubernetes analyzer is driven by AI, which automatically displays insights and recommendations to answer in real-time what is causing that anomaly. It helps automatically detect patterns, and with the help of suggested solutions, your team can narrow to the underlying issue within minutes.

Wrapping it up!

New cloud-native technologies like AWS containers are paving the way for faster innovation with resilient application development. However, it also increases the level of complexity for monitoring and troubleshooting anomalies. There are many integrations today that support AWS container infrastructure and services that make it easier for businesses to monitor their application performance, thereby maximizing the benefits of adopting containers at scale.

Which integrations do you use to monitor containerized deployment on AWS? Let us know in the comments below!

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