Getting More Value From Your Containers With Container Observability

Containers are an increasingly in-demand way to run enterprise applications. This is especially true for modern applications, such as AI, edge and IoT applications. The scalability and flexibility of containers empowers organizations to deploy and run apps faster than ever with more efficient resource utilization. 

However, most companies aren’t getting the full value out of their containers — or the data inside them — because they can’t properly observe their containers. Because of containers’ ephemeral nature, it’s difficult to check a container’s status, because a container with problems has already been deleted and recreated several times. It’s a significant security risk, not to mention a waste of resources. Here’s what you need to know about container observability, the common challenges you’ll likely run into and how to overcome them to get the full benefits of your containers. 

Container observability: A background

Container observability is the proactive approach to understanding how your containers are performing and why they’re behaving the way they are. Here’s some of the essential background on what container observability is and why it is essential for container computing.

Container computing

Containerization is the process of using containers to deploy, manage and scale applications in cloud environments. Containers give companies several advantages. Because containers, they use resources more efficiently. Containers also launch quickly, meaning you can update apps quickly and can test and maintain apps more easily. Containers also have automated scaling and, which gives you maximum uptime. 

To keep your containers functioning, it’s important to use cloud-native observability. Container observability is essential to manage, maintain and improve your container computing. With thorough container observability, you can improve your container scaling, improve resource utilization and easily troubleshoot problems. 

Observability vs monitoring

Observability is not just mere monitoring, and it’s important to understand the differences between the two. Observability includes monitoring and analyzing containers. Here are some of the key differences between observability and monitoring:

• Monitoring simply shows you what’s happening; observability is understanding why it’s happening and how to manage it in the future.
• Observability is proactive and diagnostic, while monitoring is reactive.
• Monitoring gives you data and alerts on a set of predefined metrics, such as uptime, response time, error rates, memory used, request latency and resource utilization. Observability uses those metrics and logs and traces to gain deeper insights, locate the root cause of issues effectively and de-bug faster. 

Container observability

Think of container observability like having a dashboard of everything that’s happening in your car, as though your car were being continually scanned and giving you reports. The scanning could identify small leaks and mechanical issues before they became problematic. It could also give you predictive suggestions on how to keep your car functioning smoothly, how long you have left on your tires and what may need to be refilled or replaced. You’ll understand better how your car works, how to get more mileage out of it and how to make it more gas-efficient. 

That’s what observability is for your containers. By using logs, metrics and traces, container observability offers a window into the true health of your containers. Container observability is the strategy you’ve been looking for if you want to constantly ask “How can I make my containers more effective?” 

Some of the key components of container observability include:

• Container logs
• Metrics from the container runtime and host system
• Application traces within the container
• Data from the underlying infrastructure

Container observability is a building block of Kubernetes observability. Container observability focuses on individual containers. Kubernetes observability involves the entire cluster, including nodes, pods and, of course, containers — making container observability an essential step in achieving Kubernetes observability. 

The same is true on a larger scale. If you don’t have container observability, you can’t achieve full-stack observability. Even a small issue on the container level can escalate through your tech stack. Achieving container observability is a fundamental step of achieving observability up through higher levels of your system.

Why is container observability important?

Container observability is important for several reasons. It’s essential for container security and preventing potentially data-breaching problems from happening to your containers. With proper container observability, you can detect threats early. If any problems arise, you can troubleshoot quickly. Container observability is also important for understanding the overall health and behavior of your containers. It helps you:

• Understand how well your containers are performing and why they’re behaving the way that they are
• Get a sense of your underlying infrastructure and resources that support your containers
• Find way to increase performance and increase uptime
• Identify issues, anomalies and trends
• Respond to issues faster  

Beyond threats, container observability is important for continuous improvement. Container observability seeks to understand why issues happen, how to prevent them and how to make containers function more effectively. With thorough observability, your containers will be better at resource management, will last longer and won’t fail as often. Combining security measures with effective container observability ensures that both operational efficiency and proactive threat detection are maintained, safeguarding not just the container but the broader IT ecosystem.

Common container observation challenges

Observing containers is important, but it’s easier said than done. Because containers are distributed and highly dynamic, it can be difficult to pinpoint underlying infrastructure issues. Here are some of the key challenges most organizations face when they’re trying to observe their containers.

Challenge #1: Containers are dynamic and ephemeral. 

Being dynamic is both a strength and a weakness of containers. Their dynamic nature means Kubernetes can spin up new ones on demand and then terminate them as soon as they’re no longer needed. Containers start and stop quickly, which enables fast scaling. Being dynamic means containers can shift resources for better efficiency. Unfortunately, alongside these benefits come some challenges. The short-lived nature of containers makes it difficult to track logs, metrics and traces. If there’s an underlying issue, it can be hard to track or observe consistently. 

Challenge #2: Microservices are complex. 

Microservices are great for building flexible, scalable applications. However, they make container observability much more complex. 

A single microservice is often distributed across multiple containers. Ideally, observing containers also allows you to observe the full journey of a microservice request across those several containers. Container observation gives you insights into how services interact, where errors happen and where latency occurs. However, the more containers the microservices passes through, the tricker that tracking becomes. In a distributed microservices system, it can be hard to connect logs, metrics and traces from multiple containers that are part of the same request. 

Challenge #3: There’s a massive amount of data. 

Containers generate massive amounts of data. Every app and microservice means more telemetry data. There’s an ever-increasing amount of log metrics and traces being gathered, and it’s all being demanded in real time. The volume and speed of data is constantly increasing. Although data is crucial for gaining insights, it’s also difficult to manage. Storing the information alone can be daunting, much less track and analyze it.

Not all data is equal. You have to sift through data to find what’s useful. As you’re observing your containers, you have to swim through a sea of information to find the pieces that indicate container health and give you clues about how the underlying infrastructure is functioning. That gets harder with the exponential increase in the amount of data. 

Challenge #4: Observability tools aren’t standardized. 

There are many tools on the market to help you observe your containers — Prometheus, Datadog and OpenTelemetry, to name a few. These tools can be helpful. However, they also have drawbacks, as each tool collects data in different formats. The formatting differences can lead to integration issues later. Without standardized data formats, it’s difficult to correlate metrics, logs and traces. If your data can’t easily be integrated, the valuable data you get from the tool is siloed. Siloed data means it’s difficult to identify anomalies from logs, and security breaches are harder to catch.

Tips for better container observability 

Despite the challenges, observing your containers is absolutely worth it. If you know how to address the challenges, you can unlock the benefits of container observability. Here are some strategies to get the most out of your containers and achieve maximum observability. 

Tip #1: Use persistent storage. 

Because of the ephemeral and transient nature of containers, one of the challenges mentioned above is that the short-lived container data isn’t always tracked or stored properly. To get the full value of your containers and their data, use a persistent storage solution. These solutions maintain historical data so you can analyze it. The more data you can store, the better you can identify patterns and catch anomalies early. 

Tip #2: Implement centralized monitoring. 

To leverage microservices while avoiding unnecessary complexity, implement centralized monitoring. Observing interactions and dependencies between multiple microservices is much easier when you have a dedicated tool. You could also use centralized logging and distributed tracing tools to gain deeper insights. Centralized solutions help you track microservices across containers to pinpoint where errors occur and give you an overall view of how your microservices are functioning. 

Tip #3: Invest in a scalable data storage solution

The oceans of data pouring in from your containers won’t be slowing down anytime soon. To adequately process, store and analyze your data, you’ll need a scalable data storage solution. Your data storage solution may need to be complemented by an efficient data analysis platform, especially if you need to analyze data in real time. 

To help important data float to the top, make sure you choose a data storage solution that has a fast and thorough search capability. Log aggregation and sampling techniques can also help you analyze information that’s relevant to your projects. 

Tip #4: Use integration orchestration platforms. 

All the useful container observability tools on the market won’t do you much good if you don’t integrate them carefully. The lack of standardization in these tools can cause security issues and cause difficulty in integrating data in different formats. To solve this, invest in an integration orchestration platform. Integration orchestration platforms can help you integrate your observability tools with container orchestration platforms like Kubernetes for more thorough Kubernetes observability. 

If you want to go a step further to make sure all your tools are working together, you can implement a unified observability platform. Integrated solutions that use unified observability platforms combine logging, tracing and monitoring all in one place to make sure your tools are working together. You’ll get more value out of your tools individually, and you’ll also get more value out of the data because the data will no longer be siloed in a single tool.

SUSE: Your partner in container observability

Observing your containers and their underlying infrastructure is foundational to overall container security. In turn, container security is an important part of supporting Kubernetes observability and full-stack observability to make sure your data is secure across your whole system. 

In addition to keeping your data safe, container observability’s proactive and diagnostic approach is excellent for continuous improvement. The better visibility you have into your container, the more efficient you can make your containers and the better resource utilization you’ll achieve. 

For decades, SUSE has been committed to helping organizations better observe their applications. For a fully managed solution try SUSE Cloud Observability free for 30 days.

FAQs on container observability

Container observability is a complex topic. Got questions? We have some answers for you to simplify it. Here are some commonly asked questions about container observability. 

What is Kubernetes observability?

Kubernetes observability is the process of understanding and gaining insights into how your Kubernetes is performing. True Kubernetes observability means you’ll need to be able to observe the whole cluster — nodes, pods, containers and all. 

Not only does observability help keep your Kubernetes secure, it also helps you de-bug faster, optimize Kubernetes performance and give you real-time insights. 

What is container monitoring?

Container monitoring is a strategy to track the performance and behavior of your application containers. It usually tracks metrics like CPU usage, request latency, memory usage and uptime. While container monitoring is useful for collecting data and giving you alerts about anomalies, it usually doesn’t provide deeper insights. To fully understand why your containers are behaving the way they are, you’ll need to shift to container observability, which is more proactive and focused on proactive problem-solving. 

What is a container observability tool?

A container observability tool is a solution that monitors and analyzes your containerized environment. By gathering data like traces, logs and metrics, container observability tools give you insights into why your containers are behaving the way they are. Using container observability tools can help you prevent security breaches, improve resource utilization, de-bug containers and overall manage containers successfully at scale.