How can you know how well you are doing over time without good data? And how can you continue to meet reliability objectives? With the continued adoption of cloud-native technologies, the topic of observability has become central to discussions about how best to drive performance in these complex, dynamic environments.
The modern definition for observability is that it is the combination of application logs, metrics and tracing data, allowing you to see how well your application is performing. However, the concept of observability goes back further than recent technology developments and is based on control systems theory developed in the 1960s.
By looking at the external results we get over time, we are able to understand how those internal processes are performing. Applying this to cloud and applications, looking at service performance and cloud infrastructure can show up where things are working well, where there are potential problems, and where things might not be secure.
How Does Observability Work?
The traditional approach to monitoring applications and instances involved pinging servers to see whether they respond, or simply tracking CPU and memory usage. Since then, monitoring has had to cater to far more complex software environments that utilize a combination of cloud infrastructure, container and orchestration tools, and technologies. Observability builds on this to play a more active and all-encompassing role for this application data.
Observability involves collecting data continuously in order to gain an understanding of the problems identified by monitoring and then providing recommendations on how you might fix them. Whereas monitoring will notify you of a problem, observability will tell you where that problem exists, why it’s a problem and how to fix it using a combination of tools and information sources. By combining different sets of data from multiple points across your infrastructure, you can get a more precise view into what is taking place; by keeping this set of information up to date continuously, you can see correlate your results and the impact of your changes over time.
As an example, one application within your stack can have multiple services within it, and those components also have their own cross-service dependencies. In order to understand those relationships and identify the root cause of any incident, you can use distributed tracing to find the fault. This is a great approach to get to the depth of the issue, provided the trace is within an isolated environment and the problem is already well defined with a distinct timestamp. As soon as the issue is no longer isolated to a single environment, there is a problem.
To get broader visibility around these kinds of issues that sit across numerous environments, getting a more detailed map can help determine problems. A map of each application service also helps you to understand whether a designed information flow is correct and quickly check the overall health of microservices and their dependent or supporting neighbors. This removes the need for manual correlation.
It also provides that big picture view of the entire environment including service load. For developers, this map helps to improve software development decisions and – ultimately – increases service reliability. DevOps, developers and platform engineers can resolve incidents faster, maximize availability and optimize their cloud infrastructure, microservices and application operations for reliability objectives.
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How Can Observability Go Further?
The interesting thing for many companies is how observability data can be used for other purposes too. Alongside showing how improvements in applications and services can lead to increased revenue, the same set of data can be deployed for security and operations purposes as well.
For developer teams, getting an idea of how your approach compares to other companies around the same infrastructure can be very helpful. With tools like Kubernetes and containers, seeing how your implementation performs in comparison to organizations with similar deployments can be extremely useful. Benchmarking like this can show where you can make more savings, or improve your efficiency around deployments.
Observability is not just about logs, metrics and tracing but also drives significant value for security too. Observability data can be analyzed to detect any changes in behavior such as a change in network traffic or application behavior outside of normal levels. For example, an application that suddenly starts using more CPU could indicate that the machine has been compromised and is being used for crypto mining.
This data can therefore be used by your Security Operations Center – in fact, they may already try to tap a lot of the same sources of information for their analysis. Collaborating on the data can help your team as well as others to get a better picture of what is taking place. By using application data more effectively, security teams can ensure that operations are more secure and stay compliant.
Observability is also something that business teams can use. Having a continuous intelligence stream coming through can show the impact of any change that took place over time. For developers, this will be a before and after view on a change that can show whether it had the desired effect on availability or reliability. However, if that change was for a business reason, then the data can demonstrate how successful that decision was.
Like many state-space representations, the models we build around our systems should help us improve how well we manage our operations. By helping understand the outputs from our systems, observability provides a more accurate representation of our infrastructure. But by providing continuous intelligence on what goes on within that infrastructure, we can get more insight into the decisions that we make and how effective they are.