There has been a gradual evolution happening in the world of application observability as organizations demand more flexibility in the way they monitor and observe their applications. As software architectures have become more distributed and organizations have continued their push towards the cloud, developers and operations teams have naturally begun to have higher expectations for their observability toolchain in order to ensure their applications continue to operate flawlessly in the hands of their customers.

If we look at the progression of traditional software monitoring tools, they often required that users know in advance which metrics they wanted to track or be alerted on. With the introduction of what we know today as observability, organizations began realizing that monitoring is important, but having the ability to ask real time questions of your system, that you might not have thought of in advance, is increasingly important.

Dynamic observability takes this a step further. Not only should you be able to ask real-time questions about what’s happening with a running system, but you should also be able to get real-time answers. In the rest of this blog, we’ll dive deeper into dynamic observibility, discuss why it’s important, and how it’s reshaping the way developers find and solve software issues in their applications.

Dynamic Observability

Being able to get real-time feedback is increasingly important for software development teams as the demand from the business and customers to fix issues fast is as critical as ever. But what does dynamic observibility actually mean in practice? To answer that it is helpful to dive deeper into the way traditional observability tools work.

With traditional observability tools, when you want to have access to a specific metric, trace, or other piece of information about your application it often requires you to make a new configuration change, code change, or even a redeployment of your application. Upon successfully making the change you’ll have this new piece of observibility data ready to review. Now if you couple this requirement with enterprise release management practices where environments are more heavily regulated and often require one or more approval gates, this can dramatically slow down the debugging process.

In practice, dynamic observability tools work by providing the ability to dynamically instrument your code with an agent or SDK. This can include the ability to do things like bytecode level manipulation or setting of non-breaking breakpoints in the code, in order for example, to add logging into strategic points in your code to gather debug data on-demand and without requiring any changes to the running application. This real-time approach means that any piece of data from within a running application can be gathered whenever it’s needed by a developer or production support team.

Why Dynamic Observability is Critical

Gone are the days where taking weeks or months to solve issues is acceptable. With all of the options available today to consumers, they have higher expectations for the services they use. If your service isn’t operating as expected when in the hands of your customers, they will go elsewhere and once they are gone, it’s difficult to get them back. With stakes being this high, it’s logical to want to have tools in your toolbelt that enable you to solve these issues as quickly as possible, even when you might not have thought about creating a log or metric to gather data related to that issue up front.

As much as we would like to think we know what metrics or logs we’ll need to track up front, it’s rarely the case in practice that we have all the information we need to solve defects real-time. Much of the time, teams are redeploying their applications into test or other reproduction environments where they then attempt to reproduce the issue in a controlled environment. Having the ability to gather deeper observibility data directly from the environment where the issue is occurring and where it’s easiest to reproduce is a huge time saver. Teams that can capitalize on dynamic observability will have a leg up on the competition in getting to the root cause of issues faster.

Dynamic Observability in Cloud Native Environments

As many organizations migrate from monolith to microservices, they’re finding that they need new approaches to observing and debugging applications. No longer do organizations have a set of static servers which they monitor with a set of static metrics as their applications run. Cloud Native applications are often ephemeral in nature (Kubernetes for example), meaning that servers are spun up and spun down as needed to scale and keep up with demand. Teams need an approach that is as dynamic as the cloud environments their applications are running within.

The goal of every observibility team should be to have the ability to gather any data point within their application real-time and on-demand. Cloud native applications are producing more data than ever, and far too often much of that data isn’t accessible to developers who need deeper insight in order to solve defects in their code. If a developer wants to know the value of a variable in their code, for example, and they don’t yet have a log line or a metric that gives them insight into that data, they should be able to collect that information as it’s needed without having to make a change to their code and without having to redeploy their application or service. Dynamic observability tools make this a reality and give developers the ability to have a deeper understanding of their code and how it executes.

Conclusion

As organizations migrate away from the monoliths of the past and onto Cloud-Native applications, they’re demanding more out of their observability tool vendors. Because of this, we’re seeing a push towards making these observability tools more dynamic in nature and more aligned towards the distributed and ephemeral nature of today’s applications. Dynamic Observibility is the latest in obtaining real-time access of any data point within an application when it’s needed so that teams can make better and more informed decisions about how to resolve issues.