7 principles of observability in modern applications
Organizations must keep these seven principles top of mind when monitoring and organizing data to institute observability practices that resolve problems quickly.
Modern applications increasingly rely on distributed services and cloud-native architectures to deliver compute power and versatility to businesses and users. Observability tools offer IT teams efficient ways to maintain these dynamic applications, ensure consistent performance and quickly resolve problems. Administrators can correlate performance with telemetry data to identify the root cause of a problem, whether it exists within the application or in the distributed architecture.
The need for observability also extends to the development process, where containers, APIs and multiple runtimes generate vast amounts of data. Using a single source of truth (SSOT), developers and engineers can better understand context and topology to accelerate their software builds, streamline CI/CD and improve testing.
This article explores key principles of observability and considers the approaches IT leaders can take toward adoption. As IT leaders and engineers consider the best practices for adopting observability within their organizations, they should keep these principles in mind to effectively classify and organize their data stores.
1. Understand the goals of observability
In the past, application performance monitoring (APM) was enough to maintain monolithic, on-premises applications that relied on siloed, individual resources. It was relatively easy to monitor software components, services and resources because new releases and fixes were gradual and consistent. However, rapid resource delivery and the elastic nature of virtual processing and storage have led to cloud-native applications that are constantly updated and changed.
This article is part of
What is observability? A beginner's guide
Numerous discrete, distributed services form the foundation for these dynamic apps, and those service interactions add new levels of complexity. Moreover, cloud environments and the applications they support produce far greater volumes of telemetry and system data that IT teams struggle to process. In addition to a vast increase in the number of data points, the velocity of these information flows makes it difficult for administrators and DevOps teams to keep pace.
Once observability is in place, cross-functional groups can use custom metrics and data sources to understand problems as they occur. For example, end-to-end distributed tracing can be used to map a service request, pinpoint performance issues and improve application availability while gaining key user insights.
In many instances, observability offers a natural evolution of APM because IT operations can apply more granular analysis and address the interdependency of distributed services. It also provides administrators, developers, engineers and business stakeholders access to the same sets of application insights. As teams acquire a clearer understanding of application performance and health, they also gain a more comprehensive view of their IT environment.
2. Define a single source of truth
Creating an SSOT provides an individual reference point where all data within an organization can be located. Cross-functional teams can then gain contextual insights and work collaboratively with data to isolate key application performance issues, whether they're occurring vertically in the stack or across services, processes and hosts.
Comprehensive integration represents the biggest challenge to creating an SSOT because it requires aggregating data from many disparate systems. Indeed, organizations gather massive amounts of observability data on an order of magnitude higher than other data streams. And because most cross-functional teams use data tools specific to their domain, they often lack a holistic view of modern application data.
It might be unrealistic to think that a collection of data monitoring tools or an individual observability platform can provide a single-pane view of all data flows. An alternative is to shift the source of truth from a platform to a unified data pipeline that can offer a central, logical point where data coalesces, and individual teams can use their preferred tool to extract the results they need.
3. Understand curation
Data curation ensures organizations have the information they require in the most usable format. It eases the process by which an organization collects and manages data so that software developers, business teams and analysts can use it. Using the three pillars of observability, curation ensures data in the following formats can be easily retrieved for future use or if an application issue occurs.
- Logs. Often containing huge volumes of unstructured data, logs must be aggregated in one central location. IT teams can then rely on logs for troubleshooting and debugging, as they contain up-to-the-millisecond event data.
- Metrics. IT teams employ metrics to measure application and system health factors. These measurements provide a way to quantify infrastructure and application performance in terms of latencies, traffic, memory usage and other key elements. For example, developers can capture and analyze containerized deployment metrics to assess the health and viability of key build components within a Kubernetes deployment.
- Traces. Administrators can trace and measure the performance and response times of application requests across an infrastructure. Traces provide visibility to uncover the path of a specific application request and its movement across a complex microservices infrastructure. They offer another resource to determine application performance in terms of multiple service dependencies.
4. Ensure full transparency
High levels of transparency guarantee comprehensive incident response for infrastructure stakeholders, including IT ops, development engineers, product support and business groups. Encouraging transparency increases trust among team members and ensures each incident is properly recorded, tracked and acted on, based on requisite workflows, runbooks and notifications.
5. Build speed into the process
Organizations can employ observability to help minimize the critical gap that occurs between a serious application event and the time required for remediation. IT ops and engineers can also employ observability to accelerate their responses in cloud-native and microservices environments. For example, businesses can adopt AI for IT operations to pinpoint issues and respond programmatically to problems.
6. Understand context and topology
Modern application dependencies exist both vertically in the stack and across a microservices infrastructure. Through contextual awareness, IT ops and developers can move quickly to pinpoint and resolve problems. For example, real-time topology maps based on an SSOT enable IT teams to determine the source of latency blind spots and understand the complex interplay of dynamic, multi-cloud environments.
7. Embrace change
For observability to function effectively, IT and business leaders should encourage the organization to reorient their approaches to maintaining and using applications. Placing the focus on collective improvement and responsibility instead of dictating the approach will increase the successful use of observability across an organization.