What is observability?
Observability is a management strategy focused on keeping the most relevant, important and core issues at or near the top of an operations process flow. The term is also used to describe software processes that facilitate the separation of critical information from routine information. It can also refer to the extraction and processing of critical information at the highest-level architecture of operations systems.
Observability is an element in control theory, which says that the internal states of IT systems can be deduced from the relationship between their inputs and outputs. Thus, it is also often described as a “top-down” assessment. The challenge of observability lies less in deriving the internal state from observations than in collecting the right observations.
What are the differences between monitoring and observability?
The concepts of monitoring and observability are related, but the relationship is complex. Differences include the following:
- Monitoring tools passively gather information, most of which turns out not to be particularly significant. This can drown operations personnel and even artificial intelligence (AI) tools in data. Observability actively gathers data to focus on what’s relevant, such as the factors that drive operations decisions and actions.
- Monitoring tends to gather information from available sources, such as management information bases, application programming interfaces (APIs) and logs. While observability will also use these sources, it will often add specific new points of information access to gather essential information.
- Monitoring focuses on infrastructure, where observability focuses equally on applications. That means observability will often include a focus on workflows, where monitoring focuses on point observations.
- The data made available through monitoring is often the sole expected outcome. Observability presumes that data sources will contribute to an analytic process that will then represent the state of an application or system optimally.
Why is observability important?
For decades, businesses that control and depend on complex distributed systems have struggled to deal with problems whose symptoms are often buried in floods of irrelevant data or those with high-level symptoms of underlying issues. The science of root cause analysis grew out of this problem, as did the current focus on observability. By focusing on the states of a system rather than on the state of the elements of the system, observability provides a better view of the system’s functionality and ability to serve its mission. It also provides an optimum user and customer experience.
Observability is proactive where necessary, meaning it includes techniques to add visibility to areas where it might be lacking. In addition, it is reactive in that it prioritizes existing critical data.
Observability can also tie raw data back to more useful “state of IT” measures, such as key performance indicators (KPIs), which are effectively a summation of conditions to represent broad user experience and satisfaction.
What are the three data formats of observability?
The three primary source data types for observability are logs, metrics and traces. They are also called the three pillars of observability.
- Logs are records of events, typically in textual or human-readable form. They are almost always generated by infrastructure elements, including both network devices and servers. They can also be generated by “platform” software, including operating systems and middleware. Some applications will log what the developer believes represents critical information. Log information tends to be historic or retrospective, and is often used to establish context in operations management. However, there are logs that represent collections of events or telemetry data, and the detailed information can be available in real time.
- Metrics are real-time operating data, typically accessed either through an API (a “pull” or “polling” strategy) or as a generated event or telemetry (a “push” or notification). Because they are event-driven, most fault management tasks are driven from metrics.
- Traces are records of information pathways or workflows designed to follow a unit of work, such as a transaction, through the sequence of processes that application logic directs it to follow. Because work steering is normally a function of the logic of the individual components, or of steering tools like service buses or meshes, a trace is an indirect way of assessing the logic of an application. Some trace data might be available from workflow processes, such as service buses or cloud-native microservices and service meshing. However, it might be necessary to incorporate trace tools into the software development process to gain full visibility.
Benefits of observability
The primary benefit of observability is improvement to the user experience, created by focusing operations tasks on issues that threaten that experience. Proper application of observability can improve application availability and performance.
Observability practices will also normally reduce operations costs by speeding up the handling of adverse conditions. This happens through a reduction in the amount of irrelevant or redundant information, and the prioritizing of the notification of critical events. These improvements are most noticeable in large enterprise operations where large operations teams are required.
Some users report that observability practices provide information that is helpful in reliability and performance management, and even in infrastructure design and tool selection. This is because a focus on truly critical information helps identify vulnerabilities that can be corrected by changing configurations, application design and resource levels.
Challenges of observability
Observability does come with challenges, including the following:
- Accidental invisibility of important events and data caused by a failure to properly filter or structure data sources that compete for attention. This can cause a critical condition to be missed because it’s hidden from view or processing.
- Lack of source data, particularly trace information. Not all important information is collected, particularly at the application level and as it relates to tracing of workflows. Unlike resource or component status, traces of workflows usually require special software modifications to enable.
- Multiple information formats from different sources of the same type of information can make it difficult to assemble the right information and interpret what’s available. An organized strategy for structuring information into a standard form is required to ensure optimum observability handling.
How do you implement observability?
Observability starts with a plan, then moves to an architecture and finally to an observability platform. It’s important to follow this approach or there’s a greater risk of challenges and complications.
An observability plan begins by identifying the specific benefits desired. Then, it links each to a description of the data that would be needed to achieve it. While it’s important that this linkage to data considers the available data from monitoring and telemetry, it’s equally vital to identify important information that is not currently gathered — or is gathered in a system that isn’t contributing its data for observability analysis.
The observability architecture is a diagrammatic representation of the relationship between the source data and the presentation of data to operations personnel, AI and machine learning systems, etc. All data sources must be identified, along with the information that each source is expected to contribute. Above the data sources, the diagram should identify the tools that collect and present the information, the tool choices for analysis and filtering of the data, and the tool choices for data presentation. Both proprietary and open source tools for monitoring and observability are available; it’s best to catalog the options that suit the specific target missions at this point.
The final step in implementation is a specific observability toolkit or an observability platform. The difference between the two can be subtle:
- A toolkit is a set of monitoring tools or features that can be used to support observability, but rely on a human operator or a separate software layer to support collective analysis. A toolkit approach will usually require considerable customization, but will accommodate existing software and data sources.
- An observability platform is an integrated software application that collects information, performs analysis that includes KPI derivations and presents actionable results to operations users. A platform might still require customization to accommodate all the data sources available, and it might also constrain the way data is integrated.
The value of observability depends on taking these three implementation steps in an organized way. Skipping or skimping will put the concept — and investment in it — at risk.