What is observability?

Observability refers to how closely a system’s external output reflects the performance of its internal components and processes. It is a measure of how accurately you can judge the internal state and health of a system from externally visible clues.

Your IT landscape spans technologies as varied as microservices, containers, virtual machines and databases, each of which affects performance. As you monitor, analyze and tune those technologies in real time, observability – a discipline derived from control theory – plays a role in your efforts to optimize overall performance. It enables your IT teams to maintain systems, perform root-cause analysis and keep customers satisfied.

In a system with high observability, you can take actions based on the system’s output with high confidence in how those actions will affect the system. Conversely, in a system with poor observability, the actions you take based on external outputs lead to results that seem inconsistent with or unrelated to those actions.

When you think of your databases, applications, middleware and networks as systems, observability is the measure of how readily you can ask and answer questions about them.

Your IT landscape never stops evolving. From the emergence of NoSQL and cloud-native databases to the gray area between OLAP and OLTP introduced by platforms like Snowflake, your database professionals and IT teams are constantly adapting. Step back and consider the application, middleware and network layers around your databases. How can you monitor performance and availability accurately in so many different areas, all at once?

In the context of infrastructure, IT teams have traditionally relied on statistics such as memory utilization and CPU usage to judge system health. But building an accurate picture of the performance of a database or application demands more than collecting and analyzing those statistics.

The goal of observability is that you can see the performance of your IT environment from the top down and understand it accurately. Achieving that goal requires selecting the external outputs (metrics, logs and traces) that make sense for your organization, collecting them and analyzing them over time.

When you make that top-down picture available to your IT teams, they can maintain the health of your systems across the enterprise.

The top-down picture of IT infrastructure relies on a widely held model of observability, which rests on three pillars: metrics, logs and traces.

Metrics include disk space, memory utilization, network throughput, CPU usage and other factors on which your system performance depends. Traditional performance monitoring tools use techniques like scripts, database access and APIs to gather a common set of metrics from an application, database or system. In limited contexts, for example, dashboards enable sysadmins to monitor individual factors like server activity and database locking and deadlocks, then make decisions and take action.

Logs record system activity at the lowest level. If, for example, your backup system fails, database replication and backup products can use the redo logs (external output) to determine where and when the failure occurred. Admins can then bring the system back to the optimal restore point. Logs are a precious data source for observability in enterprise management. However, the huge number of entries in an enterprise system makes logs more valuable as records of events than as indicators of overall performance.

Traces record information about operations. What’s more important, they track interdependence among those operations. For example, consider a request made in a given application stack through a web server and a middleware process back to a database. Traces capture data on the overall duration of the process and on the amount of time spent at the application, middleware, database and network layers. Taking that request as a single system, traces provide the external output your developers can use to track down poor performance and take action on it.

The importance of observability is that it demonstrates the maturity of your IT governance. With high observability, you can read the external output of a system and take action on it, confident that the system will respond as intended.

Although the model of observability rests on metrics, logs and traces, you’ll need more than just those elements for an overall view of system performance in your enterprise. After all, it’s a daunting task to synthesize the sheer volume of data generated by any system into a coherent picture that your IT teams can use. For that reason, many companies that are rich in metrics, logs and traces still have poor observability.

Observability is valuable in understanding the extent to which system input affects system output. Moreover, the growing benefit and use of observability as an approach, model and framework is apparent in other domains including the following:

• Data observability
• Database observability
• Application observability
• Infrastructure observability
• Network observability
• Security observability
• Cloud observability
• User experience (UX) observability
• Service mesh observability

Monitoring is a component of observability that tracks system logs for specific types of changes, which monitoring tools then present as alerts. Monitoring is useful for studying already-known issues. Monitoring and observability are complimentary.

The main value of visibility lies in working with individual parts of a system. If a particular application or device is visible, you know certain details about its health, such as the resources it uses and whether it is running or not.

Observability, on the other hand, focuses on unknown issues in systemwide context so that you can see, for example, the interdependence of individual system components. With the insights you derive from wider context, you can detect and respond to problems while improving performance.

The main difference is that traces play a greater role in observability than in monitoring and visibility. By enabling you to follow the entire path of, say, a database query or a call to an application, traces give you a full, chronological view of any event. Supported by metrics and logs, traces offer wide insight into your systems.

Most important, traces allow you to pose and answer almost any question about system health, including questions that may not otherwise have occurred to you. With monitoring tools, the burden is on your users to stretch their expertise and infer unanticipated questions from the external output of systems. Observability, however, provides your users with questions based on a wider context, even before those questions would normally arise. In essence, it assists IT teams in querying and understanding system output by:

• Addressing impromptu questions
• Streamlining disparate types and formats of data from disparate sources
• Generating useful insights from inconspicuous relationships
• Facilitating queries, data visualization and alerts
• Deriving answers from telemetry data

Enterprise observability offers insight across distributed IT systems to promptly identify problems and lead to their resolution.

An observability tool joins the other tools and utilities you use to manage your enterprise IT landscape. As such, a primary criterion is that the tool should have as many integrations as possible with your current tools so that they all interoperate. If the vendor does not provide the integration, then you may have to build it.

At some point, a services component will be involved in whatever your company is trying to implement. How much of that component do you have natively? How much, if any, must you add to extend your implementation?

From the functional perspective, how does the observability tool help your IT team make sense of the data? Does the tool truly provide enough context to take actions based on system output with high confidence in how those actions will affect the system? For example, if throughput on a given section of the network is slow and the applications and databases there are slow, then the applications and databases are probably not the problem. It may be a problem with the network segment or its resources. The tool should help you make sense of all the metrics, logs and traces being generated.

With observability tools, you should be able to address questions like these:

• Which queries consume the most resources and are most frequently executed?
• Which sessions, SQL statements, programs and users most affect resources in our environment?
• When do applications and databases generate abnormal levels of activity?
• What has changed in my environment, and which resources will those changes affect?
• Which changes can we make to organize our database tables optimally and normalize them? Which other, unforeseeable effects will those changes have?
• How will generative AI intersect with observability? How will that combination lead to self-healing performance?