What is observability?
Observability, a term that comes from control theory, is the ability to answer questions about the inner workings of software products and services by only observing the system’s outside or external workings. It’s a measurement of the internal system’s fitness that is inferred by observing external outputs. Observability measurements focus on why a problem is happening, rather than simply identifying that there is a problem.
If a system has a high degree of observability, that means you do not need to ship new code to answer questions about the system’s internal workings.
Because the newest systems are so complex, software engineers have developed tools to help organizations predict when something is going to break by measuring the system’s outer workings. Observability helps teams to understand how the entire system fits together.
Now that system complexity is outpacing our ability to predict what’s going to break, observability tools have become essential for large enterprises. Monitoring for unknown problems is no longer enough, since companies need tools to uncover “unknown unknowns.” With observability, the emphasis is on the development—and ongoing changes—of an application.
Many organizations use observability to analyze and track the deployment of a new system, especially experimental ones. When deploying a system, it’s important to keep a close eye on all system components, including mobile, web front-ends, back-ends, databases, and the overall infrastructure.
Investors are also noticing the importance of observability. Databand, for instance, raised a $14.5 million Series A in December 2020 to continue enhancing its data pipeline observability tools.
What do observability tools do?
Most observability tools externalize application events through logs, metrics, traces and other measurements.
- Logs are lines of structured or unstructured text that are generated by an app after an event occurs. They can help explain what happened in a specific event or to a specific part of a system at a particular time. While logs are easy to emit, they are difficult to analyze and quite expensive to store.
- Metrics are numerical values (usually counts or measurements) of aggregated data about a system that are calculated over a specific period of time.
- Traces allow you to see the activity of a specific transaction or request as it runs through the application or system. You can use traces to contextualize logs and metrics, as well as identify the most important metrics to measure in a given situation or time period. They can also help you find the most relevant logs to analyze.
Observability tools are especially useful when working with distributed systems such as microservers, serverless, and service meshes.
Who uses observability tools?
Observability tools make it possible for a variety of teams—including engineering, design, marketing, sales, HR, executives, and other stakeholders—to identify problems and pinpoint the root cause.
What is the difference between monitoring and observability?
Even before computer systems became so complex, monitoring was an important part of identifying problems. But as systems started becoming more complicated, developers started needing more advanced observability tools.
While monitoring is something that teams actually do (an action), observability is a property or measurement of a system.
Observability and monitoring go hand in hand. You shouldn’t substitute one for the other, as they complement each other.
For monitoring and observability to work, IT systems & applications need to externalize or express their state in qualitative and/or quantitative measurements.
Here’s a quick look at how monitoring is different from observability.
Before observability went mainstream, monitoring was the first step in identifying problems, and was managlabe until modern systems got too complex.
Monitoring tests monitor systems for problems against standard thresholds, basic fitness tests, and well defined health checks. In other words, you must assume that there is a “normal.”
Application monitoring tools tend to focus on sampling. In these cases you usually have access to less than 2% of the data, which can make it difficult to solve most problems.
Monitoring tools allow you to measure information passively, usually with the help of dashboards. They are a good option for relatively static systems with little variation, few changes, and a limited set of permutations.
The best monitoring tools make it easy to pinpoint the source of a problem as well as generate key insights into performance trends. Many businesses employ monitoring tools to relate coding practices to performance outcomes, especially business outcomes.
Observability, on the other hand, assumes that your system’s normal changes over time and in different circumstances. Observability is especially helpful when an organization is ushering in a new configuration, deploying new software, or scaling a system up (or down). This helps developers understand the system’s new state.
In general, teams determine metrics based on previous failures, or based on problems that they’ve addressed in the past.
Why is observability important?
Besides helping organizations deliver outstanding software, observability tools help companies build a culture centered on innovation.
Observability helps developers understand:
- Why a system is moving slowly
- What is broken
- What must be accomplished to improve the current system
- The problems that occurred during deployment
- Why performance has slipped in the past few months
- What changed
- What logs to analyze
- Whether a problem is affecting all users or a small segment of users
Observability forces developers to remain aware and embrace flexibility. Teams can use observability to make hypotheses when they are lacking crucial information about the internal system. Many complex systems have countless permutations and a high degree of variability, so observability assists teams with maintaining control over their ever-changing environment.