What is observability?
In control theory, observability is defined as how engineers can infer the internal states of a system from knowledge of that system's external outputs.
Software engineering teams use observability to understand the behavior of complex digital systems, including when and why errors occur. By looking at a system's outputs, such as metrics, events, logs, and traces, engineers can determine how well that system is performing. A common abbreviation for observability is o11y.
Observability of digital systems has four fundamental components:
- Open instrumentation gathers open source or vendor-specific telemetry data from entities that produce data. Examples of entities include services, hosts, applications, and containers.
- Correlation and context help you understand the bigger picture. Large enterprise applications have enormous amounts of raw telemetry data. The telemetry data collected must be analyzed for correlations and context, so humans can make sense of any patterns and anomalies that arise.
- Programmability gives organizations the flexibility to create their own context and curation with custom applications based on their unique business objectives.
- AIOps tools accelerate incident response to ensure modern infrastructure is always available. AIOps solutions use machine learning models to automate IT operations processes such as correlating, aggregating, and prioritizing incident data. These tools help you eliminate false alarms, proactively detect issues, and accelerate mean time to resolution (MTTR).
Why is observability important?
Observability tools empower engineers and developers to create better customer experiences despite the increasing complexity of the digital enterprise. With observability, you can collect, explore, alert, and correlate all telemetry data types.
Modern-day systems are transforming into complex, open source, cloud-native microservices running on Kubernetes clusters. They are being developed and deployed at lightning speed by distributed teams. With DevOps, continuous delivery, and agile development, the whole software delivery process is faster than ever before, which can make it more difficult to detect issues when they arise.
When things went wrong in the days of mainframes and static operations, it was pretty easy to understand why. Most older systems failed in similar ways over and over again.
As systems became more complex, monitoring tools attempted to shed light on what was happening with software performance. You could trace application performance with monitoring data and time-series analytics. It was a manageable process. But systems became more complex.
Today, the possible causes of failure are abundant and can feel infinite when you are staring at a screen, frustrated. When working on these complex, distributed systems, identifying a broken link in the chain can be nearly impossible without an observability solution. Now that microservices architectures are commonplace, every member of your software team needs to be involved. Teams need to understand, analyze, and troubleshoot application areas they don’t necessarily own. You need distributed tracing, which allows you to trace requests through all parts of a distributed system.
Everything fails at one point or another, whether due to code bugs, infrastructure overload, or changes in end-user behavior. The way that software fails is not predictable. You have to be able to dynamically react with the data.
The 2021 Observability Forecast found that 90% of respondents believe observability is important and strategic to their business, but only 26% said their observability practice was mature. Only half of the nearly 1,300 software engineers, developers, and IT leaders surveyed said their business was in the process of implementing observability.
Observability is essential, and there's a lot of room for most businesses to improve.
What can you do with observability?
Observability is not just a fancy synonym for monitoring. You can do so much more:
- Accelerate time to market.
- Ensure uptime and performance.
- Troubleshoot and resolve issues faster.
- Gain greater operating efficiency and produce high-quality software at scale.
- Understand the real-time fluctuations of your digital business performance.
- Optimize investments.
- Build a culture of innovation.
Observability makes it easier to drive operating efficiencies and fuel innovation and growth.
For example, a team can use observability to understand critical incidents that
occurred and proactively prevent them from recurring. This decreases downtime and improves MTTR.
When a new build is pushed out, they can see into the application performance and then drill down into the reasons why an error rate spikes or application latency rises. They can see which particular node has the problem. For more examples, organized into 10 principles of observability, see Observability: A 21st Century Manifesto.
Application performance monitoring is one piece of observability. To dive into the specifics of application performance monitoring, see What Is APM?