Observability vs. monitoring: What’s the difference?

Organizations are depending more and more on distributed architectures to provide application services. This trend is prompting advances in both observability and monitoring. But exactly what are the differences between observability vs. monitoring?

It’s essential to understand when something goes wrong along the application delivery chain so you can identify the root cause and correct it before it impacts your business. Monitoring and observability provide a two-pronged approach. Monitoring supplies situational awareness, and observability helps pinpoint what’s happening and what to do about it.

To get a better understanding of observability vs monitoring, we’ll explore the differences between the two. Then we’ll look at how you can best utilize both to improve business outcomes.

Monitoring vs. observability

First, let’s define what we mean by observability and monitoring.

What is meant by monitoring?

Monitoring, by textbook definition, is the process of collecting, analyzing, and using information to track a program’s progress toward reaching its objectives and to guide management decisions. Monitoring focuses on watching specific metrics. Logging provides additional data but is typically viewed in isolation of a broader system context.

What is meant by observability?

Observability is the ability to understand a system’s internal state by analyzing the data it generates, such as logs, metrics, and traces. Observability helps teams analyze what’s happening in context across multicloud environments so you can detect and resolve the underlying causes of issues.

What is the difference between observability and monitoring?

Monitoring is capturing and displaying data, whereas observability can discern system health by analyzing its inputs and outputs. For example, we can actively watch a single metric for changes that indicate a problem — this is monitoring. A system is observable if it emits useful data about its internal state, which is crucial for determining root cause.

Between observability and monitoring, which is better?

So how do you know which model is best to use across your environments?

Monitoring typically provides a limited view of system data focused on individual metrics. This approach is sufficient when systems failure modes are well understood. Because monitoring tends to focus on key indicators such as utilization rates and throughput, monitoring indicates overall system performance. For example, when monitoring a database, you’ll want to know about any latency when writing data to a disk or average query response time. Experienced database administrators learn to spot patterns that can lead to common problems. Examples include a spike in memory utilization, a decrease in cache hit ratio, or an increase in CPU utilization. These issues may indicate a poorly written query that needs to be terminated and investigated.

Conventional database performance analysis is simple, though, compared with diagnosing microservice architectures with multiple components and an array of dependencies. Monitoring is helpful when we understand how systems fail, but as applications become more complex, so do their failure modes. It is often not possible to predict how distributed applications will fail. By making a system observable, you can understand the internal state of the system and from that, you can determine what is not working correctly and why.

Correlations between a few metrics are often not enough to diagnose incidents in modern applications, however. Instead, these modern, complex applications require more visibility into the state of systems, and you can accomplish this using a combination of observability and more powerful monitoring tools.

The “three pillars” of observability

As mentioned earlier, observability is understanding what’s happening inside of a system from its logs, metrics and traces. Systems are observable when they generate and readily expose the type of data that enables you to evaluate the state of the system. Here’s a closer look at logs, metrics, and distributed traces.

• Logs include application- and system-specific data that provide details about the operations and flow of control within a system. Log entries describe events, such as starting a process, handling an error, or simply completing some part of a workload. Logging complements metrics by providing context for the state of an application when metrics are captured. For example, log messages might indicate a large percentage of errors in a particular API function. At the same time, metrics on a dashboard are showing resource exhaustion issues, such as lack of available memory. Metrics may be the first sign of a problem, but logs can provide details about what is contributing to the problem and how it impacts operations.
• Metrics in this context are sets of measurements taken over time, and there are a few types:
  • Gauge metrics measure a value at a specific point in time, such as the CPU utilization rate at the time of measurement.
  • Delta metrics capture differences between previous and current measurements, such as a change in throughput since the last time it was measured.
  • Cumulative metrics capture changes over time — for example, the number of errors returned by an API function call in the last hour.
    
• Distributed tracing is the third pillar of observability and provides insights into the performance of operations across microservices. An application may depend on multiple services, each with its own set of metrics and logs. Distributed tracing is a way of observing requests as they move through distributed cloud environments. In these complex systems, traces highlight any problems that can happen with the relationships among services.

True observability, however, relies on more kinds of data than just key indicators.

Why monitoring and observability need a next-gen approach

When trying to effectively monitor, manage, and improve complex microservices-based applications, observability and monitoring are both vital. Monitoring and observability represent a continuum from basic telemetry of single servers to deep insights about complete applications and dependencies.

Many organizations start with monitoring and realize these tools lack contextual insights. Context is critical to understanding why problems exist and how they impact the business. Organizations look to observability to provide the data they need for contextual analysis. With an understanding of the problem, they can understand the root cause and and its affects.
DevOps practitioners struggle to maintain highly available and scalable applications. That’s because these complex, interdependent systems behave in unpredictable ways and issues originate from sources that are often not apparent. Practices and tools that worked when we built monolithic applications simply can’t handle the level of data distributed environments generate. They don’t ingest enough data or provide enough insight into the state of applications to understand how to correct problems quickly. Luckily, there are tools and practices that address these challenges.

An automatic and intelligent approach to monitoring and observability

An advanced software intelligence solution like Dynatrace automatically collects and analyzes highly scalable data to make sense of these sprawling multicloud environments. Dynatrace’s causal AI engine, Davis, sifts through massive volumes of disparate, high-velocity data streams, and analyzes them through a unified interface. This single source of truth tears down information silos that traditionally separate teams that perform many different functions on many different application components. This centralized, automatic approach eliminates the need for manual diagnostics. It also provides paths to remediation to keep the technology users rely on functioning smoothly.