When something goes wrong with an application, it impacts customers and, ultimately, impacts the business. Teams need a way to find the root cause of problems and quickly resolve them. That’s where monitoring and observability come in.
Monitoring and observability are two ways to identify the underlying cause of problems. Monitoring tells you when something is wrong, while observability can tell you what’s happening, why it’s happening and how to fix it. To better understand the difference between observability and monitoring, let’s look at how each works and the roles they play today within software development.
Observability is the ability to understand a complex system’s internal state based on external outputs. When a system is observable, a user can identify the root cause of a performance problem by looking at the data it produces without additional testing or coding.
The term comes from control theory, an engineering concept that refers to the ability to assess internal problems from the outside. For example, car diagnostic systems offer observability for mechanics, giving them the ability to understand why your car won’t start without having to take it apart.
In IT, an observability solution analyzes output data, provides an assessment of the system’s health and offers actionable insights for addressing the problem. An observable system is one where DevOps teams can see the entire IT environment with context and understanding of interdependencies. The result? It allows teams to detect problems proactively and resolve issues faster.
Monitoring is the task of assessing the health of a system by collecting and analyzing aggregate data from IT systems based on a predefined set of metrics and logs. In DevOps, monitoring measures the health of the application, such as creating a rule that alerts when the app is nearing 100% disk usage, helping prevent downtime. Where monitoring truly shows its value is in analyzing long-term trends and alerting. It shows you not only how the app is functioning, but also how it’s being used over time.
Monitoring helps teams watch the system’s performance and detect known failures; however, monitoring has its limitations. For monitoring to work, you have to know what metrics and logs to track. If your team hasn’t predicted a problem, it can miss key production failures and other issues.
When it comes to monitoring vs. observability, the difference hinges upon identifying the problems you know will happen and having a way to anticipate the problems that might happen. At its most basic, monitoring is reactive, and observability is proactive. Both use the same type of telemetry data, known as the three pillars of observability.
The three pillars of observability are as follows:
When monitoring, teams use this telemetry data to internally define the metrics and create preconfigured dashboards and notifications. They also identify and document dependencies, which reveal how each application component is dependent on other components, applications and IT resources.
An observability platform takes monitoring a step further. DevOps, site reliability engineers (SREs), operations teams and IT staff can correlate the gathered telemetry in real-time to get a complete view of application performance. This way, they not only understand what’s in the system but how different elements relate to each other. The platform shows you the what, where and why of any event and what this could mean to application performance, guiding how DevOps teams perform application instrumentation, debugging and performance fixes.
Observability platforms also use telemetry, but in a proactive way. They automatically discover new sources of telemetry that might emerge within the system, such as a new API call to another software application. To manage and quickly gather insights from such a large volume of data, many platforms include machine learning and AIOps (artificial intelligence for operations) capabilities that can separate the real problems from unrelated issues.
Observability and application performance monitoring (APM) are often used interchangeably; however, it’s more accurate to view observability as an evolution of APM.
APM includes the tools and processes designed to help IT teams determine if applications are meeting performance standards and providing a valuable user experience. APM tools typically focus on infrastructure monitoring, application dependencies, business transactions and user experience. These monitoring systems aim to quickly identify, isolate and solve performance problems.
APM was the standard practice for more than two decades, but with the increased use of agile development, DevOps, microservices, multiple programming languages, serverless and other cloud-native technologies, teams needed a faster way to monitor and assess highly-complex environments. APM tools designed for a previous generation of application infrastructure could no longer provide fast, automated, contextualized visibility into the health and availability of an entire application environment. New software is deployed so quickly today, in so many small components, that APM had trouble keeping up.
Observability builds upon APM data collection methods to better address the increasingly rapid, distributed and dynamic nature of cloud-native application deployments, making it easier to understand a system and then monitor, update, repair and, ultimately, deploy it.
Observability and monitoring tools go deeper than monitoring internal states and troubleshooting problems. These platforms help teams solve problems faster, which in turn, optimizes pipelines and gives more time for core business operations and innovation.
Here, let’s dive deeper into some types of tools and approaches to observability and monitoring:
With Instana, IBM provides a fully automated enterprise observability platform that delivers the context needed to take intelligent actions and ensure optimum application performance. For example, Instana offers the following features and benefits:
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