Observability is one of the most critical ways to improve visibility and control over complex software systems. If you've ever wondered how observability differs from monitoring, then this guide will explain some of the key differences between these two popular concepts.
In the same way that telemetry is ubiquitous when referring to data in general, it is widely believed that observability can be considered to be the same as monitoring when it comes to its application across a variety of common use cases.
Monitoring is the process of analysing, combining, and interpreting telemetry data in order to make decisions such as whether to configure alerts based on certain types of events or should you upscale a subsystem based on the resources of another system being exceeded.
In some cases, monitoring is thought of as a continuous activity. In monitoring, predefined metrics are commonly used to aggregate data on the general performance of the application, so that the overall health status can be tracked.
It is sometimes said that observability is a property of a system. Observability is often regarded as the level at which a system or subsystem can be observed readily. A system may be more or less observable depending on its complexity.
The end goal of observability is that all failures within an application should have enough context to ensure that analysis and troubleshooting are effective enough to resolve any issue.
As far back as 2006, Bryan Cantrill (formerly of Sun Microsystems) discussed at great length the importance of building systems for improved observability.
Due to an excess of marketing jargon being increasingly present in guides from SaaS companies, many engineers are not a big fan of differentiating too much between monitoring vs observability.
Engineers often consider observability and monitoring to be synonymous. The rise of observability as a term has largely been influenced by thought leaders operating in this space. However, to your average software engineer, monitoring well means monitoring everything in its entirety.
Monitoring, alerting, and observing your systems via the metrics, logs, traces and events they create should be standard practice within your organisation, no matter the particular terminology used to refer to this activity.
When it comes to operating a mature application delivery ecosystem, observability and monitoring work hand in hand.
Where there are nuanced differences between observability vs monitoring as posed by engineers themselves, we've included these in further detail below.
Monitoring can be viewed as incompletely implemented observability. In order to have observability, you must be able to reconstruct what exactly went wrong during an incident.
Monitoring helps you to understand when something has gone wrong, whereas observability helps you understand why exactly something is wrong. Commonly, monitoring is considered best suited for infrastructure and shared services below the application layer, whereas observability deals with the application layer itself.
Basically, monitoring allows you to answer questions where you may already suspect the answer (is the server up or down, how many errors occur per minute), whereas observability allows you to answer questions you don't know the answer to (how many errors did a user have after logging into an app from 12 pm onwards).
Ultimately, observability and monitoring work in tandem to ensure quality application delivery by correlating data at all layers.
Here are examples of some open-source tools used for both observability and monitoring, seeing as the following tools are recommended interchangeably it is safe to say that the crossover between these two topics is very high indeed.
With Prometheus, you can monitor and alert on metrics generated from dynamic cloud environments like Kubernetes. In addition to its capabilities as an alerting and monitoring stack, Prometheus also allows you to add, average and perform other mathematical functions against your metrics data. In addition to being recommended as a monitoring tool, Prometheus is also the leading observability solution for handling metrics.
As part of Uber Technologies' open-source initiative, Jaeger's distributed tracing system was made fully open-source in 2015. Jaeger is regularly used to monitor and troubleshoot distributed systems. Jaeger, whilst being a favourite monitoring solution for engineers, is similarly often recommended as one of the most popular open-source observability tools.
The OpenTelemetry framework enables telemetry data generation and provides standardisation for four primary types of data: traces, metrics, events, and logs. OpenTelemetry is completely vendor-agnostic, open-source, and does not have a backend. While OpenTelemetry is one of the most popular up-and-coming observability frameworks, it shouldn't be overlooked for monitoring use cases, where its aims to standardise data are just as useful.
The use of open source and proprietary tools are leveraged for both monitoring and improving observability across systems. In addition, these tools are used for troubleshooting and diagnosing issues remotely within these systems. In some cases, the system that needs to be measured can be distributed. However, they can also be operating systems, servers or cloud platforms.
We hope that after reading this article you understand how vital it is to fully observe and monitor your systems as a whole, no matter the particular terminology you use to refer to the centralisation of logs, metrics, events, time-series data and traces as a whole.