Observability vs. Monitoring: What’s The Difference in DevOps?

Observability vs monitoring representation with iceberg

Updated: June 28, 2022

If you’re wondering about the difference between observability vs monitoring, you’ve come to the right place. Observability is often used as a synonym for monitoring, but by digging into these terms, we can uncover the important differences. 

Our guide covers everything you need to know about observability. You’ll learn everything from what observability and monitoring mean, the relationship between the terms, and how to utilize these practices to improve your DevOps outcomes. We even include an infographic to summarize the most important points.

Observability vs. Monitoring 

Explanation of the relationship between observability and monitoring

Monitoring and observability could be considered two sides of the same coin. Both rely on the same types of telemetry data to enable insight into software distributed systems. Those data types — metrics, traces, and logs — are often referred to as the three pillars of observability.

  • Logs: Logs record events happening in your software. These events help developers understand where an error occurred and gain actionable insights. While metrics are typically where you’ll see the first sign of an issue, logs give you additional context on how a problem impacts operations.
  • Metrics: Metrics use numerical values to represent system application performance and resource utilization. You can put this data in a graph format to see how your system performs over time. DevOps teams use the gauge, delta, and cumulative metrics.
  • Traces: Traces show how operations move from one node to another throughout your observability system. They allow you to contextualize your troubleshooting to a specific user action or API call.

Monitoring software systems is nothing new. Since the dawn of programming, logging has been around, making execution observable by writing out useful messages. These days we no longer ssh into production systems to browse logs directly. Instead, we rely on log aggregation tools and services.

Health checks are another monitoring tool that we are likely all familiar with.  We can monitor a service endpoint, for example, even if it does not make itself observable, by simply calling it every 10 seconds and recording success or failure and response times. This is a type of “Synthetic Monitoring.”

The most difficult form of observability is distributed tracing within and between application services. Creating this form of observability in an efficient and effective way requires strong experience and an understanding of the underlying principles of tracing requests that flow between services. If you can completely automate the process of creating distributed tracing observability (as Instana does), you will have found the Holy Grail of observability and monitoring.

What Is Monitoring?

Observability definition

Now that you understand the relationship between monitoring vs. observability, it’s time to deep dive into them individually. Monitoring uses a set of logs and metrics to allow you to examine the internal state of your system. Overall, monitoring will enable you to understand how your app is being utilized, see its growth, and get a more profound knowledge of your app’s functionality. 

However, the issue with using alone is that production failures aren’t linear when monitoring complex distributed applications. This makes failures difficult to predict. Enhancing your monitoring capabilities with an observability tool, such as Instana will allow you to catch the failures and find solutions in real-time. 

What Is Observability?

What is observability? meaning

In DevOps and cloud computing, observability is the ability to measure the current state of a system by examining its outputs. These data outputs include traces, logs, events, and metrics.  In a nutshell, having observability on your application allows you to understand what, how, and why a malfunction has occurred. 

In a complex microservice architecture, observability answers these questions so you can easily find a solution to application errors. Observability and monitoring have a symbiotic relationship because, without efficient observability, monitoring can’t take place. It is common for organizations to implement observability by combining Open Telemetry tools with custom instrumentation. 

Contemporary Application Observability

Over the past 20 years, many monitoring tools have enabled the monitoring of application servers. The entire APM market was built around the concept of creating monitoring. Initially, all telemetry data was gathered using manual instrumentation such as adding API calls to your code.

Eventually, APM tools evolved to enable automated instrumentation of monolithic applications.

Modern application delivery from monoliths to SOAs to microservices, leveraging tools like containerization for CI/CD. Polyglot environments create a new problem for APM vendors and for observability in general. New software is deployed so quickly, in so many small components, that the production profilers of the previous generation have trouble keeping pace. They have difficulty identifying and connecting dependencies between microservices, especially at the individual request level. Those production profilers employ various algorithms that limit the amount of data collected and therefore provide only partial data or meta-data for most of the requests flowing through the system. 

This strategy MIGHT be acceptable for SOA applications but is completely unacceptable in the microservices world. The problem is so pervasive that the Cloud Native Computing Foundation (CNCF) has multiple open-source observability projects in either the Incubation or Graduated phase.

Looking at the CNCF stack, the popular frameworks are Prometheus for time-series metrics and Jaeger for distributed tracing. These frameworks provide a collection and storage platform along with an API for generating the observed data. Thus, open-source generated observability is more than just technology. It also has to be an organizational culture, similar to DevOps, such that observability has to be embraced as part of the CI/CD process. Using open-source tools, Developers are required to implement data collection in their code, and Operations are required to configure and manage the collection infrastructure (the monitoring tool).

Automatic Observability

In the context of the CI/CD process and developing microservice applications, the current focus on observability is concerned with improving the quality of the telemetry data by making those services observable; providing internal data to augment external measurements. The resulting high-fidelity monitoring data enhances the quality of feedback in the CI/CD loop.

The practicalities of implementing open-source or manual observability can be significant, placing a considerable additional load on both developers and operators. The burden of monitoring as code results in valuable programming resources being consumed writing instrumentation rather than functional (business) code. Setting up and maintaining the data collection, dashboarding, and alerting systems take operators away from running core systems.

Automation is already extensively used throughout the CI/CD process. Automating observability frees up DevOps resources to concentrate on core tasks. The good news is that there is already an APM product that automates observability, and that product is Instana APM. Instana automatically detects language runtimes as they are deployed or scaled, then automatically performs code instrumentation to create observability. Instana collects the data, all the data (a distributed trace for every request) and automatically analyzes it all using an intelligent backend. This provides actionable information to aid troubleshooting and optimization; not monitoring as code but monitoring by robot. We’ve named our robot Stan.

AutoTrace – Automatic, zero-touch, no restart:

  • Java
  • Scala
  • Python
  • PHP 
  • dotNet

Requires Instana library inclusion:

  • NodeJS
  • Golang
  • Ruby
  • Crystal

Table of supported language runtimes for Instana automatic observability; see Instana APM documentation for full details.

Monitoring vs Observability: Which is More Important?

The bottom line in the observability vs monitoring debate is that they’re both vital when managing microservice-based applications. While monitoring tracks the system’s health of your application, observability tells you why it’s performing a certain way. It could be argued that monitoring is simply a subset of observability or a part of the process.

The issue with starting with monitoring alone is that without observability, you won’t be able to gain the data needed for contextualized analysis. Observability encompasses monitoring and allows DevOps professionals to understand the root cause of an error and how it is affecting your system. Thankfully, there are tools like Instana that collect and evaluate this data to make sense of your multicloud environment.

Utilize Instana’s Automation Capabilities 

Making applications observable by instrumenting the code to provide an internal view of their execution requires both the operation team and developers to collect and manage extra data. Utilizing Instana’s automation capabilities completely or considerably reduces the amount of effort required to generate and acquire the data and get actionable information from it.

Automation is a key ingredient for the success of CI/CD – why not automate application observability and monitoring too? Sign up for your free trial of Instana today.

Observability Resources + Infographic

To continue your journey on the path to understanding Observability vs Monitoring, here are a few great resources and infographic:

For an even deeper dive into observability and monitoring concepts you should read “From Observability to Monitoring and then Controllability“.

Observability vs Monitoring Infographic

Play with Instana’s APM Observability Sandbox

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Instana, an IBM company, provides an Enterprise Observability Platform with automated application monitoring capabilities to businesses operating complex, modern, cloud-native applications no matter where they reside – on-premises or in public and private clouds, including mobile devices or IBM Z.

Control hybrid modern applications with Instana’s AI-powered discovery of deep contextual dependencies inside hybrid applications. Instana also gives visibility into development pipelines to help enable closed-loop DevOps automation.

This provides actionable feedback needed for clients as they to optimize application performance, enable innovation and mitigate risk, helping Dev+Ops add value and efficiency to software delivery pipelines while meeting their service and business level objectives.

For further information, please visit instana.com.