Observability vs. Monitoring: Analysis of the Divide

Website Monitoring vs. Observability: Analysis of the Divide

There is an idea of the relationship between observability and monitoring, that they complement each other in an inseparable way. While true that you can only monitor a system that is observable, the line dividing observability and monitoring grows narrower with every deployment you make; making these two practices less of a pairing and more a single entity.

 

Determining Normal: Observability’s Golden Age

Observable systems are easier to diagnose because understanding the puzzle of your infrastructure gives you visibility into how its behavior will affect your application. Legacy software integrating with cloud-based solutions have created an abundance of issues with observability. The cloud comes with lots of built-in tools offering every metric under the sun. Your legacy systems? Not so much. 

How it Began

In the beginning, there was control theory but even these early systems relied on establishing feedback. Over time, the measurement of the machine’s internal state has splayed out to include outputs with metric data, events, and system logs. 

Observability is the detailed audit of how your system interacts in different situations. Each interaction gives you information for each behavior, which you then use to determine your infrastructure’s “normal” state. Once you’ve defined what normal is, you can use that data to construct intelligent and effective monitoring.

The first step in gaining observability is a human one. 

 

Manual Monitoring 

Actual human observation; reviewing logs, patterns, and manipulating data to explain systemic behavior, is a key first step in building an observable infrastructure. Though a necessary starting point, this naturally leaves room for human error, leads to paid overtime, and consumes significant chunks of time. Once the humans have organized themselves, automated processes are layered into the observability model:

  • Incorporating Tools – Once a system’s baseline has been interpreted, automation becomes necessary to sustain observability. This is the point where complimentary third party website performance monitoring services are often introduced. It’s one thing to be able to generate system data, and another to receive and interpret it in real-time. Automated monitoring ensures that when it does go down, you know before your customers do.
  • Adding Alerts – Information doesn’t do any good if it cannot be transmitted when it matters. Automation is key when mounting a first-rate response to real-time incidents. 
  • Combining – Observability is an amalgamation of the above points. And though it may seem that comparing observability to monitoring is like comparing a potato to another very similar potato, there are a few key separations.

Observability & Monitoring: The Run-book Evolving

In the game of Chutes and Ladders, observability is like an audit log of every dice roll, climb, and slide down a chute; whereas monitoring reports that you fell down the third chute on your fourth move because you rolled snake eyes. 

Put another way, observability traditionally answers what and why while monitoring has focused on when and where. 

The ultimate goal of observability is an omniscient view of your greater system but remember, observability is an internal entity that is dependent on external output for visibility. As infrastructures scale and become more distributed, visibility becomes harder to attain which complicates the process of finding the root cause of a problem. This is where automation and monitoring is in demand.

 

The Observable Ecosystem

Monitoring plus alerting, plus reliable solutions in your runbook equal observability. With a growing culture of third-party integrations and microservices, automation (yes, we’re saying it again) becomes critical for efficiency and stability. Those set and forget systems could go down too so it’s necessary to have robust observability in place for zooming in to troublespots with microscopic accuracy. 

The regenerative life cycle of an observable system

The regenerative life cycle of an observable system

 

With constant drive towards scalable systems, continuous processes are the standard; continuous monitoring, continuous testing, and constant updates/adaptations are the reality of DevOps teams. So how can we implement and support observability by using monitoring?

Planning & Testing

Planning and testing are not just occurrences at the initial onset of an application or feature release, but a part of the DevOps cycle. Monitoring supports this with real user data (check out our Real User Monitoring checks), and synthetic testing with the purpose of optimizing processes.

Continuous Website Monitoring

We mentioned before that Observability begins with human action. Monitoring is the auto-pilot extension of that initial human observation. Implementing checks and defining alerting protocols combine to create an observation and notification structure for your system.

Recognizing Your Alerts

Logs will tell you the error received and time of an incident but it can be useful to track alert types and use the data to recognize repetitive behaviors.

Added Observability with Internal Monitoring

Network monitoring is largely external; monitoring URLs, website elements, user experience, etc. and this provides a lot of detail but it doesn’t paint the whole picture. Internal monitoring, or private location monitoring, is a way to peek behind the curtain and add automated monitoring to your internal infrastructure.

When you combine internal and external monitoring, you improve the context for every alert. You can start to build data that ties downtime in one area to a specific system or process. 

Monitoring & Action

Monitoring an observable system yields an impressive amount of system data. So the final implementation in the monitoring and observability cycle is translating that data to your runbook and using it to reduce and resolve response times for future outages and events. Your runbook should also go through a constant state of testing.

Hint: check out our recommended practices and beef up your Game Day strategies.

What Do You Think?

Is monitoring a complement to observability? Or an extension of observability? In a progressively serverless environment focused on continuous scalability, how will observability and monitoring merge and evolve?

We think the future of monitoring holds more automation and intelligent alerting systems that absorb observability practices like debugging and identifying platform deficiencies. 

Let us know what you think by reaching out to support@uptime.com and sharing your feedback on our solution.

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Emily Blitstein is a technical content writer for Uptime.com. With a background in writing, editing, and global HR, Emily is committed to delivering informative and relatable content to the Uptime.com user community. Aside from travel, she enjoys making short stop-motion animations, and live music.

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