Understanding the Difference Between Observability and Monitoring

Application performance monitoring is a developer’s bread and butter when it comes to debugging and diagnosing system issues. If you’re a mobile developer, you’re likely familiar with monitoring platforms that help you keep track of such problems and identify failures that impact your app’s user experience.

Technology is advancing, though. Monitoring is still essential, but developers like you need more profound insight into when their apps crash or don’t function properly. The mobile app industry is rapidly shifting toward distributed and serverless architecture, which entails more complicated (and, unfortunately, delicate) software — meaning you can’t rely on legacy monitoring techniques the way you’re accustomed to.

Now, you need observability. What is the real difference between observability vs. monitoring, you ask? Monitoring can tell you when an issue occurs, but it doesn’t help you identify precisely what’s causing the problem or how to fix it. Observability, on the other hand, collects data from individual users sessions, providing you with a wealth of information that allows you to devise solutions much quicker before your users bounce or give up on your app entirely (and it saves you the headache of combing through code looking for what the problem is!).

Your next question is likely, which is better? How do you decide between monitoring vs. observability when choosing platforms that help you stay on top of your app’s functionality? Let’s discuss the significant differences between them and why, ultimately, you don’t have to pick one or the other.

‍

‍

First, here are more in-depth definitions of each term:

‍

What is monitoring?

Application performance monitoring is the process of ensuring an app’s system operates as it should, which includes factors like speed and scope. Monitoring tools like Instabug are ideal for monitoring apps in the background, so developers don’t have to keep a constant eye on their systems manually. Such tools collect and analyze data that track your app’s ability to achieve its objectives based on metrics you predefined.

Because you determine the metrics monitoring tools should look out for ahead of time, they detect known failure modes. Monitoring works best when the issue is predictable, which includes a wide variety of system failure types, but doesn’t always give you the necessary context to imagine a solution swiftly. However, it is useful for tracking long-term trends in your code. If a problem is consistent instead of only affecting a select few users once in a while, that indicates where the problem is and what you can do about it.

‍

What is observability?

App failures aren’t as linear in distributed systems, though. The more complicated your app, the less predictable it is, making it more challenging to identify known failure modes and metrics that will alert you when something goes wrong.

Observability compensates for what monitoring can’t do: it tells you your application’s internal state by measuring its outputs, providing you with more robust data points that give you actionable insight into system problems (such data includes traces, logs, and metrics). In other words, monitoring is excellent for tracking errors and crashes, but observability tools analyze user sessions to give you more context about what went wrong.

For example, monitoring is useful for alerting you as soon as major issues arise, but observability tools analyze individual user sessions to assess things like session replay, device battery, network connectivity, battery power, and more. All of these factors can affect a user’s experience that, to them, seem like a crash or lag when something goes wrong. These factors are also why observability is especially important in mobile app development compared to desktop versions because the situations in which people use them are more variable.

It’s important to note that monitoring is only feasible if a system is observable to some degree in the first place. Both kinds of tools survey your app for problems, meaning they complement each other and help you make your software run as smoothly as possible. One provides situational insight and the other dives into the root causes.

‍

‍

The concept of observability isn’t unique to IT or mobile application programming. The engineer Rudolf E. Kálmán developed the idea when explaining his famous control theory, which has since bled into signal processing in other fields. Decades later, developers at major tech companies began thinking of their products as “observable,” encouraging other engineers to do the same. It’s an old concept, but it’s only become common in the tech world relatively recently.

‍

Observability pillars

As mentioned previously, observability tools collect three kinds of data, which are known as the “pillars” of observability. These are:

‍

Metrics

Metrics are aggregated measurements over a given period. These are the basis for monitoring, so they can tell you long-term patterns like how many requests your app gets per second. You can further divide observability metrics into three types: gauge, delta, and cumulative metrics.

Gauge metrics measure values at particular points in time, such as CPU utilization rates. Delta metrics calculate the difference between a current measurement and a previous one, helping you monitor various changes. Similarly, cumulative metrics tell you precisely how many changes occurred over a period of time (such as the number of errors in the past hour instead of what the difference between the first and last data points are).

‍

Logs

Entries in a log refer to application-specific events, such as completing a typical task or initiating a process. These entries are time-stamped and unchangeable, so, essentially, they are records of the goings-on within your system. Logs help you understand why your system behaves unpredictably at times: if something goes wrong, you can look at what requests went through at the time of the error and narrow the source of the problem down to the most likely events. Metrics and logs complement each other to give you as much context as possible.

‍

Traces

Traces (or distributed tracing) are beneficial for monitoring performance in distributed systems. Individual microservices often have their own logs and metrics, so following requests between them is difficult without a way to display their movement. Visualizing traces between nodes enables you to track individual requests across complex environments and identify which ones are causing errors and where bottlenecks occur.

‍

‍

Observability and monitoring have a symbiotic relationship, but it might help to explore what each does differently a bit further and how they work together.

When your system experiences a failure, monitoring answers what’s happening, and observability answers why. That said, the reason why observability is more of a novelty amongst developers is that it’s possible to monitor almost any application, but observable systems have to be designed as such. When a system is made to be observable, though, it can help distinguish problems at scale and generate metrics, whereas monitoring is challenging on a broader scale and only collects metrics.

Some of monitoring’s basic functions include overseeing server CPUs, paying attention to network traffic, identifying patterns in error rates, alerting you about slow pages, and combing through your access logs to find out how long requests usually take. This kind of information empowers you as a developer to have an accurate idea of how well your system is working — if your monitoring platform alerts you about an issue, you know it’s time to take a closer look.

You can take this deeper look with an observability tool like Instabug, which uses different sources of telemetry to summarize your application’s state. With more context surrounding problems, you can answer questions like, why are requests (or a specific request) failing? How are microservices processing them, and how did they do so differently than expected? Were there occasions where a request did go through, so the problem only affected certain users? The more robust your insight, the more information you have to fix the problem.

Try thinking of an observable system like a house. If something goes wrong with the plumbing or electrical wiring, you don’t need to tear apart the walls to determine what’s happening. Instead, you use visible signs (your “data” in this metaphor) like a backed-up sink or malfunctioning light switch and whatever tools you have — like a pressure gauge or a fuse box — to tell you what’s going on internally. Walking around your house and checking for problems regularly is akin to monitoring, ensuring you have situational awareness instead of letting issues slide by, and observability is like using your tools to find out more information you can’t glean just from looking at the problem’s consequences.

You need both monitoring and observability to keep your house in running order. Keeping a sharp eye on its plumbing and electrical functionality — without actually looking at the plumbing and wiring — prevents problems from escalating beyond repair, and your investigative tools enable you to find a solution quickly.

‍

‍

Hopefully, you understand the difference between observability vs. monitoring a bit more, but another question is, why bother with either? Are they superfluous to keeping your app running? Definitely not — monitoring and observability tools are essential to maintaining a well-functioning app that consumers actually use.

If your app crashes or lags too frequently, users might give up and migrate to another. Losing customers is costly whether you rely on ad revenue or subscription payments. It’s every developer’s nightmare for customers to complain about functionality in App Store reviews, so the smoother your app works, the better your customer acquisition and retention will be — and the same goes for your bottom line.

However, it’s too strenuous and time-consuming — if not outright impossible — to monitor and observe your app manually (even if you have a whole team of other developers), so it’s well worth the investment to have tools operating in the background and keeping closer watch than a human could. Plus, monitoring and observability tools are especially critical the more complicated your system becomes. It’s unlikely that your app is a monolith; you likely have a host of distributed microservices that have their own needs and frameworks, so platforms that empower you to debug your system quickly are invaluable.

‍

‍

There are many digital tools that offer monitoring and observability features. When deciding on a platform suitable for you and your app’s needs, it’s essential to pay attention to what it’s capable of: does it offer both performance monitoring and management? What about crash and bug reporting? It’s also essential to keep debugging throughout the development process, not just once you’ve launched a product and put it into users' hands.

Instabug is a monitoring and observability platform that helps you debug your app’s stability and performance issues throughout the development lifecycle. App crashes and poor app performance can make a significant impact on your business, so if you want to ensure user satisfaction and avoid risking losing revenue, Instabug is ideal for both monitoring your system for problems and understanding what you can do about it without a great deal of trial and error.