What is the difference between observability and monitoring in DevOps?

In the world of the software delivery process, observability and monitoring have become important terms, important when you’re discussing software development. It doesn’t matter how much hard work you put into creating good quality software applications, there will always be errors and bugs, like the rapid increase in users and the software application is becoming more complex. For all these reasons our system should be observable.

The software delivery culture is changing and it is shifting from monitoring to cloud-native. Although applications in both on-premises and cloud-native environments are expected to be highly available and resilient to failure, the methods that are used to achieve those goals are different. There are many benefits of monitoring such as improving productivity and performance, you can easily allocate resources according to the need of the users, you can easily detect and solve the problems before they affect your business, and you can better allocate time and upgrade to new projects.

What is observability in DevOps?

Monitoring the CI-CD pipeline requires each individual part of the pipeline to be observable. Each individual part of the pipeline must produce appropriate data to support automated problem detection and alerting, manual debugging when necessary, and analysis of system health (historical trends and analytics).

“Observability is the ability to ask questions to the inside system from outside.”

These are the types of data, that a system should produce in order to be observable.

• Health checks: they are often custom HTTP endpoints, and help orchestrators, like Kubernetes or Cloud Foundry, they are performed in order to maintain the good health of the system.
• Metrics: they are a numeric representation of data that is collected at regular intervals into a time series. The Numerical time series data is very simple to store and can query quickly, it helps when looking for historical trends. Over a longer period, numerical data can be compressed into a shorter period, for example, monthly, weekly, and daily.
• Log entries: they represent discrete events. The Log entries are essential for debugging, as they often include stack traces and other contextual information that can help identify the root cause of observed failures.
• Distributed, request, or end-to-end tracing: they capture the end-to-end flow of a request through the system. Tracing essentially captures both relationships between services (the services the request touched), and the structure of work through the system (synchronous or asynchronous processing, child-of or follows-from relationships).

What is monitoring in DevOps?

In the software delivery process, an effective continuous monitoring activity increases productivity and performance. Continuous monitoring helps us to reduce downtime, allows us to better allocate time and resources, and we can plan our upgrades and new projects easily. It alerts us by giving notifications whenever there is any failure in the system before it affects our business. In DevOps monitoring is done by tools such as Nagios, tensable, snort, etc. continuous Monitoring gives feedback from the production environment. Continuous Monitoring produces information about an application’s performance and usage patterns.

The main aim of continuous monitoring is to achieve high availability by minimizing time to detect and time to mitigate. The benefit of continuous monitoring is to, Automating work that used to be manual, repetitive, and error-prone results in faster speed, productivity, and scalability — and the assurance of standardized configurations across test, dev, and production environments. Eliminating errors and bugs reduce the wastage of time and let you deploy software faster and more reliably.

What does continuous monitoring do? in general, it does the following activities.

• Problem Detection: it let you know the problems, by alerting, or seeing issues on dashboards.
• Problem Resolution: after knowing the problem, the root cause, and troubleshooting.
• Continuous Improvement: by all these, capacity planning, financial planning, trending, performance engineering, and reporting we can improve the software delivery process.

Comparing observability and monitoring:

Let’s take an example of a large and complex data center’s infrastructure system which is monitored using log analysis and monitoring and ITSM tools. Analyzing too many data points continuously will generate large volumes of unnecessary alerts, data, and false flags. The software delivery process may have fewer observability characteristics unless the correct metrics are calculated and the unwanted noise is carefully removed using Artificial intelligence and continuous monitoring solutions.

On the other hand, an application with a single server can be easily monitored using health checks, metrics, logs, and parameters such as response time, throughput, efficiency, etc. These parameters are widely interrelated with the health of internal system components. Therefore, the system has demonstrated high observability. Using essential continuous monitoring tools, such as energy and temperature measurement instruments, software-based continuous monitoring tools, or web-based continuous monitoring, the performance, life cycle, and risk of potential performance incidents can be calculated before any negative impact on the business.

The system’s simplicity, the insightful representation of the performance metrics, and the capability of the continuous monitoring tools to identify the correct metrics are responsible for the observability of the system. This combination yields the necessary insights to construct an accurate representation of the internal states, despite a system’s inherent complexity.

Conclusion:

By using all the above knowledge about observability and monitoring, we can conclude that Monitoring is similar to taking your vehicle to a mechanic for routine maintenance and when the check engine light comes on. You want to keep your vehicle running as smoothly as possible and avoid breakdowns. On the server, you want to know whether users can access your application and services and if they are performing within appropriate limitations.

Notifications are sent to teams when something is not performing accurately, is about to break, or is broken. But before the notification is sent, it is important to create a baseline and understand the current state of the environment. And here, observability first comes in. Without observing the architecture, it is impossible to know what is wrong and what is not correct. Only by observing application, architecture, system, and service can we determine what is wrong and what is correct.