What is observability in DevOps? Practical guide


What is observability in DevOps? Practical guide


In recent years, DevOps has become the most popular method of software development in the digital world. Various companies have successfully adopted the DevOps practices in creating and deploying high quality software in a fast and secure manner. There is one part though that affects the effectiveness of DevOps that can not be omitted – observability. In this article, we explain the role of observability in DevOps and how we may help in utilizing the full potential of this method.

Table of contents:

What is observability?

Observability is the ability to define the internal state of complex systems, which is based on the external baseline data. We use the term “system” to describe the collection of elements which creates the IT environment or the application and the relationships between them.

It can say that the system is observable when the data gathered from it and processed allow  for examining and understanding:

  • how does the system work?
  • what issues are present in the system?
  • how do those issues affect the operation of the system?

The three pillars of observability

Logs, metrics, and traces are the three pillars of observability – each one of them provides insight into the state of the system from a different perspective. Combined usage of the above-mentioned pillars helps in visualizing the state of the whole system at all of its layers: application, integration, database, system, and hardware. It also ensures an effective detection and solving of the problems appearing within the system.


Observability is used primarily to understand the internal state of the system and the processes within it. This is made possible by the collected logs, i.e., chronologically ordered information about events that took place in the system. They consist of a timestamp and a description of the event.


Beside logs, an important role is played by metrics. Metrics are a description of the general behavior of each particular component of the system. Data from the metrics are stored in the pair of key-value and label, which gives the data its context.


The last component giving the insight in the processes occurring within the systems are the traces. Thanks to them, it is possible to estimate the path which the data travel through the system.

Observability in DevOps

DevOps is a work culture based on the constant communication of the teams working on software development and system operations. DevOps teams, who wish to effectively advance their products, have to have an insight into the condition of their products – during their making and implementing alike.

In order to effectively execute the CI/CD method, the knowledge of the impact of the changes to the application is needed. Systematic migration from the basic, monolithic application structures to those scattered based on many services makes primary metrics monitoring not sufficient. Issues occur much more frequently, and simultaneously the cause is much less frequently easy to determine.

The main premise of the DevOps method is the fast delivery of the produced software. If the error in the system is not known, then it can not be solved simply, which is reflected in delays in the delivery of the new, innovative solutions. For the DevOps to be efficient, the teams should have a full insight into the system. Only the ability to control the incidents at the moment, enabling fixing or preventing it, gives the full control over scattered systems – that is just what observability is.

Why observability is crucial for DevOps?

The monolithic structured systems created in the past were relatively simple to supervise. Predicting the potential area of failures did not present a big problem. Gathering data from the primary system metrics, such as disk or CPU usage, in most cases proved enough to locate and fix the issue.

The complexity of modern IT systems makes it increasingly difficult to detect, understand, repair and prevent the occurring malfunctions. In the last few years, many systems have been transformed into cloud-based microservices. They are developed and implemented by DevOps teams in a very fast manner. Such action is convenient and innovative, yet it also results in the appearance of many, new, often impossible to understand errors.

Observability gives the ability to find the answer to the inquiries about the system. It identifies hard to detect errors and accelerates the process of fixing them. Quick troubleshooting is extremely important when working with a system. One malfunction, if not fixed, may entail further ones and a single error left unfixed lowers the effectiveness of the DevOps practices in an organization.

Observability benefits in DevOps

Implementing observability in a system provides numerous advantages.

Full insight into the operation of the system and the processes taking place within it

For the DevOps teams, it is essential to understand the production systems, and, in this respect, observability is extremely helpful. The main advantage and purpose of using observability is in fact achieving full insight into the operation of the system and also the processes occurring within it.

Preventing issues within the system and shortening the time to repair

Another value of observability, which is extremely important for DevOps teams, is the reduction in the time it takes to resolve problems in the system and the ability to prevent them. This translates into a reduction in MTTD (Mean Time To Detection) and MTTR (Mean Time To Resolve) parameters. It is achievable thanks to the in-depth analysis of the system data. Anomalies occurring, for example, in the values of metrics, can indicate that a fault will take place, and detecting anomalies early enough allows for appropriate prevention. This feature is extremely significant in the context of system continuity.

Understanding the source of the issues and how to solve it

However, if a failure cannot be prevented and needs to be fixed, understanding the problem is key. Observability gives you not only the ability to see where the issue occurred, but also to understand why the issue occurred and how it can be resolved. This allows DevOps teams to focus on solving the problem in the system, rather than spending their time searching for the cause.

Increased automation and efficiency

Observability allows for better control of the system, resulting in increased automation and efficiency.

Useful solutions in terms of engineering techniques

Some examples of engineering techniques in which observability is an extremely important component.

1. Feature toggles

One example is feature toggles, a technique that allows development teams to modify the operation of a system without changing the source code. When using it, observability is essential to have a good understanding of the impact of each functionality, but also the impact of a set of functionalities on the application’s performance.

The concept of monitoring component-by-component behavior is no longer relevant, because endpoints can execute in multiple ways depending on which user they are called by.

2. Chaos engineering

Chaos engineering is a technique consisting in constant experimenting with the system, for it to acquire a resistance for working in different, often extreme conditions.

In this case, for it to be able to constantly monitor the state of the system throughout experimenting on it, implementing observability also proves to be indispensable. Without observability, it would not be possible to determine what the initial state of the system was and to explain the deviations from the expected behavior.

3. Blameless postmortem

Observability is also crucial when utilizing the so-called blameless postmortem – the analysis of the whole course of action during the incident, which has taken place in the past.

This technique, thanks to utilizing observability, allows the DevOps specialists to understand what has happened and why, and also to examine the reaction of the team. Its aim is to prevent similar incidents in the future and to improve the reactions of the team during the next occurrence of an incident.

Introducing innovation to the system

The gathered data do not only serve in detecting and eliminating the issues. They can also be used to identify the areas in the system to which innovation can be introduced to.

Observability tools in DevOps

By the term ‘observability tools,’ we mean all of the applications, programs, and platforms designed for constant monitoring of systems. These tools deliver feedback about the state of the system on an ongoing basis.

Even though observability is a new approach and is still gaining popularity, the market offers quite a number of utilities in that field. So, which tool is the best one? There is no definite answer to this question. For each individual system, one must choose a suitable tool considering many factors, for instance to what degree the tool will be helpful in working with the data. The more functionalities it offers, the better.

Learn more in the article: Observability – overview of the most popular solutions.

Implementing observability – what is worth paying attention to

  1. Successful implementation of observability is not an easy task and requires in the first place to prepare the system properly. It is recommended to abandon the concept of creating systems oriented on the individual components and to adopt a unified approach within the whole system. The purpose of observability, in fact, is to understand how the system functions as a one entity.
  2. The next step is to determine the goal of implementing observability. At this point, it is worth focusing on defining the types and sources of data, which are the most important to the functioning of the system. These are the data whose analysis makes it possible to effectively prevent errors, as well as detect and correct them when errors occur.
  3. For the DevOps teams, the data, thanks to which it is possible to improve the system and positively affect its efficiency, is also significant. It should thereby be correctly determined whether the gathered data have any value to the system. Accumulating large amounts of useless data is a discouraged practice that significantly lowers the effectiveness of observability.
  4. After determining which data will be gathered and analyzed, one can proceed to develop a scale of desired values and thresholds, which, after being crossed by a given measure, will deem it invalid. The gathered data should be properly indexed, so that an individual entry can be quickly found, without the need to browse the hundreds or thousands of unrelated entries.
  5. When collecting data, pay attention to how the data is transmitted. The manually gathered data do not provide information about the current system status as well as those provided, for instance, by the data forwarder. Data forwarder transmits the data on an ongoing basis throughout the time of system’s activity, while using the right format.
  6. What’s more, the data collected must be properly analyzed to prevent and detect errors, as well as to locate places in the system that are worth improving in terms of performance.
  7. When interpreting data visualizations, for instance in the form of tables, charts, diagrams, as well as proper data filtering in order to shell the records concerning the vital areas of the system, may prove to be helpful.

Implementing observability – how to avoid the common mistakes

  1. The most common mistake is applying the observability approach without using proper tools – only working with the right utility ensures an effective observability.
  2. Software designed to work with data should offer a wide range of functionalities, which would give the leeway to the users. The most important are:
    • reliable data gathering system;
    • ability to arbitrarily browse the gathered data with the use of filtering, categorizing and prioritizing;
    • data visualization;
    • alerting mechanisms in case of an error occurring within the system.
  3. An extremely important nexus, affecting to what extent the observability tools are going to be helpful, are the employees who use them. The lack of proper training for the team causes the full potential of observability to be underutilized.
  4. A good practice is also to utilize and make use of the output data from the observability tools on business meetings and hence increasing the abilities and awareness of the team on this matter.
  5. The next mistake in implementing observability is the improper distribution of data within an organization. Data from the system should be transferred not only to the DevOps teams, but also to all developers working with the system. Such action improves the distribution of data in the company, which results in faster problem-solving.
  6. Another common mistake is ignoring alert notifications. This can happen due to delivering all alerts concerning the entire team through a single route, or because of a poor quality alert system. To prevent this, consider using different routes to deliver alert information to the team.
  7. Moreover, it is discouraged to ignore alerts that provide information about the cause of the problem and prioritize those that indicate which locations are affected. Additionally, writing alerts for every single error in the system without including their potential causes is also inadvisable.


The evolution of IT systems makes them increasingly difficult to control. Implementation of observability approach and utilization of the proper tools helps the DevOps teams to:

  • gather, correlate and analyze large amounts of efficiency regarding data from scattered applications;
  • gain insight into the functioning of applications in the real time.

As a result, the DevOps teams can more effectively monitor, modernize and improve applications, in order to deliver new products in a faster manner and to improve the experiences of the clients.

If you wish to find out more about how observability can help you to fully utilize the full potential of DevOps, please contact our experts

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