Currently, detecting and fixing failures in complex systems has become practically impossible without the use of appropriate tools. In this situation, it is especially valuable to pay attention to observability, often incorrectly equated with monitoring, which enables the inspection and understanding of the application stack. What is observability, and how does it differ from monitoring? How can you make a system observable? We explain in the article.
Table of Contents:
- What exactly is observability and monitoring
- Differences between observability and monitoring
- Observability vs. monitoring – which to choose?
- Implementing observability and monitoring – where to start?
- What features must a system have in order to be observable?
- Observability – what tools to pick?
- How to implement observability into the system?
- How to prepare the team to work with observability
- How to measure the quality of observability?
What exactly are observability and monitoring?
Observability is the ability of understanding the internal state of the systems using the data generated by these systems. This concept is being used to analyze the performance of an environment and to utilize the acquired information with the aim to detect, understand and solve the issues occurring within it.
Implementation of this concept is based on the three pillars, which are:
- logs – text-only representation of the events occurring in the given environment in the form of texts containing the most important information about the given incident (including the time of occurrence and a description of the incident); they provide the context to the changes taking place within the system;
- metrics – descriptions of behavior of systems’ components represented as numeric data; through them, we obtain a comprehensive overview of system’s state and efficiency;
- traces – pathways traveled by data invoked by the request; they are used to track the dataflow in the infrastructure of observable components; data gathered that way are utilized to control the correctness of relations between the services which form the given environment.
Monitoring is the process of gathering and analyzing data from the system with the aim to measure its state and efficiency. The approach is based primarily on metrics, though in implementation of this concept logs are also used as an additional, isolated source of information.
Differences between observability and monitoring
For those unfamiliar to the discussed terms, they may seem very similar, if not identical. This statement is somewhat true. Both of these concepts are very, heavily connected – they use the information generated by the system to discover the existing issues. However, considerable differences exist between them, mainly in utilization of mechanisms, which excludes using both of these terms interchangeably.
Monitoring is focused on gathering data. The observability tools, besides gathering the needed information, additionally conduct the correlation on the data and also look for patterns and anomalies contained within the data.
In case of an issue, monitoring only provides information about the location of the error. Thanks to observability, it is also possible to determine the cause of the error. We can think about this situation analogically to the function of a car’s dashboard. It provides information about the issues with the car by lighting the appropriate indicator, but it does not give any information on why the failure has occurred. Whereas, observability is like a mechanic’s diagnostic computer – provides information about the issue and its probable cause.
Moreover, monitoring focuses primarily on a limited portion of data. This solution works best in the case of less complicated systems, where the impact of the issues on the operation of the system is well recognized.
Along with the growth of the environment, it is more difficult to determine the location of the occurring errors based upon their effects. In such a situation, observability works better, because it provides a comprehensive insight into the operation of the system and the above-mentioned data interpretation.
In summary, monitoring is characterized by a reactive approach to the detection of the issues. Using it, we are not able to detect the upcoming failures, without a comprehensive knowledge of the system – we only receive information about the current issues in the examined environment.
Whereas by using observability we are able to detect the potential threats (using the analysis of the downloaded data) and proactively eliminate them, before they start affecting the operation of the system.
Observability vs. monitoring – what to choose?
Although when discussing observability and monitoring these concepts are often presented as contrasting processes, they should not be thought of as two separate entities, but rather as a pair of complementary ideas. Observability adds context to the data gathered by monitoring, and monitoring allows the observability to correctly operate for a given system. Using both of the issues we obtain an extensive insight into the monitored infrastructure which enables fast and effective detection of error and its causes.
Implementation observability and monitoring – where to start?
When beginning the implementation of monitoring and observability, it is worth to consider the following matters:
- business and development goals;
- the scale and method of construction of the monitored system;
- integration of observability tools with the other services;
- utilizing the machine learning to analyze the obtained information;
- trends and possible directions of development of our environment in the future;
- key functionalities.
The above information is crucial in the process of selecting the tools best suited to the needs and requirements of our implementation.
What qualities must a system possess to be observable?
Before selecting the proper tools, make sure that the system meets certain criteria that will allow it to be observable.
Uniformity
One of the most important and at the same time the most basic properties of an observable system is its uniformity. It relies on treating the entire environment as one living organism, as opposed to the approach of focusing on individual components of a given environment.
Access to the data
Another significant component of observability is providing access to the data contained in the pillars of observability: logs, metrics and traces. Additionally, to ensure the proper operation of observability, the metrics of high level of power of the set should be retrieved from the system. In order to reduce the risk of data overload and to increase efficiency, a good practice is to limit the alerting to the necessary metrics. Similar restrictions should also be applied to the collected logs. The process of collecting logs should focus on the perspective of the request and the pathway she has taken in the environment.
Automatic discover of components
Another feature that makes observability possible is the automatic discovery of components. It serves to ensure a maximum data availability within the system with minimal effort.
Surroundings of observability
At last, to ensure observability of the system, besides the changes in itself, the practices that surround it should also be changed. In order to keep up with rapidly changing systems, it may be necessary, for example, to introduce the Agile and DevOps philosophy within the organization.
Observability – what tools to choose?
When we have established the goals we want to achieve and provided an environment prepared for the implementation, we can decide on the choice of the optimal tool.
Observability related software can be divided into two categories:
- observability tools,
- observability platforms.
The observability tools provide insight into the performance of individual system components. Due to a lack of a uniform data source and an infrequent communication between the components, users working with these tools must rely on incomplete data.
The solution to these problems can be the usage of an observability platform. These platforms act as a more effective implementation of observability in an environment. A single platform that collects data in the form of the three pillars of observability is able to cover an entire organizational system of a given corporation. Centrally placed data increases the effectiveness of automation, as well as shortening the response time in the event of an error. Moreover, these platforms are often equipped with additional tools for data analysis and interpretation.
Due to continuous development of IT systems and the departure from the idea of creating monolithic applications for the distributed systems, a significant part of observability tools has been replaced by the functionalities offered by these platforms. Therefore, you may frequently find the term ‘observability tools’ being used interchangeably when describing the ‘observability platforms’.
How to implement observability into the system?
The first step of the implementation of observability is selecting a suitable platform, which accomplishes the goals specified before. The next phase is the implementation of the observability platform and collecting the data according to the three pillars of observability. That way, we obtain an insight into the state of the whole environment. This aspect is especially important, because even the slightest modification in a microservice can cause side effects on the scale of the entire environment. The final phase of the implementation is inserting the created observability implementation into the process of incident management.
How to prepare the team to work with observability?
Implementation of observability does not end when the system becomes observable. The next step on the way to ensure an effective usage of observability is to train the personnel dedicated to its operation.
Training
It is worth remembering to develop the competencies with the aid of training concerning the issues and tools of monitoring and observability.
Access to a test environment
The next, important component is ensuring access to the test environment is being provided, on which the team can develop their skills practically, without disrupting the operation of the system itself.
Culture of observability
Another vital aspect is creating a culture based on the idea of observability. The level of complexity of systems and functionalities offered by the observability platforms is constantly evolving. That is why it is necessary to motivate the employees to continuously develop their skills alongside it. This development may be motivated externally by systematic training, or internally by appreciating the individual progress of each member of the team.
The culture of observability is related to changing the mindset of the team from “what has happened?” to “why has it happened?”. It results in the creation of a new work manner focused not only on solving the issues, but also on eliminating the cause of errors. Though the observability concept itself naturally directs the team towards the new way of thinking and working, it is worthwhile to educate and promote the change of perspective within the organization.
How to measure the quality of observability?
After implementing observability, the next natural step is to measure its results
Comparison of systems efficiency
To achieve that, we might for instance compare the efficiency of the system before and after the implementation of observability.
Comparison of the number of detected mistakes
Similarly, it is also possible to examine the number of detected mistakes after and before the implementation. A significant increase is definitely a positive sign. However, an opposite situation does not mean that implementing observability has not had any effect or the operation of the system has deteriorated. Such a situation may be caused by a more efficient elimination of the causes of errors.
Comparison of the time of detecting and fixing of the error
Another metric, which may help us to evaluate the effect of implementation, is the time of detecting and fixing of the error.
Summary
The IT sector is considerably different from its state from a few years ago. Former, straightforward monolithic systems have eventually evolved into enormous, dispersed networks. These changes did not happen without some difficulties. The environments consisting of numerous components are becoming more and more abstract. It has led to a hindered process of tracking of system’s activity and slower detection of the issues. The answer to these problems have been the ideas of monitoring and observability. They have been adapted to the needs of modern companies. They ensure straightforward scalability, security, and fast and comprehensible finding. Thanks to these and other features, these concepts have become an essential part of the company’s operation model around the world.
