Currently, the demands of the clients and business are growing exponentially. Creating a competitive portfolio of products or services, which would meet their expectations, requires continuous expansion and improvement of the software. It leads to a systematical increase of systems’ complexity and causes problems in controlling them. Detection, understanding and fixing the crashes occurring in complex systems becomes virtually impossible without using the proper tools. In this situation it is especially worth noting observability, which is often incorrectly equated with monitoring, which allows for inspecting and understanding the application stack. Observability is a quality of a system, which is an ability to control the incidents at the exact time of them happening, allowing for correction or fixing of the error. A full observability ensures an immediate insight into the essence of the potential issue. What is observability, and how does it differ from monitoring? How to make the system observable? What is the role of DevOps in observability alongside the work with containers, databases or operating systems? We will try our best to explain it in our guide.
Table of contents:
What is observability?
Observability is an ability to define the internal state of complex systems based on their external output data. One can say that a system is observable when gathering and processing of the data which came from it enables a possibility to research and understand how the system works. This includes identifying what problems occur in it and how these problems affect its operation.
Observability – key benefits
The complexity of contemporary systems hinders the ability to detect, comprehend and repair failures. In recent years, various systems have been transformed into cloud based open source type microservices. They are developed and implemented in a very quick manner. This approach, though convenient, causes many additional, often incomprehensible issues to appear.
System troubleshooting is crucial because a single defect often leads another to occur. There can be many causes of problems, and finding the root of the problem without a proper tool can often be impossible.
Observability allows finding the answers to the questions concerning the system. It detects difficult to notice issues and accelerates their fixing. The observability tools allow for a constant tracking of a system’s parameters, thanks to which one can predict where an issue will occur. Observability aids in increasing the efficiency and reliability of the system, which is reflected in lowering the cost of its maintenance.
One of the most important components of observability are the logs of events. They are lines of text containing the information about the event i.a. the time of incident and error message. They can occur in an organized form, i.e., formatted to a JSON file, or in an unorganized form as a sequence of characters understandable to a human.
The log’s analysis helps to understand the problem, define its source and cause. Logs are generated by every component of the system from the boot-up to the shutdown. At times, in order to analyze the log and understand it completely, an extraction of the most important components from the log’s content is needed.
Many applications which analyze the data and programming languages with their libraries allow for unhindered operation and processing of the logs so as to, by using the tools designed for analyzing, achieve the desired outcome.
The next important component of observability are metrics. Metric is a description of a general behavior of a given system’s component in the form of numeric representation of data. Each metric has its name and value, usually in a pair of key-value and label. Thanks to labels, the data have context, which is helpful during the troubleshooting.
A metric may consist of information about every aspect of a system’s operation, i.e., consumption of the processing power by a specific application or the time in which the system stays operational. Thanks to these metrics, one can obtain an overall image of the system’s health and efficiency through access to all of its parameters. This information aids in detecting the errors and determining their impact on the whole system.
The next essential component of observability is the trace type functionality. It is a route of request or action through all the system’s nodes, and it is traced by assigning a unique identifier to the data, which travel that route.
Tracing is being used whenever data is transmitted between components of the system. Thanks to that, one can gather information about the route which the data travel – i.e., how long it is or what its architecture or throughput on every level is.
The gathered data show which areas of the system need improvement or optimization. Trace is necessary during working with very complex systems in which the adequate assessment of resources is crucial and ensures an efficient operation of the entire system.
The above-mentioned components are known as the three pillars of observability. Each one of them provides an insight into the state of the system from different perspectives. Utilizing every one of them allows one to visualize the condition of the whole system on every one of its layers: application, integration, database, system, and hardware. It ensures an efficient detection and trouble solving of the occurring problems. Said pillars are crucial for the system to work effectively and to achieve the best possible results.
How to make the system observable?
Developing the observable systems requires primarily the understanding of the abovementioned pillars of observability. However, this is not sufficient. To build a fully observable system, one has to let go of the conception of building systems oriented to individual components and adapt a unified approach within the whole system. Indeed, the goal of observability is understanding how the system functions as one entity.
During the creation of observable systems, the most important aspect is to define the types and sources of the data, which are crucial to the functioning of the system. Most crucial meaning these which, after being analyzed, can prevent bugs and, in the rare instance of them occurring, can detect and fix them.
Significant data are also these thanks to which the improvements of the systems can be conducted and which can positively impact its efficiency. Gathering large amounts of unnecessary data is an undesirable practice, which significantly decrease the effectiveness of observability.
After defining the most essential metrics of an observable system, one should define the desirable values and thresholds. After exceeding either of them, a given benchmark is considered incorrect.
Focussing on the most important data, makes observability very credible. A large amount of malfunctions in metrics not relevant to the functioning of the system is not a bigger problem than only one failure, which can also cause a system to function incorrectly or to not operate at all.
Building an observable system does not come down only to a proper design of the system itself. If we want the observability to be the most effective, the system must be properly administered.
On the shoulders of the administrator lies the great responsibility of insightful analysis of the results of the system’s monitoring parameters and is the one who is obliged to come to the right conclusions. The administrator has to know how to differentiate between the events that are crucial to the functioning of the system from the ones which are not relevant.
DevOps, an agent responsible for the communication between the system’s development and operations sectors, also plays an important role. Observability allows the programmers to control the actions during the delivery of the products to the users.
Thanks to that, it is clear whether the fresh changes will cause failure. This is the task of DevOps, because it delivers the feedback about what and where does not function properly or does not function at all. Thanks to DevOps, it is possible to significantly shorten the time to repair in the system.
The last component which is worth noticing is the means of registering and interpreting the data gathered from the reports. Throughout gathering data, an emphasis should be placed on the vital aspect of the way that the data is transmitted.
Data gathered manually to the observability tools, do not inform about the current state of the system as well as those provided by for instance data forwarder, which transfers the data on an ongoing basis employing an appropriate format.
When interpreting the data visualizations, i.e., in the form of tables, charts, diagrams, are also helpful. It is also worth it to properly filter the data, in order to shell the records, which relate to the areas in the system which we are interested in.
When implementing observability, selecting the right platform designed to monitor the system’s parameters is the most important. The choice of the utility should be dictated by not only present but also future needs of the company.
In order to do that, it is worth considering what the environment will look like in a few years and what requires it will have. The question arises which type of tool will be better – open source or commercial. Usually the commercial tools offer more functionalities and are more convenient to use. On the other hand, the open source tools do not strain the company’s finances. The most significant thing when choosing the tool should first and foremost be to what extent it fulfills the requirements of the environment.
Modern observability platforms are extremely efficient when it comes to monitoring the operations of the system. In practice, however, the extent to which they are helpful in managing the system, depends on the abilities of employees who use them.
The lack of sufficient training and transfer of knowledge makes the potential of observability not used to its fullest. If we want to achieve good results, then we can not forget about investing in the team. We can start by building the relevant competencies within a small team, which subsequently will aid in introducing the observability policies to the rest of the company.
A good practice is also using baseline data from the observability tools during business meetings, in reports or presentations. It leads to increasing the skills and the awareness in this regard amongst the members of the organization.
In summary, implementing observability is not an easy task, but it is certainly worth taking this initiative. Thanks to observability we gain a better control over the system, which, in consequence, is reflected in increased automation as well as improvement of efficiency and financial results of the company.
Observability and DevOps IT
Fast, reliable and secure software deployment is the basis of the technological transformation and also influences the company’s results. The key to success is accessibility to data about the software and functional communication in the DevOps team.
Observability in DevOps is mostly the process of sharing the data from an observable system. The main task of observability in this branch of IT is answering the question of what was not working and why it was not working. Thanks to observability, the DevOps team is given the information about the causes of the problems in the software, which leads to improvement in debugging and eliminating abnormalities.
Observability and containerization
The access to metrics, logs, and the tracing services brings considerable benefits also while working with container platforms. Acquiring the information about what is happening not only on the cluster or host level, but also on container and application level, provides a wide range of possibilities. Thanks to observability, it is possible to make informed decisions about the whole system.
The concept of observability in the applications based on containers is not especially different to the one occurring in the traditional applications. The data is gathered both on the container itself and on the infrastructure level in order to achieve better resource management. It is extremely useful, especially in the scaling of applications.
Observability and software engineering
In large corporations involved in software manufacturing, observability is one of the most important factors in achieving success. It is used mainly in identifying the behavior of manufactured software and detecting the anomalies throughout production. It speeds up its implementation and increases the efficiency of the company.
The gathered data are not only related to the technical aspects but also to user experience of the manufactured software. Thanks to analyzing this data, the engineers can find out why the user interacts with the product, as well as gather the information about the future innovations and scalability.
Observability and operating systems
Observability is heavily connected to operating systems. Monitoring the use of resources, like disc or network, allows efficient management of the processes. Gathering the data from the system, in the form of logs or metrics, gives the administrator full control over the system and aids in solving the problems related to its functioning. Observability also helps in looking after the safety of the system’s users by gathering and analyzing the logs regarding the cyberattacks.
Observability and databases
Diagnosing and trouble solving the issues in the case of databases is an extremely hard and time-consuming task. Observability focuses in that case mostly on the telemetric data, Telemetry along with the context of applications facilitates the understanding of database instances as well as helps in their maintenance. In the observable databases, it is easier to identify the difficulties and tendencies in their occurrence, which leads to their quicker elimination.
The data gathered through observability help in the configuration of new instances, in such a way that they can gather the relevant data right from the start of their operation. Observability helps to locate the source of slowdowns and downtimes in the operation of databases and analyze their causes. It happens thanks to gathering and controlling the information about tables, fields, columns, queries and many other components.
Splunk and observability
Splunk is a utility, which brings observability to the next level. The platform enables quicker detection and trouble solving within the system, which translates into avoiding downtime and improving efficiency. It also increases the operational efficiency and TCO, by achieving better visibility and control over the usage of the cloud. In addition, it reduces the necessity of unplanned work by delivering the tools to monitor, problem solve and quickly react to errors in the system.
The data collected by Splunk can be visualized in the form of tables, juxtapositions, charts, or maps compiled on dashboards (panels) which aid in understanding the data.
Splunk Observability Cloud is a tool, which was made to respond to the challenges related to system monitoring. It proves to be very useful when working with complex systems, which can generate numerous errors. Splunk Observability Cloud provides the users with the best tools to work and as a result the effectiveness and efficiency of the organization increases.
Splunk Infrastructure Monitoring enables monitoring of a given set of data in any scale in real time. Splunk APM gives the possibility of solving the issues with microservices and applications thanks to scattered tracking.
Splunk Log Observer facilitates the research and exploration of the logs without the need of learning the query language.
Splunk Real User Monitoring enables identifying the issues which have impact on the clients, starting with the web browsers and native mobile apps to the backend services.
Splunk Synthetic Monitoring facilitates the proactive detection and solving of the problems related to the duration, functionality, and speed of the work and also offers the best optimization efficiency abilities in its class of websites, mobile devices and APIs.
Splunk ensures a coherent and intuitive user interface. In the applications, a specialized language is used, and their menu allows quick access to all the functionalities. The whole application has a vast array of useful tools, which are quite helpful on many levels of analysis and data processing. The vast community and meticulously prepared documentation are also noteworthy. The above-mentioned advantages make Splunk one of the best observability tools available on the market.
Observability in Elastic
Elastic offers the ability to quickly detect and fix the issues and analyze their cause in the observable systems.
Elastic observability allows gathering the current data and transfer them to Elasticsearch, whose function is to process and analyze them.
Then, thanks to the Kibana tool, the data can be visualized in many, different forms i.e., as charts, maps, or graphs.
The log-dedicated application called Logs enables an analysis of logs from hosts, services and various other sources. Logs are gathered in real time and moreover there is a possibility of filtering, pinning and marking the ones that we are interested in.
Metrics is responsible for monitoring the metrics of the system and services from the servers, as well as for creating nonstandard groups, i.e., accessibility or namespace.
Application Performance Monitoring (APM) allows the user to monitor programming services and applications in the real time. It is done by gathering the in-depth data about the efficiency regarding the time of the response to the incoming requests, queries to the databases, cache invokes, external HTTP demands and others.
Heartbeat is a functionality, which enables the ability of monitoring the accessibility of hosts, the duration of the service, endpoints, and API. The Uptime application is responsible for this.
Kibana utility, which is integrated with the above-mentioned applications, has a function of alerts and actions. Thanks to it, the user receives the information about the potential problems occurring in the system on an ongoing basis. Kibana enables the central management of all rules from the level of Kibana Management.
Elastic is a very convenient and an enjoyable tool in terms of the user. The large amount of available resources in the form of extensive documentation and videos allows the user to absorb the knowledge easily. Elastic provides many functionalities necessary in observability. The platform is able to meet the expectations of even the most demanding analysts.
Datadog and observability
Datadog is a modern environment dedicated, among others, to observability. It gives insight into the systems, applications, and services from a single platform. Thanks to Datadog, the data can be intercepted in the real-time from any source and be observed and analyzed.
The platform combines many, useful functionalities:
- comprehensive insight into the efficiency of the infrastructure on different levels,
- overview of functioning of on-premise and cloud networks, including performance tasting of the application layer;
- multidimensional revision of container environments;
- solving issues with the efficiency of serverless applications;
- review and analysis of logs and metrics in any scale.
In addition, Datadog gives the ability to visualize the gathered data in a form of dashboards, which facilitates the analysis. It also has mechanisms for eliminating displaying the false positive errors, thanks to implementation of machine learning.
The utility is characterized by a simplicity of operation and legible interface, even for the advanced users. It allows full freedom of action. The drag-and-drop method facilitates a quick creation of dashboards and sending data. A big advantage is also the fluent navigation between logs, metrics and environment-related alerts.
Observability or monitoring?
The basic difference between observability and monitoring are their purpose and scope.
Monitoring is the process of gathering, analyzing and using the data in order to determine to what extent the program accomplishes its goals. It serves only to intercept and display the data predefined by the administrator.
It focuses on making the right decisions regarding the management of the system using the observable indicators. Monitoring is primarily the act of searching, which is observing the metrics and waiting for abnormalities in order to eliminate them.
Observability allows determining the condition of the system thanks to the analysis of all of its input and output data. It focuses on drawing conclusions from the data useful for a given system, which will serve to determine the cause of the problem.
How to determine which approach would be advisable in a particular case?
If we are dealing with systems composed of many components, from which every single one is dependent on the other, finding the source of the failure can be problematic. Often it can not be predicted in what location the failure occurs and how it would affect the rest of the system. The modern applications demand a greater transparency of the system’s condition, and it can only be achieved by utilizing observability. The analysis of logs, metrics, and trace enable to provide the answer to every possible question about the system.
In the case of less complex applications, monitoring is sufficient. When the cause of the failure is known and the origin of the issue is clear, monitoring of the predefined indicators allows detecting the abnormalities and eliminate them.
Observability and monitoring frequently work together. The main task of monitoring is to inform the administrator about a potential occurrence of the issues, whereas the purpose of observability is to detect and analyze the root of the problem.
Interested in learning more about observability and finding the optimal observability solution for your company? Seeking assistance in selecting the right observability tools for your business? Explore our observability comprehensive services and connect with our experts today to gain insights into every aspect of your IT ecosystem and enhance your system’s performance!