Monitoring vs Observability: What to choose?

In complex IT environments, monitoring and tracking events is a key element of infrastructure management. However, there is a growing discussion about the Monitoring vs. Observability clash, a concept that goes a step further than just monitoring, offering deeper insight into system performance and application availability. In the following article, we will look at both Monitoring and Observability, discussing practical aspects of service management and effective application troubleshooting.

How does IT monitoring support business goals?

The goal of monitoring is to ensure the availability, security, and performance of the system in accordance with SLOs (Service Level Objectives). SLOs are specific metrics that must be met to comply with the SLA (Service Level Agreement), which specifies the level of service availability, e.g. at 99.9%. Infrastructure monitoring – both at the hardware and software level – is necessary to control whether these conditions are met and to respond to any deviations from the assumptions.

Monitoring layers

Monitoring covers all layers from hardware to applications:

• Hardware layer: monitoring the status of servers.
• Network layer: monitoring switches and routers.
• Software layer: monitoring and tracking the operating systems on which our services run, as well as external services.
• Application layer: monitoring databases, response times to requests to external services, such as payment gateways or e-mail services.

In distributed environments, consisting of many microservices, problems appear more often, so continuous data analysis and event tracking are crucial to maintain control over the entire infrastructure and implement corrective actions. It is a good practice to provide advanced monitoring functionalities that guard the performance and security of systems and services.

Monitoring and its tools (pillars) – metrics, logs and traces

Monitoring is based on three pillars: metrics, logs and traces. Each of these pillars collects information from different layers of the infrastructure. It can also use different tools to aggregate this information.

Metrics

Metrics are numerical data that change over time, e.g. CPU load, network throughput, remaining disk space. This data can be analyzed in real time for different infrastructure components. This translates into detecting bottlenecks, and consequently – into decisions about, for example, rescaling resources.

Logs

Logs are textual information about events that come from different levels of the infrastructure. They can be informational, warning, or error logs that need to be analyzed in the right order to identify the causes of problems. To do this, each log contains a timestamp.

Trace

A trace is a trace that a user action leaves in our system. It informs us about the flow of data through the system after users perform an action, e.g. clicking a button in an application. Traces help us understand how the system processes data and what delays occur in communication between different services.

The problem of locating the causes of problems and errors

A common problem for large companies or distributed organizations is the difficulty of locating the causes of failures, especially in distributed IT environments. For example, when an internal employee using an ERP system encounters an error, it is often difficult to determine where the problem lies.

• The network administrator claims that his system is working properly.
• The systems administrator believes that all processes on the virtual machines are working properly.
• The developers point to problems in the database.
• The database administrator first blames the network.

As a result, the end user does not understand why the problem is not being resolved. This scenario shows the limitations of traditional monitoring in solving infrastructure problems and requires a more advanced approach, such as Observability.

Observability: Tracking at a Higher Level

Observability is a concept that extends monitoring analytics to provide a comprehensive view of the entire IT infrastructure. It allows you to aggregate data from different sources and visualize it, allowing you to identify issues faster and optimize resources.

Unlike monitoring, which only collects data, Observability offers the ability to analyze and optimize processes in real time. This allows companies to better understand which elements of the infrastructure need improvement – ​​whether in terms of performance, operational costs, or application response speed.

Integration and optimization with Observability

The first step towards full implementation of the Observability tool is to integrate data from various sources (servers, virtual machines, operating systems, security systems, databases, libraries, application code). Then this data must be properly analyzed.

Based on the collected information, you can automate reactions to increased load, anticipated problems or other changes in the IT environment. This process is increasingly supported by artificial intelligence, which allows for taking corrective actions automatically.

The key advantage of the Observability platform is the ability to visualize data, which allows for easier management of IT resources and making optimization decisions. This allows for dynamic scaling of resources depending on current business needs. And this translates into powerful observability efficiency.

Monitoring vs. Observability

Observability is the next step in the evolution of IT monitoring. It allows you to not only monitor application performance, but also understand how systems behave in real time.

With full visibility into the infrastructure, companies can reduce the time it takes to resolve issues, as well as predict them and minimize their effects. This is important not only for resource optimization, but also for the security of the entire IT infrastructure.

In the future, Observability will become a standard in IT management, allowing for better control and operational efficiency.