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POSTED Dec, 2020 dot IN Observability

The difference between Monitoring and Observability

Ismail Egilmez

Written by Ismail Egilmez

Business Development Manager @Thundra


Legacy methods based on handling predictable failures often do not work well while monitoring modern distributed applications. Efficient debugging and diagnostics require that the system be observable with a microservice architecture now the de facto standard for web applications. Being observable means that the application’s internal state can be inferred by observing its output.

It is confusing for the development teams, to distinguish between monitoring and observability. We discuss monitoring, observability, and the liaison between the two in this post. We also mention some of the tools you can use to achieve observability.


DevOps-framed-of-mind organizations usually start dissociating the application to microservice architecture to gain operability and to reduce the mean time to resolve when an incident takes place. But as the complexity increases in their systems, they must ensure that they can not only gain visibility on system failures but also act within firm timelines.

Your monitoring system needs to answer two simple questions: “what’s broken, and why?” according to the SRE book by Google. Monitoring allows you to watch and understand your system’s state using a predefined set of metrics and logs. Monitoring applications lets you detect a known set of failure modes.

Monitoring is very important for analyzing long-term trends, for building dashboards, and for alerting. It lets you know how your apps are functioning, how they’re growing, and how they’re being utilized. However, the problem with monitoring complex distributed applications is that production failures are not linear and therefore are difficult to predict.

If you want to build and run microservice-based systems, monitoring is still an indispensable tool for it. Monitoring will provide a reasonably good view of your system’s health if the monitoring rules and metrics are straightforward and focused on actionable data. Although monitoring may not make your system wholly immune to failure, it will provide a panoramic view of system behavior and performance in the wild, allowing you to see the impact of any failure and consequent fixes.


Observability looks at the outputs of the systems and measures how well you can understand a system’s internal states. It is which originated from control theory. Observability uses instrumentation to provide insights that aid monitoring. Especially, monitoring is what you do after a system is observable. Without some level of observability, monitoring is impossible.

Even in complex microservice architectures, observable systems enable you to understand the system behaviors, so that you can more easily navigate from the effects to the cause. It helps you find answers to questions like what services did a request go through, and where were the performance bottlenecks. What is the difference between the execution of the request and the expected system behavior? What is the root cause of the failed request? How did each microservice process the request?

The are three main pillars of Observability:

  • Logs: Logs are the abiding records of discrete events that can identify unpredictable behavior in a system and provide insight into what changed in the system’s behavior when things went wrong. It’s highly recommended to ingest logs in a structured way, such as in JSON format so that log visualization systems can auto-index and make logs easily queryable.
  • Metrics: Metrics are considered as the foundations of monitoring. They are the measurements or simply the counts that are aggregated over a period of time. Metrics will tell you how much of the total amount of memory is used by a method, or how many requests a service handles per second.
  • Traces: A single trace displays the operation as it moves from one node to another in a distributed system for an individual transaction or request. Traces enable you to dig into the details of particular requests to understand which components cause system errors, monitor flow through the modules, and discover the bottlenecks in the performance of the system.

Liaison of Observability and Monitoring

The two notions of observability and monitoring are complemental but they serve different purposes.

Observability is a superset of monitoring and monitoring is a key action for observability. Only observable systems can be monitored. When you only monitor your system, you can only understand if something is wrong or not. But if your system is observable, you can understand the root cause of the failures. 

You can track the overall health of your application by monitoring it. Monitoring aggregates data on how the system is performing in terms of access speeds, connectivity, downtime, and bottlenecks. On the other hand, you can drill down into the “what” and “why” of application operations with observability, by providing granular and contextual insight into its specific failure modes.

While monitoring provides answers only for known problems or occurrences, software instrumented for observability allows developers to ask new questions in order to debug a problem or gain insight into the general state of what is typically a dynamic system with changing complexities and unknown permutations.

Never Ending Observability

Observability is not a frightening thing to achieve for the development and operation teams. There are numerous key metrics pertaining to your application that you can begin with; such as your application’s CPU, network, and memory.

If you want to ensure the observability of your systems, system logs may also be necessary. Managing and storing logs is cumbersome in the sense of workforce and cost for many organizations because they can grow really rapidly. But there are tools that can increase the effectiveness of logging. An example is OpenTelemetry, which is used not only for logging, but also for metric collation and tracing. OpenTelemetry integrates with popular frameworks and libraries as well, such as Spring, ASP.NET Core, and Express.

The modern way of observing distributed systems is tracing for sure. Tracing helps you to identify the root cause of an issue in a distributed system. Tracing can be seen as the most important part of observability implementation: understanding the causal relationship in your microservice architecture and being able to follow the issue from the effect to the cause, and vice versa.

Continuous automated observability lets you stay on top of any risks or problems throughout the software development lifecycle. It provides visibility across the entire CI/CD pipeline and your infrastructure, giving you fast feedback on the health of your environment at any time — including in pre-production phases.

Observability Tools

After building complex systems like distributed infrastructures, you will have to manage them in the best possible correct way to prevent the impact of failures. A set of tools need to be actualized to help you visualize our distributed systems. These tools may notify you when a failure occurs. These tools allow you to understand system behaviors and prevent future system problems.

We describe in some depth the three leading observability platforms: OpenTelemetry, Jaeger, and Zipkin in our white paper “Monitoring vs. Observability”.

Some third-party tools like Thundra help you instrument your applications automatically. It provides an automated plug-play solution for observability while keeping the door open for manual instrumentation that’s compatible with OpenTracing — and soon OpenTelemetry.

Continuous Improvement of Observability

There are a lot of causes for the failure of the applications in production. Something will definitely go wrong regardless of your effort. Debugging the issues in production will be a nightmare and give you sleepless nights unless you make your application’s components observable in the right way. It is not enough to build or make your application observable. The answers to the issues will not come as a divine inspiration. You need to continuously examine the data you have, to determine its usefulness. Observability must have the right data to help you get answers to known and unknown problems in production. You have to constantly adapt your system’s instrumentation until it’s appropriately observable, to the point where you can get answers to any questions needed to support your application at the level of quality you want.