Monitoring vs. Observability
A discussion modern monitoring and observability practices and how they help development teams better understand the bugs and other errors in their software.
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The IT sector has become exponentially complex in recent times – more environments, more connected devices, more data, and more updates. As such, the legacy methods used to monitor modern disseminated applications and the management of predictive failures do not work optimally. Monitoring is a crucial factor to growth and keeping pace with the challenges that technology brings.
Observability tends to streamline complexities. To efficiently diagnose and debug code, the system must be observable in the lines of the microservices' architecture. But what makes this new IT buzzword different from monitoring?
Monitoring is an integral element of IT operations and gives visibility into today’s highly distributed environments. But it isn’t entirely what we know as observability. Machine learning and logging need to be integrated into a single environment to make it more efficient. To put it simply, monitoring is a function of observability; however, the latter has more components that enable the team to move from their approach of reactive problem solving to proactive decision making.
In this post, we will take a look at the difference between Monitoring and Observability, and why it matters to your organization.
Organizations based on DevOps focus on breaking down an application into a microservice-based architecture to minimize repair time and gain improved operability in case an incident occurs. However, as modern systems are highly complex, monitoring systems should be able to provide visibility and the ability to react quickly in the event of system failures.
Monitoring helps determine the state of a system or application. From standard testing of the application to performing health checks, monitoring aids in identifying anomalies and problems. Through monitoring, organizations can detect the root cause of a problem and gain actionable insights into performance trends and capacity requirements.
However, monitoring has been continuously evolving, improving the scope for stakeholders. For instance, architects use this technique to determine which models or cloud patterns can help achieve the best ROI. In application development, monitoring tools can be used to measure coding practices against performance outcomes.
Therefore, monitoring is important for building dashboards, analyzing long-term trends, and sending alerts in case an event occurs. It enables you to understand how your applications or systems are functioning, their growth trends, and whether they are appropriately utilized. But the limitation of monitoring highly dispersed and complex systems is the lack of linear production failures. As such, they are hard to predict.
Observability – What Is it After All?
Though this term has recently become popular, the concept of observability has always been there for linear dynamic systems. According to control theory, the concept of observability is defined as "a count of how well the internal states of the framework can be inferred from the information of their external outputs." It leverages instrumentation to gain valuable insights, which aids in effective monitoring. That means without a certain extent of observability, the process of monitoring becomes impossible.
With observability, you can measure and understand the internal factors affecting a system. This will enable you to seamlessly navigate from the effects through the cause, even in a multifaceted microservice architecture. An observable system helps you derive answers for questions like:
- Where are the performance bottlenecks and which services does a request undergo?
- How does the request execution process differ from the system behavior?
- What are the reasons for request failure?
- How is a request processed by every microservice?
Observability is crucial for today’s overly complex IT infrastructures where two important factors need to be considered – characteristics of the application and the speed at which it is being delivered. Most organizations today are embracing dynamic microservice architectures and containerized workloads. Therefore, solely depending on monitoring will not help.
It is crucial to have modern instrumentation in place that helps better understand an application’s properties and performance. This enables organizations to efficiently handle the challenges of a highly complex and distributed system that is affecting the production and delivery pipeline.
Major Pillars of Observability
Primarily, there are three pillars of observability:
- Logs: These are absolute and time-stamped records of various discrete events that can be used to detect unpredictable behavior in an application or system. It helps you to identify changes in the application’s behavior when a problem occurs.
- Tracing: For individual requests and transactions, traces demonstrate the process as they transfer from one node to another in a complex distributed system. Traces enable you to capture the details of a specific request or transaction. This enables you to: determine which elements are causing system errors, detect performance bottlenecks, and track flow through the nodes.
- Metrics: This is a powerful tool that helps teams understand the total amount of memory used by a method and the number of requests or transactions a service can handle every second.
Relationship Between Monitoring and Observability
Despite the differences, it is important to know that monitoring and observability complement each other. However, they both have a unique purpose to serve.
Monitoring enables you to detect errors in the system, while observability takes it a step ahead in helping you to understand why the problem occurred. Monitoring lets you track the overall health and performance of an application or system. It collects data on how the application is functioning in terms of connectivity, bottlenecks, downtime, and access speeds.
On the other hand, observability takes you deep down into a problem by helping understand the “what” and “why” of a system operation. It provides more contextual and granular insight into particular modes of failure. So, monitoring provides answers only to known occurrences and problems, while observability lets you gain newer and deeper insights into the state of a dynamic and complex system.
In this sense, monitoring is a key subset and function of observability, i.e., a system can be monitored only when it is observable. It is important to understand that observability is not a product; rather, it is an integral property for today’s highly complex and distributed environments, where application monitoring and modern instrumentation are one of the biggest contributors.
Thus, organizations should focus on building an environment of observability that is intertwined with monitoring to achieve greater results. Effective training should go into helping troubleshooters and stakeholders learn how to make their systems more observable. This will involve generating deeper insights, assisting in monitoring designs, and building improvement strategies.
To state the fact, many organizations have walked that extra mile and built “observability engineers,” while several others have already included observability practices and learnings into their new-recruit training programs.
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