What is Observability?

Observability is a measure of how well the internal states of a system can be inferred from its external outputs. In control theory, observability is a key concept that determines whether or not a system can be controlled. If a system is not observable, then it is not possible to know what its internal state is and therefore it cannot be controlled.

There are two types of observability: weak and strong. Weak observability means that the internal states of a system can be inferred from its external outputs, but only to a limited extent. Strong observability means that the internal states of a system can be completely inferred from its external outputs.

What is the purpose of Observability?

Observability is key in understanding an organization’s internal system. It is particularly important in distributed systems, where software engineers may not have direct access to the internals of a system, and in Kubernetes clusters and microservices, where there are many individual components that make up the overall system.

What are the three pillars of Observability?

Observability relies on three pillars of data to efficiently and optimally function: logs, metrics, and traces.

1. Logs are records of events that have happened in a system. They are typically unstructured and include a timestamp, message, and details about the event. Logs are important for understanding what has happened in a system in the past and for troubleshooting issues.

2. Metrics are numerical values that represent the current state of a system. They are typically structured and include a timestamp, name, value, and tags. Metrics are relevant in understanding the current state of a system and for monitoring trends over time.

3. Traces are a record of the path that a request takes as it flows through software systems. They include information about the start and end time of the request, the source and destination of the request, and the intermediate steps that the request took. Traces are useful for understanding how requests flow through a system and for identifying bottlenecks and slowdowns.

What are the benefits of Observability?

There are many benefits to using observability to monitor your system.

1. DevOps: One of the key benefits of observability is that it enables DevOps teams to work more effectively. By understanding the internal state of a system, DevOps teams can identify and fix problems more quickly. This leads to shorter development cycles and faster time to market for new features.

2. Application Performance Monitoring: Another benefit of observability is that it can be used to monitor the performance of applications. By understanding how an application is performing, engineers can identify and fix bottlenecks more quickly. This leads to improved performance and better user experiences.

3. User experience: The third benefit of observability is that it can be used to improve the user experience. By understanding how users interact with a system, engineers can identify and fix problems that lead to poor user experiences. This leads to happier users and improved customer satisfaction.

4. Cost savings: Relying on observability can also save companies money. By identifying and fixing problems quickly, companies can avoid the cost of downtime and lost productivity. In addition, using observability to monitor performance can help companies save on infrastructure costs by ensuring that resources are used efficiently.

5. Improved uptime: Finally, using observability can improve uptime by helping engineers recognize and target issues before they cause outages. This leads to fewer disruptions and happier customers.

What are the differences between Observability and Monitoring?

Monitoring is the process of collecting data from a system for the purpose of understanding its current state. This data can be used to identify and fix problems, or to monitor trends over time.

Observability goes a step further than monitoring by allowing us to understand the internal state of a system by observing its external outputs. This is possible because of the three pillars of observability: logs, metrics, and traces. Collecting data from these sources, as well as from events, provides a more complete picture of a system and can be used to identify and fix problems more quickly.

As such, observability and monitoring differ in their focus. Observability focuses on understanding the internal state of a system while monitoring focuses on understanding the external state of a system. The internal state of a system includes information about the individual components that make up the system and how they interact with each other. The external state of a system includes information about how the system is performing for users. This means that observability is more helpful for troubleshooting issues while monitoring is more helpful for understanding how users interact with a system.

A further difference between the two is that observability is active, while monitoring is passive. This means that observability requires engineers to actively instrument their code and collect data from a multitude of sources while monitoring relies solely on existing data sources such as application programming interfaces (APIs).

While these major differences distinguish the two from one another, observability and monitoring are still closely related. In many cases, data from observability can be used to improve the accuracy of monitoring. For example, data collected from observability can be used to identify and fix problems with a system before they cause outages. This leads to improved uptime and fewer disruptions for users. Observability also relies on the concept of monitoring for application performance monitoring (APM), an observability practice that uses data from monitoring applications to target and fix bottlenecks rapidly and regulate application function.

How can you implement Observability?

There are many ways to implement observability in a system.

1. Collect data using open instrumentation: The first step is to collect data from all the different parts of your system such as logs, metrics, and traces. This helps ensure that all the relevant data is captured and that no data is missed.

2. Store data: The next step is to store this data in a central location. This can be done using a data warehouse, logging platform, or metric platform. This step is key in ensuring that the data is accessible and that it can be effectively analyzed in line with your company's goals.

3. Analyze data: Once the data is collected and stored, it can be analyzed to identify issues and trends. This analysis can be done using a monitoring tool, log analyzer, or tracing tool.

4. Act on data: The final step is to take action based on the data that has been collected and analyzed. This can involve fixing issues, optimizing performance, or improving the user experience.