Many organizations adopt cloud-native technologies either through microservices, containers, or serverless solutions. It became increasingly difficult to trace an event to its source with these distributed technologies.
In the past, monitoring tools could not adequately track communication pathways and interdependencies in cloud computing systems. Observability tools were then introduced to improve the performance of information technology (IT) systems by watching system performance.
What is Observability?
Observability is the process of monitoring and measuring the internal status of a system by evaluating its output.
The Difference between Observability and Monitoring
Many DevOps teams refer to monitoring and observability interchangeably. There are significant differences between these two concepts, though.
Monitoring allows you to watch the state of your system based on predetermined metrics and logs.
Observability is derived from control theory and deals with understanding the status of your internal systems by the outputs.
Monitoring requires teams to know what metrics to follow and helps keep track of them. On the other hand, observability tells teams what metrics are essential by watching overall system performance, asking relevant questions, and noting important information. In other words, observability identifies areas that need to be monitored.
Benefits of Data Observability
Observability gives development teams real-time visibility into their distributed system, allowing them to optimize the debugging process when there’s an error in the code. That is achieved by tracking the system and providing relevant data used to make decisions swiftly.
Asides from identifying valuable metrics, here are some other functions of observability:
Better alerting. Observability platforms allow developers to identify and solve problems faster by providing insights that show what changes have occurred in the system and the issues caused by those changes. This makes debugging and troubleshooting easy for teams.
Consistent workflow. With observability, development teams can see the entire journey of each request along with contextual data from traces. This capability optimizes performance and the debugging process.
Time saving. Effective observability software helps reduce the time spent figuring out where an issue is from, what part of the deployment process the error is in or what third-party application led to the problem. Observability saves time by readily providing necessary data.
Accelerated developer velocity. Observability performs some functions of monitoring tools and makes troubleshooting swift and effective by removing areas of unease for developers. This feature provides development teams with time to develop innovative ideas and carry out forward-facing activities.
What to Consider When Choosing Observability Tools
There are many observability tools available in the market. Using the tool that best suits your organization’s needs is vital for success.
Depending on your systems, here are some factors to look out for when deciding on an observability tool:
Integration with modern tools. An adequate observability tool should not only work with your current stack but also have a proven history of updates that make it compatible with new platforms.
Ease of use. Your observability architecture should be easy to learn, understand, and use. Tools that are difficult to understand do not get added to workflows, defeating the architecture’s purpose.
Provision of real-time data. Good observability platforms should provide information in your distributed systems via queries, dashboards, and reports so that teams can take the necessary action in time.
Adoption of machine learning. Observability software should adopt a machine learning model and automate processes and data curation. This enables detection and makes response to anomalies fast.
Accordance with business value. All technology used by your organization should be in line with your business purpose. Observability tools should identify and evaluate data — such as system stability and deployment speed — that improve your business.
Difference between Observability and Visibility
Although observability and visibility have many similarities, they are two different concepts in development and operations:
Visibility is the ability to monitor every stage in the development process and align it with the needs of stakeholders. In an attempt to undergo security modernization, organizations channeled multiple resources into achieving visibility. API-driven architectures enabled the aggregation of multiple logs, giving companies a clear view of systems. Visibility birthed the first generation of analytics.
Observability expands on the goals of monitoring software and not only provides organizations with a view of their systems but also enables correlation and inspection of data to provide insights that align with business objectives. Observability tracks systems to determine essential attributes that should be monitored.
Three Pillars of Observability
There are three primary data classes used in observability, and they are often referred to as the pillars of observability. These three pillars are logs, traces, and metrics.
Logs are text records that a system makes of events while codes are run. A log often includes a timestamp that reflects the time the event occurred and a payload of details about the event itself. A log’s format could be plain, structured, or binary. Although the plain text logs are most common, structured logs that include easily queried metadata are gaining prominence.
Log files provide in-depth system details and are often the first place you look when you detect a fault. By reviewing logs, teams can easily troubleshoot codes and discover the reason why an error occurred.
Metrics are numerical representations of data measured over a period of time. These metrics usually include name, timestamp, KPIs, and labels. Metrics are useful in determining a service’s overall behavior as they are structured by default. This means that the data derived from metrics can easily be optimized and stored for longer periods.
Many teams prefer metrics because one can match them across other system components and get a clear picture of performance and system behavior.
A trace describes the full journey as it moves along a distributed system. As requests pass through the system, each action performed on it — referred to as a span — is filled with data concerning the action performed by the microservice.
Tracing is the observability technique that allows teams to see and understand the lifecycle of action across all nodes of a distributed system. Traces provide context to the data from logs and metrics in observability as they allow you to profile systems.
Learn More About Observability at The New Stack
For The New Stack’s coverage of the observability space, we look at how pre-existing monitoring technologies such as New Relic and Dynatrace are optimized to support this new environment. We also examine the technologies from companies formed specifically to deal with observability and monitoring, such as Honeycomb.io and SignalFx.
Find out more about developments in observability technologies with our articles in this category.