Keeping track of metrics requires teams to know what metrics to follow. Conversely, observability helps teams determine what metrics are essential by monitoring system performance, asking pertinent questions and noting relevant information. Thus, observability identifies areas for monitoring.
Many organizations adopt cloud-native technologies through microservices, containers, or serverless solutions. Tracing an event to its source with these distributed technologies became increasingly difficult.
In the past, monitoring tools could not adequately track communication pathways and interdependencies in cloud computing systems. Observability tools were introduced to improve the performance of information technology (IT) systems by watching system performance.
Observability is the process of monitoring and measuring the internal status of a system by evaluating its output. The output is comprised of logs, traces, and metrics. Observability aims to understand what happens across various environments, networks, and technologies, with the goal of resolving issues sooner rather than later.
Many DevOps teams refer to monitoring and observability interchangeably. There are significant differences between these concepts. Monitoring allows you to watch the state of your system based on predetermined metrics and logs. Observability is derived from control theory and understands 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.
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 to make decisions swiftly.
Monitoring and Observability — What’s the Difference and Why Does It Matter? Let’s Find Out.
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 developers’ uneasy areas. This feature gives development teams time to develop innovative ideas and carry out forward-facing activities.
There are many observability tools available in the market. The tools best suited for your organization’s needs are 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. Difficult to understand tools 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 align with your business purpose. Observability tools should identify and evaluate data — such as system stability and deployment speed — that improve your business.
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 provides organizations with a view of their systems, and 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 primary data classes are used in observability, often referred to as the pillars of observability. These three pillars are logs, traces, and metrics.
Logs: Logs are text records that a system makes of events while codes are run. A log often includes a timestamp that reflects the event’s time and a payload of details about the event itself. A log’s format could be plain, structured, or binary. Although plain text logs are the 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 why an error occurred.
Metrics: Metrics are numerical representations of data measured over some 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.
Traces: 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 action lifecycle across all distributed system nodes. Traces provide context to the data from logs and metrics in observability as they allow you to profile systems.
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.
Discover more about developments in observability and monitoring.