What is observability?
Observability is the ability to measure and infer a system’s current state based on the data it generates. This data is usually in the form of logs, metrics, traces, and events. As a brief example, you could observe the health of your microservices application by examining its metrics.
How does observability work?
Observability provides organizations with a holistic view of how a complex system is functioning. Through data collection, storage, and analysis, developers gain the ability to identify and troubleshoot issues in their applications.
Observability starts with collecting data in real time. The collected data is then stored in a centralized location for analysis. This analysis can be done through a machine learning algorithm, visualization, or combination of statistical techniques.
The outcome of this analysis alerts developers, along with operations, security, and other relevant teams, to any anomalies within an application or system. Alerts can be automated and triggered by established thresholds, severity levels, or other criteria based on business or application needs. Once the anomaly is identified and located, developers can use the data to debug and resolve the issue.
Examples of observability data
The primary types of observability data are logs, metrics, traces, and events.
- Logs: A timestamped text record with metadata. These recordings or messages are usually generated by an application or system. Logging is one of the most common ways to implement observability in software development.
- Metrics: A measurement about a service, captured at runtime. These numerical measurements include CPU usage, memory usage, and error rates. All of these measurements track the performance and health of an application or system.
- Traces: An account of the request’s journey or an action as it moves through the nodes of a distributed system. Traces document how a request is processed and how long it takes to complete. This data can help identify bottlenecks and other latency issues.
- Events: A record of significant changes or occurrences at a specific point in time. Event data might include state changes, user actions, security alerts, or critical deployments within an environment. The record provides contextual information to help teams understand why changes happened.
Is monitoring different from observability?
Monitoring is the ability to observe and check the progress of processes happening within a system or application. Monitoring relies heavily on metrics. In short, it provides visualization of the environment and enables you test against known problems. Observability, on the other hand, supplies new and deeper data that allows you to infer that an issue may exist. You can then dive into the issue’s cause to gain insight into the future.
Monitoring and observability are not completely distinct. Rather, they are data analysis options and visualization techniques that allow developers to reach insights faster.
With those definitions established, the table below takes a closer look at four subtle differences between monitoring and observability in software applications. These four differences are divided into scope, granularity, flexibility, and analysis.
| Monitoring | Observability | |
|---|---|---|
Scope | Measures metrics (e.g., system uptime, CPU usage, error rates) | Understands the system’s mechanisms at work based on its outputs |
Granularity | Aggregates or samples collected data at a regular cadence (based on predefined metrics) | Collects and analyzes granular data to get deeper insight and understanding of system behavior |
Flexibility | Depending on the vendor, implements predefined dashboards or alert thresholds that are difficult to modify once deployed | Uses a flexible and adaptable approach with easy-to-change tools that accommodate evolving situations and requirements |
Analysis | Identifies and reacts to specific events or anomalies | Emphasizes proactive analysis and troubleshooting by giving developers the tools they need to identify a problem’s cause and implement solutions over time |
What role does telemetry play in observability?
Telemetry in software observability refers to the practice of collecting and transmitting data about the performance and behavior of a software system in real time. This data (response times, error rates, resource consumption, etc.) is used to monitor and understand the system’s current state, and help developers identify opportunities to improve performance.
In conversations on telemetry, OpenTelemetry (OTel) often becomes a talking point because it offers a simplified approach to make observability easier for developers. OTel is a set of open source tools and libraries that standardize the collection of telemetry data (logs, metrics, and traces) from software systems.
You can learn more about OTel and ways it affects the cloud-native landscape in How OpenTelemetry Is Changing the Way We Trace and Design Apps.
Advanced observability technologies
In addition to using traditional solutions, organizations are increasingly exploring advanced technologies to streamline data collection and enable more dynamic, real-time observability.
eBPF (extended Berkeley Packet Filter) gathers telemetry data from the Linux kernel. This sandboxed execution engine, built directly into the Linux kernel, allows developers to run custom, lightweight kernel-level tracing without modifying kernel source code or running heavy agents. Teams can map microservice topologies and track container-to-container traffic, for example, quickly identifying performance issues or bottlenecks.
AIOps (AI for IT operations) platforms provide near-real-time insights and execution capabilities. These platforms use AI and machine learning to rapidly detect anomalies, analyze patterns, enable users to visualize insights, and automate actions. The platforms can automatically execute functions that improve user performance, optimize traffic for costs, and remediate security vulnerabilities.
Benefits of observability
Observability provides developers with a better understanding of their applications, which enables:
- Faster debugging: Detailed, analyzed data expedites a developer’s ability to diagnose and debug system issues.
- Better performance: Monitoring key metrics and identifying blockers helps developers make data-driven decisions to improve application performance.
- Improved reliability: Observability data allows developers to proactively resolve system failures that may disrupt user experience.
- Better collaboration: A standard set of data over time enables teams to readily work together to solve problems based on a universal set of metrics.
Disadvantages of observability
Observability does come with some drawbacks, and the most common include:
- Increased overhead: Implementing observability can mean adding cost for specialized tools used to track application or system metrics, along with the need to provide additional data storage.
- Added complexity: Additional instrumentation and monitoring are required, and accommodating these extra tools can make an application more complex.
- Information overload: Observability can create large amounts of data that quickly become cumbersome for teams to manage, and too much data can make it hard to prioritize which issues require immediate resolution.
Frequently asked questions
What is the main difference between application observability and traditional monitoring?
Traditional monitoring uses metrics to track processes and then generates visualizations of the current state of an environment. By contrast, observability collects and analyzes granular data to produce deep insights and proactively uncover potentially hidden issues.
What are the four primary types of observability data?
Observability platforms typically collect and analyze four types of data: logs, metrics, traces, and events. Logs are timestamped records generated by a system. Metrics are measurements about an app or service at runtime. Traces document how a request is processed and how long it takes. Events are records of important changes in an environment at a point in time.
What is OpenTelemetry (OTel), and why is it important?
OpenTelemetry (OTel) is a set of open source tools and libraries that standardize collection of telemetry data from software systems. OTel enables teams to avoid vendor lock-in for observability platforms by decoupling application instrumentation from the backend tool. It also provides consistent conventions and APIs across all major programming languages.
How do you implement observability in cloud-native Kubernetes tools and serverless platforms?
Traditionally, it was difficult to implement observability for both Kubernetes tools and serverless platforms because they formatted, named, and transmitted performance data in incompatible ways. Open source OpenTelemetry (OTel) tools and libraries enable teams to implement observability for Kubernetes and serverless by providing a single, universal protocol (the OpenTelemetry Protocol [OTLP]) along with unified field definitions.
How does eBPF improve application observability?
The extended Berkeley Packet Filter (eBPF) is a sandboxed execution engine built directly into the Linux kernel. It enables developers to run custom tracing without modifying source code or running heavy agents. Developers can identify performance issues while minimizing resource usage and capturing a full picture of activity.