Observability is the ability to understand the internal state or condition of a complex system based solely on knowledge of its external outputs, specifically its telemetry.
Observability plays a crucial role in maintaining the availability, performance and security of modern software systems and cloud computing environments.
The term “observability” comes from control theory, an engineering theory concerned with automating control of dynamic systems, such as regulating the flow of water through a pipe based on feedback from a flow control system.
Observability provides deep visibility into modern, distributed tech stacks for automated, real-time problem identification and resolution. The more observable a system, the more quickly and accurately IT teams can shift from an identified performance issue to its root cause, without extra testing or coding.
In IT operations (ITOps) and cloud computing, observability requires software tools that aggregate, correlate and analyze a steady stream of performance data from applications and the hardware and networks they run on. Teams can then use the data to monitor, troubleshoot and debug apps and networks, and ultimately optimize the customer experience and meet service level agreements (SLAs) and other business requirements.
Observability is often confused with application performance monitoring and network performance management (NPM). However, observability tools are a natural evolution of application performance monitoring and NPM data collection methods. They are better suited to address the increasingly distributed and dynamic nature of cloud-native application deployments.
Observability doesn’t replace other monitoring approaches; it improves and expands upon them.
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Observability platforms continuously discover and collect performance telemetry by integrating with instrumentation built into app and infrastructure components, adding features and instrumentation to these components.
Observability focuses on three main telemetry types:
Logs
Logs are granular, time-stamped, complete and immutable records of application events. Among other things, logs can be used to create a high-fidelity, millisecond-by-millisecond record of every event, complete with surrounding context. Developers use logs for troubleshooting and debugging.
Traces
Traces record the end-to-end “journey” of every user request, from the user interface or mobile app, through the entire architecture, and back to the user.
Metrics
Metrics (sometimes called time series metrics) are fundamental measures of application and system health over time. For example, metrics are used to measure how much memory or CPU capacity an application uses in five minutes, or how much latency an application experiences during a usage spike.
Observability tools also produce dependency maps that reveal how each application component depends on other components, applications and IT resources.
After gathering telemetry, the platform correlates the data in real time, providing DevOps teams, site reliability engineering (SRE) teams and IT staff complete contextual information. Teams get to understand the “what, where and why” of any event that might indicate, cause or address an application performance issue.
Many observability platforms also automatically discover new sources of telemetry as they emerge within the system, such as when a new application programming interface (API) is added to the network. Leading platforms also include artificial intelligence for operations (AIOps) capabilities that can separate the signals, which are indications of real problems, from the “noise,” which is data unrelated to current or potential issues.
Observability tools typically automate three key processes to help businesses understand their tech stacks more clearly:
Data collection
Continuous data collection makes observability possible. Observability tools facilitate the collection and aggregation of, and access to, CPU memory data, app logs, high availability numbers, average latency and other metrics.
Monitoring
Teams must be able to view app and system data with relative ease, so observability tools set up dashboards to monitor application health, any related services and any relevant business objectives.
Monitoring features also help clarify how services work with each other, by using tools like dependency graphs, and fit into the overall architecture.
Analysis
Previously, data analysis tasks were performed by using application performance management (APM) tools, which would aggregate the data collected from each data source to create digestible reports, dashboards and visualizations, similar to monitoring features in observability software.
Because modern architectures often rely on containerized microservices, observability tools often offload basic telemetry to the Kubernetes layer, enabling IT teams to focus data analysis on service-level objectives (SLOs) and service-level indicators (SLIs). Observability software compiles data from multiple sources, vets it to find what’s pertinent and delivers actionable insights back to development teams.
It's worth noting that the automation capabilities of observability software extend beyond these three processes. Observability tools can also automate debugging processes, instrumentation and monitoring dashboard updates as new services are added to the network. They manage agent handling, where agents are small software components deployed throughout an ecosystem to continuously gather telemetry data, and more.
For the past few decades, IT teams have relied primarily on APM tools to monitor and troubleshoot applications. APM, which includes—but is not limited to—application performance monitoring, periodically samples and aggregates application and system data that can help identify application performance issues.
APM analyzes the telemetry relative to key performance indicators (KPIs) and assembles the results in easy-to-read dashboards, which alert operations and support teams to any abnormal conditions causing, or threatening to cause, system performance issues.
APM tools are effective for monitoring and troubleshooting monolithic apps and traditional, distributed applications. In these configurations, new code releases occur periodically, and workflows and dependencies between application components, servers and related resources are well-known or relatively easy to trace.
Today, however, organizations are embracing digital transformation. They’re rapidly shifting toward modern development practices, such as agile development, continuous integration and continuous deployment (CI/CD), DevOps, and adopting cloud-native technologies, such as Docker containers and serverless functions.
Modern applications often rely on microservices architectures, often running within containerized Kubernetes clusters. As a result, developers can bring more services to market faster than ever.
But, in doing so, they deploy new application components throughout the architecture. These components operate in different languages and data formats and function for varying durations, sometimes only for seconds or fractions of a second, as seen with serverless functions. That means multiple runtimes, with each runtime outputting logs in different locations within the architecture.
APM's once-a-minute data sampling and traditional monitoring protocols can’t keep pace with such an immense amount of data.
Instead, businesses need the fine-grained, high-volume, automated telemetry and real-time insight generation that observability tools provide. These tools enable development teams to create and store real-time, high-fidelity, context-rich, fully correlated records of every application, user request and data transaction on the network.
The topic of observability has become central to modern DevOps, which accelerates the delivery of apps and services by combining and automating the work of software development and IT operations teams. A DevOps methodology uses shared tools and practices, and smaller, frequent updates, to make software development faster, more efficient and more reliable.
An effective DevOps strategy requires teams to identify potential performance bottlenecks and issues in the end-user experience and use observability tools to address the issue. With an observability platform, DevOps teams can quickly identify problematic components and events by using relevant data insights.
Observability platforms also empower DevOps teams with tools and observability engineering methods for better understanding their systems. These tools and methods include incident analysis to help find causes for unexpected system events and improve future incident response tactics; feature flagging to allow teams to enable and disable app functions without modifying source code; and continuous verification, which uses machine learning (ML) to analyze historical deployment data and establish a performance baseline.
High-quality observability data insights mean faster, better feedback in the software development and testing processes and a more efficient CI/CD pipeline.
Artificial intelligence is transforming observability, integrating advanced analytics, automation and predictive features into IT operations. Traditional observability offers detailed visibility into systems, but AI enhances that visibility by intelligently analyzing data to foresee and prevent issues before they occur.
AI-driven observability enables development teams to proactively protect enterprise IT infrastructure instead of solving problems as they arise. By using ML algorithms, observability tools can parse through extensive data streams to find patterns, trends and anomalies, revealing insights that a human worker might overlook.
Some AI-driven observability tools and features include:
Automated remediation
Observability tools can use AI technologies to emulate and automate human decision-making in the remediation process.
Let’s say a team is using a digital experience management (DEM) platform. Currently, these platforms use a range of remediation scripts that enable IT staff to perform one-click fixes and suggest self-service options to users.
Using continuous monitoring, AI-based observability functions can analyze incoming data to find anomalies and activities that surpass established thresholds. The observability platform can then perform a series of corrective actions, similar to remediation scripts, to address the issue.
If, for some reason, the software is unable to resolve the problem, it will automatically generate a ticket with all the pertinent details. These details include the location of the issue, its priority level and any relevant insights from the AI model in the IT team’s issue management platform.
This process enables IT staff to focus solely on the issues the software can’t handle and to resolve system performance issues as quickly as possible.
Large language models (LLMs)
LLMs excel at recognizing patterns in vast quantities of repetitive textual data, which closely resembles log and telemetry data in complex, dynamic systems. And today’s LLMs can be trained for specific IT processes—or driven by prompt engineering protocols—to return information and insights by using human language syntax and semantics.
Advancements in LLMs can help users of observability tools to write and explore queries in natural language, moving away from complex query languages. This development can significantly benefit users of all skill levels, especially people with limited technical expertise, helping them to manage complex data more effectively.
LLMs aren't yet appropriate for real-time analysis and troubleshooting, because they often lack the precision to capture complete context. However, LLMs have the advanced text processing capabilities to help simplify data insights in observability platforms.
More accessible insights enable better awareness of system behavior and better, broader understanding of IT issues and failure points.
Causal AI
Causal AI is a branch of AI that focuses on clarifying and modeling causal relationships between variables, rather than merely identifying correlations.
Traditional AI techniques, such as ML, often rely on statistical correlation to make predictions. Causal AI instead aims to find the underlying mechanisms that produce correlations, to improve predictive power and enable more targeted decision-making.
Incorporating causal AI into observability systems can significantly enhance organizations' insights into their IT environments.
Causal AI enables IT teams to analyze the relationships and interdependencies between infrastructure components, so they can better pinpoint the root causes of operational and quality issues. It empowers developers to understand not just the “when and where” of system issues but the “why,” helping teams resolve problems faster and boosting system reliability.
Full-stack observability can make a system easier to understand and monitor, easier and safer to update with new code, and easier to repair. It helps enable IT teams to:
Discover and address “unknown unknowns”
A chief limitation of monitoring tools is that they only watch for “known unknowns”—exceptional conditions that IT teams already know to watch for. Observability tools discover conditions teams might never know or think to look for and then track their relationship to specific performance issues. This insight provides greater context to help identify root causes and accelerate resolution.
Identify and resolve issues early in development
Observability integrates monitoring into the early phases of the software development process. This integration helps DevOps teams identify and fix issues in new code before they impact the customer experience or SLAs.
Improve the user experience
Observability tools enable developers to collect, analyze, correlate and discover a broad range of telemetry data to better understand user behavior and optimize the user experience.
Scale automatically
Observability tools enable teams to specify instrumentation and data aggregation in a Kubernetes cluster configuration, for instance, and start gathering telemetry from the moment it spins up, until it spins down.
Automate remediation and self-healing application infrastructure
IT teams can combine observability with AIOps, ML and automation capabilities to predict issues based on system outputs and resolve them without human intervention.
Minimize downtime and MTTR
Observability solutions accelerate the issue discovery and resolution processes. This acceleration helps teams keep app availability high, mean time to repair (MTTR) low and outages to a minimum.
Observability solutions take a holistic, cloud-native approach to application logging and monitoring. They facilitate seamless process automation and work with historical contextual data to help teams better optimize enterprise applications in a range of use cases.
- Real-time system monitoring. Observability tools provide real-time, end-to-end health and performance telemetry so that IT teams can accelerate debugging protocols and keep apps running optimally.
- Informed digital transformation and cloud migration. As companies modernize their app and cloud infrastructures, they often increase overall system complexity. Observability platforms enable teams to maintain visibility into distributed, highly dynamic multicloud and hybrid cloud environments.
- Improved business performance. Observability enables teams to find and fix issues faster, so they can spend more time focusing on other aspects of the business.
- Better DevSecOps. Observable systems enable development, security and operations teams to build more secure, resilient applications by providing continuous, real-time feedback from the software delivery lifecycle.
- AI-driven system optimization. AI-driven observability tools can work across complex, distributed systems, deploying AI algorithms to quickly analyze and correlate data. ML models within observability tools can also generate predictive analytics and forecast performance trends.
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