Observability is the extent you can understand the internal state or condition of a complex system based only on knowledge of its external outputs. The more observable a system, the more quickly and accurately you can navigate from an identified performance problem to its root cause, without additional testing or coding.
Observability provides deep visibility into modern distributed applications for faster, automated problem identification and resolution.
In IT and cloud computing, observability involves using software tools and practices. These tools are for aggregating, correlating and analyzing a steady stream of performance data from a distributed application along with the hardware and network it runs on. This process helps to effectively monitor, troubleshoot and debug applications and networks. The goal is to meet customer experience expectations, service level agreements (SLAs) and other business requirements.
A relatively new IT topic, observability is often mischaracterized as an overhyped buzzword, or a “rebranding” of system monitoring, application performance monitoring (APM), and network performance management (NPM). In fact, observability is a natural evolution of APM and NPM data collection methods that better addresses the increasingly rapid, distributed and dynamic nature of cloud-native application deployments. Observability doesn’t replace monitoring—it enables better monitoring, and better APM and NPM.
The term “observability” comes from control theory, an area of engineering concerned with automating control of a dynamic system. Examples include regulating the flow of water through a pipe, or controlling the speed of an automobile over inclines and declines, based on feedback from the system.
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For the past 20 years or so, IT teams have relied primarily on APM to monitor and troubleshoot applications. APM periodically samples and aggregates application and system data, called telemetry, that's known to be related to application performance issues.
APM analyzes the telemetry relative to key performance indicators (KPIs) and assembles the results in a dashboard. These findings alert operations and support teams to abnormal conditions that need addressing to resolve or prevent issues.
APM is effective enough for monitoring and troubleshooting monolithic applications or traditional distributed applications. In these setups, new code releases occur periodically and workflows and dependencies between application components, servers and related resources are well-known or easy to trace.
Today, organizations are rapidly adopting modern development practices. These practices include agile development, continuous integration and continuous deployment (CI/CD), DevOps, multiple programming languages.
Organizations are also adopting cloud-native technologies such as microservices, Docker containers, Kubernetes and serverless functions. As a result, they're bringing more services to market faster than ever. But in the process they're deploying new application components. They do so in many places, in different languages and for widely varying periods of time, even seconds or fractions of a second, for serverless functions. APM's once-a-minute data sampling can't keep pace with this.
What's needed is higher quality telemetry–and a lot more of it–that can be used to create a high-fidelity, context-rich, fully correlated record of every application user request or transaction. Enter observability.
Observability platforms discover and collect performance telemetry continuously by integrating with existing instrumentation built into application and infrastructure components, and by providing tools to add instrumentation to these components. Observability focuses on four main telemetry types:
- Logs. Logs are granular, timestamped, 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 can use these logs to 'play back' for troubleshooting and debugging purposes.
- Metrics. Metrics(sometimes called time series metrics) are fundamental measures of application and system health over a given period of time. The metrics measure, for example, how much memory or CPU capacity an application uses over a five-minute span, or how much latency an application experiences during a spike in usage.
- Traces. Traces record the end-to-end 'journey' of every user request, from the UI or mobile app through the entire distributed architecture and back to the user.
- Dependencies(also called dependency maps) reveal how each application component depends on other components, applications and IT resources.
After gathering this telemetry, the platform correlates it in real-time. This process provides DevOps teams, site reliability engineering (SREs) teams and IT staff complete, contextual information. The teams understand the what, where and why of any event that could indicate, cause or be used to address an application performance issue.
Many observability platforms automatically discover new sources of telemetry as that might emerge within the system (such as a new API call to another software application). The platforms deal with more data than a standard APM solution. Many platforms include AIOps (artificial intelligence for operations) capabilities that sift the signals, indications of real problems, from noise (data unrelated to issues).
Observability makes a system easier to understand (in general and in great detail) and monitor, easier and safer to update with new code, and easier to repair than a less observable system. More specifically, observability directly supports the Agile/DevOps/SRE goals of delivering higher-quality software faster by enabling an organization to:
- Discover and address 'unknown unknowns'–issues you don't know exist. A chief limitation of monitoring tools is that they only watch for 'known unknowns'–exceptional conditions you already know to watch for. Observability discovers conditions you might never know or think to look for, then tracks their relationship to specific performance issues and provides the context for identifying root causes to speed resolution.
- Catch and resolve issues early in development. Observability bakes monitoring into the early phases of the software development process. DevOps teams can identify and fix issues in new code before they impact the customer experience or SLAs.
- Scale observability automatically. For example, you can specify instrumentation and data aggregation as part of a Kubernetes cluster configuration and start gathering telemetry from the moment it spins up, until it spins down.
- Enable automated remediation and self-healing application infrastructure. Combine observability with AIOps machine learning and automation capabilities to predict issues based on system outputs and resolved them without management intervention.
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