Observability

As explained in Building Distributed Applications that Work

Observability is the capability to infer the internal behavior and condition of a system from its externally emitted telemetry.

Observability makes a distributed application’s behavior visible through logs, metrics, and distributed traces. When a request crosses independently running components, these signals help verify the complete workflow and diagnose failures across service boundaries. section 1.1

Following an order

A shopper’s request moves through the storefront, API, data stores, and background processing. Tracing helps follow that request across services; logs and metrics provide further evidence when processing fails or slows down. section 1.1 section 1.4

Aspire integrates with OpenTelemetry to provide tracing, logging, and metrics without extensive manual instrumentation. Its dashboard helps make system behavior visible during local development. Treat that visibility as part of the application, alongside explicit configuration and infrastructure. section 1.4 section 10.5

Figure 1.1 A shopper’s request crosses the storefront, API, data stores, and background-processing components. Telemetry makes the complete workflow observable.

Follow the shopper’s request across components to see why observing one service alone cannot explain the whole workflow. section 1.1

How Building Distributed Applications that Work covers it

How deep it goes

Identifies logs, metrics, and distributed traces and explains their diagnostic role, but does not specify instrumentation steps here.

Examples

Follows a shopper’s request through a storefront, API, data stores, and background-processing components.

What it assumes

Introduces the need for telemetry through a multi-component workflow before building on distributed-systems concepts.

What you can do after

Diagnose/debug with it by following requests across services and identifying failures or performance bottlenecks.

How it connects

Connects observability to distributed tracing, integration testing, microservice orchestration, and performance monitoring.

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Also explained in

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  Effective Platform EngineeringObservability is the ability to determine and explain a software system’s internal state and usefulness from its output data.
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  Secure APIsAPI observability is the ability to infer the health, behavior, and security-relevant activity of APIs from their telemetry.
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  Grokking AI ApplicationsThe capability to understand an application's internal behavior from its outputs, telemetry, logs, traces, and related runtime signals.
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  Cloud Observability in ActionObservability is the capability to infer and continuously discover a system’s internal state from externally available signals in order to influence that system.
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  Architecting an Apache Iceberg LakehouseObservability is the capability to infer a system's internal behavior and resource usage from its emitted telemetry and operational data.
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  Generative AI in ActionObservability is the practice of monitoring, logging, and tracing a system to understand its internal behavior and diagnose problems from its outputs and operational data.
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  Machine Learning Platform EngineeringObservability is the ability to infer the internal state and behavior of a deployed system from its outputs and telemetry, including application-specific information flows.
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  Automating API DeliveryObservability is the ability to query and analyze instrumented system behavior to determine the causes of previously unanticipated problems.
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  Troubleshooting Java, Second EditionObservability is the ability to infer a system's internal behavior from its emitted telemetry and other external outputs.
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  AI Agents in Action, Second EditionObservability is the practice of inferring and diagnosing a software system's internal state from its externally emitted telemetry.
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  Logs and TelemetryObservability is the ability to understand a system's internal state from externally emitted telemetry such as logs, metrics, and traces.
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  Digital Twins in ActionObservability is the ability to understand a system's internal condition and behavior from its externally generated outputs and operational signals.
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  Google Anthos in ActionObservability is the ability to understand a system's internal state from the outputs it produces, such as metrics, logs, and traces.
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  AI Agents in ActionObservability is the collection and analysis of execution signals that reveal an agent system's behavior, performance, and failures.
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  Bootstrapping Microservices, Second Edition: With Docker, Kubernetes, GitHub Actions, and TerraformObservability is the practice of exposing and analyzing detailed telemetry to understand a system's internal behavior from its external outputs.

Compare how 16 books explain observability →

Related concepts

• trajectoryhas part · A trajectory is an ordered sequence of states, actions, events, or operations that records or describes progression through successive steps or over time.
• monitoringhas part · Monitoring is the ongoing observation and measurement of a system’s behavior, state, or operational signals to assess its condition and detect changes or problems.
• telemetryuses · Telemetry is automatically generated or collected operational data describing a system’s activity, state, behavior, or performance.
• OpenTelemetryimplemented by · OpenTelemetry is an open, vendor-neutral observability standard and framework for instrumenting software and generating, collecting, and exporting telemetry such as traces, metrics, and logs.
• performanceuses · Performance is the measurable effectiveness or operational behavior of a system under specified conditions, assessed through relevant qualities such as task results, speed, responsiveness, throughput, or resource use.
• metricuses · A metric is a quantitative measure used to characterize or assess a property, condition, behavior, performance, or outcome.

This explainer was generated by Manning's knowledge graph from the text of Building Distributed Applications that Work. The section links point to the parts of the book it draws on.