OpenTelemetry data

OpenTelemetry is an open-source observability framework designed to collect, process, and export telemetry data from software applications and systems. Telemetry data provides insights into how software systems are performing, where bottlenecks might exist, and how requests flow through distributed systems. OpenTelemetry simplifies the process of instrumenting code and integrates with various observability tools, enabling organizations to gain better visibility into their applications, infrastructure, and services.

OpenTelemetry is part of the Cloud Native Computing Foundation (CNCF) and is widely adopted in the context of modern, distributed, and cloud-native applications. It provides standard APIs, libraries, and SDKs for collecting telemetry data and exporting it to a wide range of backend tools, avoiding vendor lock-in and ensuring interoperability and consistency across systems.

Key components of OpenTelemetry include:

• Traces: Track the lifecycle of a request or transaction as it flows through services in a distributed system. This includes spans, which represent individual operations within a trace.
• Metrics: Quantifiable data that measures system performance, such as response times, CPU usage, or request rates
• Logs: Textual records of events that occur during the operation of applications, often used to troubleshoot issues
• Instrumentation: Libraries and tools that enable developers to collect telemetry data without manually writing extensive code
• Exporters: Modules that send collected telemetry data to observability backends for storage, visualization, and analysis

OpenTelemetry data typically includes:

• Distributed tracing in microservices: For example, a retail application consists of multiple microservices: `frontend`, `payment`, `inventory`, and `shipping`
• Monitoring system performance with metrics: For example, a cloud-based SaaS application needs to monitor key performance metrics like request latency, error rates, and resource utilization
• Logging for debugging and troubleshooting: For example, a developer needs to debug an issue where requests are intermittently failing in a web service
• Observability in Kubernetes environments: For example, a company runs containerized applications on Kubernetes and needs to monitor the health of pods and services
• Frontend and user experience monitoring: For example, a single-page web application (SPA) needs to track user interactions and performance metrics
• Observability for serverless architectures: For example, an application uses serverless functions (for example, AWS Lambda, Azure Functions) to process user requests
• Real-time alerting for service downtime: For example, an e-commerce platform needs proactive alerts when a service becomes unavailable
• Custom instrumentation for legacy applications: For example, a company has a legacy on-premises application with no built-in support for telemetry