加载中
正在获取最新内容,请稍候...
正在获取最新内容,请稍候...
Explore and leverage community-contributed extensions, instrumentations, and exporters for OpenTelemetry-Go, expanding its capabilities for robust observability.
This project is a collection of community-contributed extensions for the OpenTelemetry-Go library, providing instrumentations, exporters, propagators, and other utilities to enhance observability in Go applications.
While OpenTelemetry-Go provides core APIs and SDKs, the need exists for off-the-shelf integrations and extensions for specific libraries, databases, and protocols. This collection addresses that gap, simplifying the adoption and use of OpenTelemetry in diverse Go applications.
Discover and integrate a wide range of instrumentations for popular Go libraries and frameworks.
Utilize exporters to send telemetry data to various backends and monitoring systems.
Access propagation mechanisms for tracing context across different services and protocols.
The extensions in this collection can be applied in various scenarios where robust application performance monitoring and distributed tracing are required:
Instrument an existing Go microservice to trace requests across different services and identify performance bottlenecks.
Gain end-to-end visibility into distributed transactions and improve debugging efficiency.
Add tracing spans and metrics to database calls (e.g., SQL, Redis, MongoDB) made by your application.
Monitor database query performance and understand their impact on overall request latency.
Configure an exporter to send collected telemetry data to a specific backend like Jaeger, Prometheus, or a cloud provider's monitoring service.
Centralize observability data for analysis, visualization, and alerting within your preferred monitoring platform.
You might be interested in these projects
This project provides an efficient and modular solution for automating complex workflows and data processing tasks. Built with modern technologies, it offers scalability and ease of use for developers and businesses alike.
A powerful open-source container-native workflow engine for orchestrating parallel jobs on Kubernetes. Easily define complex workflows as sequences of tasks.
TensorZero creates a feedback loop for optimizing LLM applications — turning production data into smarter, faster, and cheaper models.