There is no easy way to migrate or swap-in-place one provider for another. The issue with current observability and monitoring systems is that you are locked once you choose the platform to use and analyze your data. Having several open-source, commercial tools connected into such an application to drain telemetry data used to be the only way to gain insights from every backend component. Some services could be programmed using Golang, while still (at the backend), several others have Rust, Python, and even Ruby in their source code to create the smoothest performing multiservice architecture.Įxtracting performance data from scenarios like the multi-language/framework example above can be a tough task, limiting the observability of cloud-hosted (distributed) microservice application models. This allows developers to reap the best features from different languages and frameworks. Why you need OpenTelemetry collectors and exportersīuilding applications in the cloud and through connecting microservices is a prevailing trend nowadays, and with that comes polyglot applications. This article is an introduction to OpenTelemetry collectors and exporters which aims to leave you convinced of the efficacy of its methods and ready to integrate it with your projects. These are core elements of the implementation that help developers source telemetry data to feed observability tools from applications’ backends. The above is possible, thanks to OpenTelemetry’s collectors and exporters. Think of OpenTelemetry as a collecting instrument that leaves open ends for engineers to plug third-party data analysis tools to make the most of logs, traces, and metrics through visualization. Constructed from the amalgamation of OpenCensus and OpenTracing, OpenTelemetry is a suite of integrations, APIs, SDKs, and tools that generate telemetry data for consumption on a variety of backends.
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