Distributed tracing is crucial for modern observability, offering richer context than logs. However, the volume of tracing data can be overwhelming. Sampling addresses this by selectively retaining data, with two main approaches: head sampling (deciding upfront) and tail sampling (deciding after collecting all spans). Head sampling is simpler but can miss localized issues. Tail sampling, while more accurate, is complex to implement at scale, requiring buffering, stateful processing, and potentially impacting system resilience. Furthermore, sampling inherently affects the accuracy of RED metrics (request rate, error rate, duration), necessitating metric materialization *before* sampling.
This article provides an overview of OpenTelemetry, an open-source observability framework, and guides on integrating it with Go applications. It covers key concepts like logs, metrics, and traces, and demonstrates setting up a reusable telemetry package using OpenTelemetry in Go.
Jaeger, a leading open-source distributed tracing platform, releases its version 2, which aligns with the OpenTelemetry framework, bringing improved performance, streamlined architecture, and native support for OTLP. The update includes advanced sampling techniques, expanded ecosystem access, and a more flexible storage implementation.
Jaeger, a popular open-source distributed tracing platform, has released version 2, which aligns its architecture with the OpenTelemetry Collector. This new version brings significant improvements, including native OpenTelemetry support, batched data processing, and access to OpenTelemetry features and ecosystem.
