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Optimizing Tail Sampling in OpenTelemetry with Retroactive Sampling

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Why This Matters

This article highlights the importance of retroactive sampling in OpenTelemetry, demonstrating how it can significantly optimize data collection by reducing outbound traffic, CPU, and memory usage. These improvements are crucial for managing the high volume of distributed traces in microservices architectures, leading to more efficient and scalable observability solutions for both industry providers and consumers.

Key Takeaways

Last month, the VictoriaMetrics team gave a talk on retroactive sampling at KubeCon Europe 2026.

By writing this blog post, as a transcript of the session, we want to explain how retroactive sampling reduces outbound traffic, CPU, and memory usage in the data collection pipeline significantly compared to tail sampling in OpenTelemetry.

Benchmark Comparison (Lower is better. Details in section 3)

1. Background

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We’ll start with concepts for the beginners, but feel free to skip ahead to section 2 if you’re already familiar with these concepts - you won’t miss anything important.

1.1 Distributed Tracing

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A Trace

The diagram above shows how a trace is visualized. The concept of distributed tracing is straightforward: you have a distributed system with dozens of microservices that call each other as they process a single request. Each microservice records its work as a span, and spans are organized into a trace by the parent-child relationships.

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