Tail Sampling with Otel + Gigapipe

In modern observability, capturing and storing every trace can quickly become impractical due to storage costs and noise from less relevant data. Tail sampling is a powerful technique that enables smarter trace retention by evaluating full traces before deciding whether to keep them. Let’s see how we can leverage this in combination the gigapipe polyglot stack.

What is Tail Sampling?

Unlike head-based sampling, which makes decisions at the start of a trace, tail sampling occurs after a trace is completed. This approach provides richer context, ensuring important traces—such as slow requests, errors, or specific customer interactions—are retained for analysis. The OpenTelemetry Collector supports tail sampling through its tailsamplingprocessor, allowing for advanced filtering and retention policies.

Leveraging qryn as an OpenTelemetry Receiver

qryn is a high-performance observability backend, acts as a native OpenTelemetry receiver, ingesting traces, logs, and metrics offering native LogQL, PromQL and Tempo compatibility.

By integrating the OpenTelemetry Collector with qryn, organizations can benefit from a seamless pipeline where tail sampling decisions are made before storing data in qryn. This setup optimizes both storage efficiency and query performance.

Configuring Tail Sampling with qryn

To enable tail sampling with qryn and OpenTelemetry, follow these key steps:

1. Deploy OpenTelemetry Collector – Ensure your collector is set up to receive traces from applications and forward them to qryn.

2. Enable the Tail Sampling Processor – Define sampling rules in your otel-collector-config.yaml, such as retaining traces based on status codes, duration, or custom attributes.

3. Export to qryn – Configure the collector to send selected traces to qryn’s OpenTelemetry-compatible API.

Example OpenTelemetry Collector Configuration:

receivers:
  otlp:
    protocols:
      grpc:
      http:

processors:
  tailsampling:
    decision_wait: 10s
    policies:
      - name: error_traces
        type: status_code
        status_code:
          status_codes: [ERROR]
      - name: long_traces
        type: latency
        latency:
          threshold_ms: 1000

exporters:
  otlphttp:
    endpoint: "http://qryn-gigapipe/api/v1/traces"

service:
  pipelines:
    traces:
      receivers: [otlp]
      processors: [tailsampling]
      exporters: [otlphttp]

Benefits of Tail Sampling

• Reduced Storage Costs – By retaining only high-value traces, organizations can significantly cut down on observability storage expenses.

• Improved Query Performance – Less noise in the dataset leads to faster and more meaningful trace analysis.

• Enhanced Decision-Making – Tail sampling enables intelligent data retention, keeping critical issues and performance bottlenecks visible.

It’s that simple.

By combining OpenTelemetry’s tail sampling capabilities with qryn’s scalable and efficient backend, teams can fine-tune their observability pipelines for optimal performance and cost-effectiveness. Implementing tail sampling ensures that only the most relevant traces are retained, enabling deeper insights and better troubleshooting without unnecessary data overload.

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