# Tail Sampling with Otel + Gigapipe

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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](https://gigapipe.com) as an OpenTelemetry Receiver

[qryn](https://gigapipe.com) 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](https://gigapipe.com), organizations can benefit from a seamless pipeline where tail sampling decisions are made before storing data in [qryn](https://gigapipe.com). 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:

```yaml
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.

**Sign up for a free trial account at** [**Gigapipe**](https://gigapipe.com)**.** Bring your own OTEL Logs, Metrics and Traces to enjoy our truly polyglot observability platform.

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