Deprecation Notice: This feature is deprecated and will be removed in version 1.0.180.

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Considerations

Before enabling probabilistic sampler, please note the following:

  • Currently supports only traces.
  • The action will arbitrarily filter out traces regardless of their properties, potentially dropping important traces, even if they were sampled by other samplers.
  • All spans in a trace will be either entirely dropped or entirely sampled.
  • The action will be positioned after all other sampling actions in the pipeline.

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Use Cases

Cost Reduction

  • Some vendors charge based on the amount of data ingested. For self hosted destinations, the cost is correlated to the use of cloud resources which grows with the based on the amount of data you process and store. By Reducing traces volumes, you can reduce the amount of data ingested and reduce costs.

Stability

  • Performance Trend Analysis - In certain scenarios, users may seek to collect sufficient data to understand performance trends without overwhelming the system with excessive telemetry data. For example, track latency trend.
  • Telemetry Volume Management - When experiencing a sudden influx of telemetry data, probabilistic sampling can temporarily reduce data volume sent to your data destination.

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Configuration Options

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Basic Example

The following example demonstrates how to add a sampler with a 15% sampling percentage.

1

Create a YAML file with the following content:

probabilistic-sampler.yaml
apiVersion: actions.odigos.io/v1alpha1
kind: ProbabilisticSampler
metadata:
  name: example-probabilistic-sampler
  namespace: odigos-system
spec:
  actionName: "configure probabilistic_sampler"
  sampling_percentage: "15"
  signals:
    - TRACES
2

Apply the action to the cluster:

kubectl apply -f probabilistic-sampler.yaml