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SDK version v0.0.24 includes the MetadataSampler, which allows you to easily decide whether or not to log a trace based on metadata fields.

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How it works

At configuration time, you can pass a sampler parameter to the configure function. The MetadataSampler accepts any function that maps metadata Optional[dict[str, str]] to a boolean.

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Basic Usage

from typing import Optional
from atla_insights import configure
from atla_insights.sampling import MetadataSampler

def sampling_fn(metadata: Optional[dict[str, str]]) -> bool:
    if metadata is None:
        return False
    return metadata.get("feature") == "feature_1"

configure(
    token="<YOUR_ATLA_INSIGHTS_TOKEN>", 
    sampler=MetadataSampler(sampling_fn)
)

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Complete Example

Check out our full example on GitHub that demonstrates metadata-based sampling in action.

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Use Cases

Metadata sampling is particularly useful for:
  • Environment-based filtering: Different sampling rates for dev/staging/prod
  • Feature flagging: Only trace when specific features are enabled
  • User-based sampling: Focus on specific user segments or cohorts
  • Compliance: Exclude traces containing sensitive user data based on metadata flags
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