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Sifter: Scalable Sampling for Distributed Tracing

Distributed tracing can be ridiculously expensive if you try to trace a hundred percent of requests. A common technique to reduce costs is to sample only a small portion of the traffic. But naive sampling techniques like uniform sampling will inevitably capture more common-case executions and might miss the more interesting edge cases. Instead, [Sifter’s approach][1] is to bias sampling decisions towards outliers and anomalous traces. This way, anomalous traces have a higher chance of being sampled, and the more uninteresting traces are discarded.

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