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revert accidental change to sampling doc
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content/en/docs/collector/deployment/multiple-collectors.md

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@@ -20,10 +20,10 @@ report on the same data can lead to data loss or, at least, degraded data
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quality. In gateway collector deployments, applying this principle guards
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against sending inconsistent data to the backend. All metric data streams within
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OTLP must have a
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[single writer](/docs/specs/otel/metrics/data-model/#single-writer). In a system
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with multiple collectors, the single-writer principle is most relevant for
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receivers that create their own metrics, such a pull-based scrapers or a host
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metrics receiver.
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[single writer](/docs/specs/otel/metrics/data-model/#single-writer).
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In a system with multiple collectors, the single-writer principle is most
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relevant for receivers that create their own metrics, such a pull-based scrapers
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or a host metrics receiver.
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### Deployment Considerations
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content/en/docs/concepts/sampling/index.md

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SDK you will find support for sampling at the respective documentation pages:
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{{% sampling-support-list " " %}}
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### Single-Writer Principle
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When scaling collectors for distributed tracing with tail sampling, it's important to consider
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the Single-Writer Principle. This principle ensures a single logical writer makes sampling decisions for a
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particular trace, avoiding conflicts that might arise from multiple collectors evaluating the same trace for
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sampling. Techniques like using load balancing exporters in front of tail-sampling collectors can help achieve this.
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