Database / InfluxDb interview questions II

What are practical retention tiers for observability data?

Common tiers are short-lived raw high-resolution data, medium-term rolled-up metrics, and long-term coarse trend archives.

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After reading the explanation, which idea is correct for: What are practical retention tiers for observability data? Use retention tiers with rollups so cost, speed, and historical insight stay balanced.

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Use one infinite retention policy for every bucket regardless of workload.

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Delete old data first and decide aggregation strategy later.

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Keep all raw high-resolution data forever to avoid downsampling complexity.

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A team asks for a practical next step. What would you recommend for: What are practical retention tiers for observability data? Disable scheduled tasks to prevent accidental data transformation.

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Define lifecycle tiers and automate downsampling tasks with clear SLO checks.

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Run ad-hoc manual rollups only when storage alerts become critical.

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Treat retention as a billing concern rather than a query design decision.

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Database / InfluxDb interview questions II

What are practical retention tiers for observability data?

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