What Are Time-series Database Best Practices?

Time-series database best practices are guidelines for efficiently storing, querying, and managing time-stamped data to achieve high performance and scalability.

When to Use

Use a time-series database when dealing with continuous streams of time-ordered data. Examples include IoT sensor data, financial market prices, and application performance monitoring metrics.

Example

A DevOps team might log server CPU usage every second and query it later to analyze performance trends.

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Why Is It Important

Following best practices ensures scalability, prevents uncontrolled storage growth, and keeps queries fast. Retention policies, partitioning by time, and compression are critical to maintaining system efficiency.

Interview Tips

Explain how time-series databases optimize for high write throughput and efficient range queries. Highlight differences from relational databases and give a practical example to show interviewers you understand real-world trade-offs.

Trade-offs

A time-series DB excels in write-heavy workloads and fast time-range queries but may sacrifice complex joins and strict relational guarantees. The gain is speed; the loss is flexibility.

Pitfalls

Skipping retention policies or using too many unique tags can lead to storage bloat and degraded performance—common mistakes interviewers love to test for.