<|fingerprint="2b3ff272bd399f4c"|>` Moving from relational to time series databases can be a significant shift in approach, requiring a change in thinking from "what objects do I need and how are they related?" to "what measurements am I taking and when?" This mental transformation is necessary as time series databases are designed for constant writes with occasional reads, not balanced read/write patterns. The performance improvements are substantial, but the trade-offs include losing rich object models, automatic SQL generation, and language-integrated queries. Time series databases offer schema flexibility without migrations, purpose-built time aggregation functions, and massive performance gains for time-based queries. They make sense when dealing with high-frequency data ingestion causing database locks, acting as a "data historian," or when patterns over time matter more than current values. Sticking with relational databases is suitable when individual records have critical business context, complex queries are needed, or data volume isn't causing performance issues. A quick test and hybrid approach can help determine the usefulness of time series databases for specific use cases.