How Different Databases Handle High-Cardinality Data

High cardinality, a characteristic of modern data streams such as time-series data, IoT sensor readings, and user behavior logs, poses significant challenges for database systems due to the exponential increase in unique combinations during joins. This can lead to performance degradation, slower query execution times, or system failures. To address this issue, databases like InfluxDB and TimescaleDB employ different strategies. InfluxDB's custom-built Time Series Index (TSI) relies on a log-structured merge tree-based system, while TimescaleDB leverages the power of B-tree data structures, providing a robust foundation for handling high-cardinality data sets with superior query performance and flexibility. By understanding these approaches, organizations can make informed decisions about their data architecture to build efficient and scalable systems.