High cardinality in time series databases refers to handling millions or billions of unique values across dimensions like device IDs, user IDs, and tenant IDs. Traditional time series databases struggle with high cardinality because they were designed for fixed metrics and predictable queries, not event-driven analytics with dynamic dimensions. CrateDB addresses this by offering SQL-based analytics without pre-aggregation, supporting real-time queries on raw events, distributing data automatically across nodes, and maintaining predictable performance as cardinality grows. The system is particularly suited for IoT telemetry, multi-tenant SaaS analytics, and operational intelligence where dimensions grow continuously and queries are exploratory rather than predefined.
Table of contents
What Is a High Cardinality Database?Why High Cardinality Breaks Traditional Time Series DatabasesWhy "Unlimited Cardinality" Is the Wrong PromiseDimensions Are the Hard Part of Time Series AnalyticsCrateDB’s Unique Strengths for High-Cardinality Time SeriesReal-World Use Cases That Depend on High CardinalityConclusionSort: