How Time Series Databases Work—and Where They Don’t

Honeycomb's implementation as a distributed column store, rather than a time series database (TSDB), addresses limitations inherent to TSDBs, particularly in handling high-cardinality data and maintaining raw data for contextual insights. While TSDBs like Facebook Gorilla offer efficient storage and retrieval of time-stamped data through advanced compression algorithms, they fall short in providing true observability due to their reliance on pre-aggregated metrics and limited capacity to manage high-cardinality tags. Honeycomb, in contrast, optimizes for storing raw, high-cardinality data, allowing for comprehensive analysis by computing aggregates only at query time, thus preserving all original data. This design supports both metrics and tracing, enabling a more nuanced view of system behavior without the constraints of a TSDB's structure. Ultimately, Honeycomb's approach offers a more flexible and contextualized solution for observability, while acknowledging the trade-offs between the specialized efficiencies of TSDBs and the broader capabilities of distributed column stores.