The Problem with Pre-aggregated Metrics: Part 1, the “Pre”

Pre-aggregated metrics, although efficient and easy to understand, often fall short in addressing new and unpredictable questions that arise as systems grow more complex. Initially, using default metrics, like those provided by a Cassandra cluster, may seem adequate, but they can limit insights, as they rely on predefined data, potentially obscuring root causes of issues. This approach necessitates constant updates to metrics, which can become overwhelming. Honeycomb offers a solution by preserving data for query-time aggregation, allowing for flexible analysis by grouping or filtering data based on various attributes, thus providing deeper insights and addressing the limitations of pre-aggregated metrics.