Traditional metrics are shifting from the center of observability stacks to an optimization layer. While metrics remain useful for known failure modes and system-level signals like CPU and memory, they struggle with high-cardinality debugging and require pre-defining what to measure. Modern columnar databases like ClickHouse enable efficient rollups over rich, structured event data, allowing engineers to store high-fidelity logs and traces that can be aggregated on-demand. This approach moves curation from development time to investigation time, making metrics a performance optimization rather than the primary interface for understanding production systems.