Addressing metric overload: a deep dive on Adaptive Metrics

Adaptive Metrics, a feature in Grafana Cloud, addresses the challenge of metric overload by aggregating unused and partially used metrics into lower cardinality versions, helping to cut costs and reduce noisy data signals. Highlighted in the podcast "Grafana's Big Tent," the feature was developed to combat the common issue of explosive growth in time series data due to teams' tendencies to collect extensive metrics without considering monitoring costs. Grafana Labs Engineering Director Mat Ryer, alongside engineers Patrick Oyarzun and Mauro Stettler, discussed how Adaptive Metrics offers recommendations for reducing cardinality by identifying unnecessary labels and allows users to implement these recommendations through a metrics ingestion aggregator. The feature's dynamic capability to adapt over time and generate new recommendations based on usage patterns distinguishes it from traditional solutions that rely on static lists of essential metrics. Additionally, the discussion touched on the potential future of Adaptive Metrics and observability, where a "just-in-time metrics" approach could enable organizations to rapidly increase data collection during incidents for thorough investigation and analysis without incurring consistently high storage costs.