Today, I'm diving into a problem I frequently encounter in operational metrics, but one that isn't discussed enough: High Cardinality Metrics. We face this situation especially in large-scale systems when we want to keep a separate metric record for each request, each connection, or each user. While this means our system provides highly detailed information, it can also turn into a monster that rapidly drains our budget. Based on my experience, I'll explain how we can manage these high cardinality metrics in three steps and how to strike that delicate balance between cost and detail level. What is High Cardinality and Why is it a Problem? First, let's clarify the concept of high cardinality. As the number of unique label combinations in a metric increases, its cardinality rises. For example, when monitoring an HTTP request, using labels like method , path , status_code is common and manageable.…