PromQL, the querying language for Prometheus, is designed to extract and manipulate time-series data from metrics collected and stored in Prometheus's time series database. Prometheus employs three primary data types: scalars, instant vectors, and range vectors, each serving different needs for data representation and manipulation. Labels in Prometheus enhance metric flexibility by allowing users to distinguish metrics with shared names but different attributes, though they can lead to unexpectedly large query results. Prometheus supports four metric types—Counter, Gauge, Histogram, and Summary—each with distinct conventions and uses, such as monitoring cumulative values, fluctuating metrics, or distribution of values. Functions like delta, increase, and rate, alongside aggregation and binary operators, enable complex data analysis and visualization, particularly when used with tools like Grafana. Despite its strengths, PromQL's syntax can be non-intuitive and requires careful attention to detail to ensure queries align with user intentions, as illustrated by the author's personal exploration and recommendations for further reading on Prometheus's documentation.