The last concept to grasp when thinking about time series is how aggregations work on an abstract level. One of Prometheus' core strengths is that it makes the manipulation of time series data easy, and this slicing and dicing of data usually boils down to two kinds of aggregations, which are often used together: longitudinal and cross-sectional aggregations.
In the context of time series, an aggregation is a process that reduces or summarizes the raw data, which is to say that it receives a set of data points as input and produces a smaller set (often a single element) as output. Some of the most common aggregation functions in time series databases are minimum, maximum, average, count, and sum.
To better understand how these aggregations work, let's look at some data using the example time series we presented earlier in...
