One of the benefits of InfluxDB is the ability to store raw events, which might come in at varying intervals, as an irregular time series. However, irregular time series present some unique challenges, and in some cases, common operations on the data simply will not work. Fortunately, InfluxDB allows you to convert an irregular time series to a regular one on the fly by calculating an aggregate of individual values for arbitrary windows of time. This gives you the best of both worlds when capturing events from your systems and working with that data.
We can take a look at a few actual data points in order to get a better understanding of what considerations need to be made when working with irregular time series. For the sake of example, we’ll use five data points, and give them values of 10, 20, 30, 40, and 50.
