![]() In general, the time series panel will look for columns that are of date/time types, or columns that look like UNIX times (seconds since the epoch). ![]() The thing to note about this data is that it has an amount field, which is the metric we want to see in our time series, and it has a payment_date, which is a datetime field we’ll want to use. In the below screenshot, we’re simply running SELECT * FROM sakila.payment. Let’s start with simple payment data from a video service, stored in MySQL. You can inspect it yourself on Grafana Play, a sample Grafana instance where we share sample dashboards and help teach people how they work. We will be building the following finished visualization. In the process, you’ll learn about what kind of data the time series panel can visualize, and how to use SQL macros in Grafana. ![]() In this article, we’re going to show you how to visualize any time series from any SQL database in Grafana using the time series visualization. Often this data can be used to enhance observability dashboards, or keep track of important application factors, like how many users have signed up for a service. Relational databases like MySQL, PostgreSQL, Oracle, and others have a wealth of time series data locked inside of them. ![]()
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