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#Grafana dashboard how to
#Grafana dashboard software

Grafana supports pretty much every data source you can think of, from traditional databases like MySQL to log aggregation platforms like AWS Cloudwatch. Your data source could be a database or a collection of logs. It also helped introduce an overhaul, after which end-users stopped complaining about loading speed.įinally, it made us happy as developers, because we could find out exactly how our changes affect the system.Configuring your data source is the first step to setting up your Grafana dashboard. Our presentation comparing app performance before/after optimization made the business department really happy (thanks for the charts, Grafana!). With a deep understanding of how data collection and presentation layers work, it’s easy to extract practical information from an application. It delivered necessary information and provided integrations with many programming languages and Kubernetes out of the box. Here, Prometheus was the best tool for us.

#Grafana dashboard software

We needed software which will help solve problems. It’s important to define goals and analyze exactly what should be measured. The optimization process is a long journey, which is hard to accomplish without additional help. Life with all this information on multiple graphs is so much easier!

With alerts, it’s easy to get information that something is wrong.

It makes it possible to prepare a business report about progress.

It provides information on actions increasing/decreasing speed and resource utilization.

“sum(app_request_execution_time_seconds_sum)/sum(app_request_execution_time_seconds_count)”Īs an open source tool, Grafana helps us visualize and understand what happens in the software: Usually, we have many pods that collect data, and we want to see and analyze data per routing.įirst, we need to use the “sum” function which will add up information from different pods: Unfortunately, in real project situations, that might be not enough. “app_request_execution_time_seconds_sum/app_request_execution_time_seconds_count”. The easiest way to display that section is to write: In our example, we are going to show execution time per request by using “sum” and “count” to prepare what we want to see.

Bucket – if we configured buckets, we can use them to approximate data.

Sum – a summary of logged information from our queries.

In our case, “app_request_execution_time_seconds” is a histogram. Remember to run any endpoint in your app first to generate a minimum amount of data. If the application hasn’t collected a particular type of data, it will not be on the list. Here, we collected “app_request_execution_time_seconds” so that we can search for this metric. Metrics – we use them to choose the data for monitoring. We are not going to show and describe all options in Grafana, just the ones that we needed to configure our charts (hey, it’s a crash course, after all!).ĭisplaying registered information on a dashboard

#Grafana dashboard how to

We are going to show an example of how to configure a dashboard, what is important, and why we did it this way. Today it’s time to show ugly metrics data in a beautiful way.Īs you know from previous articles, we collect data from Kubernetes and our application. Another important thing is that the data, which we collected with Prometheus, doesn’t have an easy-to-read format.

It will not just make pretties, but also far more useful. The true purpose of it is to present data in a way that gives us what we need, rather than just as a disorganized pile of trash. Since we have now a lot of data to show we should think about the presentation layer. In the previous three articles, we talked about why you should measure your app’s metrics, how you can integrate it with Prometheus and how to go about collecting statistics.