Build a Monitoring Dashboard With QuestDB and Grafana
Use QuestDB as a data source for your Grafana dashboards and create visualizations using aggregate functions and sampling.
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.CSVfiles into QuestDB and use this as a data source for a Grafana dashboard. The dashboard will have line charts as data visualizations that make use of aggregate SQL functions and Grafana global variables for sampling data based on dashboard settings.
What Is Grafana?
Grafana is an open-source visualization tool. It consists of a server that connects to one or more data-sources to retrieve data, which is then visualized by the user in a browser.
The following three Grafana features will be used in this tutorial:
- Data source - this is how you tell Grafana where your data is stored and how you want to access it. For this tutorial, we will have a QuestDB server running, which we will access via Postgres Wire using the PostgreSQL data source plugin.
- Dashboard - A group of widgets that are displayed together on the same screen.
- Panel - A single visualization which can be a graph or table.
Once the Grafana server has started, you can access it via port 3000 (http://locahost:3000). The default login credentials are as follows:
The Docker version for QuestDB can be run exposing the port
8812 for the PostgreSQL connection and port
9000 for the web and REST interface:
Loading the Dataset
There should be two datasets available as
These can be imported via curl using the
/imp REST entry point:
Creating Your First Visualization
Create a Data Source
In Grafana, select to the cog icon to expand the Configuration menu, select Data Sources and click the Add data source button. Choose the PostgreSQL plugin and configure it with the following settings:
localhost cannot be resolved by the Grafana Docker image, the local IP address of your machine should be used for the host field, e.g.
Note that Grafana does not validate that queries are read-only. This means it's possible to run queries such as
drop table x in Grafana which would be destructive to a dataset. To protect against this, set a dedicated QuestDB instance to read-only mode by setting the property
http.security.readonly=true in your
server.conf. Details of setting this configuration can be found on Grafana's configuration page.
Create a New Dashboard and Add a Panel
Now that we have a data source and a dashboard, we can add a panel. Navigate to + Create and select Dashboard:
The new panel has a graphing area on the top half of the window and a query builder in the bottom half:
Toggle the query editor to text edit mode by clicking the pencil icon or by clicking the Edit SQL button. The query editor will now accept SQL statements that we can input directly:
Click the time range selector in above the chart and set the following date range:
- Set the From value to
- Set the To value to
- Click Apply time range
We have built our first panel with aggregations:
To graph the average trip distance above, we use the
avg() function on the
tripDistance column. This function aggregates data over the specified sampling interval. If the sampling interval is 1-hour, we are calculating the average distance traveled during each 1-hour interval. You can find more information on QuestDB aggregate functions in our documentation.
There are also two key Grafana-specific expressions used which can be identified by the
$__intervalThis is a dynamic interval based on the time range applied to the dashboard. By using this function, the sampling interval changes automatically as the user zooms in and out of the panel.
$__timeFilter(pickupDatetime)tells Grafana to send the start-time and end-time defined in the dashboard to the QuestDB server. Given the settings we have configured so far with our date range, Grafana translates this to the following:
pickupDatetime BETWEEN '2018-02-01T00:00:00Z' AND '2018-02-28T23:59:59Z'
Adding Multiple Queries
You can add multiple queries to the same panel which will display multiple lines on a graph. To demonstrate this, separate the taxi data into two series, one for cash payments and one for card payments. The first query will have a default name of
Click + Query to add a second query (automatically labeled
B) and paste the following in text mode:
We can see in this graph that the distance traveled by those paying with cards is longer than for those paying with cash. This could be due to the fact that users usually carry less cash than the balance in their card.
Let’s add another panel:
Zooming in to a single day shows more detailed data points as we are sampling by Grafana's
The daily cycle of activity is visible, with rides peaking in the early evening and reaching a low in the middle of the night.
Published at DZone with permission of Joan Augsburger. See the original article here.
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