That's all the sensor data. The radiation sensor is off-line right now and how it looks on-screen. Each of those dashboard elements is created and updated via an InfluxQL query via Chronograf. So, let's look at some of the queries and what they look like in the Chronograf Dashboard Builder today. We'll start with that CO2 reading at the upper left.

To be perfectly clear: I did not have to write that query. I simply started exploring the database, clicking on measurements, tags and values, and adjusting the settings until I got the visualization I was looking for. Easy enough.

So, let's try building that same query in using InfluxDB 1.7, Chronograf 1.7, and using the new Flux Builder along with the Schema Explorer:

Wow! That was easy!

It's very, very similar. In the Flux example, we start with where we're going to get the data-the 'from bucket,' and then give it the time interval, and finally, we filtered it by the measurement and field.

Next, we add the window(every: autoInterval) to the query. Flux's window() function groups records based on a time value. Using the every: parameter along with autoInterval allows Chronograf to dynamically determine the size of the window based on the screen real-estate that is available for visualizing the results.

And here's what the Flux query looks like in the end:

from ( bucket: "telegraf/autogen" )
   |> range(start: dashboardTime)
   |> filter (fn: (r) => r._measurement == "k30_reader" and (r_field == "co2" )) 
   |> window (every: autoInterval)

Flux takes input tables, performs a function, and produces one or more output tables. So far, we've defined an input table. Now, we add the mean() aggregate function to get the mean CO2 reading within each window. Each window is returned as an output table. By adding the group() function with the none:true parameter, we remove the window partitions and combine the aggregates into a single output table.

The resulting query looks like this:

from(bucket: “telegraf/autogen”)
  |> range(start: dashboardTime)
  |> filter(fn:(r =>r._measurement == “k30_reader” and (r._field == “co2”))
  |> window(every: autoInterval)
  |> mean()
  |> group(none:true)

The original query used the template variable :dashboardTime: to allow the user to adjust the time range of the query through the user interface controls provided within Chronograf, without forcing them to re-adjust the query itself. This same functionality is supported within Flux, too. The ScriptWizard drops this into the |> range filter by default.

Now, we can visually compare the results by flipping back and forth between the InfluxQL and the Flux version of these queries to see that they indeed yield the same results!

What About FILL?

In the technical preview of Flux, the fill functionality hasn't been implemented. We understand this is an important part of how to visualize sparse data, and, rest assured, we are working to implement this in a future update.