This transformation goes in two steps. First you specify one or multiple fields to group the data by. This will group all the same values of those fields together, as if you sorted them. For instance if we `Group By` the `Server ID` field, it would group the data this way:
| Time | Server ID | CPU Temperature | Server Status
All rows with the same value of `Server ID` are grouped together.
After choosing which field you want to group your data by, you can add various calculations on the other fields, and the calculation will be applied on each group of rows. For instance, we could want to calculate the average `CPU temperature` for each of those servers. So we can add the _mean_ calculation applied on the `CPU Temperature` field to get the following:
| Server ID | CPU Temperature (mean)
|-----------|--------------------------
| server 1 | 82
| server 2 | 88.6
| server 3 | 59.6
| Server ID | CPU Temperature (mean) |
| --------- | ---------------------- |
| server 1 | 82 |
| server 2 | 88.6 |
| server 3 | 59.6 |
And we can add more than one of those calculation. For instance :
- For field `Time`, we can calculate the *Last* value, to know when the last data point was received for each server
- For field `Server Status`, we can calculate the *Last* value to know what is the last state value for each server
- For field `Temperature`, we can also calculate the *Last* value to know what is the latest monitored temperature for each server
- For field `Time`, we can calculate the _Last_ value, to know when the last data point was received for each server
- For field `Server Status`, we can calculate the _Last_ value to know what is the last state value for each server
- For field `Temperature`, we can also calculate the _Last_ value to know what is the latest monitored temperature for each server
We would then get :
| Server ID | CPU Temperature (mean) | CPU Temperature (last) | Time (last) | Server Status (last)
