TL;DR InfluxDB Tech Tips – How to Monitor States with InfluxDB

2 minutes

In this post, we learn about monitoring states with InfluxDB. This TL;DR assumes that you already know how to create a check. If you are new to checks, please read this “TL;DR – Using Tasks and Checks for Monitoring with InfluxDB”.

Q: What ways can I monitor states with InfluxDB?
A: There are several ways to monitor states with InfluxDB. Some Flux functions for monitoring states include:

Q: Can you provide an example of how I might use monitor.stateChanges()?
A: Let’s use monitor.stateChanges() to calculate the average charging cycle of a solar battery like in the previous TL;DR. Before using monitor.StateChages, you typically create a threshold check first. In this example we create custom status with conditional logic within a task:

from(bucket: “solar”)
|> range(start: -task.every)
|> filter(fn: ® => r["_measurement"] == “battery”)
|> filter(fn: ® => r["_field"] == “kWh”)
|> derivative(unit: 3s, nonNegative: false, columns: ["_value"], timeColumn: “_time”)
|> map(fn: ® => ({
r with
_level:
if r._value > 0.0 then “CH”
else “DH”
}))

Now you want to determine when a charging cycle occurs. For example, you want to monitor when the _level changes from CH to DH.

You can do so with:

|> monitor.stateChanges(
fromLevel: “CH”,
toLevel: “DH”
)

This function will return a table with all of the times that the _level first changed to DH. Now apply the elapsed() function and mean() function to find the average charging cycle time.

Q: What if I’ve used a task to assign custom statuses, and I want to apply the monitor package functions to other columns other than the  _level column?
A: Unfortunately, this feature doesn’t exist. However, you can use the rename() function to rename your column to _level.

Note: If this approach is cumbersome, and this type of feature enhancement is important to you, please comment on this issue #3075.

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