Assign custom states to data
This page documents an earlier version of . is the latest stable version. View this page in the documentation.
Problem
You may want to use the monitor package and take advantage of functions like monitor.stateChangesOnly(). However, monitor.stateChangesOnly() only allows you to monitor four states: “crit”, “warn”, “ok”, and “info”. What if you want to be able to assign and monitor state changes across custom states or more than four states?
Solution
Define your own custom stateChangesOnly() function. Use the function from the source code here and alter it to accommodate more than four levels. Here we account for six different levels instead of just four.
import "dict"
import "experimental"
stateChangesOnly = (tables=<-) => {
levelInts =
[
"customLevel1": 1,
"customLevel2": 2,
"customLevel3": 3,
"customLevel4": 4,
"customLevel5": 5,
"customLevel6": 6,
]
return
tables
|> map(fn: (r) => ({r with level_value: dict.get(dict: levelInts, key: r._level, default: 0)}))
|> duplicate(column: "_level", as: "____temp_level____")
|> drop(columns: ["_level"])
|> rename(columns: {"____temp_level____": "_level"})
|> sort(columns: ["_source_timestamp", "_time"], desc: false)
|> difference(columns: ["level_value"])
|> filter(fn: (r) => r.level_value != 0)
|> drop(columns: ["level_value"])
|> experimental.group(mode: "extend", columns: ["_level"])
}
Construct some example data with array.from() and map custom levels to it:
array.from(
rows: [
{_value: 0.0},
{_value: 3.0},
{_value: 5.0},
{_value: 7.0},
{_value: 7.5},
{_value: 9.0},
{_value: 11.0},
],
)
|> map(
fn: (r) =>
({r with _level:
if r._value <= 2.0 then
"customLevel2"
else if r._value <= 4.0 and r._value > 2.0 then
"customLevel3"
else if r._value <= 6.0 and r._value > 4.0 then
"customLevel4"
else if r._value <= 8.0 and r._value > 6.0 then
"customLevel5"
else
"customLevel6",
}),
)
Where the example data looks like:
| _value | _level |
|---|---|
| 0.0 | customLevel2 |
| 3.0 | customLevel3 |
| 5.0 | customLevel4 |
| 7.0 | customLevel5 |
| 7.5 | customLevel5 |
| 9.0 | customLevel6 |
| 11.0 | customLevel6 |
Now apply our custom stateChangesOnly() function:
import "array"
import "dict"
import "experimental"
stateChangesOnly = (tables=<-) => {
levelInts =
[
"customLevel1": 1,
"customLevel2": 2,
"customLevel3": 3,
"customLevel4": 4,
"customLevel5": 5,
"customLevel6": 6,
]
return
tables
|> map(fn: (r) => ({r with level_value: dict.get(dict: levelInts, key: r._level, default: 0)}))
|> duplicate(column: "_level", as: "____temp_level____")
|> drop(columns: ["_level"])
|> rename(columns: {"____temp_level____": "_level"})
|> sort(columns: ["_source_timestamp", "_time"], desc: false)
|> difference(columns: ["level_value"])
|> filter(fn: (r) => r.level_value != 0)
|> drop(columns: ["level_value"])
|> experimental.group(mode: "extend", columns: ["_level"])
}
data =
array.from(
rows: [
{_value: 0.0},
{_value: 3.0},
{_value: 5.0},
{_value: 7.0},
{_value: 7.5},
{_value: 9.0},
{_value: 11.0},
],
)
|> map(
fn: (r) =>
({r with _level:
if r._value <= 2.0 then
"customLevel2"
else if r._value <= 4.0 and r._value > 2.0 then
"customLevel3"
else if r._value <= 6.0 and r._value > 4.0 then
"customLevel4"
else if r._value <= 8.0 and r._value > 6.0 then
"customLevel5"
else
"customLevel6",
}),
)
data
|> stateChangesOnly()
This returns:
| _value | _level |
|---|---|
| 3.0 | customLevel3 |
| 5.0 | customLevel4 |
| 7.0 | customLevel5 |
| 9.0 | customLevel6 |
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