Grafana: Graph - Noise Range: Unterschied zwischen den Versionen
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| − | If you want a Grafana Graph that shows the average CPU usage as a line but also the min and max value as a area in the background, then following this site. <br> | + | If you want a Grafana Graph that shows the '''average CPU usage''' as a line but also the '''min''' and '''max''' value as a area in the background for example like the image below, then following this site. <br> |
[[Datei:01-noise range.png|1200px]] <br> | [[Datei:01-noise range.png|1200px]] <br> | ||
| + | == Example Flux Query == | ||
| + | The image above is from the following Flux query: | ||
| + | <pre> | ||
| + | //define variables | ||
| + | bucket = "telegraf" | ||
| + | data = from(bucket: bucket) | ||
| + | |> range(start: v.timeRangeStart, stop: v.timeRangeStop) | ||
| + | |> filter(fn: (r) => | ||
| + | r._measurement == "hyperv_logical_processor" and | ||
| + | r._field == "Percent_Total_Run_Time" | ||
| + | ) | ||
| + | |> group(columns: ["instance"]) | ||
| + | mean = data | ||
| + | |> aggregateWindow(every: 10m, fn: mean, createEmpty: false) | ||
| + | |> set(key: "_field", value: "mean") | ||
| + | |||
| + | min = data | ||
| + | |> aggregateWindow(every: 10m, fn: min, createEmpty: false) | ||
| + | |> set(key: "_field", value: "min") | ||
| + | |||
| + | max = data | ||
| + | |> aggregateWindow(every: 10m, fn: max, createEmpty: false) | ||
| + | |> set(key: "_field", value: "max") | ||
| + | |||
| + | union(tables: [mean, min, max]) | ||
| + | |> pivot(rowKey:["_time"], columnKey: ["_field"], valueColumn: "_value") | ||
| + | |> keep(columns: ["_time", "instance", "mean", "min", "max"]) | ||
| + | </pre> | ||
Version vom 16. September 2021, 13:25 Uhr
If you want a Grafana Graph that shows the average CPU usage as a line but also the min and max value as a area in the background for example like the image below, then following this site.
Example Flux Query
The image above is from the following Flux query:
//define variables
bucket = "telegraf"
data = from(bucket: bucket)
|> range(start: v.timeRangeStart, stop: v.timeRangeStop)
|> filter(fn: (r) =>
r._measurement == "hyperv_logical_processor" and
r._field == "Percent_Total_Run_Time"
)
|> group(columns: ["instance"])
mean = data
|> aggregateWindow(every: 10m, fn: mean, createEmpty: false)
|> set(key: "_field", value: "mean")
min = data
|> aggregateWindow(every: 10m, fn: min, createEmpty: false)
|> set(key: "_field", value: "min")
max = data
|> aggregateWindow(every: 10m, fn: max, createEmpty: false)
|> set(key: "_field", value: "max")
union(tables: [mean, min, max])
|> pivot(rowKey:["_time"], columnKey: ["_field"], valueColumn: "_value")
|> keep(columns: ["_time", "instance", "mean", "min", "max"])
