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The aggregation function "reduce()" is useful if you want to aggregate (sum, count,..) foreach series. <br>
 
The aggregation function "reduce()" is useful if you want to aggregate (sum, count,..) foreach series. <br>
 
For example you have multiple Clusters with multiple Nodes in it and you want to calculate how many Nodes are in each Cluster and also calculate the sum of a "_value" field, then you can use reduce(). <br>
 
For example you have multiple Clusters with multiple Nodes in it and you want to calculate how many Nodes are in each Cluster and also calculate the sum of a "_value" field, then you can use reduce(). <br>
''I think it is more clearly when you have read the examples below.''
+
''I think it is more clearly when you have read the example below.''
  
  

Aktuelle Version vom 19. Mai 2021, 13:08 Uhr

When should I use reduce()

The aggregation function "reduce()" is useful if you want to aggregate (sum, count,..) foreach series.
For example you have multiple Clusters with multiple Nodes in it and you want to calculate how many Nodes are in each Cluster and also calculate the sum of a "_value" field, then you can use reduce().
I think it is more clearly when you have read the example below.


Important

By default, reduce() drops any columns that:

  • Are not part of the input table’s group key.
  • Are not explicitly mapped in the reduce() function.


Example

Because the raw data in my example measurement "asset_hyperv_local" has a lot of tags I use "keep()" to decrease the number of columns.

Flux

bucket = "<YOUR_BUCKET>"

from(bucket: bucket)
  |> range(start: v.timeRangeStart, stop: v.timeRangeStop)
  |> filter(fn: (r) => 
    r._measurement == "asset_hyperv_local" and
    r._field == "physicalmemory"
  )
  |> last()
  |> group(columns: ["cluster", "manufacturer"])
  |> keep(columns: ["_time", "_value", "cluster", "manufacturer", "_field"])

00-before reduce.png


Same Flux Query but with "reduce()" in action

bucket = "<YOUR_BUCKET>"

from(bucket: bucket)
  |> range(start: v.timeRangeStart, stop: v.timeRangeStop)
  |> filter(fn: (r) => 
    r._measurement == "asset_hyperv_local" and
    r._field == "physicalmemory"
  )
  |> last()
  |> group(columns: ["cluster", "manufacturer"])
  |> keep(columns: ["_time", "_value", "cluster", "manufacturer", "_field"])
  |> reduce(
    fn: (r, accumulator) => ({ 
      NumberOfNodes: accumulator.NumberOfNodes + 1.0,
      SumOfPhysicalMemory: r._value + accumulator.SumOfPhysicalMemory,
    }), 
    identity: {NumberOfNodes: 0.0, SumOfPhysicalMemory: 0.0}
  )

01-after reduce.png


Influxdata documentation: https://docs.influxdata.com/influxdb/v2.0/reference/flux/stdlib/built-in/transformations/aggregates/reduce/