SignalFx Developers Guide

mean()

Calculates the mean of metric timeseries in the input stream

Syntax

Aggregation

mean([by=grp])

Table 1. Parameter definitions
Parameter Type Description

grp

string or list of strings. Default is None.

Optional: Names of properties to group the output by

Transformation

mean(over=duration [,cycle=type][,cycle_start=index][,shift_cycles=shift][,partial_values=partials_flag])

Table 2. Parameter definitions
Parameter Type Description

duration

Duration (number and duration units)
Durations are specified as a number followed by a single character:

  • s: seconds

  • m: minutes

  • h: hours

  • d: days

The default is None.

Optional: Duration over which to get the average of the input stream

type

One of the following:

  • "quarter"

  • "month"

  • "week"

  • "day"

  • "hour"

The default is None.

Clock or calendar duration over which to get the average of the input stream.1

index

String or number, depending on the value of cycle. Default is None.

Index within cycle=type at which the transformation starts. The default is the first position within type. Format depends on the value of type.1
NOTE: You can’t specify cycle_start=index if you specify cycle="hour".

shift

Number. Default is 0.

Shifts the cycle window a number of cycles back from the current time and day. For example, cycle="hour" and shift_cycles=1 shifts the window to the previous hour.+ NOTE: If you specify shift_cycles=shift, partial_values must be False (the default).1

partials_flag

Boolean

If True, SignalFlow emits partial results during the time period of cycle; otherwise, SignalFlow only emits a result at the end of the cycle. Default is False.
NOTE: If you specify partial_values=True, you can’t use shift_cycles.1

1 To learn more about calendar window transformations, see Calendar window transformations.

All forms return a reference to the input stream object.

Examples

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#Aggregation - mean by env
data('cpu.utilization').mean(by='env')

#Aggregation - mean by env and datacenter
data('cpu.utilization').mean(by=['env', 'datacenter'])

#Transformation - mean over 1 hour
data('cpu.utilization').mean(over='1h')

#Transformation - Average over last day, starting at 6:00 AM
#Emit values during the period
data('cpu.utilization').mean(cycle="day", cycle_start="06h", partial_values=True)

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