derivative

README

Derivative Aggregator Plugin

The Derivative Aggregator Plugin estimates the derivative for all fields of the aggregated metrics.

Global configuration options

In addition to the plugin-specific configuration settings, plugins support additional global and plugin configuration settings. These settings are used to modify metrics, tags, and field or create aliases and configure ordering, etc. See the CONFIGURATION.md for more details.

Configuration

# Calculates a derivative for every field.
[[aggregators.derivative]]
  ## The period in which to flush the aggregator.
  period = "30s"
  ##
  ## Suffix to append for the resulting derivative field.
  # suffix = "_rate"
  ##
  ## Field to use for the quotient when computing the derivative.
  ## When using a field as the derivation parameter the name of that field will
  ## be used for the resulting derivative, e.g. *fieldname_by_parameter*.
  ## By default the timestamps of the metrics are used and the suffix is omitted.
  # variable = ""
  ##
  ## Maximum number of roll-overs in case only one measurement is found during a period.
  # max_roll_over = 10

This aggregator will estimate a derivative for each field of a metric, which is contained in both the first and last metric of the aggregation interval. Without further configuration the derivative will be calculated with respect to the time difference between these two measurements in seconds. The following formula is applied is for every field

derivative = (value_last - value_first) / (time_last - time_first)

The resulting derivative will be named <fieldname>_rate if no suffix is configured.

To calculate a derivative for every field use

[[aggregators.derivative]]
  ## Specific Derivative Aggregator Arguments:

  ## Configure a custom derivation variable. Timestamp is used if none is given.
  # variable = ""

  ## Suffix to add to the field name for the derivative name.
  # suffix = "_rate"

  ## Roll-Over last measurement to first measurement of next period
  # max_roll_over = 10

  ## General Aggregator Arguments:

  ## calculate derivative every 30 seconds
  period = "30s"

Time Derivatives

In its default configuration it determines the first and last measurement of the period. From these measurements the time difference in seconds is calculated. This time difference is than used to divide the difference of each field using the following formula:

derivative = (value_last - value_first) / (time_last - time_first)

For each field the derivative is emitted with a naming pattern <fieldname>_rate.

Custom Derivation Variable

The plugin supports to use a field of the aggregated measurements as derivation variable in the denominator. This variable is assumed to be a monotonically increasing value. In this feature the following formula is used:

derivative = (value_last - value_first) / (variable_last - variable_first)

Make sure the specified variable is not filtered and exists in the metrics passed to this aggregator!

When using a custom derivation variable, you should change the suffix of the derivative name. See the next section on customizing the derivative name for details.

Customize the Derivative Name

The derivatives generated by the aggregator are named <fieldname>_rate, i.e. they are composed of the field name and a suffix _rate. You can configure the suffix to be used by changing the suffix parameter.

Roll-Over to next Period

Calculating the derivative for a period requires at least two distinct measurements during that period. Whether those are available depends on the configuration of the aggregator period and the agent interval. By default the last measurement is used as first measurement in the next aggregation period. This enables a continuous calculation of the derivative. If within the next period an earlier timestamp is encountered this measurement will replace the roll-over metric. A main benefit of this roll-over is the ability to cope with multiple "quiet" periods, where no new measurement is pushed to the aggregator. The roll-over will take place at most max_roll_over times.

Example of Roll-Over

Let us assume we have an input plugin, that generates a measurement with a single metric "test" every 2 seconds. Let this metric increase the first 10 seconds from 0.0 to 10.0 and then decrease the next 10 seconds form 10.0 to 0.0:

timestamp	value
0	0.0
2	2.0
4	4.0
6	6.0
8	8.0
10	10.0
12	8.0
14	6.0
16	4.0
18	2.0
20	0.0

To avoid thinking about border values, we consider periods to be inclusive at the start but exclusive in the end. Using period = "10s" and max_roll_over = 0 we would get the following aggregates:

timestamp	value	aggregate	explanation
0	0.0
2	2.0
4	4.0
6	6.0
8	8.0
		1.0	(8.0 - 0.0) / (8 - 0)
10	10.0
12	8.0
14	6.0
16	4.0
18	2.0
		-1.0	(2.0 - 10.0) / (18 - 10)
20	0.0

If we now decrease the period with period = 2s, no derivative could be calculated since there would only one measurement for each period. The aggregator will emit the log messages Same first and last event for "test", skipping.. This changes, if we use max_roll_over = 1, since now end measurements of a period are taking as start for the next period.

timestamp	value	aggregate	explanation
0	0.0
2	2.0	1.0	(2.0 - 0.0) / (2 - 0)
4	4.0	1.0	(4.0 - 2.0) / (4 - 2)
6	6.0	1.0	(6.0 - 4.0) / (6 - 4)
8	8.0	1.0	(8.0 - 6.0) / (8 - 6)
10	10.0	1.0	(10.0 - 8.0) / (10 - 8)
12	8.0	-1.0	(8.0 - 10.0) / (12 - 10)
14	6.0	-1.0	(6.0 - 8.0) / (14 - 12)
16	4.0	-1.0	(4.0 - 6.0) / (16 - 14)
18	2.0	-1.0	(2.0 - 4.0) / (18 - 16)
20	0.0	-1.0	(0.0 - 2.0) / (20 - 18)

The default max_roll_over = 10 allows for multiple periods without measurements either due to configuration or missing input.

There may be a slight difference in the calculation when using max_roll_over compared to running without. To illustrate this, let us compare the derivatives for period = "7s".

timestamp	value	max_roll_over = 0	explanation	max_roll_over = 1	explanation
0	0.0
2	2.0
4	4.0
6	6.0
7		0.8571...	(6-0) / (7-0)	0.8571...	(6-0) / (7-0)
8	8.0
10	10.0
12	8.0
14	8.0	0.0	(8-8) / (14-7)	0.2857...	(8-6) / (14-7)
16	4.0
18	2.0
20	0.0
		-1.0	-1.0

The difference stems from the change of the value between periods, e.g. from 6.0 to 8.0 between first and second period. Those changes are omitted with max_roll_over = 0 but are respected with max_roll_over = 1. That there are no more differences in the calculated derivatives is due to the example data, which has constant derivatives in during the first and last period, even when including the gap between the periods. Using max_roll_over with a value greater 0 may be important, if you need to detect changes between periods, e.g. when you have very few measurements in a period or quasi-constant metrics with only occasional changes.

Tags

No tags are applied by this aggregator. Existing tags are passed throug the aggregator untouched.

Example Output

net bytes_recv=15409i,packets_recv=164i,bytes_sent=16649i,packets_sent=120i 1508843640000000000
net bytes_recv=73987i,packets_recv=364i,bytes_sent=87328i,packets_sent=452i 1508843660000000000
net bytes_recv_by_packets_recv=292.89 1508843660000000000
net packets_sent_rate=16.6,bytes_sent_rate=3533.95 1508843660000000000
net bytes_sent_by_packet=292.89 1508843660000000000

Documentation

Index

• type Derivative
  • func NewDerivative() *Derivative
  • func (d *Derivative) Add(in telegraf.Metric)
  • func (d *Derivative) Init() error
  • func (d *Derivative) Push(acc telegraf.Accumulator)
  • func (d *Derivative) Reset()
  • func (*Derivative) SampleConfig() string

Types

type Derivative

type Derivative struct {
	Variable    string          `toml:"variable"`
	Suffix      string          `toml:"suffix"`
	MaxRollOver uint            `toml:"max_roll_over"`
	Log         telegraf.Logger `toml:"-"`
	// contains filtered or unexported fields
}

func NewDerivative

func NewDerivative() *Derivative

func (*Derivative) Add

func (d *Derivative) Add(in telegraf.Metric)

func (*Derivative) Init

func (d *Derivative) Init() error

func (*Derivative) Push

func (d *Derivative) Push(acc telegraf.Accumulator)

func (*Derivative) Reset

func (d *Derivative) Reset()

func (*Derivative) SampleConfig

func (*Derivative) SampleConfig() string