mock

README

Mock Data Input Plugin

The mock input plugin generates random data based on a selection of different algorithms. For example, it can produce random data between a set of values, fake stock data, sine waves, and step-wise values.

Additionally, users can set the measurement name and tags used to whatever is required to mock their situation.

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

# Generate metrics for test and demonstration purposes
[[inputs.mock]]
  ## Set the metric name to use for reporting
  metric_name = "mock"

  ## Optional string key-value pairs of tags to add to all metrics
  # [inputs.mock.tags]
  # "key" = "value"

  ## One or more mock data fields *must* be defined.
  # [[inputs.mock.constant]]
  #   name = "constant"
  #   value = value_of_any_type
  # [[inputs.mock.random]]
  #   name = "rand"
  #   min = 1.0
  #   max = 6.0
  # [[inputs.mock.sine_wave]]
  #   name = "wave"
  #   amplitude = 1.0
  #   period = 0.5
  # [[inputs.mock.step]]
  #   name = "plus_one"
  #   start = 0.0
  #   step = 1.0
  # [[inputs.mock.stock]]
  #   name = "abc"
  #   price = 50.00
  #   volatility = 0.2

The mock plugin only requires that:

1. Metric name is set
2. One of the data field algorithms is defined

Available Algorithms

The available algorithms for generating mock data include:

• Constant - generate a field with the given value of type string, float, int or bool
• Random Float - generate a random float, inclusive of min and max
• Sine Wave - produce a sine wave with a certain amplitude and period
• Step - always add the step value, negative values accepted
• Stock - generate fake, stock-like price values based on a volatility variable

Metrics

Metrics are entirely based on the user's own configuration and settings.

Example Output

The following example shows all available algorithms configured with an additional two tags as well:

mock_sensors,building=5A,site=FTC random=4.875966794516125,abc=50,wave=0,plus_one=0 1632170840000000000
mock_sensors,building=5A,site=FTC random=5.738651873834452,abc=45.095549448434774,wave=5.877852522924732,plus_one=1 1632170850000000000
mock_sensors,building=5A,site=FTC random=1.0429328917205203,abc=51.928560083072924,wave=9.510565162951535,plus_one=2 1632170860000000000
mock_sensors,building=5A,site=FTC random=5.290188595384418,abc=44.41090520217027,wave=9.510565162951536,plus_one=3 1632170870000000000
mock_sensors,building=5A,site=FTC random=2.0724967227069135,abc=47.212167806890314,wave=5.877852522924733,plus_one=4 1632170880000000000

Documentation

Index

• type Mock
  • func (m *Mock) Gather(acc telegraf.Accumulator) error
  • func (m *Mock) Init() error
  • func (*Mock) SampleConfig() string

Types

type Mock

type Mock struct {
	MetricName string            `toml:"metric_name"`
	Tags       map[string]string `toml:"tags"`

	Constant []*constant `toml:"constant"`
	Random   []*random   `toml:"random"`
	Step     []*step     `toml:"step"`
	Stock    []*stock    `toml:"stock"`
	SineWave []*sineWave `toml:"sine_wave"`
	// contains filtered or unexported fields
}

func (*Mock) Gather

func (m *Mock) Gather(acc telegraf.Accumulator) error

func (*Mock) Init

func (m *Mock) Init() error

func (*Mock) SampleConfig

func (*Mock) SampleConfig() string