mock

Mock Data Input Plugin

The plugin generates mock-metrics based on different algorithms like sine-wave functions, random numbers and more with the configured names and tags. Those metrics are usefull during testing (e.g. processors) or if random data is required.

⭐ Telegraf v1.22.0 🏷️ testing 💻 all

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
  #   phase = 20.0
  #   base_line = 0.0
  # [[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: generate a random float, inclusive of min and max
• sine_wave: produce a sine wave with a certain amplitude, period and baseline
• 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