89 lines
2.9 KiB
Markdown
89 lines
2.9 KiB
Markdown
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# Mock Data Input Plugin
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The plugin generates mock-metrics based on different algorithms like sine-wave
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functions, random numbers and more with the configured names and tags. Those
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metrics are usefull during testing (e.g. processors) or if random data is
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required.
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⭐ Telegraf v1.22.0
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🏷️ testing
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💻 all
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## Global configuration options <!-- @/docs/includes/plugin_config.md -->
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In addition to the plugin-specific configuration settings, plugins support
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additional global and plugin configuration settings. These settings are used to
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modify metrics, tags, and field or create aliases and configure ordering, etc.
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See the [CONFIGURATION.md][CONFIGURATION.md] for more details.
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[CONFIGURATION.md]: ../../../docs/CONFIGURATION.md#plugins
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## Configuration
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```toml @sample.conf
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# Generate metrics for test and demonstration purposes
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[[inputs.mock]]
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## Set the metric name to use for reporting
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metric_name = "mock"
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## Optional string key-value pairs of tags to add to all metrics
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# [inputs.mock.tags]
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# "key" = "value"
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## One or more mock data fields *must* be defined.
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# [[inputs.mock.constant]]
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# name = "constant"
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# value = value_of_any_type
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# [[inputs.mock.random]]
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# name = "rand"
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# min = 1.0
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# max = 6.0
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# [[inputs.mock.sine_wave]]
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# name = "wave"
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# amplitude = 1.0
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# period = 0.5
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# phase = 20.0
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# base_line = 0.0
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# [[inputs.mock.step]]
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# name = "plus_one"
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# start = 0.0
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# step = 1.0
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# [[inputs.mock.stock]]
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# name = "abc"
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# price = 50.00
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# volatility = 0.2
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```
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The mock plugin only requires that:
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1) Metric name is set
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2) One of the data field algorithms is defined
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## Available Algorithms
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The available algorithms for generating mock data include:
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* `constant`: generate a field with the given value of type string, float, int
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or bool
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* `random`: generate a random float, inclusive of min and max
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* `sine_wave`: produce a sine wave with a certain amplitude, period and baseline
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* `step`: always add the step value, negative values accepted
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* `stock`: generate fake, stock-like price values based on a volatility variable
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## Metrics
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Metrics are entirely based on the user's own configuration and settings.
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## Example Output
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The following example shows all available algorithms configured with an
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additional two tags as well:
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```text
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mock_sensors,building=5A,site=FTC random=4.875966794516125,abc=50,wave=0,plus_one=0 1632170840000000000
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mock_sensors,building=5A,site=FTC random=5.738651873834452,abc=45.095549448434774,wave=5.877852522924732,plus_one=1 1632170850000000000
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mock_sensors,building=5A,site=FTC random=1.0429328917205203,abc=51.928560083072924,wave=9.510565162951535,plus_one=2 1632170860000000000
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mock_sensors,building=5A,site=FTC random=5.290188595384418,abc=44.41090520217027,wave=9.510565162951536,plus_one=3 1632170870000000000
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mock_sensors,building=5A,site=FTC random=2.0724967227069135,abc=47.212167806890314,wave=5.877852522924733,plus_one=4 1632170880000000000
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```
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