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Input Plugins

This section is for developers who want to create new collection inputs. Telegraf is entirely plugin driven. This interface allows for operators to pick and chose what is gathered and makes it easy for developers to create new ways of generating metrics.

Plugin authorship is kept as simple as possible to promote people to develop and submit new inputs.

Input Plugin Guidelines

  • A plugin must conform to the telegraf.Input interface.
  • Input Plugins should call inputs.Add in their init function to register themselves. See below for a quick example.
  • Input Plugins must be added to the github.com/influxdata/telegraf/plugins/inputs/all/all.go file.
  • The SampleConfig function should return valid toml that describes how the plugin can be configured. This is included in telegraf config. Please consult the Sample Config page for the latest style guidelines.
  • The Description function should say in one line what this plugin does.
  • Follow the recommended Code Style.

Let's say you've written a plugin that emits metrics about processes on the current host.

Input Plugin Example

package simple

// simple.go

import (
    "github.com/influxdata/telegraf"
    "github.com/influxdata/telegraf/plugins/inputs"
)

type Simple struct {
    Ok  bool            `toml:"ok"`
    Log telegraf.Logger `toml:"-"`
}

func (s *Simple) Description() string {
    return "a demo plugin"
}

func (s *Simple) SampleConfig() string {
    return `
  ## Indicate if everything is fine
  ok = true
`
}

// Init is for setup, and validating config.
func (s *Simple) Init() error {
	return nil
}

func (s *Simple) Gather(acc telegraf.Accumulator) error {
    if s.Ok {
        acc.AddFields("state", map[string]interface{}{"value": "pretty good"}, nil)
    } else {
        acc.AddFields("state", map[string]interface{}{"value": "not great"}, nil)
    }

    return nil
}

func init() {
    inputs.Add("simple", func() telegraf.Input { return &Simple{} })
}

Development

  • Run make static followed by make plugin-[pluginName] to spin up a docker dev environment using docker-compose.
  • [Optional] When developing a plugin, add a dev directory with a docker-compose.yml and telegraf.conf as well as any other supporting files, where sensible.

Typed Metrics

In addition to the AddFields function, the accumulator also supports functions to add typed metrics: AddGauge, AddCounter, etc. Metric types are ignored by the InfluxDB output, but can be used for other outputs, such as prometheus.

Data Formats

Some input plugins, such as the exec plugin, can accept any supported input data formats.

In order to enable this, you must specify a SetParser(parser parsers.Parser) function on the plugin object (see the exec plugin for an example), as well as defining parser as a field of the object.

You can then utilize the parser internally in your plugin, parsing data as you see fit. Telegraf's configuration layer will take care of instantiating and creating the Parser object.

Add the following to the SampleConfig():

  ## Data format to consume.
  ## Each data format has its own unique set of configuration options, read
  ## more about them here:
  ## https://github.com/influxdata/telegraf/blob/master/docs/DATA_FORMATS_INPUT.md
  data_format = "influx"

Service Input Plugins

This section is for developers who want to create new "service" collection inputs. A service plugin differs from a regular plugin in that it operates a background service while Telegraf is running. One example would be the statsd plugin, which operates a statsd server.

Service Input Plugins are substantially more complicated than a regular plugin, as they will require threads and locks to verify data integrity. Service Input Plugins should be avoided unless there is no way to create their behavior with a regular plugin.

To create a Service Input implement the telegraf.ServiceInput interface.

Metric Tracking

Metric Tracking provides a system to be notified when metrics have been successfully written to their outputs or otherwise discarded. This allows inputs to be created that function as reliable queue consumers.

To get started with metric tracking begin by calling WithTracking on the telegraf.Accumulator. Add metrics using the AddTrackingMetricGroup function on the returned telegraf.TrackingAccumulator and store the TrackingID. The Delivered() channel will return a type with information about the final delivery status of the metric group.

Check the amqp_consumer for an example implementation.