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Steps for Contributing:

  1. Sign the CLA
  2. Make changes or write plugin (see below for details)
  3. Add your plugin to one of: plugins/{inputs,outputs,aggregators,processors}/all/all.go
  4. If your plugin requires a new Go package, add it
  5. Write a README for your plugin, if it's an input plugin, it should be structured like the input example here. Output plugins READMEs are less structured, but any information you can provide on how the data will look is appreciated. See the OpenTSDB output for a good example.
  6. Optional: Help users of your plugin by including example queries for populating dashboards. Include these sample queries in the README.md for the plugin.
  7. Optional: Write a tickscript for your plugin and add it to Kapacitor.

GoDoc

Public interfaces for inputs, outputs, processors, aggregators, metrics, and the accumulator can be found on the GoDoc

GoDoc

Sign the CLA

Before we can merge a pull request, you will need to sign the CLA, which can be found on our website

Adding a dependency

Assuming you can already build the project, run these in the telegraf directory:

  1. dep ensure -vendor-only
  2. dep ensure -add github.com/[dependency]/[new-package]

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 SampleConfig page for the latest style guidelines.
  • The Description function should say in one line what this plugin does.

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
}

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

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

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{} })
}

Input Plugin 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.

Adding Typed Metrics

In addition the the AddFields function, the accumulator also supports an AddGauge and AddCounter function. These functions are for adding typed metrics. Metric types are ignored for the InfluxDB output, but can be used for other outputs, such as prometheus.

Input Plugins Accepting Arbitrary Data Formats

Some input plugins (such as exec) accept arbitrary input data formats. An overview of these data formats can be found here.

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.

You should also add the following to your SampleConfig() return:

  ## 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"

Below is the Parser interface.

// Parser is an interface defining functions that a parser plugin must satisfy.
type Parser interface {
    // Parse takes a byte buffer separated by newlines
    // ie, `cpu.usage.idle 90\ncpu.usage.busy 10`
    // and parses it into telegraf metrics
    Parse(buf []byte) ([]telegraf.Metric, error)

    // ParseLine takes a single string metric
    // ie, "cpu.usage.idle 90"
    // and parses it into a telegraf metric.
    ParseLine(line string) (telegraf.Metric, error)
}

And you can view the code here.

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.

Their interface is quite similar to a regular plugin, with the addition of Start() and Stop() methods.

Service Plugin Guidelines

Output Plugins

This section is for developers who want to create a new output sink. Outputs are created in a similar manner as collection plugins, and their interface has similar constructs.

Output Plugin Guidelines

  • An output must conform to the telegraf.Output interface.
  • Outputs should call outputs.Add in their init function to register themselves. See below for a quick example.
  • To be available within Telegraf itself, plugins must add themselves to the github.com/influxdata/telegraf/plugins/outputs/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 SampleConfig page for the latest style guidelines.
  • The Description function should say in one line what this output does.

Output Example

package simpleoutput

// simpleoutput.go

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

type Simple struct {
    Ok bool
}

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

func (s *Simple) SampleConfig() string {
    return `
  ok = true
`
}

func (s *Simple) Connect() error {
    // Make a connection to the URL here
    return nil
}

func (s *Simple) Close() error {
    // Close connection to the URL here
    return nil
}

func (s *Simple) Write(metrics []telegraf.Metric) error {
    for _, metric := range metrics {
        // write `metric` to the output sink here
    }
    return nil
}

func init() {
    outputs.Add("simpleoutput", func() telegraf.Output { return &Simple{} })
}

Output Plugins Writing Arbitrary Data Formats

Some output plugins (such as file) can write arbitrary output data formats. An overview of these data formats can be found here.

In order to enable this, you must specify a SetSerializer(serializer serializers.Serializer) function on the plugin object (see the file plugin for an example), as well as defining serializer as a field of the object.

You can then utilize the serializer internally in your plugin, serializing data before it's written. Telegraf's configuration layer will take care of instantiating and creating the Serializer object.

You should also add the following to your SampleConfig() return:

  ## Data format to output.
  ## 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_OUTPUT.md
  data_format = "influx"

Service Output Plugins

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

Their interface is quite similar to a regular output, with the addition of Start() and Stop() methods.

Service Output Guidelines

  • Same as the Output guidelines, except that they must conform to the output.ServiceOutput interface.

Processor Plugins

This section is for developers who want to create a new processor plugin.

Processor Plugin Guidelines

  • A processor must conform to the telegraf.Processor interface.
  • Processors should call processors.Add in their init function to register themselves. See below for a quick example.
  • To be available within Telegraf itself, plugins must add themselves to the github.com/influxdata/telegraf/plugins/processors/all/all.go file.
  • The SampleConfig function should return valid toml that describes how the processor can be configured. This is include in the output of telegraf config.
  • The Description function should say in one line what this processor does.

Processor Example

package printer

// printer.go

import (
	"fmt"

	"github.com/influxdata/telegraf"
	"github.com/influxdata/telegraf/plugins/processors"
)

type Printer struct {
}

var sampleConfig = `
`

func (p *Printer) SampleConfig() string {
	return sampleConfig
}

func (p *Printer) Description() string {
	return "Print all metrics that pass through this filter."
}

func (p *Printer) Apply(in ...telegraf.Metric) []telegraf.Metric {
	for _, metric := range in {
		fmt.Println(metric.String())
	}
	return in
}

func init() {
	processors.Add("printer", func() telegraf.Processor {
		return &Printer{}
	})
}

Aggregator Plugins

This section is for developers who want to create a new aggregator plugin.

Aggregator Plugin Guidelines

  • A aggregator must conform to the telegraf.Aggregator interface.
  • Aggregators should call aggregators.Add in their init function to register themselves. See below for a quick example.
  • To be available within Telegraf itself, plugins must add themselves to the github.com/influxdata/telegraf/plugins/aggregators/all/all.go file.
  • The SampleConfig function should return valid toml that describes how the aggregator can be configured. This is include in telegraf config.
  • The Description function should say in one line what this aggregator does.
  • The Aggregator plugin will need to keep caches of metrics that have passed through it. This should be done using the builtin HashID() function of each metric.
  • When the Reset() function is called, all caches should be cleared.

Aggregator Example

package min

// min.go

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

type Min struct {
	// caches for metric fields, names, and tags
	fieldCache map[uint64]map[string]float64
	nameCache  map[uint64]string
	tagCache   map[uint64]map[string]string
}

func NewMin() telegraf.Aggregator {
	m := &Min{}
	m.Reset()
	return m
}

var sampleConfig = `
  ## period is the flush & clear interval of the aggregator.
  period = "30s"
  ## If true drop_original will drop the original metrics and
  ## only send aggregates.
  drop_original = false
`

func (m *Min) SampleConfig() string {
	return sampleConfig
}

func (m *Min) Description() string {
	return "Keep the aggregate min of each metric passing through."
}

func (m *Min) Add(in telegraf.Metric) {
	id := in.HashID()
	if _, ok := m.nameCache[id]; !ok {
		// hit an uncached metric, create caches for first time:
		m.nameCache[id] = in.Name()
		m.tagCache[id] = in.Tags()
		m.fieldCache[id] = make(map[string]float64)
		for k, v := range in.Fields() {
			if fv, ok := convert(v); ok {
				m.fieldCache[id][k] = fv
			}
		}
	} else {
		for k, v := range in.Fields() {
			if fv, ok := convert(v); ok {
				if _, ok := m.fieldCache[id][k]; !ok {
					// hit an uncached field of a cached metric
					m.fieldCache[id][k] = fv
					continue
				}
				if fv < m.fieldCache[id][k] {
                    // set new minimum
					m.fieldCache[id][k] = fv
				}
			}
		}
	}
}

func (m *Min) Push(acc telegraf.Accumulator) {
	for id, _ := range m.nameCache {
		fields := map[string]interface{}{}
		for k, v := range m.fieldCache[id] {
			fields[k+"_min"] = v
		}
		acc.AddFields(m.nameCache[id], fields, m.tagCache[id])
	}
}

func (m *Min) Reset() {
	m.fieldCache = make(map[uint64]map[string]float64)
	m.nameCache = make(map[uint64]string)
	m.tagCache = make(map[uint64]map[string]string)
}

func convert(in interface{}) (float64, bool) {
	switch v := in.(type) {
	case float64:
		return v, true
	case int64:
		return float64(v), true
	default:
		return 0, false
	}
}

func init() {
	aggregators.Add("min", func() telegraf.Aggregator {
		return NewMin()
	})
}

Unit Tests

Before opening a pull request you should run the linter checks and the short tests.

Execute linter

execute make lint

Execute short tests

execute make test

Execute integration tests

Running the integration tests requires several docker containers to be running. You can start the containers with:

make docker-run

And run the full test suite with:

make test-all

Use make docker-kill to stop the containers.