PROCESSORS.md

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 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 processor does.

• Follow the recommended CodeStyle.

Processor Plugin 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) Init() error {
	return nil
}

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{}
	})
}

Streaming Processors

Streaming processors are a new processor type available to you. They are particularly useful to implement processor types that use background processes or goroutines to process multiple metrics at the same time. Some examples of this are the execd processor, which pipes metrics out to an external process over stdin and reads them back over stdout, and the reverse_dns processor, which does reverse dns lookups on IP addresses in fields. While both of these come with a speed cost, it would be significantly worse if you had to process one metric completely from start to finish before handling the next metric, and thus they benefit significantly from a streaming-pipe approach.

Some differences from classic Processors:

• Streaming processors must conform to the telegraf.StreamingProcessor interface.
• Processors should call processors.AddStreaming in their init function to register themselves. See below for a quick example.

Streaming 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) Init() error {
	return nil
}

func (p *Printer) Start(acc telegraf.Accumulator) error {
}

func (p *Printer) Add(metric telegraf.Metric, acc telegraf.Accumulator) error {
	// print!
	fmt.Println(metric.String())
	// pass the metric downstream, or metric.Drop() it.
	// Metric will be dropped if this function returns an error.
	acc.AddMetric(metric)

	return nil
}

func (p *Printer) Stop() error {
}

func init() {
	processors.AddStreaming("printer", func() telegraf.StreamingProcessor {
		return &Printer{}
	})
}