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Telegraf Input Data Formats

Telegraf is able to parse the following input data formats into metrics:

  1. InfluxDB Line Protocol
  2. JSON
  3. Graphite
  4. Value, ie: 45 or "booyah"
  5. Nagios (exec input only)
  6. Collectd
  7. Dropwizard
  8. Grok

Telegraf metrics, like InfluxDB points, are a combination of four basic parts:

  1. Measurement Name
  2. Tags
  3. Fields
  4. Timestamp

These four parts are easily defined when using InfluxDB line-protocol as a data format. But there are other data formats that users may want to use which require more advanced configuration to create usable Telegraf metrics.

Plugins such as exec and kafka_consumer parse textual data. Up until now, these plugins were statically configured to parse just a single data format. exec mostly only supported parsing JSON, and kafka_consumer only supported data in InfluxDB line-protocol.

But now we are normalizing the parsing of various data formats across all plugins that can support it. You will be able to identify a plugin that supports different data formats by the presence of a data_format config option, for example, in the exec plugin:

[[inputs.exec]]
  ## Commands array
  commands = ["/tmp/test.sh", "/usr/bin/mycollector --foo=bar"]

  ## measurement name suffix (for separating different commands)
  name_suffix = "_mycollector"

  ## 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 = "json"

  ## Additional configuration options go here

Each data_format has an additional set of configuration options available, which I'll go over below.

Influx:

There are no additional configuration options for InfluxDB line-protocol. The metrics are parsed directly into Telegraf metrics.

Influx Configuration:

[[inputs.exec]]
  ## Commands array
  commands = ["/tmp/test.sh", "/usr/bin/mycollector --foo=bar"]

  ## measurement name suffix (for separating different commands)
  name_suffix = "_mycollector"

  ## 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"

JSON:

The JSON data format flattens JSON into metric fields. NOTE: Only numerical values are converted to fields, and they are converted into a float. strings are ignored unless specified as a tag_key (see below).

So for example, this JSON:

{
    "a": 5,
    "b": {
        "c": 6
    },
    "ignored": "I'm a string"
}

Would get translated into fields of a measurement:

myjsonmetric a=5,b_c=6

The measurement name is usually the name of the plugin, but can be overridden using the name_override config option.

JSON Configuration:

The JSON data format supports specifying "tag keys". If specified, keys will be searched for in the root-level of the JSON blob. If the key(s) exist, they will be applied as tags to the Telegraf metrics.

For example, if you had this configuration:

[[inputs.exec]]
  ## Commands array
  commands = ["/tmp/test.sh", "/usr/bin/mycollector --foo=bar"]

  ## measurement name suffix (for separating different commands)
  name_suffix = "_mycollector"

  ## 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 = "json"

  ## List of tag names to extract from top-level of JSON server response
  tag_keys = [
    "my_tag_1",
    "my_tag_2"
  ]

with this JSON output from a command:

{
    "a": 5,
    "b": {
        "c": 6
    },
    "my_tag_1": "foo"
}

Your Telegraf metrics would get tagged with "my_tag_1"

exec_mycollector,my_tag_1=foo a=5,b_c=6

If the JSON data is an array, then each element of the array is parsed with the configured settings. Each resulting metric will be output with the same timestamp.

For example, if the following configuration:

[[inputs.exec]]
  ## Commands array
  commands = ["/usr/bin/mycollector --foo=bar"]

  ## measurement name suffix (for separating different commands)
  name_suffix = "_mycollector"

  ## 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 = "json"

  ## List of tag names to extract from top-level of JSON server response
  tag_keys = [
    "my_tag_1",
    "my_tag_2"
  ]

with this JSON output from a command:

[
    {
        "a": 5,
        "b": {
            "c": 6
        },
        "my_tag_1": "foo",
        "my_tag_2": "baz"
    },
    {
        "a": 7,
        "b": {
            "c": 8
        },
        "my_tag_1": "bar",
        "my_tag_2": "baz"
    }
]

Your Telegraf metrics would get tagged with "my_tag_1" and "my_tag_2"

exec_mycollector,my_tag_1=foo,my_tag_2=baz a=5,b_c=6
exec_mycollector,my_tag_1=bar,my_tag_2=baz a=7,b_c=8

Value:

The "value" data format translates single values into Telegraf metrics. This is done by assigning a measurement name and setting a single field ("value") as the parsed metric.

Value Configuration:

You must tell Telegraf what type of metric to collect by using the data_type configuration option. Available options are:

  1. integer
  2. float or long
  3. string
  4. boolean

Note: It is also recommended that you set name_override to a measurement name that makes sense for your metric, otherwise it will just be set to the name of the plugin.

[[inputs.exec]]
  ## Commands array
  commands = ["cat /proc/sys/kernel/random/entropy_avail"]

  ## override the default metric name of "exec"
  name_override = "entropy_available"

  ## 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 = "value"
  data_type = "integer" # required

Graphite:

The Graphite data format translates graphite dot buckets directly into telegraf measurement names, with a single value field, and without any tags. By default, the separator is left as ".", but this can be changed using the "separator" argument. For more advanced options, Telegraf supports specifying "templates" to translate graphite buckets into Telegraf metrics.

Templates are of the form:

"host.mytag.mytag.measurement.measurement.field*"

Where the following keywords exist:

  1. measurement: specifies that this section of the graphite bucket corresponds to the measurement name. This can be specified multiple times.
  2. field: specifies that this section of the graphite bucket corresponds to the field name. This can be specified multiple times.
  3. measurement*: specifies that all remaining elements of the graphite bucket correspond to the measurement name.
  4. field*: specifies that all remaining elements of the graphite bucket correspond to the field name.

Any part of the template that is not a keyword is treated as a tag key. This can also be specified multiple times.

NOTE: field* cannot be used in conjunction with measurement*!

Measurement & Tag Templates:

The most basic template is to specify a single transformation to apply to all incoming metrics. So the following template:

templates = [
    "region.region.measurement*"
]

would result in the following Graphite -> Telegraf transformation.

us.west.cpu.load 100
=> cpu.load,region=us.west value=100

Multiple templates can also be specified, but these should be differentiated using filters (see below for more details)

templates = [
    "*.*.* region.region.measurement", # <- all 3-part measurements will match this one.
    "*.*.*.* region.region.host.measurement", # <- all 4-part measurements will match this one.
]

Field Templates:

The field keyword tells Telegraf to give the metric that field name. So the following template:

separator = "_"
templates = [
    "measurement.measurement.field.field.region"
]

would result in the following Graphite -> Telegraf transformation.

cpu.usage.idle.percent.eu-east 100
=> cpu_usage,region=eu-east idle_percent=100

The field key can also be derived from all remaining elements of the graphite bucket by specifying field*:

separator = "_"
templates = [
    "measurement.measurement.region.field*"
]

which would result in the following Graphite -> Telegraf transformation.

cpu.usage.eu-east.idle.percentage 100
=> cpu_usage,region=eu-east idle_percentage=100

Filter Templates:

Users can also filter the template(s) to use based on the name of the bucket, using glob matching, like so:

templates = [
    "cpu.* measurement.measurement.region",
    "mem.* measurement.measurement.host"
]

which would result in the following transformation:

cpu.load.eu-east 100
=> cpu_load,region=eu-east value=100

mem.cached.localhost 256
=> mem_cached,host=localhost value=256

Adding Tags:

Additional tags can be added to a metric that don't exist on the received metric. You can add additional tags by specifying them after the pattern. Tags have the same format as the line protocol. Multiple tags are separated by commas.

templates = [
    "measurement.measurement.field.region datacenter=1a"
]

would result in the following Graphite -> Telegraf transformation.

cpu.usage.idle.eu-east 100
=> cpu_usage,region=eu-east,datacenter=1a idle=100

There are many more options available, More details can be found here

Graphite Configuration:

[[inputs.exec]]
  ## Commands array
  commands = ["/tmp/test.sh", "/usr/bin/mycollector --foo=bar"]

  ## measurement name suffix (for separating different commands)
  name_suffix = "_mycollector"

  ## 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 = "graphite"

  ## This string will be used to join the matched values.
  separator = "_"

  ## Each template line requires a template pattern. It can have an optional
  ## filter before the template and separated by spaces. It can also have optional extra
  ## tags following the template. Multiple tags should be separated by commas and no spaces
  ## similar to the line protocol format. There can be only one default template.
  ## Templates support below format:
  ## 1. filter + template
  ## 2. filter + template + extra tag(s)
  ## 3. filter + template with field key
  ## 4. default template
  templates = [
    "*.app env.service.resource.measurement",
    "stats.* .host.measurement* region=eu-east,agent=sensu",
    "stats2.* .host.measurement.field",
    "measurement*"
  ]

Nagios:

There are no additional configuration options for Nagios line-protocol. The metrics are parsed directly into Telegraf metrics.

Note: Nagios Input Data Formats is only supported in exec input plugin.

Nagios Configuration:

[[inputs.exec]]
  ## Commands array
  commands = ["/usr/lib/nagios/plugins/check_load -w 5,6,7 -c 7,8,9"]

  ## measurement name suffix (for separating different commands)
  name_suffix = "_mycollector"

  ## 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 = "nagios"

Collectd:

The collectd format parses the collectd binary network protocol. Tags are created for host, instance, type, and type instance. All collectd values are added as float64 fields.

For more information about the binary network protocol see here.

You can control the cryptographic settings with parser options. Create an authentication file and set collectd_auth_file to the path of the file, then set the desired security level in collectd_security_level.

Additional information including client setup can be found here.

You can also change the path to the typesdb or add additional typesdb using collectd_typesdb.

Collectd Configuration:

[[inputs.socket_listener]]
  service_address = "udp://127.0.0.1:25826"
  name_prefix = "collectd_"

  ## 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 = "collectd"

  ## Authentication file for cryptographic security levels
  collectd_auth_file = "/etc/collectd/auth_file"
  ## One of none (default), sign, or encrypt
  collectd_security_level = "encrypt"
  ## Path of to TypesDB specifications
  collectd_typesdb = ["/usr/share/collectd/types.db"]

  # Multi-value plugins can be handled two ways.
  # "split" will parse and store the multi-value plugin data into separate measurements
  # "join" will parse and store the multi-value plugin as a single multi-value measurement.
  # "split" is the default behavior for backward compatability with previous versions of influxdb.
  collectd_parse_multivalue = "split"

Dropwizard:

The dropwizard format can parse the JSON representation of a single dropwizard metric registry. By default, tags are parsed from metric names as if they were actual influxdb line protocol keys (measurement<,tag_set>) which can be overriden by defining custom measurement & tag templates. All field value types are supported, string, number and boolean.

A typical JSON of a dropwizard metric registry:

{
	"version": "3.0.0",
	"counters" : {
		"measurement,tag1=green" : {
			"count" : 1
		}
	},
	"meters" : {
		"measurement" : {
			"count" : 1,
			"m15_rate" : 1.0,
			"m1_rate" : 1.0,
			"m5_rate" : 1.0,
			"mean_rate" : 1.0,
			"units" : "events/second"
		}
	},
	"gauges" : {
		"measurement" : {
			"value" : 1
		}
	},
	"histograms" : {
		"measurement" : {
			"count" : 1,
			"max" : 1.0,
			"mean" : 1.0,
			"min" : 1.0,
			"p50" : 1.0,
			"p75" : 1.0,
			"p95" : 1.0,
			"p98" : 1.0,
			"p99" : 1.0,
			"p999" : 1.0,
			"stddev" : 1.0
		}
	},
	"timers" : {
		"measurement" : {
			"count" : 1,
			"max" : 1.0,
			"mean" : 1.0,
			"min" : 1.0,
			"p50" : 1.0,
			"p75" : 1.0,
			"p95" : 1.0,
			"p98" : 1.0,
			"p99" : 1.0,
			"p999" : 1.0,
			"stddev" : 1.0,
			"m15_rate" : 1.0,
			"m1_rate" : 1.0,
			"m5_rate" : 1.0,
			"mean_rate" : 1.0,
			"duration_units" : "seconds",
			"rate_units" : "calls/second"
		}
	}
}

Would get translated into 4 different measurements:

measurement,metric_type=counter,tag1=green count=1
measurement,metric_type=meter count=1,m15_rate=1.0,m1_rate=1.0,m5_rate=1.0,mean_rate=1.0
measurement,metric_type=gauge value=1
measurement,metric_type=histogram count=1,max=1.0,mean=1.0,min=1.0,p50=1.0,p75=1.0,p95=1.0,p98=1.0,p99=1.0,p999=1.0
measurement,metric_type=timer count=1,max=1.0,mean=1.0,min=1.0,p50=1.0,p75=1.0,p95=1.0,p98=1.0,p99=1.0,p999=1.0,stddev=1.0,m15_rate=1.0,m1_rate=1.0,m5_rate=1.0,mean_rate=1.0

You may also parse a dropwizard registry from any JSON document which contains a dropwizard registry in some inner field. Eg. to parse the following JSON document:

{
	"time" : "2017-02-22T14:33:03.662+02:00",
	"tags" : {
		"tag1" : "green",
		"tag2" : "yellow"
	},
	"metrics" : {
		"counters" : 	{
			"measurement" : {
				"count" : 1
			}
		},
		"meters" : {},
		"gauges" : {},
		"histograms" : {},
		"timers" : {}
	}
}

and translate it into:

measurement,metric_type=counter,tag1=green,tag2=yellow count=1 1487766783662000000

you simply need to use the following additional configuration properties:

dropwizard_metric_registry_path = "metrics"
dropwizard_time_path = "time"
dropwizard_time_format = "2006-01-02T15:04:05Z07:00"
dropwizard_tags_path = "tags"
## tag paths per tag are supported too, eg.
#[inputs.yourinput.dropwizard_tag_paths]
#  tag1 = "tags.tag1"
#  tag2 = "tags.tag2"

For more information about the dropwizard json format see here.

Dropwizard Configuration:

[[inputs.exec]]
  ## Commands array
  commands = ["curl http://localhost:8080/sys/metrics"]
  timeout = "5s"

  ## 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 = "dropwizard"

  ## Used by the templating engine to join matched values when cardinality is > 1
  separator = "_"

  ## Each template line requires a template pattern. It can have an optional
  ## filter before the template and separated by spaces. It can also have optional extra
  ## tags following the template. Multiple tags should be separated by commas and no spaces
  ## similar to the line protocol format. There can be only one default template.
  ## Templates support below format:
  ## 1. filter + template
  ## 2. filter + template + extra tag(s)
  ## 3. filter + template with field key
  ## 4. default template
  ## By providing an empty template array, templating is disabled and measurements are parsed as influxdb line protocol keys (measurement<,tag_set>)
  templates = []

  ## You may use an appropriate [gjson path](https://github.com/tidwall/gjson#path-syntax)
  ## to locate the metric registry within the JSON document
  # dropwizard_metric_registry_path = "metrics"

  ## You may use an appropriate [gjson path](https://github.com/tidwall/gjson#path-syntax)
  ## to locate the default time of the measurements within the JSON document
  # dropwizard_time_path = "time"
  # dropwizard_time_format = "2006-01-02T15:04:05Z07:00"

  ## You may use an appropriate [gjson path](https://github.com/tidwall/gjson#path-syntax)
  ## to locate the tags map within the JSON document
  # dropwizard_tags_path = "tags"

  ## You may even use tag paths per tag
  # [inputs.exec.dropwizard_tag_paths]
  #   tag1 = "tags.tag1"
  #   tag2 = "tags.tag2"

Grok

The grok data format parses line delimited data using a regular expression like language.

The best way to get acquainted with grok patterns is to read the logstash docs, which are available here: https://www.elastic.co/guide/en/logstash/current/plugins-filters-grok.html

Grok Configuration:

[[inputs.reader]]
  ## Files to parse each interval.
  ## These accept standard unix glob matching rules, but with the addition of
  ## ** as a "super asterisk". ie:
  ##   /var/log/**.log     -> recursively find all .log files in /var/log
  ##   /var/log/*/*.log    -> find all .log files with a parent dir in /var/log
  ##   /var/log/apache.log -> only tail the apache log file
  files = ["/var/log/apache/access.log"]

  ## The dataformat to be read from files
  ## 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 = "grok"

  ## This is a list of patterns to check the given log file(s) for.
  ## Note that adding patterns here increases processing time. The most
  ## efficient configuration is to have one pattern per logparser.
  ## Other common built-in patterns are:
  ##   %{COMMON_LOG_FORMAT}   (plain apache & nginx access logs)
  ##   %{COMBINED_LOG_FORMAT} (access logs + referrer & agent)
  grok_patterns = ["%{COMBINED_LOG_FORMAT}"]

  ## Full path(s) to custom pattern files.
  grok_custom_pattern_files = []

  ## Custom patterns can also be defined here. Put one pattern per line.
  grok_custom_patterns = '''
  '''

  ## Timezone allows you to provide an override for timestamps that
  ## don't already include an offset
  ## e.g. 04/06/2016 12:41:45 data one two 5.43µs
  ##
  ## Default: "" which renders UTC
  ## Options are as follows:
  ##   1. Local             -- interpret based on machine localtime
  ##   2. "Canada/Eastern"  -- Unix TZ values like those found in https://en.wikipedia.org/wiki/List_of_tz_database_time_zones
  ##   3. UTC               -- or blank/unspecified, will return timestamp in UTC
  grok_timezone = "Canada/Eastern"

The Telegraf grok parser uses a slightly modified version of logstash "grok" patterns, with the format

%{<capture_syntax>[:<semantic_name>][:<modifier>]}

The capture_syntax defines the grok pattern that's used to parse the input line and the semantic_name is used to name the field or tag. The extension modifier controls the data type that the parsed item is converted to or other special handling.

By default all named captures are converted into string fields. Timestamp modifiers can be used to convert captures to the timestamp of the parsed metric. If no timestamp is parsed the metric will be created using the current time.

You must capture at least one field per line.

  • Available modifiers:
    • string (default if nothing is specified)
    • int
    • float
    • duration (ie, 5.23ms gets converted to int nanoseconds)
    • tag (converts the field into a tag)
    • drop (drops the field completely)
  • Timestamp modifiers:
    • ts (This will auto-learn the timestamp format)
    • ts-ansic ("Mon Jan _2 15:04:05 2006")
    • ts-unix ("Mon Jan _2 15:04:05 MST 2006")
    • ts-ruby ("Mon Jan 02 15:04:05 -0700 2006")
    • ts-rfc822 ("02 Jan 06 15:04 MST")
    • ts-rfc822z ("02 Jan 06 15:04 -0700")
    • ts-rfc850 ("Monday, 02-Jan-06 15:04:05 MST")
    • ts-rfc1123 ("Mon, 02 Jan 2006 15:04:05 MST")
    • ts-rfc1123z ("Mon, 02 Jan 2006 15:04:05 -0700")
    • ts-rfc3339 ("2006-01-02T15:04:05Z07:00")
    • ts-rfc3339nano ("2006-01-02T15:04:05.999999999Z07:00")
    • ts-httpd ("02/Jan/2006:15:04:05 -0700")
    • ts-epoch (seconds since unix epoch, may contain decimal)
    • ts-epochnano (nanoseconds since unix epoch)
    • ts-syslog ("Jan 02 15:04:05", parsed time is set to the current year)
    • ts-"CUSTOM"

CUSTOM time layouts must be within quotes and be the representation of the "reference time", which is Mon Jan 2 15:04:05 -0700 MST 2006.
To match a comma decimal point you can use a period. For example %{TIMESTAMP:timestamp:ts-"2006-01-02 15:04:05.000"} can be used to match "2018-01-02 15:04:05,000" To match a comma decimal point you can use a period in the pattern string. See https://golang.org/pkg/time/#Parse for more details.

Telegraf has many of its own built-in patterns, as well as support for most of logstash's builtin patterns. Golang regular expressions do not support lookahead or lookbehind. logstash patterns that depend on these are not supported.

If you need help building patterns to match your logs, you will find the https://grokdebug.herokuapp.com application quite useful!

Timestamp Examples

This example input and config parses a file using a custom timestamp conversion:

2017-02-21 13:10:34 value=42

[[inputs.logparser]]
  [inputs.logparser.grok]
    patterns = ['%{TIMESTAMP_ISO8601:timestamp:ts-"2006-01-02 15:04:05"} value=%{NUMBER:value:int}']

This example input and config parses a file using a timestamp in unix time:

1466004605 value=42
1466004605.123456789 value=42

[[inputs.logparser]]
  [inputs.logparser.grok]
    patterns = ['%{NUMBER:timestamp:ts-epoch} value=%{NUMBER:value:int}']

This example parses a file using a built-in conversion and a custom pattern:

Wed Apr 12 13:10:34 PST 2017 value=42

[[inputs.logparser]]
  [inputs.logparser.grok]
	patterns = ["%{TS_UNIX:timestamp:ts-unix} value=%{NUMBER:value:int}"]
    custom_patterns = '''
      TS_UNIX %{DAY} %{MONTH} %{MONTHDAY} %{HOUR}:%{MINUTE}:%{SECOND} %{TZ} %{YEAR}
    '''

For cases where the timestamp itself is without offset, the timezone config var is available to denote an offset. By default (with timezone either omit, blank or set to "UTC"), the times are processed as if in the UTC timezone. If specified as timezone = "Local", the timestamp will be processed based on the current machine timezone configuration. Lastly, if using a timezone from the list of Unix timezones, the logparser grok will attempt to offset the timestamp accordingly. See test cases for more detailed examples.

TOML Escaping

When saving patterns to the configuration file, keep in mind the different TOML string types and the escaping rules for each. These escaping rules must be applied in addition to the escaping required by the grok syntax. Using the Multi-line line literal syntax with ''' may be useful.

The following config examples will parse this input file:

|42|\uD83D\uDC2F|'telegraf'|

Since | is a special character in the grok language, we must escape it to get a literal |. With a basic TOML string, special characters such as backslash must be escaped, requiring us to escape the backslash a second time.

[[inputs.logparser]]
  [inputs.logparser.grok]
    patterns = ["\\|%{NUMBER:value:int}\\|%{UNICODE_ESCAPE:escape}\\|'%{WORD:name}'\\|"]
    custom_patterns = "UNICODE_ESCAPE (?:\\\\u[0-9A-F]{4})+"

We cannot use a literal TOML string for the pattern, because we cannot match a ' within it. However, it works well for the custom pattern.

[[inputs.logparser]]
  [inputs.logparser.grok]
    patterns = ["\\|%{NUMBER:value:int}\\|%{UNICODE_ESCAPE:escape}\\|'%{WORD:name}'\\|"]
    custom_patterns = 'UNICODE_ESCAPE (?:\\u[0-9A-F]{4})+'

A multi-line literal string allows us to encode the pattern:

[[inputs.logparser]]
  [inputs.logparser.grok]
    patterns = ['''
	  \|%{NUMBER:value:int}\|%{UNICODE_ESCAPE:escape}\|'%{WORD:name}'\|
	''']
    custom_patterns = 'UNICODE_ESCAPE (?:\\u[0-9A-F]{4})+'

Tips for creating patterns

Writing complex patterns can be difficult, here is some advice for writing a new pattern or testing a pattern developed online.

Create a file output that writes to stdout, and disable other outputs while testing. This will allow you to see the captured metrics. Keep in mind that the file output will only print once per flush_interval.

[[outputs.file]]
  files = ["stdout"]
  • Start with a file containing only a single line of your input.
  • Remove all but the first token or piece of the line.
  • Add the section of your pattern to match this piece to your configuration file.
  • Verify that the metric is parsed successfully by running Telegraf.
  • If successful, add the next token, update the pattern and retest.
  • Continue one token at a time until the entire line is successfully parsed.