grok_exporter Configuration

This page describes the grok_exporter configuration file. The configuration is in YAML format. An example configuration can be found in example/config.yml. The path to the configuration file is passed as a command line parameter when starting grok_exporter:

grok_exporter -config ./example/config.yml

Updating from Config Version 2

Configuration files are versioned. The config_version is specified in the global section of the configuration file, see global Section below.

The versions allow for backwards compatibility: If an incompatible change in the config format is introduced, we increment the version number. New grok_exporter versions support old configuration versions.

grok_exporter 1.0.0.RC3 introduced configuration version 3. While version 2 is still supported, we recommend updating to version 3. You can use the -showconfig command line option to convert the configuration automatically and print the result to the console:

grok_exporter -config /path/to/config_v2.yml -showconfig

The main difference between configuration version 2 and configuration version 3 is that the new version introduces an imports section for importing grok_patterns and metrics from external configuration files. The old grok section was renamed to grok_patterns and now contains just a plain list of grok patterns. There's also a minor change: poll_interval_seconds was renamed to poll_interval. The format is described in How to Configure Durations below.

The old documentation for configuration version 2 can be found in CONFIG_v2.md.

Overall Structure

The grok_exporter configuration file consists of six main sections:

global:
    # Config version
input:
    # How to read log lines (file or stdin).
imports:
    # External configuration files for grok patterns and for metrics.
grok_patterns:
    # Grok patterns.
metrics:
    # How to map Grok fields to Prometheus metrics.
server:
    # How to expose the metrics via HTTP(S).

The following shows the configuration options for each of these sections.

global Section

The global section is as follows:

global:
    config_version: 3
    retention_check_interval: 53s

The config_version specifies the version of the config file format. Specifying the config_version is mandatory, it has to be included in every configuration file. The current config_version is 3.

The config file format is versioned independently of the grok_exporter program. When a new version of grok_exporter does not introduce incompatible changes to the config file, the config_version will remain the same.

The following table shows which grok_exporter version uses which config_version:

grok_exporter	config_version
≤ 0.1.4	1
0.2.X, 1.0.0.RC1, 1.0.0.RC2	2
≥ 1.0.0.RC3	3

The retention_check_interval is the interval at which grok_exporter checks for expired metrics. By default, metrics don't expire so this is relevant only if retention is configured explicitly with a metric. The retention_check_interval is optional, the value defaults to 53s. The default value is reasonable for production and should not be changed. This property is intended to be used in tests, where you might not want to wait 53 seconds until an expired metric is cleaned up. The format is described in How to Configure Durations below.

Input Section

grok_exporter supports three input types: file, stdin, and webhook. The following three sections describe the input types respectively:

File Input Type

The configuration for the file input type is as follows:

Example 1:

input:
    type: file
    path: /var/logdir1/*.log
    readall: false
    fail_on_missing_logfile: true
    poll_interval: 5s # should NOT be needed in most cases, see below

Example 2:

input:
    type: file
    paths:
    - /var/logdir1/*.log
    - /var/logdir2/*.log
    readall: false
    fail_on_missing_logfile: true
    poll_interval: 5s # should NOT be needed in most cases, see below

The path is the path to the log file. path is used if you want to monitor a single path. If you want to monitor a list of paths, use paths instead, as in example 2 above. Glob patterns are supported on the file level, but not on the directory level. If you want to monitor multiple logfiles, see also restricting a metric to specific log files and pre-defined label variables below.

The readall flag defines if grok_exporter starts reading from the beginning or the end of the file. True means we read the whole file, false means we start at the end of the file and read only new lines. True is good for debugging, because we process all available log lines. False is good for production, because we avoid to process lines multiple times when grok_exporter is restarted. The default value for readall is false.

If fail_on_missing_logfile is true, grok_exporter will not start if the path is not found. This is the default value, and it should be used in most cases because a missing logfile is likely a configuration error. However, in some scenarios you might want grok_exporter to start successfully even if the logfile is not found, because you know the file will be created later. In that case, set fail_on_missing_logfile: false.

On poll_interval: You probably don't need this. The internal implementation of grok_exporter's file input is based on the operating system's file system notification mechanism, which is inotify on Linux, kevent on BSD (or macOS), and ReadDirectoryChangesW on Windows. These tools will inform grok_exporter as soon as a new log line is written to the log file, and let grok_exporter sleep as long as the log file doesn't change. There is no need for configuring a poll interval. However, there is one combination where the above notifications don't work: If the logging application keeps the logfile open and the underlying file system is NTFS (see #17). For this specific case you can configure a poll_interval. This will disable file system notifications and instead check the log file periodically. The format is described in How to Configure Durations below.

Stdin Input Type

The configuration for the stdin input type does not have any additional parameters:

input:
    type: stdin

This is useful if you want to pipe log data to the grok_exporter command. For example if you want to monitor the output of journalctl:

journalctl -f | grok_exporter -config config.yml

Note that grok_exporter terminates as soon as it finishes reading from stdin. That means, if you run cat sample.log | grok_exporter -config config.yml, the exporter will terminate as soon as sample.log is processed. In order to keep grok_exporter running, always use a command that keeps the output open, like tail -f -n +1 sample.log | grok_exporter -config config.yml.

Webhook Input Type

The grok_exporter is capable of receive log entries from webhook sources. It supports webhook reception in various formats... plain-text or JSON, single entries or bulk entries.

The following input configuration example which demonstrates how to configure grok_exporter to receive HTTP webhooks from the Logstash HTTP Output Plugin configured in json_batch mode, which allows the transmission of multiple json log entries in a single webhook.

input:

    type: webhook

    # HTTP Path to POST the webhook
    # Default is `/webhook`
    webhook_path: /webhook

    # HTTP Body POST Format
    # text_single: Webhook POST body is a single plain text log entry
    # text_bulk: Webhook POST body contains multiple plain text log entries
    #   separated by webhook_text_bulk_separator (default: \n\n)
    # json_single: Webhook POST body is a single json log entry.  Log entry
    #   text is selected from the value of a json key determined by
    #   webhook_json_selector.
    # json_bulk: Webhook POST body contains multiple json log entries.  The
    #   POST body envelope must be a json array "[ <entry>, <entry> ]".  Log
    #   entry text is selected from the value of a json key determined by
    #   webhook_json_selector.
    # json_lines: Webhook POST body contains multiple json log entries, with
    #   newline-separated log lines holding an individual json object. JSON
    #   object itself may not contain newlines. For example:
    #   example:
    #       { app="foo", stage="prod", log="example log message" }
    #       { app="bar", stage="dev", log="another line" }
    #   Log entry text is selected from the value of a json key determined
    #   by webhook_json_selector. 
    # Default is `text_single`
    webhook_format: json_bulk

    # JSON Path Selector
    # Within an json log entry, text is selected from the value of this json selector
    #   Example ".path.to.element"
    # Default is `.message`
    webhook_json_selector: .message

    # Bulk Text Separator
    # Separator for text_bulk log entries
    # Default is `\n\n`
    webhook_text_bulk_separator: "\n\n"

This configuration example may be found in the examples directory here.

Kafka Input Type

The grok_exporter is also capable of consuming log entries from Kafka. Currently, only plain-text encoded messages are supported.

input:
  type: kafka
  # Version corresponding to the kafka cluster
  kafka_version: 2.1.0

  # The list of the Kafka brokers part of the Kafka cluster.  Please note that you need an instance of grok_exporter per Kafka cluster if you plan on consuming from topics from multiple clusters.
  kafka_brokers:
    - localhost:9092

  # The list of Kafka topics to consume from.
  kafka_topics: 
    - grok_exporter_test

  # The assignor to use, which can be either range, roundrobin, sticky (range by default)
  kafka_partition_assignor: range

  # The name of the consumer group to register as on the broker.  If not specified, the default is 'grok_exporter'
  kafka_consumer_group_name: grok_exporter

  # Indicates if the exporter should start consuming as of the most recent messages in the topic (true), or consume from the earliest messages in the topic (false).
  kafka_consume_from_oldest: false

This configuration example may be found in the examples directory here.

imports Section

The imports section is used to load grok_patterns and metrics from external configuration files. This is optional, the configuration can be defined directly in the configuration file, as described in the grok_patterns Section and the metrics Section below.

Example:

imports:
- type: grok_patterns
  dir: ./logstash-patterns-core/patterns
- type: metrics
  file: /etc/grok_exporter/metrics.d/*.yaml
  defaults:
    path: /var/log/syslog/*
    retention: 2h30m0s
    buckets: [0, 1, 2, 3]
    quantiles: {0.5: 0.05, 0.9: 0.02, 0.99: 0.002}
    labels:
      logfile: '{{base .logfile}}'

The type can either be grok_patterns or metrics. Each import can either specify a file or a dir. The file is either a path to a config file, or a Glob pattern matching multiple config files. The dir is a directory, all files in that directory will be imported.

grok_patterns import type

The grok_exporter releases contain a patterns/ directory with the pre-defined grok patterns from github.com/logstash-patterns-core. If you want to use them, configure an import for this directory. See the grok_patterns Section below for more information on the grok_patterns.

metrics import type

The external metrics configuration files are YAML files containing a list of metrics definitions. The contents is the same as in the metrics Section.

When importing metrics from external files, you can specify some default values in the imports section. If an imported metric configuration does not contain that value, the default from the imports is used. You can specify defaults for the following values:

path, paths
retention
buckets
quantiles
labels (will be merged with the labels defined in the imported metrics)

The meaning of these values is defined in the metrics Section below.

grok_patterns Section

As described in the metrics Section below, each metric uses a regular expression to match log lines. Regular expressions quickly become complex and hard to read. Grok patterns are a way to break down regular expression into smaller snippets to improve readability.

The grok_patterns section configures these Grok patterns as a list of name regular-expression-snippet pairs. The regular expression snippets may themselves reference Grok patterns with the %{name} syntax.

An example of a grok_patterns section is as follows:

grok_patterns:
  - 'EXIM_MESSAGE [a-zA-Z ]*'
  - 'EXIM_SENDER_ADDRESS F=<%{EMAILADDRESS}>'

See the metrics Section below for more examples of Grok patterns and how to use them.

The grok_patterns section is optional. If you want to use plain regular expressions, you don't need to define Grok patterns.

The grok_exporter distribution includes a directory of pre-defined Grok patterns. These are taken from github.com/logstash-patterns-core. This directory can be imported as defined in the imports Section above.

Metrics Section

Metric Types Overview

The metrics section contains a list of metric definitions, specifying how log lines are mapped to Prometheus metrics. Four metric types are supported:

Counter
Gauge
Histogram
Summary

Example Log Lines

To exemplify the different metrics configurations, we use the following example log lines:

30.07.2016 14:37:03 alice 1.5
30.07.2016 14:37:33 alice 2.5
30.07.2016 14:43:02 bob 2.5
30.07.2016 14:45:59 alice 2.5

The following is a simple counter counting the number of log lines containing alice.

metrics:
    - type: counter
      name: alice_occurrences_total
      help: number of log lines containing alice
      match: 'alice'
      labels:
          logfile: '{{base .logfile}}'

Match

The match is a regular expression. In the simple example above, alice is a regular expression matching the string alice.

Regular expressions quickly become hard to read. Grok patterns are pre-defined regular expression snippets that you can use in your match patterns. For example, a complete match pattern for the log lines above looks like this:

%{DATE} %{TIME} %{USER} %{NUMBER}

The actual regular expression snippets referenced by DATE, TIME, USER, and NUMBER are defined in github.com/logstash-patterns-core.

Labels

One of the main features of Prometheus is its multi-dimensional data model: A Prometheus metric can be further partitioned using labels.

In order to define a label, you need two steps. First: Define a Grok field name in the match: pattern. Second: Add label template under labels:.

Define Grok field names. In Grok, each field, like %{USER}, can be given a name, like %{USER:user}. The name user can then be used in label templates.
Define label templates. Each metric type supports labels, which is a map of name/template pairs. The name will be used in Prometheus as the label name. The template is a Go template that may contain references to Grok fields, like {{.user}}.

Example: In order to define a label user for the example log lines above, use the following fragment:

match: '%{DATE} %{TIME} %{USER:user} %{NUMBER:val}'
labels:
    user: '{{.user}}'

The match stores whatever matches the %{USER} pattern under the Grok field name user. The label defines a Prometheus label user with the value of the Grok field user as its content.

This simple example shows a one-to-one mapping of a Grok field to a Prometheus label. However, the label definition is pretty flexible: You can combine multiple Grok fields in one label, and you can define constant labels that don't use Grok fields at all.

Pre-Defined Label Variables

Two pre-defined label variables, that are independent of Grok patterns are defined, namely:

logfile: Which contains the full path of the log file the line was read from (for input type file).
extra: Which contains the entire JSON object parsed from the input (for input type webhook, with format=json_*).

logfile

The logfile variable is always present for input type file, and contains the full path to the log file the line was read from. You can use it like this:

match: '%{DATE} %{TIME} %{USER:user} %{NUMBER:val}'
labels:
    user: '{{.user}}'
    logfile: '{{.logfile}}'

If you don't want the full path but only the file name, you can use the base template function, see next section.

extra

The extra variable is always present for input type webhook with format being either json_single, json_lines or json_bulk. It contains the entire JSON object that was parsed. You can use it like this:

match: 'Login occured'
labels:
    user: '{{ index .extra "user" }}'
    ip: '{{ index .extra "ip" }}'

With the incoming log object being:

{"message": "Login occured", "user": "Skeen", "ip": "1.1.1.1"}'

Label Template Functions

Label values are defined as Go templates. grok_exporter supports the following template functions: gsub, base, add, subtract, multiply, divide.

For example, let's assume we have the match from above:

match: '%{DATE} %{TIME} %{USER:user} %{NUMBER:val}'

We apply this pattern to the first log line of our example:

30.07.2016 14:37:03 alice 1.5

This results in three variables that are available for defining labels:

user has the value alice
val has the value 1.5
logfile has the value /tmp/example/example.log, if that's where the logfile was located.

The following examples show how to use these fields as label values using the Go template language:

'{{.user}}' -> alice
'user {{.user}} with number {{.val}}.' -> user alice with number 1.5.
'{{gsub .user "ali" "beatri"}}' -> beatrice
'{{multiply .val 1000}}' -> 1500
'{{if eq .user "alice"}}1{{else}}0{{end}}' -> 1
'{{base .logfile}}' -> example.log
'{{gsub .logfile "/tmp/" ""}} -> example/example.log

The syntax of the gsub function is {{gsub input pattern replacement}}. The pattern and replacement are is similar to Elastic's mutate filter's gsub (derived from Ruby's String.gsub()), except that you need to double-escape backslashes (\\ instead of \). A more complex example (including capture groups) can be found in this comment.

The arithmetic functions add, subtract, multiply, and divide are straightforward. These functions may not be useful for label values, but they can be useful as the value: in gauge, histogram, or summary metrics. For example, they could be used to convert milliseconds to seconds.

Conditionals like '{{if eq .user "alice"}}1{{else}}0{{end}} are described in the Go template documentation. For example, they can be used to define boolean metrics, i.e. gauge metrics with a value of 1 or 0. Another example can be found in this comment.

The base function is like Golang's path.Base(). If you want something other than either the full path or the file name, use gsub.

Restricting a Metric to Specific Log Files

In the input section above, we showed that you can monitor multiple logfiles. By default, all metrics are applied to all log files. If you want to restrict a metric to specific log files, you can specify either a path or a list of paths:

- type: counter
  name: alice_occurrences_total
  help: number of log lines containing alice
  match: 'alice'
  paths: /tmp/example/*.log
  labels:
      logfile: '{{base .logfile}}'

In the example, the alice_occurrences_total would only be applied to files matching /tmp/example/*.log and not to other files. If you have only one single path, you can use path as an alternative to paths. Note that path and paths are Glob patterns, which is not the same as Grok patterns or regular expressions.

Expiring Old Labels

By default, metrics are kept forever. However, sometimes you might want metrics with old labels to expire. There are two ways to do this in grok_exporter:

`delete_labels`

As of version 0.2.2, grok_exporter supports delete_match and delete_labels configuration:

delete_match: '%{DATE} %{TIME} %{USER:user} logged out'
delete_labels:
    user: '{{.user}}'

Without delete_match and delete_labels, all labels are kept forever (until grok_exporter is restarted). However, it might sometimes be desirable to explicitly remove metrics with specific labels. For example, if a service shuts down, it might be desirable to remove metrics labeled with that service name.

Using delete_match you can define a regular expression that will trigger removal of metrics. For example, delete_match could match a shutdown message in a log file.

Using delete_labels you can restrict which labels are deleted if a line matches delete_match. If no delete_labels are specified, all labels for the given metric are deleted. If delete_labels are specified, only those metrics are deleted where the label values are equal to the delete label values.

`retention`

As of version 0.2.3, grok_exporter supports retention configuration for metrics:

metrics:
    - type: ...
      name: retention_example
      help: ...
      match: ...
      labels:
          ...
      retention: 2h30m

The example above means that if label values for the metrics named retention_example have not been observed for 2 hours and 30 minutes, the retention_example metrics with these label values will be removed. For the format of the retention value, see How to Configure Durations below. Note that grok_exporter checks the retention every 53 seconds by default, so it may take 53 seconds until the metric is actually removed after the retention time is reached, see retention_check_interval above.

Counter Metric Type

The counter metric counts the number of matching log lines.

metrics:
    - type: counter
      name: grok_example_lines_total
      help: Example counter metric with labels.
      match: '%{DATE} %{TIME} %{USER:user} %{NUMBER:val}'
      value: '{{.val}}'
      labels:
          user: '{{.user}}'

The configuration is as follows:

type is counter.
name is the name of the metric. Metric names are described in the [Prometheus data model documentation].
help is a comment describing the metric.
match is the Grok expression. See the Grok documentation for more info.
value is an optional Go template for the value to be monitored. The template must evaluate to a valid positive number. The template may use to Grok fields from the match patterns, like the label templates described above.
labels is an optional map of name/template pairs, as described above.

Output for the example log lines above:

# HELP grok_example_lines_total Example counter metric with labels.
# TYPE grok_example_lines_total counter
grok_example_lines_total{user="alice"} 3
grok_example_lines_total{user="bob"} 1

Gauge Metric Type

The gauge metric is used to monitor values that are logged with each matching log line.

metrics:
    - type: gauge
      name: grok_example_values
      help: Example gauge metric with labels.
      match: '%{DATE} %{TIME} %{USER:user} %{NUMBER:val}'
      value: '{{.val}}'
      cumulative: false
      labels:
          user: '{{.user}}'

The configuration is as follows:

type is gauge.
name, help, match, and labels have the same meaning as for counter metrics.
value is a Go template for the value to be monitored. The template must evaluate to a valid number. The template may use to Grok fields from the match patterns, like the label templates described above.
cumulative is optional. By default, the last observed value is measured. With cumulative: true, the sum of all observed values is measured.

Output for the example log lines above::

# HELP grok_example_values Example gauge metric with labels.
# TYPE grok_example_values gauge
grok_example_values{user="alice"} 6.5
grok_example_values{user="bob"} 2.5

Histogram Metric Type

Like gauge metrics, the histogram metric monitors values that are logged with each matching log line. However, instead of just summing up the values, histograms count the observed values in configurable buckets.

    - type: histogram
      name: grok_example_values
      help: Example histogram metric with labels.
      match: '%{DATE} %{TIME} %{USER:user} %{NUMBER:val}'
      value: '{{.val}}'
      buckets: [1, 2, 3]
      labels:
          user: '{{.user}}'

The configuration is as follows:

type is histogram.
name, help, match, labels, and value have the same meaning as for gauge metrics.
buckets configure the categories to be observed. In the example, we have 4 buckets: One for values < 1, one for values < 2, one for values < 3, and one for all values (i.e. < infinity). Buckets are optional. The default buckets are [0.005, 0.01, 0.025, 0.05, 0.1, 0.25, 0.5, 1, 2.5, 5, 10], which is useful for HTTP response times in seconds.

Output for the example log lines above::

# HELP grok_example_values Example histogram metric with labels.
# TYPE grok_example_values histogram
grok_example_values_bucket{user="alice",le="1"} 0
grok_example_values_bucket{user="alice",le="2"} 1
grok_example_values_bucket{user="alice",le="3"} 3
grok_example_values_bucket{user="alice",le="+Inf"} 3
grok_example_values_sum{user="alice"} 6.5
grok_example_values_count{user="alice"} 3
grok_example_values_bucket{user="bob",le="1"} 0
grok_example_values_bucket{user="bob",le="2"} 0
grok_example_values_bucket{user="bob",le="3"} 1
grok_example_values_bucket{user="bob",le="+Inf"} 1
grok_example_values_sum{user="bob"} 2.5
grok_example_values_count{user="bob"} 1

Summary Metric Type

Like gauge and histogram metrics, the summary metric monitors values that are logged with each matching log line. Summaries measure configurable φ quantiles, like the median (φ=0.5) or the 95% quantile (φ=0.95). See histograms and summaries for more info.

metrics:
   - type: summary
      name: grok_example_values
      help: Summary metric with labels.
      match: '%{DATE} %{TIME} %{USER:user} %{NUMBER:val}'
      value: '{{.val}}'
      quantiles: {0.5: 0.05, 0.9: 0.01, 0.99: 0.001}
      max_age: 10m
      labels:
          user: '{{.user}}'

The configuration is as follows:

type is summary.
name, help, match, labels, and value have the same meaning as for gauge metrics.
quantiles is a list of quantiles to be observed. grok_exporter does not provide exact values for the quantiles, but only estimations. For each quantile, you also specify an uncertainty that is tolerated for the estimation. In the example, we measure the median (0.5 quantile) with uncertainty 5%, the 90% quantile with uncertainty 1%, and the 99% quantile with uncertainty 0.1%. quantiles is optional, the default value is {0.5: 0.05, 0.9: 0.01, 0.99: 0.001}.
max_age is a summary sliding window. By default, summaries represent a sliding time window of 10 minutes, i.e. if you observe a 0.5 quantile (median) of x, the value x represents the median within the last 10 minutes. The time window is moved forward every 2 minutes.

Output for the example log lines above::

# HELP grok_example_values Example summary metric with labels.
# TYPE grok_example_values summary
grok_example_values{user="alice",quantile="0.5"} 2.5
grok_example_values{user="alice",quantile="0.9"} 2.5
grok_example_values{user="alice",quantile="0.99"} 2.5
grok_example_values_sum{user="alice"} 6.5
grok_example_values_count{user="alice"} 3
grok_example_values{user="bob",quantile="0.5"} 2.5
grok_example_values{user="bob",quantile="0.9"} 2.5
grok_example_values{user="bob",quantile="0.99"} 2.5
grok_example_values_sum{user="bob"} 2.5
grok_example_values_count{user="bob"} 1

Server Section

The server section configures the HTTP(S) server for exposing the metrics:

server:
    protocol: https
    host: localhost
    port: 9144
    path: /metrics
    cert: /path/to/cert
    key: /path/to/key
    client_ca: /path/to/client_ca
    client_auth: RequireAndVerifyClientCert

protocol can be http or https. Default is http.
host can be a hostname or an IP address. If host is specified, grok_exporter will listen on the network interface with the given address. If host is omitted, grok_exporter will listen on all available network interfaces. If host is set to [::], grok_exporter will listen on all IPV6 addresses.
port is the TCP port to be used. Default is 9144.
path is the path where the metrics are exposed. Default is /metrics, i.e. by default metrics will be exported on http://localhost:9144/metrics.
cert is the path to the SSL certificate file for protocol https. It is optional. If omitted, a hard-coded default certificate will be used.
key is the path to the SSL key file for protocol https. It is optional. If omitted, a hard-coded default key will be used.
client_ca is the CA certificate used for client authentication. It is optional. If omitted, grok_exporter will not validate client certificates.
client_auth is the policy used for client authentication. It can only be used together with client_ca. It is optional. The default is RequireAndVerifyClientCert, meaning if you specify a client_ca, you want to allow only clients with a valid certificate. Golang's tls.ClientAuthType documentation contains a list of valid values: NoClientCert, RequestClientCert, RequireAnyClientCert, VerifyClientCertIfGiven, and RequireAndVerifyClientCert.

Example commands for creating SSL test certificates:

The following will generate server.crt and server.key:

openssl req -x509 -sha256 -subj "/CN=localhost" -nodes -days 365 -newkey rsa:2048 -keyout server.key -out server.crt

These can be configured in grok_exporter as follows:

server:
    protocol: https
    cert: ./server.crt
    key: ./server.key

The following will generate client.crt and client.key:

openssl req -x509 -sha256 -subj "/CN=localhost" -nodes -days 365 -newkey rsa:2048 -keyout client.key -out client.crt

The client.crt can be configured in grok_exporter as follows:

server:
  protocol: https
  cert: ./server.crt
  key: ./server.key
  client_ca: ./client.crt
  client_auth: RequireAndVerifyClientCert

The client.key can be used to authenticate client requests. With curl, you can test this as follows:

curl --cacert server.crt --cert client.crt --key client.key https://localhost:9144/metrics

How to Configure Durations

grok_exporter uses the format from golang's time.ParseDuration() for configuring time intervals. Some examples are:

2h30m: 2 hours and 30 minutes
100ms: 100 milliseconds
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5m: 5 minutes

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CONFIG.md

CONFIG.md

grok_exporter Configuration

Updating from Config Version 2

Overall Structure

global Section

Input Section

File Input Type

Stdin Input Type

Webhook Input Type

Kafka Input Type

imports Section

grok_patterns import type

metrics import type

grok_patterns Section

Metrics Section

Metric Types Overview

Example Log Lines

Match

Labels

Pre-Defined Label Variables

logfile

extra

Label Template Functions

Restricting a Metric to Specific Log Files

Expiring Old Labels

`delete_labels`

`retention`

Counter Metric Type

Gauge Metric Type

Histogram Metric Type

Summary Metric Type

Server Section

How to Configure Durations

Files

CONFIG.md

Latest commit

History

CONFIG.md

File metadata and controls

grok_exporter Configuration

Updating from Config Version 2

Overall Structure

global Section

Input Section

File Input Type

Stdin Input Type

Webhook Input Type

Kafka Input Type

imports Section

grok_patterns import type

metrics import type

grok_patterns Section

Metrics Section

Metric Types Overview

Example Log Lines

Match

Labels

Pre-Defined Label Variables

logfile

extra

Label Template Functions

Restricting a Metric to Specific Log Files

Expiring Old Labels

delete_labels

retention

Counter Metric Type

Gauge Metric Type

Histogram Metric Type

Summary Metric Type

Server Section

How to Configure Durations

`delete_labels`

`retention`