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Copy grok documentation from logparser to data format doc (#4475)

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docs/DATA_FORMATS_INPUT.md
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@ -2,14 +2,14 @@
Telegraf is able to parse the following input data formats into metrics: Telegraf is able to parse the following input data formats into metrics:
1. [InfluxDB Line Protocol](https://github.com/influxdata/telegraf/blob/master/docs/DATA_FORMATS_INPUT.md#influx) 1. [InfluxDB Line Protocol](#influx)
1. [JSON](https://github.com/influxdata/telegraf/blob/master/docs/DATA_FORMATS_INPUT.md#json) 1. [JSON](#json)
1. [Graphite](https://github.com/influxdata/telegraf/blob/master/docs/DATA_FORMATS_INPUT.md#graphite) 1. [Graphite](#graphite)
1. [Value](https://github.com/influxdata/telegraf/blob/master/docs/DATA_FORMATS_INPUT.md#value), ie: 45 or "booyah" 1. [Value](#value), ie: 45 or "booyah"
1. [Nagios](https://github.com/influxdata/telegraf/blob/master/docs/DATA_FORMATS_INPUT.md#nagios) (exec input only) 1. [Nagios](#nagios) (exec input only)
1. [Collectd](https://github.com/influxdata/telegraf/blob/master/docs/DATA_FORMATS_INPUT.md#collectd) 1. [Collectd](#collectd)
1. [Dropwizard](https://github.com/influxdata/telegraf/blob/master/docs/DATA_FORMATS_INPUT.md#dropwizard) 1. [Dropwizard](#dropwizard)
1. [Grok](https://github.com/influxdata/telegraf/blob/master/docs/DATA_FORMATS_INPUT.md#grok) 1. [Grok](#grok)
Telegraf metrics, like InfluxDB Telegraf metrics, like InfluxDB
[points](https://docs.influxdata.com/influxdb/v0.10/write_protocols/line/), [points](https://docs.influxdata.com/influxdb/v0.10/write_protocols/line/),
@ -481,9 +481,9 @@ You can also change the path to the typesdb or add additional typesdb using
## Path of to TypesDB specifications ## Path of to TypesDB specifications
collectd_typesdb = ["/usr/share/collectd/types.db"] collectd_typesdb = ["/usr/share/collectd/types.db"]
# Multi-value plugins can be handled two ways. # Multi-value plugins can be handled two ways.
# "split" will parse and store the multi-value plugin data into separate measurements # "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. # "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. # "split" is the default behavior for backward compatability with previous versions of influxdb.
collectd_parse_multivalue = "split" collectd_parse_multivalue = "split"
``` ```
@ -566,7 +566,7 @@ measurement,metric_type=histogram count=1,max=1.0,mean=1.0,min=1.0,p50=1.0,p75=1
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 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. 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: Eg. to parse the following JSON document:
```json ```json
@ -577,7 +577,7 @@ Eg. to parse the following JSON document:
"tag2" : "yellow" "tag2" : "yellow"
}, },
"metrics" : { "metrics" : {
"counters" : { "counters" : {
"measurement" : { "measurement" : {
"count" : 1 "count" : 1
} }
@ -641,16 +641,16 @@ For more information about the dropwizard json format see
## By providing an empty template array, templating is disabled and measurements are parsed as influxdb line protocol keys (measurement<,tag_set>) ## By providing an empty template array, templating is disabled and measurements are parsed as influxdb line protocol keys (measurement<,tag_set>)
templates = [] templates = []
## You may use an appropriate [gjson path](https://github.com/tidwall/gjson#path-syntax) ## You may use an appropriate [gjson path](https://github.com/tidwall/gjson#path-syntax)
## to locate the metric registry within the JSON document ## to locate the metric registry within the JSON document
# dropwizard_metric_registry_path = "metrics" # dropwizard_metric_registry_path = "metrics"
## You may use an appropriate [gjson path](https://github.com/tidwall/gjson#path-syntax) ## 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 ## to locate the default time of the measurements within the JSON document
# dropwizard_time_path = "time" # dropwizard_time_path = "time"
# dropwizard_time_format = "2006-01-02T15:04:05Z07:00" # dropwizard_time_format = "2006-01-02T15:04:05Z07:00"
## You may use an appropriate [gjson path](https://github.com/tidwall/gjson#path-syntax) ## You may use an appropriate [gjson path](https://github.com/tidwall/gjson#path-syntax)
## to locate the tags map within the JSON document ## to locate the tags map within the JSON document
# dropwizard_tags_path = "tags" # dropwizard_tags_path = "tags"
@ -660,79 +660,32 @@ For more information about the dropwizard json format see
# tag2 = "tags.tag2" # tag2 = "tags.tag2"
``` ```
#### Grok # Grok
Parse logstash-style "grok" patterns. Patterns can be added to patterns, or custom patterns read from custom_pattern_files.
# View logstash grok pattern docs here: The grok data format parses line delimited data using a regular expression like
# https://www.elastic.co/guide/en/logstash/current/plugins-filters-grok.html language.
# All default logstash patterns are supported, these can be viewed here:
# https://github.com/logstash-plugins/logstash-patterns-core/blob/master/patterns/grok-patterns
# Available modifiers: The best way to get acquainted with grok patterns is to read the logstash docs,
# string (default if nothing is specified) which are available here:
# int https://www.elastic.co/guide/en/logstash/current/plugins-filters-grok.html
# 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-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)
# ts-epochnano (nanoseconds since unix epoch)
# 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
# See https://golang.org/pkg/time/#Parse for more details.
# Example log file pattern, example log looks like this:
# [04/Jun/2016:12:41:45 +0100] 1.25 200 192.168.1.1 5.432µs
# Breakdown of the DURATION pattern below:
# NUMBER is a builtin logstash grok pattern matching float & int numbers.
# [nuµm]? is a regex specifying 0 or 1 of the characters within brackets.
# s is also regex, this pattern must end in "s".
# so DURATION will match something like '5.324ms' or '6.1µs' or '10s'
DURATION %{NUMBER}[nuµm]?s
RESPONSE_CODE %{NUMBER:response_code:tag}
RESPONSE_TIME %{DURATION:response_time_ns:duration}
EXAMPLE_LOG \[%{HTTPDATE:ts:ts-httpd}\] %{NUMBER:myfloat:float} %{RESPONSE_CODE} %{IPORHOST:clientip} %{RESPONSE_TIME}
# Wider-ranging username matching vs. logstash built-in %{USER}
NGUSERNAME [a-zA-Z0-9\.\@\-\+_%]+
NGUSER %{NGUSERNAME}
# Wider-ranging client IP matching
CLIENT (?:%{IPORHOST}|%{HOSTPORT}|::1)
##
## COMMON LOG PATTERNS
##
# apache & nginx logs, this is also known as the "common log format"
# see https://en.wikipedia.org/wiki/Common_Log_Format
COMMON_LOG_FORMAT %{CLIENT:client_ip} %{NOTSPACE:ident} %{NOTSPACE:auth} \[%{HTTPDATE:ts:ts-httpd}\] "(?:%{WORD:verb:tag} %{NOTSPACE:request}(?: HTTP/%{NUMBER:http_version:float})?|%{DATA})" %{NUMBER:resp_code:tag} (?:%{NUMBER:resp_bytes:int}|-)
# Combined log format is the same as the common log format but with the addition
# of two quoted strings at the end for "referrer" and "agent"
# See Examples at http://httpd.apache.org/docs/current/mod/mod_log_config.html
COMBINED_LOG_FORMAT %{COMMON_LOG_FORMAT} %{QS:referrer} %{QS:agent}
# HTTPD log formats
HTTPD20_ERRORLOG \[%{HTTPDERROR_DATE:timestamp}\] \[%{LOGLEVEL:loglevel:tag}\] (?:\[client %{IPORHOST:clientip}\] ){0,1}%{GREEDYDATA:errormsg}
HTTPD24_ERRORLOG \[%{HTTPDERROR_DATE:timestamp}\] \[%{WORD:module}:%{LOGLEVEL:loglevel:tag}\] \[pid %{POSINT:pid:int}:tid %{NUMBER:tid:int}\]( \(%{POSINT:proxy_errorcode:int}\)%{DATA:proxy_errormessage}:)?( \[client %{IPORHOST:client}:%{POSINT:clientport}\])? %{DATA:errorcode}: %{GREEDYDATA:message}
HTTPD_ERRORLOG %{HTTPD20_ERRORLOG}|%{HTTPD24_ERRORLOG}
#### Grok Configuration: #### Grok Configuration:
```toml ```toml
[[inputs.reader]] [[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. ## This is a list of patterns to check the given log file(s) for.
## Note that adding patterns here increases processing time. The most ## Note that adding patterns here increases processing time. The most
## efficient configuration is to have one pattern per logparser. ## efficient configuration is to have one pattern per logparser.
@ -741,9 +694,6 @@ HTTPD_ERRORLOG %{HTTPD20_ERRORLOG}|%{HTTPD24_ERRORLOG}
## %{COMBINED_LOG_FORMAT} (access logs + referrer & agent) ## %{COMBINED_LOG_FORMAT} (access logs + referrer & agent)
grok_patterns = ["%{COMBINED_LOG_FORMAT}"] grok_patterns = ["%{COMBINED_LOG_FORMAT}"]
## Name of the outputted measurement name.
grok_name_override = "apache_access_log"
## Full path(s) to custom pattern files. ## Full path(s) to custom pattern files.
grok_custom_pattern_files = [] grok_custom_pattern_files = []
@ -761,4 +711,179 @@ HTTPD_ERRORLOG %{HTTPD20_ERRORLOG}|%{HTTPD24_ERRORLOG}
## 2. "Canada/Eastern" -- Unix TZ values like those found in https://en.wikipedia.org/wiki/List_of_tz_database_time_zones ## 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 ## 3. UTC -- or blank/unspecified, will return timestamp in UTC
grok_timezone = "Canada/Eastern" 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](./grok/patterns/influx-patterns),
as well as support for most of
[logstash's builtin patterns](https://github.com/logstash-plugins/logstash-patterns-core/blob/master/patterns/grok-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
```
```toml
[[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
```
```toml
[[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
```
```toml
[[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](https://en.wikipedia.org/wiki/List_of_tz_database_time_zones), 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](https://github.com/toml-lang/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.
```toml
[[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.
```toml
[[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:
```toml
[[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](https://grokdebug.herokuapp.com).
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`.
```toml
[[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.

4
plugins/inputs/logparser/README.md
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@ -1,8 +1,6 @@
# Logparser Input Plugin # Logparser Input Plugin
### **Deprecated in version 1.8**: Please use the ### **Deprecated in version 1.8**: Please use the [tail](/plugins/inputs/tail) plugin with the `grok` [data format](/docs/DATA_FORMATS_INPUT.md).
[tail](/plugins/inputs/tail) plugin with the `grok`
[data format](/docs/DATA_FORMATS_INPUT.md).
The `logparser` plugin streams and parses the given logfiles. Currently it The `logparser` plugin streams and parses the given logfiles. Currently it
has the capability of parsing "grok" patterns from logfiles, which also supports has the capability of parsing "grok" patterns from logfiles, which also supports