opensearch-docs-cn/_data-prepper/common-use-cases/text-processing.md

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Text processing

Data Prepper provides text processing capabilities with the grok processor. The grok processor is based on the java-grok library and supports all compatible patterns. The java-grok library is built using the java.util.regex regular expression library.

You can add custom patterns to your pipelines by using the patterns_definitions option. When debugging custom patterns, the Grok Debugger can be helpful.

Basic usage

To get started with text processing, create the following pipeline:

patten-matching-pipeline:
  source
    ...
  processor:
    - grok:
        match:
          message: ['%{IPORHOST:clientip} \[%{HTTPDATE:timestamp}\] %{NUMBER:response_status:int}']
  sink:
    - opensearch:
        # Provide an OpenSearch cluster endpoint

{% include copy-curl.html %}

An incoming message might contain the following contents:

{"message": "127.0.0.1 198.126.12 [10/Oct/2000:13:55:36 -0700] 200"}

{% include copy-curl.html %}

In each incoming event, the pipeline will locate the value in the message key and attempt to match the pattern. The keywords IPORHOST, HTTPDATE, and NUMBER are built into the plugin.

When an incoming record matches the pattern, it generates an internal event such as the following with identification keys extracted from the original message:

{ 
  "message":"127.0.0.1 198.126.12 [10/Oct/2000:13:55:36 -0700] 200",
  "response_status":200,
  "clientip":"198.126.12",
  "timestamp":"10/Oct/2000:13:55:36 -0700"
}

{% include copy-curl.html %}

The match configuration for the grok processor specifies which record keys to match against which patterns.

In the following example, the match configuration checks incoming logs for a message key. If the key exists, it matches the key value against the SYSLOGBASE pattern and then against the COMMONAPACHELOG pattern. It then checks the logs for a timestamp key. If that key exists, it attempts to match the key value against the TIMESTAMP_ISO8601 pattern.

processor:
  - grok:
      match:
        message: ['%{SYSLOGBASE}', "%{COMMONAPACHELOG}"]
        timestamp: ["%{TIMESTAMP_ISO8601}"]  

{% include copy-curl.html %}

By default, the plugin continues until it finds a successful match. For example, if there is a successful match against the value in the message key for a SYSLOGBASE pattern, the plugin doesn't attempt to match the other patterns. If you want to match logs against every pattern, include the break_on_match option.

Including named and empty captures

Include the keep_empty_captures option in your pipeline configuration to include null captures or the named_captures_only option to include only named captures. Named captures follow the pattern %{SYNTAX:SEMANTIC} while unnamed captures follow the pattern %{SYNTAX}.

For example, you can modify the preceding Grok configuration to remove clientip from the %{IPORHOST} pattern:

processor:
  - grok:
      match:
        message: ['%{IPORHOST} \[%{HTTPDATE:timestamp}\] %{NUMBER:response_status:int}']

{% include copy-curl.html %}

The resulting grokked log will look like this:

{
  "message":"127.0.0.1 198.126.12 [10/Oct/2000:13:55:36 -0700] 200",
  "response_status":200,
  "timestamp":"10/Oct/2000:13:55:36 -0700"
}

{% include copy-curl.html %}

Notice that the clientip key no longer exists because the %{IPORHOST} pattern is now an unnamed capture.

However, if you set named_captures_only to false:

processor:
  - grok:
      match:
        named_captures_only: false
        message: ['%{IPORHOST} \[%{HTTPDATE:timestamp}\] %{NUMBER:message:int}']

{% include copy-curl.html %}

Then the resulting grokked log will look like this:

{
  "message":"127.0.0.1 198.126.12 [10/Oct/2000:13:55:36 -0700] 200",
  "MONTH":"Oct",
  "YEAR":"2000",
  "response_status":200,
  "HOUR":"13",
  "TIME":"13:55:36",
  "MINUTE":"55",
  "SECOND":"36",
  "IPORHOST":"198.126.12",
  "MONTHDAY":"10",
  "INT":"-0700",
  "timestamp":"10/Oct/2000:13:55:36 -0700"
}

{% include copy-curl.html %}

Note that the IPORHOST capture now shows up as a new key, along with some internal unnamed captures like MONTH and YEAR. The HTTPDATE keyword is currently using these patterns, which you can see in the default patterns file.

Overwriting keys

Include the keys_to_overwrite option to specify which existing record keys to overwrite if there is a capture with the same key value.

For example, you can modify the preceding Grok configuration to replace %{NUMBER:response_status:int} with %{NUMBER:message:int} and add message to the list of keys to overwrite:

processor:
  - grok:
      match:
        keys_to_overwrite: ["message"]
        message: ['%{IPORHOST:clientip} \[%{HTTPDATE:timestamp}\] %{NUMBER:message:int}']

{% include copy-curl.html %}

In the resulting grokked log, the original message is overwritten with the number 200:

{ 
  "message":200,
  "clientip":"198.126.12",
  "timestamp":"10/Oct/2000:13:55:36 -0700"
}

{% include copy-curl.html %}

Using custom patterns

Include the pattern_definitions option in your Grok configuration to specify custom patterns.

The following configuration creates custom regex patterns named CUSTOM_PATTERN-1 and CUSTOM_PATTERN-2. By default, the plugin continues until it finds a successful match.

processor:
  - grok:
      pattern_definitions:
        CUSTOM_PATTERN_1: 'this-is-regex-1'
        CUSTOM_PATTERN_2: '%{CUSTOM_PATTERN_1} REGEX'
      match:
        message: ["%{CUSTOM_PATTERN_2:my_pattern_key}"]

{% include copy-curl.html %}

If you specify break_on_match as false, the pipeline attempts to match all patterns and extract keys from the incoming events:

processor:
  - grok:
      pattern_definitions:
        CUSTOM_PATTERN_1: 'this-is-regex-1'
        CUSTOM_PATTERN_2: 'this-is-regex-2'
        CUSTOM_PATTERN_3: 'this-is-regex-3'
        CUSTOM_PATTERN_4: 'this-is-regex-4'
      match:
        message: [ "%{PATTERN1}”, "%{PATTERN2}" ]
        log: [ "%{PATTERN3}", "%{PATTERN4}" ]
        break_on_match: false

{% include copy-curl.html %}

You can define your own custom patterns to use for pipeline pattern matching. In the previous example, my_pattern will be extracted after matching the custom patterns.

Storing captures with a parent key

Include the target_key option in your Grok configuration to wrap all record captures in an additional outer key value.

For example, you can modify the preceding Grok configuration to add a target key named grokked:

processor:
   - grok:
       target_key: "grokked"
       match:
         message: ['%{IPORHOST} \[%{HTTPDATE:timestamp}\] %{NUMBER:response_status:int}']

The resulting grokked log will look like this:

{ 
  "message":"127.0.0.1 198.126.12 [10/Oct/2000:13:55:36 -0700] 200",
  "grokked": {
     "response_status":200,
     "clientip":"198.126.12",
     "timestamp":"10/Oct/2000:13:55:36 -0700"
  }
}