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* Add new doc for grok processor --------- (cherry picked from commit 06fdfd7) Signed-off-by: Melissa Vagi <[email protected]> Signed-off-by: Fanit Kolchina <[email protected]> Signed-off-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com> Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com> Co-authored-by: Heemin Kim <[email protected]> Co-authored-by: Fanit Kolchina <[email protected]>
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| --- | ||
| layout: default | ||
| title: Grok | ||
| parent: Ingest processors | ||
| grand_parent: Ingest pipelines | ||
| nav_order: 140 | ||
| --- | ||
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| # Grok | ||
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| The `grok` processor is used to parse and structure unstructured data using pattern matching. You can use the `grok` processor to extract fields from log messages, web server access logs, application logs, and other log data that follows a consistent format. | ||
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| ## Grok basics | ||
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| The `grok` processor uses a set of predefined patterns to match parts of the input text. Each pattern consists of a name and a regular expression. For example, the pattern `%{IP:ip_address}` matches an IP address and assigns it to the field `ip_address`. You can combine multiple patterns to create more complex expressions. For example, the pattern `%{IP:client} %{WORD:method} %{URIPATHPARM:request} %{NUMBER:bytes %NUMBER:duration}` matches a line from a web server access log and extracts the client IP address, the HTTP method, the request URI, the number of bytes sent, and the duration of the request. | ||
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| The `grok` processor is built on the [Oniguruma regular expression library](https://github.com/kkos/oniguruma/blob/master/doc/RE) and supports all the patterns from that library. You can use the [Grok Debugger](https://grokdebugger.com/) tool to test and debug your grok expressions. | ||
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| ## Grok processor syntax | ||
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| The following is the basic syntax for the `grok` processor: | ||
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| ```json | ||
| { | ||
| "grok": { | ||
| "field": "your_message", | ||
| "patterns": ["your_patterns"] | ||
| } | ||
| } | ||
| ``` | ||
| {% include copy-curl.html %} | ||
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| ## Configuration parameters | ||
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| To configure the `grok` processor, you have various options that allow you to define patterns, match specific keys, and control the processor's behavior. The following table lists the required and optional parameters for the `grok` processor. | ||
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| Parameter | Required | Description | | ||
| |-----------|-----------|-----------| | ||
| `field` | Required | The name of the field containing the text that should be parsed. | | ||
| `patterns` | Required | A list of grok expressions used to match and extract named captures. The first matching expression in the list is returned. | | ||
| `pattern_definitions` | Optional | A dictionary of pattern names and pattern tuples used to define custom patterns for the current processor. If a pattern matches an existing name, it overrides the pre-existing definition. | | ||
| `trace_match` | Optional | When the parameter is set to `true`, the processor adds a field named `_grok_match_index` to the processed document. This field contains the index of the pattern within the `patterns` array that successfully matched the document. This information can be useful for debugging and understanding which pattern was applied to the document. Default is `false`. | | ||
| `description` | Optional | A brief description of the processor. | | ||
| `if` | Optional | A condition for running this processor. | | ||
| `ignore_failure` | Optional | If set to `true`, failures are ignored. Default is `false`. | | ||
| `ignore_missing` | Optional | If set to `true`, the processor does not modify the document if the field does not exist or is `null`. Default is `false`. | | ||
| `on_failure` | Optional | A list of processors to run if the processor fails. | | ||
| `tag` | Optional | An identifier tag for the processor. Useful for debugging to distinguish between processors of the same type. | | ||
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| ## Creating a pipeline | ||
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| The following steps guide you through creating an [ingest pipeline]({{site.url}}{{site.baseurl}}/ingest-pipelines/index/) with the `grok` processor. | ||
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| **Step 1: Create a pipeline.** | ||
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| The following query creates a pipeline, named `log_line`. It extracts fields from the `message` field of the document using the specified pattern. In this case, it extracts the `clientip`, `timestamp`, and `response_status` fields: | ||
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| ```json | ||
| PUT _ingest/pipeline/log_line | ||
| { | ||
| "description": "Extract fields from a log line", | ||
| "processors": [ | ||
| { | ||
| "grok": { | ||
| "field": "message", | ||
| "patterns": ["%{IPORHOST:clientip} %{HTTPDATE:timestamp} %{NUMBER:response_status:int}"] | ||
| } | ||
| } | ||
| ] | ||
| } | ||
| ``` | ||
| {% include copy-curl.html %} | ||
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| **Step 2 (Optional): Test the pipeline.** | ||
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| {::nomarkdown}<img src="{{site.url}}{{site.baseurl}}/images/icons/alert-icon.png" class="inline-icon" alt="alert icon"/>{:/} **NOTE**<br>It is recommended that you test your pipeline before you ingest documents. | ||
| {: .note} | ||
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| To test the pipeline, run the following query: | ||
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| ```json | ||
| POST _ingest/pipeline/log_line/_simulate | ||
| { | ||
| "docs": [ | ||
| { | ||
| "_source": { | ||
| "message": "127.0.0.1 198.126.12 10/Oct/2000:13:55:36 -0700 200" | ||
| } | ||
| } | ||
| ] | ||
| } | ||
| ``` | ||
| {% include copy-curl.html %} | ||
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| #### Response | ||
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| The following response confirms that the pipeline is working as expected: | ||
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| ```json | ||
| { | ||
| "docs": [ | ||
| { | ||
| "doc": { | ||
| "_index": "_index", | ||
| "_id": "_id", | ||
| "_source": { | ||
| "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" | ||
| }, | ||
| "_ingest": { | ||
| "timestamp": "2023-09-13T21:41:52.064540505Z" | ||
| } | ||
| } | ||
| } | ||
| ] | ||
| } | ||
| ``` | ||
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| **Step 3: Ingest a document.** | ||
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| The following query ingests a document into an index named `testindex1`: | ||
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| ```json | ||
| PUT testindex1/_doc/1?pipeline=log_line | ||
| { | ||
| "message": "127.0.0.1 198.126.12 10/Oct/2000:13:55:36 -0700 200" | ||
| } | ||
| ``` | ||
| {% include copy-curl.html %} | ||
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| **Step 4 (Optional): Retrieve the document.** | ||
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| To retrieve the document, run the following query: | ||
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| ```json | ||
| GET testindex1/_doc/1 | ||
| ``` | ||
| {% include copy-curl.html %} | ||
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| ## Custom patterns | ||
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| You can use default patterns, or you can add custom patterns to your pipelines using the `patterns_definitions` parameter. Custom grok patterns can be used in a pipeline to extract structured data from log messages that do not match the built-in grok patterns. This can be useful for parsing log messages from custom applications or for parsing log messages that have been modified in some way. Custom patterns adhere to a straightforward structure: each pattern has a unique name and the corresponding regular expression that defines its matching behavior. | ||
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| The following is an example of how to include a custom pattern in your configuration. In this example, the issue number is between 3 and 4 digits and is parsed into the `issue_number` field and the status is parsed into the `status` field: | ||
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| ```json | ||
| PUT _ingest/pipeline/log_line | ||
| { | ||
| "processors": [ | ||
| { | ||
| "grok": { | ||
| "field": "message", | ||
| "patterns": ["The issue number %{NUMBER:issue_number} is %{STATUS:status}"], | ||
| "pattern_definitions" : { | ||
| "NUMBER" : "\\d{3,4}", | ||
| "STATUS" : "open|closed" | ||
| } | ||
| } | ||
| } | ||
| ] | ||
| } | ||
| ``` | ||
| {% include copy-curl.html %} | ||
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| ## Tracing which patterns matched | ||
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| To trace which patterns matched and populated the fields, you can use the `trace_match` parameter. The following is an example of how to include this parameter in your configuration: | ||
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| ```json | ||
| PUT _ingest/pipeline/log_line | ||
| { | ||
| "description": "Extract fields from a log line", | ||
| "processors": [ | ||
| { | ||
| "grok": { | ||
| "field": "message", | ||
| "patterns": ["%{HTTPDATE:timestamp} %{IPORHOST:clientip}", "%{IPORHOST:clientip} %{HTTPDATE:timestamp} %{NUMBER:response_status:int}"], | ||
| "trace_match": true | ||
| } | ||
| } | ||
| ] | ||
| } | ||
| ``` | ||
| {% include copy-curl.html %} | ||
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| When you simulate the pipeline, OpenSearch returns the `_ingest` metadata that includes the `grok_match_index`, as shown in the following output: | ||
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| ```json | ||
| { | ||
| "docs": [ | ||
| { | ||
| "doc": { | ||
| "_index": "_index", | ||
| "_id": "_id", | ||
| "_source": { | ||
| "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" | ||
| }, | ||
| "_ingest": { | ||
| "_grok_match_index": "1", | ||
| "timestamp": "2023-11-02T18:48:40.455619084Z" | ||
| } | ||
| } | ||
| } | ||
| ] | ||
| } | ||
| ``` | ||
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