[DOC] Add new doc for grok processor (#5019)
* Add new doc for grok processor Signed-off-by: Melissa Vagi <vagimeli@amazon.com> * Writing Signed-off-by: Melissa Vagi <vagimeli@amazon.com> * Writing Signed-off-by: Melissa Vagi <vagimeli@amazon.com> * Add custom patterns Signed-off-by: Melissa Vagi <vagimeli@amazon.com> * Update _api-reference/ingest-apis/processors/grok.md Co-authored-by: Heemin Kim <heemin@amazon.com> Signed-off-by: Melissa Vagi <vagimeli@amazon.com> * Update _api-reference/ingest-apis/processors/grok.md Co-authored-by: Heemin Kim <heemin@amazon.com> Signed-off-by: Melissa Vagi <vagimeli@amazon.com> * Address tech feedback Signed-off-by: Melissa Vagi <vagimeli@amazon.com> * Address tech feedback Signed-off-by: Melissa Vagi <vagimeli@amazon.com> * Revise content Signed-off-by: Melissa Vagi <vagimeli@amazon.com> * Revise intro section based on doc review Signed-off-by: Melissa Vagi <vagimeli@amazon.com> * Revise intro section based on doc review Signed-off-by: Melissa Vagi <vagimeli@amazon.com> * Revise intro section based on doc review Signed-off-by: Melissa Vagi <vagimeli@amazon.com> * Change example and intro sentences to examples Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Link fix Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Address doc review feedback Signed-off-by: Melissa Vagi <vagimeli@amazon.com> --------- Signed-off-by: Melissa Vagi <vagimeli@amazon.com> Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> Co-authored-by: Heemin Kim <heemin@amazon.com> Co-authored-by: Fanit Kolchina <kolchfa@amazon.com>
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Melissa Vagi
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---
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layout: default
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title: Grok
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parent: Ingest processors
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grand_parent: Ingest pipelines
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nav_order: 140
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---
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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
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{
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"grok": {
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"field": "your_message",
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"patterns": ["your_patterns"]
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}
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}
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```
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{% 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 |
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|-----------|-----------|-----------|
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`field` | Required | The name of the field containing the text that should be parsed. |
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`patterns` | Required | A list of grok expressions used to match and extract named captures. The first matching expression in the list is returned. |
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`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. |
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`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`. |
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`description` | Optional | A brief description of the processor. |
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`if` | Optional | A condition for running this processor. |
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`ignore_failure` | Optional | If set to `true`, failures are ignored. Default is `false`. |
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`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`. |
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`on_failure` | Optional | A list of processors to run if the processor fails. |
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`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
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PUT _ingest/pipeline/log_line
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{
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"description": "Extract fields from a log line",
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"processors": [
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{
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"grok": {
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"field": "message",
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"patterns": ["%{IPORHOST:clientip} %{HTTPDATE:timestamp} %{NUMBER:response_status:int}"]
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}
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}
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]
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}
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```
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{% 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.
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{: .note}
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To test the pipeline, run the following query:
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```json
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POST _ingest/pipeline/log_line/_simulate
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{
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"docs": [
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{
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"_source": {
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"message": "127.0.0.1 198.126.12 10/Oct/2000:13:55:36 -0700 200"
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}
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}
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]
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}
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```
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{% 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
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{
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"docs": [
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{
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"doc": {
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"_index": "_index",
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"_id": "_id",
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"_source": {
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"message": "127.0.0.1 198.126.12 10/Oct/2000:13:55:36 -0700 200",
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"response_status": 200,
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"clientip": "198.126.12",
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"timestamp": "10/Oct/2000:13:55:36 -0700"
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},
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"_ingest": {
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"timestamp": "2023-09-13T21:41:52.064540505Z"
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}
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}
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}
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]
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}
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```
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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
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PUT testindex1/_doc/1?pipeline=log_line
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{
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"message": "127.0.0.1 198.126.12 10/Oct/2000:13:55:36 -0700 200"
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}
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```
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{% 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
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GET testindex1/_doc/1
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```
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{% 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
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PUT _ingest/pipeline/log_line
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{
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"processors": [
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{
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"grok": {
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"field": "message",
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"patterns": ["The issue number %{NUMBER:issue_number} is %{STATUS:status}"],
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"pattern_definitions" : {
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"NUMBER" : "\\d{3,4}",
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"STATUS" : "open|closed"
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}
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}
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}
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]
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}
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```
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{% 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
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PUT _ingest/pipeline/log_line
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{
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"description": "Extract fields from a log line",
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"processors": [
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{
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"grok": {
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"field": "message",
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"patterns": ["%{HTTPDATE:timestamp} %{IPORHOST:clientip}", "%{IPORHOST:clientip} %{HTTPDATE:timestamp} %{NUMBER:response_status:int}"],
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"trace_match": true
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}
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}
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]
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}
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```
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{% 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
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{
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"docs": [
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{
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"doc": {
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"_index": "_index",
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"_id": "_id",
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"_source": {
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"message": "127.0.0.1 198.126.12 10/Oct/2000:13:55:36 -0700 200",
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"response_status": 200,
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"clientip": "198.126.12",
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"timestamp": "10/Oct/2000:13:55:36 -0700"
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},
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"_ingest": {
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"_grok_match_index": "1",
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"timestamp": "2023-11-02T18:48:40.455619084Z"
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}
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}
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}
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]
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}
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```
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