Add documentation for Grok debugger

.github/vale/styles/Vocab/OpenSearch/Products/accept.txt

@@ -79,6 +79,7 @@ Google Normalized Distance
 Google Workspace
 Gradle
 Grafana
+Grok Debugger
 Hanja
 Helm
 Hjson

_dashboards/discover/run-queries.md

@@ -151,4 +151,20 @@ To update your preferences, select **Settings** from the top menu:
 
 ## Using keyboard shortcuts
 
-To view all available keyboard shortcuts, select **Help** from the top menu.
+To view all available keyboard shortcuts, select **Help** from the top menu.
+
+## Using the Grok Debugger
+
+The **Grok Debugger** is a built-in tool in Dev Tools that helps you build and test [Grok patterns]({{site.url}}{{site.baseurl}}/ingest-pipelines/processors/grok/) before using them in ingest pipelines. Grok patterns parse unstructured log data into structured fields.
+
+To use the Grok Debugger, follow these steps:
+
+1. On the top menu in Dev Tools, select **Grok Debugger**.
+1. Enter a sample log message in the **Sample Data** field.
+1. Enter your Grok pattern in the **Grok Pattern** field.
+1. Select **Simulate** to test the pattern.
+
+The debugger displays the parsed fields in the output pane, as shown in the following image.
+
+![Grok Debugger]({{site.url}}{{site.baseurl}}/images/dev-tools/grok-debugger.png)
+

_ingest-pipelines/processors/grok.md

@@ -2,24 +2,24 @@
 layout: default
 title: Grok
 parent: Ingest processors
-nav_order: 140
+nav_order: 120
 ---
 
-This documentation describes using the `grok` processor in OpenSearch ingest pipelines. Consider using the [Data Prepper `grok` processor]({{site.url}}{{site.baseurl}}/data-prepper/pipelines/configuration/processors/grok/), which runs on the OpenSearch cluster, if your use case involves large or complex datasets.
-{: .note}
-
 # Grok processor 
 
 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.
 
+This documentation describes using the `grok` processor in OpenSearch ingest pipelines. Consider using the [Data Prepper `grok` processor]({{site.url}}{{site.baseurl}}/data-prepper/pipelines/configuration/processors/grok/), which runs on the OpenSearch cluster, if your use case involves large or complex datasets.
+{: .note}
+
 ## Grok basics
 
 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.
 
 For a list of available predefined patterns, see [Grok patterns](https://github.com/opensearch-project/OpenSearch/blob/main/libs/grok/src/main/resources/patterns/grok-patterns).
 {: .tip}
 
-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.
+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 built-in [Grok Debugger]({{site.url}}{{site.baseurl}}/dashboards/discover/run-queries/#using-the-grok-debugger) in OpenSearch Dashboards Dev Tools to test and debug your grok expressions.
 
 Note that patterns are *not anchored*. For performance and reliability, include a start-of-line anchor (`^`) in your pattern.
 {: .note}