Add CI with link checker. (#3584)

* Add CI with link checker.

Signed-off-by: dblock <[email protected]>

* Capture URI::InvalidURIError.

Signed-off-by: dblock <[email protected]>

* Use HEAD and catch URI errors.

Signed-off-by: dblock <[email protected]>

* Retry on a 405 with a GET.

Signed-off-by: dblock <[email protected]>

* Replaced external link checker with ruby-link-checker.

Signed-off-by: dblock <[email protected]>

* Don't exit with an exception.

Signed-off-by: dblock <[email protected]>

* Run internal link checker on build/ci.

Signed-off-by: dblock <[email protected]>

* Added broken links issue template.

Signed-off-by: dblock <[email protected]>

* Added host exclusions that 404 or fail on bots.

Signed-off-by: dblock <[email protected]>

* Raise anyway because Jekyll does it for us.

Signed-off-by: dblock <[email protected]>

* Fix broken links.

Signed-off-by: dblock <[email protected]>

* Only run link checker on main.

Signed-off-by: dblock <[email protected]>

* Re-add check-links.sh.

Signed-off-by: dblock <[email protected]>

* Run once a day on cron.

Signed-off-by: dblock <[email protected]>

---------

Signed-off-by: dblock <[email protected]>

.github/ISSUE_TEMPLATE/broken_links.md

@@ -0,0 +1,7 @@
+---
+title: '[AUTOCUT] Broken links'
+labels: 'bug'
+---
+
+Links checker has failed on push of your commit.
+Please examine the workflow log {{ env.WORKFLOW_URL }}.

.github/workflows/jekyll-build.yml

@@ -0,0 +1,16 @@
+name: Jekyll Build Verification
+
+on: [pull_request]
+
+jobs:
+  check:
+    runs-on: ubuntu-latest
+
+    steps:
+    - uses: actions/checkout@v3
+    - uses: ruby/setup-ruby@v1
+      with:
+        ruby-version: '3.0'
+        bundler-cache: true
+    - run: |
+        JEKYLL_LINK_CHECKER=internal bundle exec jekyll build --future

.github/workflows/link-checker.yml

@@ -0,0 +1,25 @@
+name: Check Links
+on:
+  workflow_dispatch:
+  schedule:
+    - cron: "30 11 * * *"
+jobs:
+  check:
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/checkout@v3
+    - uses: ruby/setup-ruby@v1
+      with:
+        ruby-version: '3.0'
+        bundler-cache: true
+    - run: |
+        JEKYLL_FATAL_LINK_CHECKER=all bundle exec jekyll build --future
+    - name: Create Issue On Build Failure
+      if: ${{ failure() }}
+      uses: dblock/create-a-github-issue@v3
+      env:
+        GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+        WORKFLOW_URL: "${{ github.server_url }}/${{ github.repository }}/actions/runs/${{ github.run_id }}"
+      with:
+        update_existing: true
+        filename: .github/ISSUE_TEMPLATE/broken_links.md

Gemfile

@@ -32,4 +32,8 @@ gem "tzinfo-data", platforms: [:mingw, :mswin, :x64_mingw, :jruby]
 gem "wdm", "~> 0.1.0" if Gem.win_platform?
 
 # Installs webrick dependency for building locally
-gem "webrick", "~> 1.7"
+gem "webrick", "~> 1.7"
+
+# Link checker
+gem "typhoeus"
+gem "ruby-link-checker"

_api-reference/explain.md

@@ -10,7 +10,7 @@ Introduced 1.0
 
 Wondering why a specific document ranks higher (or lower) for a query? You can use the explain API for an explanation of how the relevance score (`_score`) is calculated for every result.
 
-OpenSearch uses a probabilistic ranking framework called [Okapi BM25](https://en.wikipedia.org/wiki/Okapi_BM25) to calculate relevance scores. Okapi BM25 is based on the original [TF/IDF](http://lucene.apache.org/core/{{site.lucene_version}}/core/org/apache/lucene/search/package-summary.html#scoring) framework used by Apache Lucene.
+OpenSearch uses a probabilistic ranking framework called [Okapi BM25](https://en.wikipedia.org/wiki/Okapi_BM25) to calculate relevance scores. Okapi BM25 is based on the original [TF/IDF](https://lucene.apache.org/core/{{site.lucene_version}}/core/org/apache/lucene/search/package-summary.html#scoring) framework used by Apache Lucene.
 
 The explain API is an expensive operation in terms of both resources and time. On production clusters, we recommend using it sparingly for the purpose of troubleshooting.
 {: .warning }

_clients/OSC-dot-net.md

@@ -15,7 +15,7 @@ This getting started guide illustrates how to connect to OpenSearch, index docum
 
 ## Installing OpenSearch.Client
 
-To install OpenSearch.Client, download the [OpenSearch.Client NuGet package](https://www.nuget.org/packages/OpenSearch.Client) and add it to your project in an IDE of your choice. In Microsoft Visual Studio, follow the steps below: 
+To install OpenSearch.Client, download the [OpenSearch.Client NuGet package](https://www.nuget.org/packages/OpenSearch.Client/) and add it to your project in an IDE of your choice. In Microsoft Visual Studio, follow the steps below: 
 - In the **Solution Explorer** panel, right-click on your solution or project and select **Manage NuGet Packages for Solution**.
 - Search for the OpenSearch.Client NuGet package, and select **Install**.
 

_config.yml

@@ -5,10 +5,10 @@ baseurl: "/docs/latest" # the subpath of your site, e.g. /blog
 url: "https://opensearch.org" # the base hostname & protocol for your site, e.g. http://example.com
 permalink: /:path/
 
-opensearch_version: 2.6.0
-opensearch_dashboards_version: 2.6.0
-opensearch_major_minor_version: 2.6
-lucene_version: 9_5_0
+opensearch_version: '2.6.0'
+opensearch_dashboards_version: '2.6.0'
+opensearch_major_minor_version: '2.6'
+lucene_version: '9_5_0'
 
 # Build settings
 markdown: kramdown

_dashboards/reporting.md

@@ -54,7 +54,7 @@ This problem can occur for two reasons:
 
 - You don't have the correct version of `headless-chrome` to match the operating system on which OpenSearch Dashboards is running. Download the [correct version](https://github.com/opensearch-project/reporting/releases/tag/chromium-1.12.0.0).
 
-- You're missing additional dependencies. Install the required dependencies for your operating system from the [additional libraries](https://github.com/opensearch-project/dashboards-reports/blob/main/dashboards-reports/rendering-engine/headless-chrome/README.md#additional-libaries) section.
+- You're missing additional dependencies. Install the required dependencies for your operating system from the [additional libraries](https://github.com/opensearch-project/dashboards-reports/blob/1.x/dashboards-reports/rendering-engine/headless-chrome/README.md#additional-libaries) section.
 
 ### Characters not loading in reports
 

_data-prepper/common-use-cases/trace-analytics.md

@@ -39,7 +39,7 @@ The [OpenTelemetry source]({{site.url}}{{site.baseurl}}/data-prepper/pipelines/c
 There are three processors for the trace analytics feature:
 
 * *otel_trace_raw* - The *otel_trace_raw* processor receives a collection of [span](https://github.com/opensearch-project/data-prepper/blob/fa65e9efb3f8d6a404a1ab1875f21ce85e5c5a6d/data-prepper-api/src/main/java/org/opensearch/dataprepper/model/trace/Span.java) records from [*otel-trace-source*]({{site.url}}{{site.baseurl}}/data-prepper/pipelines/configuration/sources/otel-trace/), and performs stateful processing, extraction, and completion of trace-group-related fields.
-* *otel_trace_group* - The *otel_trace_group* processor fills in the missing trace-group-related fields in the collection of [span](https://github.com/opensearch-project/data-prepper/blob/fa65e9efb3f8d6a404a1ab1875f21ce85e5c5a6d/data-prepper-api/src/main/java/com/amazon/dataprepper/model/trace/Span.java) records by looking up the OpenSearch backend.
+* *otel_trace_group* - The *otel_trace_group* processor fills in the missing trace-group-related fields in the collection of [span](https://github.com/opensearch-project/data-prepper/blob/298e7931aa3b26130048ac3bde260e066857df54/data-prepper-api/src/main/java/org/opensearch/dataprepper/model/trace/Span.java) records by looking up the OpenSearch backend.
 * *service_map_stateful* – The *service_map_stateful* processor performs the required preprocessing for trace data and builds metadata to display the `service-map` dashboards.
 
 

_data-prepper/managing-data-prepper/configuring-log4j.md

@@ -11,7 +11,7 @@ You can configure logging using Log4j in Data Prepper.
 
 ## Logging 
 
-Data Prepper uses [SLF4J](http://www.slf4j.org/) with a [Log4j 2 binding](http://logging.apache.org/log4j/2.x/log4j-slf4j-impl/). 
+Data Prepper uses [SLF4J](https://www.slf4j.org/) with a [Log4j 2 binding](https://logging.apache.org/log4j/2.x/log4j-slf4j-impl.html).
 
 For Data Prepper versions 2.0 and later, the Log4j 2 configuration file can be found and edited in `config/log4j2.properties` in the application's home directory. The default properties for Log4j 2 can be found in `log4j2-rolling.properties` in the *shared-config* directory.
 

_data-prepper/managing-data-prepper/monitoring.md

@@ -11,11 +11,11 @@ You can monitor Data Prepper with metrics using [Micrometer](https://micrometer.
 
 ## JVM and system metrics
 
-JVM and system metrics are runtime metrics that are used to monitor Data Prepper instances. They include metrics for classloaders, memory, garbage collection, threads, and others. For more information, see [JVM and system metrics](https://micrometer.io/docs/ref/jvm). 
+JVM and system metrics are runtime metrics that are used to monitor Data Prepper instances. They include metrics for classloaders, memory, garbage collection, threads, and others. For more information, see [JVM and system metrics](https://micrometer.io/?/docs/ref/jvm).
 
 ### Naming
 
-JVM and system metrics follow predefined names in [Micrometer](https://micrometer.io/docs/concepts#_naming_meters). For example, the Micrometer metrics name for memory usage is `jvm.memory.used`. Micrometer changes the name to match the metrics system. Following the same example, `jvm.memory.used` is reported to Prometheus as `jvm_memory_used`, and is reported to Amazon CloudWatch as `jvm.memory.used.value`.
+JVM and system metrics follow predefined names in [Micrometer](https://micrometer.io/?/docs/concepts#_naming_meters). For example, the Micrometer metrics name for memory usage is `jvm.memory.used`. Micrometer changes the name to match the metrics system. Following the same example, `jvm.memory.used` is reported to Prometheus as `jvm_memory_used`, and is reported to Amazon CloudWatch as `jvm.memory.used.value`.
 
 ### Serving
 

_data-prepper/pipelines/configuration/sources/http-source.md

@@ -19,7 +19,7 @@ request_timeout | No | Integer | The request timeout, in milliseconds. Default v
 thread_count | No | Integer | The number of threads to keep in the ScheduledThreadPool. Default value is `200`.
 max_connection_count | No | Integer | The maximum allowed number of open connections. Default value is `500`.
 max_pending_requests | No | Integer | The maximum allowed number of tasks in the `ScheduledThreadPool` work queue. Default value is `1024`.
-authentication | No | Object | An authentication configuration. By default, this creates an unauthenticated server for the pipeline. This uses pluggable authentication for HTTPS. To use basic authentication define the `http_basic` plugin with a `username` and `password`. To provide customer authentication, use or create a plugin that implements [ArmeriaHttpAuthenticationProvider](https://github.com/opensearch-project/data-prepper/blob/main/data-prepper-plugins/armeria-common/src/main/java/com/amazon/dataprepper/armeria/authentication/ArmeriaHttpAuthenticationProvider.java).
+authentication | No | Object | An authentication configuration. By default, this creates an unauthenticated server for the pipeline. This uses pluggable authentication for HTTPS. To use basic authentication define the `http_basic` plugin with a `username` and `password`. To provide customer authentication, use or create a plugin that implements [ArmeriaHttpAuthenticationProvider](https://github.com/opensearch-project/data-prepper/blob/1.2.0/data-prepper-plugins/armeria-common/src/main/java/com/amazon/dataprepper/armeria/authentication/ArmeriaHttpAuthenticationProvider.java).
 ssl | No | Boolean | Enables TLS/SSL. Default value is false.
 ssl_certificate_file | Conditionally | String | SSL certificate chain file path or Amazon Simple Storage Service (Amazon S3) path. Amazon S3 path example `s3://<bucketName>/<path>`. Required if `ssl` is set to true and `use_acm_certificate_for_ssl` is set to false.
 ssl_key_file | Conditionally | String | SSL key file path or Amazon S3 path. Amazon S3 path example `s3://<bucketName>/<path>`. Required if `ssl` is set to true and `use_acm_certificate_for_ssl` is set to false.

_data-prepper/pipelines/configuration/sources/otel-metrics-source.md

@@ -25,7 +25,7 @@ sslKeyFile | Conditionally | String | File-system path or Amazon S3 path to the
 useAcmCertForSSL | No | Boolean | Whether to enable TLS/SSL using a certificate and private key from AWS Certificate Manager (ACM). Default value is `false`.
 acmCertificateArn | Conditionally | String | Represents the ACM certificate ARN. ACM certificate take preference over S3 or local file system certificates. Required if `useAcmCertForSSL` is set to `true`.
 awsRegion | Conditionally | String | Represents the AWS Region used by ACM or Amazon S3. Required if `useAcmCertForSSL` is set to `true` or `sslKeyCertChainFile` and `sslKeyFile` is the Amazon S3 path.
-authentication | No | Object | An authentication configuration. By default, an unauthenticated server is created for the pipeline. This uses pluggable authentication for HTTPS. To use basic authentication, define the `http_basic` plugin with a `username` and `password`. To provide customer authentication, use or create a plugin that implements [GrpcAuthenticationProvider](https://github.com/opensearch-project/data-prepper/blob/main/data-prepper-plugins/armeria-common/src/main/java/com/amazon/dataprepper/armeria/authentication/GrpcAuthenticationProvider.java).
+authentication | No | Object | An authentication configuration. By default, an unauthenticated server is created for the pipeline. This uses pluggable authentication for HTTPS. To use basic authentication, define the `http_basic` plugin with a `username` and `password`. To provide customer authentication, use or create a plugin that implements [GrpcAuthenticationProvider](https://github.com/opensearch-project/data-prepper/blob/1.2.0/data-prepper-plugins/armeria-common/src/main/java/com/amazon/dataprepper/armeria/authentication/GrpcAuthenticationProvider.java).
 
 <!--- ## Configuration
 

_data-prepper/pipelines/configuration/sources/otel-trace.md

@@ -31,7 +31,7 @@ sslKeyFile | Conditionally | String | File system path or Amazon S3 path to the
 useAcmCertForSSL | No | Boolean | Whether to enable TLS/SSL using a certificate and private key from AWS Certificate Manager (ACM). Default value is `false`.
 acmCertificateArn | Conditionally | String | Represents the ACM certificate ARN. ACM certificate take preference over S3 or local file system certificate. Required if `useAcmCertForSSL` is set to `true`.
 awsRegion | Conditionally | String | Represents the AWS region used by ACM or Amazon S3. Required if `useAcmCertForSSL` is set to `true` or `sslKeyCertChainFile` and `sslKeyFile` are Amazon S3 paths.
-authentication | No | Object | An authentication configuration. By default, an unauthenticated server is created for the pipeline. This parameter uses pluggable authentication for HTTPS. To use basic authentication, define the `http_basic` plugin with a `username` and `password`. To provide customer authentication, use or create a plugin that implements [GrpcAuthenticationProvider](https://github.com/opensearch-project/data-prepper/blob/main/data-prepper-plugins/armeria-common/src/main/java/com/amazon/dataprepper/armeria/authentication/GrpcAuthenticationProvider.java).
+authentication | No | Object | An authentication configuration. By default, an unauthenticated server is created for the pipeline. This parameter uses pluggable authentication for HTTPS. To use basic authentication, define the `http_basic` plugin with a `username` and `password`. To provide customer authentication, use or create a plugin that implements [GrpcAuthenticationProvider](https://github.com/opensearch-project/data-prepper/blob/1.2.0/data-prepper-plugins/armeria-common/src/main/java/com/amazon/dataprepper/armeria/authentication/GrpcAuthenticationProvider.java).
 
 
 <!--- ## Configuration

_ml-commons-plugin/algorithms.md

@@ -59,7 +59,7 @@ The training process supports multi-threads, but the number of threads should be
 
 ## Linear regression
 
-Linear regression maps the linear relationship between inputs and outputs. In ML Commons, the linear regression algorithm is adopted from the public machine learning library [Tribuo](https://tribuo.org/), which offers multidimensional linear regression models. The model supports the linear optimizer in training, including popular approaches like Linear Decay, SQRT_DECAY, [ADA](http://chrome-extension//gphandlahdpffmccakmbngmbjnjiiahp/https://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf), [ADAM](https://tribuo.org/learn/4.1/javadoc/org/tribuo/math/optimisers/Adam.html), and [RMS_DROP](https://tribuo.org/learn/4.1/javadoc/org/tribuo/math/optimisers/RMSProp.html). 
+Linear regression maps the linear relationship between inputs and outputs. In ML Commons, the linear regression algorithm is adopted from the public machine learning library [Tribuo](https://tribuo.org/), which offers multidimensional linear regression models. The model supports the linear optimizer in training, including popular approaches like Linear Decay, SQRT_DECAY, [ADA](https://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf), [ADAM](https://tribuo.org/learn/4.1/javadoc/org/tribuo/math/optimisers/Adam.html), and [RMS_DROP](https://tribuo.org/learn/4.1/javadoc/org/tribuo/math/optimisers/RMSProp.html). 
 
 ### Parameters
 

_observing-your-data/ad/index.md

@@ -14,7 +14,7 @@ An anomaly in OpenSearch is any unusual behavior change in your time-series data
 
 It can be challenging to discover anomalies using conventional methods such as creating visualizations and dashboards. You could configure an alert based on a static threshold, but this requires prior domain knowledge and isn't adaptive to data that exhibits organic growth or seasonal behavior.
 
-Anomaly detection  automatically detects anomalies in your OpenSearch data in near real-time using the Random Cut Forest (RCF) algorithm. RCF is an unsupervised machine learning algorithm that models a sketch of your incoming data stream to compute an `anomaly grade` and `confidence score` value for each incoming data point. These values are used to differentiate an anomaly from normal variations. For more information about how RCF works, see [Random Cut Forests](https://api.semanticscholar.org/CorpusID:927435).
+Anomaly detection  automatically detects anomalies in your OpenSearch data in near real-time using the Random Cut Forest (RCF) algorithm. RCF is an unsupervised machine learning algorithm that models a sketch of your incoming data stream to compute an `anomaly grade` and `confidence score` value for each incoming data point. These values are used to differentiate an anomaly from normal variations. For more information about how RCF works, see [Random Cut Forests](https://www.semanticscholar.org/paper/Robust-Random-Cut-Forest-Based-Anomaly-Detection-on-Guha-Mishra/ecb365ef9b67cd5540cc4c53035a6a7bd88678f9).
 
 You can pair the anomaly detection plugin with the [alerting plugin]({{site.url}}{{site.baseurl}}/monitoring-plugins/alerting/) to notify you as soon as an anomaly is detected.