[FSTORE-1921] Improve documentation building process

.github/workflows/mkdocs-release.yml

@@ -3,6 +3,9 @@ name: mkdocs-release
 on:
   push:
     branches: [branch-*\.*]
+  repository_dispatch:
+    types:
+      - trigger-rebuild
 
 concurrency:
   group: ${{ github.workflow }}
@@ -13,25 +16,72 @@ jobs:
     runs-on: ubuntu-latest
 
     steps:
-      - uses: actions/checkout@v4
+      - name: Extract branch name (push)
+        if: ${{ github.event_name == 'push' }}
+        run: echo "BRANCH=${GITHUB_REF#refs/heads/}" >> "$GITHUB_ENV"
+
+      - name: Extract branch name (repository_dispatch)
+        if: ${{ github.event_name == 'repository_dispatch' }}
+        run: echo "BRANCH=${{ github.event.client_payload.branch }}" >> "$GITHUB_ENV"
+
+      - name: Extract version from branch name
+        run: echo "HOPSWORKS_VERSION=${BRANCH#branch-}" >> "$GITHUB_ENV"
+
+      - name: Checkout main repo
+        uses: actions/checkout@v4
         with:
           fetch-depth: 0
+          ref: ${{ env.BRANCH }}
+
+      - name: Checkout the API repo
+        uses: actions/checkout@v4
+        with:
+          # TODO: replace aversey with logicalclocks
+          repository: aversey/hopsworks-api
+          ref: ${{ env.BRANCH }}
+          path: hopsworks-api
+
+      - name: Cache local Maven repository
+        uses: actions/cache@v4
+        with:
+          path: ~/.m2/repository
+          key: ${{ runner.os }}-maven-${{ hashFiles('java/pom.xml') }}
+          restore-keys: |
+            ${{ runner.os }}-maven-
+
+      - name: Set up JDK 8
+        uses: actions/setup-java@v5
+        with:
+          java-version: "8"
+          distribution: "adopt"
+
+      - name: Build javadoc documentation
+        working-directory: hopsworks-api/java
+        run: mvn clean install javadoc:javadoc javadoc:aggregate -DskipTests && cp -r target/site/apidocs ../../docs/javadoc
 
       - uses: actions/setup-python@v5
         with:
           python-version: "3.10"
 
-      - name: Install ubuntu dependencies
-        run: sudo apt update && sudo apt-get install -y libxml2-dev libxslt-dev
+      - name: Install uv
+        uses: astral-sh/setup-uv@v7
+        with:
+          activate-environment: true
+          working-directory: hopsworks-api/python
 
-      - name: install deps
-        run: pip3 install -r requirements-docs.txt
+      - name: Install Python API dependencies
+        run: uv sync --extra dev --group docs --project hopsworks-api/python
+
+      - name: Install Python dependencies
+        run: uv pip install -r requirements-docs.txt
+
+      - name: Install Ubuntu dependencies
+        run: sudo apt update && sudo apt-get install -y libxml2-dev libxslt-dev
 
-      - name: setup git
+      - name: Setup git for mike
         run: |
           git config --global user.name Mike
           git config --global user.email [email protected]
 
-      # Put this back and increment version when cutting a new release branch
-      # - name: mike deploy docs
-      #  run: mike deploy 3.0 latest -u --push
+      - name: Deploy the docs with mike
+        run: mike deploy ${HOPSWORKS_VERSION} latest -u --push

.github/workflows/mkdocs-test.yml

@@ -12,25 +12,60 @@ jobs:
         with:
           fetch-depth: 0
 
+      - name: Checkout the API repo
+        uses: actions/checkout@v4
+        with:
+          # TODO: replace aversey with logicalclocks
+          repository: aversey/hopsworks-api
+          ref: ${{ github.base_ref }}
+          path: hopsworks-api
+
+      - name: Markdownlint
+        uses: DavidAnson/markdownlint-cli2-action@v21
+        with:
+          globs: '**/*.md'
+
+      - name: Cache local Maven repository
+        uses: actions/cache@v4
+        with:
+          path: ~/.m2/repository
+          key: ${{ runner.os }}-maven-${{ hashFiles('java/pom.xml') }}
+          restore-keys: |
+            ${{ runner.os }}-maven-
+
+      - name: Set up JDK 8
+        uses: actions/setup-java@v5
+        with:
+          java-version: "8"
+          distribution: "adopt"
+
+      - name: Build javadoc documentation
+        working-directory: hopsworks-api/java
+        run: mvn clean install javadoc:javadoc javadoc:aggregate -DskipTests && cp -r target/site/apidocs ../../docs/javadoc
+
       - uses: actions/setup-python@v5
         with:
           python-version: "3.10"
 
-      - name: Install ubuntu dependencies
-        run: sudo apt update && sudo apt-get install -y libxml2-dev libxslt-dev
+      - name: Install uv
+        uses: astral-sh/setup-uv@v7
+        with:
+          activate-environment: true
+          working-directory: hopsworks-api/python
 
-      - name: install deps
-        run: pip3 install -r requirements-docs.txt
+      - name: Install Python API dependencies
+        run: uv sync --extra dev --group docs --project hopsworks-api/python
 
-      - name: setup git
-        run: |
-          git config --global user.name Mike
-          git config --global user.email [email protected]
+      - name: Install Python dependencies
+        run: uv pip install -r requirements-docs.txt
+
+      - name: Install Ubuntu dependencies
+        run: sudo apt update && sudo apt-get install -y libxml2-dev libxslt-dev
 
-      - name: test broken links
+      - name: Check for broken links
         run: |
           # run the server
-          mkdocs serve  > /dev/null 2>&1 &
+          mkdocs serve > /dev/null 2>&1 &
           SERVER_PID=$!
           echo "mk server in PID $SERVER_PID"
           # Give enough time for deployment
@@ -41,5 +76,10 @@ jobs:
           # If ok just kill the server
           kill -9 $SERVER_PID
 
-      - name: mike deploy docs
+      - name: Setup git for mike
+        run: |
+          git config --global user.name Mike
+          git config --global user.email [email protected]
+
+      - name: Generate the docs with mike
         run: mike deploy 3.2-SNAPSHOT dev -u

.markdownlint.yaml

@@ -0,0 +1,7 @@
+MD041: false
+MD013: false
+MD033: false
+MD045: false
+MD046: false
+MD004:
+  style: dash

README.md

@@ -1,6 +1,6 @@
 # Documentation landing page
 
-This is the source of the landing page for https://docs.hopsworks.ai
+This is the source of the landing page for <https://docs.hopsworks.ai>
 
 ## Build instructions
 
@@ -35,11 +35,11 @@ Use mkdocs to build the documentation and serve it locally
 {PY_ENV}/bin/mkdocs serve
 ```
 
-The documentation should now be available locally on the following URL: http://127.0.0.1:8000/
+The documentation should now be available locally on the following URL: <http://127.0.0.1:8000/>
 
 ## Adding new pages
 
-The `mkdocs.yml` file of this repository defines the pages to show in the navigation. 
+The `mkdocs.yml` file of this repository defines the pages to show in the navigation.
 After adding your new page in the docs folder, you also need to add it to this file for it to show up in the navigation.
 
 ## Checking links
@@ -56,4 +56,4 @@ linkchecker http://127.0.0.1:8000/
 
 # If ok just kill the server
 kill -9 $SERVER_PID
-```
+```

docs/concepts/dev/inside.md

@@ -1,34 +1,46 @@
-Hopsworks provides a complete self-service development environment for feature engineering and model training. You can develop programs as Jupyter notebooks or jobs, customize the bundled FTI (feature, training and inference pipeline) python environments, you can manage your source code with Git, and you can orchestrate jobs with Airflow.
 
-<img src="../../../assets/images/concepts/dev/dev-inside.svg">
+Hopsworks provides a complete self-service development environment for feature engineering and model training.
+You can develop programs as Jupyter notebooks or jobs, customize the bundled FTI (feature, training and inference pipeline) python environments, you can manage your source code with Git, and you can orchestrate jobs with Airflow.
+
+<img src="../../../assets/images/concepts/dev/dev-inside.svg" alt="Hopsworks Development Environment" />
 
 ### Jupyter Notebooks
 
-Hopsworks provides a Jupyter notebook development environment for programs written in Python, Spark, Flink, and SparkSQL. You can also develop in your IDE (PyCharm, IntelliJ, etc), test locally, and then run your programs as Jobs in Hopsworks. Jupyter notebooks can also be run as Jobs.
+Hopsworks provides a Jupyter notebook development environment for programs written in Python, Spark, Flink, and SparkSQL.
+You can also develop in your IDE (PyCharm, IntelliJ, etc), test locally, and then run your programs as Jobs in Hopsworks.
+Jupyter notebooks can also be run as Jobs.
 
 ### Source Code Control
 
-Hopsworks provides source code control support using Git (GitHub, GitLab or BitBucket). You can securely checkout code into your project and commit and push updates to your code to your source code repository. 
+Hopsworks provides source code control support using Git (GitHub, GitLab or BitBucket).
+You can securely check out code into your project and commit and push updates to your code to your source code repository.
 
 ### FTI Pipeline Environments
 
-Hopsworks postulates that building ML systems following the FTI pipeline architecture is best practice. This architecture consists of three independently developed and operated ML pipelines:
+Hopsworks postulates that building ML systems following the FTI pipeline architecture is best practice.
+This architecture consists of three independently developed and operated ML pipelines:
 
-* Feature pipeline: takes as input raw data that it transforms into features (and labels)
-* Training pipeline: takes as input features (and labels) and outputs a trained model
-* Inference pipeline: takes new feature data and a trained model and makes predictions
+- Feature pipeline: takes as input raw data that it transforms into features (and labels)
+- Training pipeline: takes as input features (and labels) and outputs a trained model
+- Inference pipeline: takes new feature data and a trained model and makes predictions
 
-In order to facilitate the development of these pipelines Hopsworks bundles several python environments containing necessary dependencies. Each of these environments may then also be customized further by cloning it and installing additional dependencies from PyPi, Conda channels, Wheel files, GitHub repos or a custom Dockerfile. Internal compute such as Jobs and Jupyter is run in one of these environments and changes are applied transparently when you install new libraries using our APIs. That is, there is no need to write a Dockerfile, users install libraries directly in one or more of the environments. You can setup custom development and production environments by creating separate projects or creating multiple clones of an environment within the same project.
+In order to facilitate the development of these pipelines Hopsworks bundles several python environments containing necessary dependencies.
+Each of these environments may then also be customized further by cloning it and installing additional dependencies from PyPi, Conda channels, Wheel files, GitHub repos or a custom Dockerfile.
+Internal compute such as Jobs and Jupyter is run in one of these environments and changes are applied transparently when you install new libraries using our APIs.
+That is, there is no need to write a Dockerfile, users install libraries directly in one or more of the environments.
+You can setup custom development and production environments by creating separate projects or creating multiple clones of an environment within the same project.
 
 ### Jobs
 
-In Hopsworks, a Job is a schedulable program that is allocated compute and memory resources. You can run a Job in Hopsworks:
+In Hopsworks, a Job is a schedulable program that is allocated compute and memory resources.
+You can run a Job in Hopsworks:
 
-* From the UI
-* Programmatically with the Hopsworks SDK (Python, Java) or REST API
-* From Airflow programs (either inside our outside Hopsworks)
-* From your IDE using a plugin ([PyCharm/IntelliJ plugin](https://plugins.jetbrains.com/plugin/15537-hopsworks))
+- From the UI
+- Programmatically with the Hopsworks SDK (Python, Java) or REST API
+- From Airflow programs (either inside our outside Hopsworks)
+- From your IDE using a plugin ([PyCharm/IntelliJ plugin](https://plugins.jetbrains.com/plugin/15537-hopsworks))
 
 ### Orchestration
 
-Airflow comes out-of-the box with Hopsworks, but you can also use an external Airflow cluster (with the Hopsworks Job operator) if you have one. Airflow can be used to schedule the execution of Jobs, individually or as part of Airflow DAGs.
+Airflow comes out-of-the box with Hopsworks, but you can also use an external Airflow cluster (with the Hopsworks Job operator) if you have one.
+Airflow can be used to schedule the execution of Jobs, individually or as part of Airflow DAGs.

docs/concepts/dev/outside.md

@@ -1,5 +1,8 @@
-You can write programs that use Hopsworks in any [Python, Spark, PySpark, or Flink environment](../../user_guides/integrations/index.md). Hopsworks also running SQL queries to compute features in external data warehouses. The Feature Store can also be queried with SQL.
+You can write programs that use Hopsworks in any [Python, Spark, PySpark, or Flink environment](../../user_guides/integrations/index.md).
+Hopsworks also running SQL queries to compute features in external data warehouses.
+The Feature Store can also be queried with SQL.
 
-There is REST API for Hopsworks that can be used with a valid API key, generated in Hopsworks. However, it is often easier to develop your programs against SDKs available in Python and Java/Scala for HSFS, in Python for HSML, and in Python for the Hopsworks API.
+There is REST API for Hopsworks that can be used with a valid API key, generated in Hopsworks.
+However, it is often easier to develop your programs against SDKs available in Python and Java/Scala for HSFS, in Python for HSML, and in Python for the Hopsworks API.
 
 <img src="../../../assets/images/concepts/dev/dev-outside.svg">

docs/concepts/fs/feature_group/external_fg.md

@@ -1,6 +1,9 @@
-External feature groups are offline feature groups where their data is stored in an external table. An external table requires a data source, defined with the Connector API (or more typically in the user interface), to enable HSFS to retrieve data from the external table. An external feature group doesn't allow for offline data ingestion or modification; instead, it includes a user-defined SQL string for retrieving data. You can also perform SQL operations, including projections, aggregations, and so on. The SQL query is executed on-demand when HSFS retrieves data from the external Feature Group, for example, when creating training data using features in the external table.
+External feature groups are offline feature groups where their data is stored in an external table.
+An external table requires a data source, defined with the Connector API (or more typically in the user interface), to enable HSFS to retrieve data from the external table.
+An external feature group doesn't allow for offline data ingestion or modification; instead, it includes a user-defined SQL string for retrieving data.
+You can also perform SQL operations, including projections, aggregations, and so on.
+The SQL query is executed on-demand when HSFS retrieves data from the external Feature Group, for example, when creating training data using features in the external table.
 
 In the image below, we can see that HSFS currently supports a large number of data sources, including any JDBC-enabled source, Snowflake, Data Lake, Redshift, BigQuery, S3, ADLS, GCS, RDS, and Kafka
 
 <img src="../../../../assets/images/concepts/fs/fg-connector-api.svg">
-

docs/concepts/fs/feature_group/feature_monitoring.md

@@ -8,13 +8,12 @@ HSFS supports monitoring features on your Feature Group by:
 
 ## Scheduled Statistics
 
-After creating a Feature Group in HSFS, you can setup statistics monitoring to compute statistics over one or more features on a scheduled basis. Statistics are computed on the whole or a subset of feature data (i.e., detection window) already inserted into the Feature Group.
+After creating a Feature Group in HSFS, you can setup statistics monitoring to compute statistics over one or more features on a scheduled basis.
+Statistics are computed on the whole or a subset of feature data (i.e., detection window) already inserted into the Feature Group.
 
 ## Statistics Comparison
 
 In addition to scheduled statistics, you can enable the comparison of statistics against a reference subset of feature data (i.e., reference window) and define the criteria for this comparison including the statistics metric to compare and a threshold to identify anomalous values.
 
 !!! info "Feature Monitoring Guide"
     More information can be found in the [Feature monitoring guide](../../../user_guides/fs/feature_monitoring/index.md).
-
-