Feature/lrangine master

.github/workflows/java_master_only.yml

@@ -72,13 +72,13 @@ jobs:
           java-version: '11'
           java-package: jdk
           architecture: x64
-      - uses: actions/cache@v2
+      - uses: actions/cache@v4
         with:
           path: ~/.m2/repository
           key: ${{ runner.os }}-it-maven-${{ hashFiles('**/pom.xml') }}
           restore-keys: |
             ${{ runner.os }}-it-maven-
-      - uses: actions/cache@v2
+      - uses: actions/cache@v4
         with:
           path: ~/.m2/repository
           key: ${{ runner.os }}-ut-maven-${{ hashFiles('**/pom.xml') }}

.github/workflows/java_pr.yml

@@ -53,13 +53,13 @@ jobs:
           java-version: '11'
           java-package: jdk
           architecture: x64
-      - uses: actions/cache@v2
+      - uses: actions/cache@v4
         with:
           path: ~/.m2/repository
           key: ${{ runner.os }}-it-maven-${{ hashFiles('**/pom.xml') }}
           restore-keys: |
             ${{ runner.os }}-it-maven-
-      - uses: actions/cache@v2
+      - uses: actions/cache@v4
         with:
           path: ~/.m2/repository
           key: ${{ runner.os }}-ut-maven-${{ hashFiles('**/pom.xml') }}
@@ -97,11 +97,11 @@ jobs:
           python-version: "3.11"
           architecture: x64
       - name: Authenticate to Google Cloud
-        uses: 'google-github-actions/auth@v1'
+        uses: google-github-actions/auth@v2
         with:
           credentials_json: '${{ secrets.GCP_SA_KEY }}'
       - name: Set up gcloud SDK
-        uses: google-github-actions/setup-gcloud@v1
+        uses: google-github-actions/setup-gcloud@v2
         with:
           project_id: ${{ secrets.GCP_PROJECT_ID }}
       - run: gcloud auth configure-docker --quiet
@@ -137,18 +137,18 @@ jobs:
         with:
           python-version: '3.11'
           architecture: 'x64'
-      - uses: actions/cache@v2
+      - uses: actions/cache@v4
         with:
           path: ~/.m2/repository
           key: ${{ runner.os }}-it-maven-${{ hashFiles('**/pom.xml') }}
           restore-keys: |
             ${{ runner.os }}-it-maven-
       - name: Authenticate to Google Cloud
-        uses: 'google-github-actions/auth@v1'
+        uses: google-github-actions/auth@v2
         with:
           credentials_json: '${{ secrets.GCP_SA_KEY }}'
       - name: Set up gcloud SDK
-        uses: google-github-actions/setup-gcloud@v1
+        uses: google-github-actions/setup-gcloud@v2
         with:
           project_id: ${{ secrets.GCP_PROJECT_ID }}
       - name: Use gcloud CLI

.github/workflows/lint_pr.yml

@@ -14,7 +14,7 @@ jobs:
     name: Validate PR title
     runs-on: ubuntu-latest
     steps:
-      - uses: amannn/action-semantic-pull-request@v4
+      - uses: amannn/action-semantic-pull-request@v5
         with:
           # Must use uppercase
           subjectPattern: ^(?=[A-Z]).+$

.github/workflows/operator_pr.yml

@@ -7,7 +7,7 @@ jobs:
     steps:
       - uses: actions/checkout@v4
       - name: Install Go
-        uses: actions/setup-go@v2
+        uses: actions/setup-go@v5
         with:
           go-version: 1.21.x
       - name: Operator tests

.github/workflows/pr_local_integration_tests.yml

@@ -45,7 +45,7 @@ jobs:
       - name: Get uv cache dir
         id: uv-cache
         run: |
-          echo "::set-output name=dir::$(uv cache dir)"
+          echo "dir=$(uv cache dir)" >> $GITHUB_OUTPUT
       - name: uv cache
         uses: actions/cache@v4
         with:

.github/workflows/smoke_tests.yml

@@ -31,7 +31,7 @@ jobs:
       - name: Get uv cache dir
         id: uv-cache
         run: |
-          echo "::set-output name=dir::$(uv cache dir)"
+          echo "dir=$(uv cache dir)" >> $GITHUB_OUTPUT
       - name: uv cache
         uses: actions/cache@v4
         with:

.github/workflows/unit_tests.yml

@@ -33,8 +33,8 @@ jobs:
           curl -LsSf https://astral.sh/uv/install.sh | sh
       - name: Get uv cache dir
         id: uv-cache
-        run: |
-          echo "::set-output name=dir::$(uv cache dir)"
+        run: |          
+          echo "dir=$(uv cache dir)" >> $GITHUB_OUTPUT
       - name: uv cache
         uses: actions/cache@v4
         with:
@@ -52,7 +52,7 @@ jobs:
       NPM_TOKEN: ${{ secrets.NPM_TOKEN }}
     steps:
       - uses: actions/checkout@v4
-      - uses: actions/setup-node@v3
+      - uses: actions/setup-node@v4
         with:
           node-version-file: './ui/.nvmrc'
           registry-url: 'https://registry.npmjs.org'

docs/README.md

@@ -42,12 +42,15 @@ serving system must make a request to the feature store to retrieve feature valu
 
 ## Who is Feast for?
 
-Feast helps ML platform/MLOps teams with DevOps experience productionize real-time models. Feast also helps these teams 
-build a feature platform that improves collaboration between data engineers, software engineers, machine learning 
-engineers, and data scientists.
+Feast helps ML platform/MLOps teams with DevOps experience productionize real-time models. Feast also helps these teams build a feature platform that improves collaboration between data engineers, software engineers, machine learning engineers, and data scientists.
 
-Feast is likely **not** the right tool if you
-* are in an organization that’s just getting started with ML and is not yet sure what the business impact of ML is
+* *For Data Scientists*: Feast is a a tool where you can easily define, store, and retrieve your features for both model development and model deployment. By using Feast, you can focus on what you do best: build features that power your AI/ML models and maximize the value of your data.
+
+* *For MLOps Engineers*: Feast is a library that allows you to connect your existing infrastructure (e.g., online database, application server, microservice, analytical database, and orchestration tooling) that enables your Data Scientists to ship features for their models to production using a friendly SDK without having to be concerned with software engineering challenges that occur from serving real-time production systems. By using Feast, you can focus on maintaining a resilient system, instead of implementing features for Data Scientists.
+
+* *For Data Engineers*: Feast provides a centralized catalog for storing feature definitions allowing one to maintain a single source of truth for feature data. It provides the abstraction for reading and writing to many different types of offline and online data stores. Using either the provided python SDK or the feature server service, users can write data to the online and/or offline stores and then read that data out again in either low-latency online scenarios for model inference, or in batch scenarios for model training.
+
+* *For AI Engineers*: Feast provides a platform designed to scale your AI applications by enabling seamless integration of richer data and facilitating fine-tuning. With Feast, you can optimize the performance of your AI models while ensuring a scalable and efficient data pipeline.
 
 ## What Feast is not?
 

docs/getting-started/quickstart.md

@@ -10,6 +10,9 @@ Feast (Feature Store) is an open-source feature store designed to facilitate the
 
 * *For Data Engineers*: Feast provides a centralized catalog for storing feature definitions allowing one to maintain a single source of truth for feature data. It provides the abstraction for reading and writing to many different types of offline and online data stores. Using either the provided python SDK or the feature server service, users can write data to the online and/or offline stores and then read that data out again in either low-latency online scenarios for model inference, or in batch scenarios for model training.
 
+* *For AI Engineers*: Feast provides a platform designed to scale your AI applications by enabling seamless integration of richer data and facilitating fine-tuning. With Feast, you can optimize the performance of your AI models while ensuring a scalable and efficient data pipeline.
+
+
 For more info refer to [Introduction to feast](../README.md)
 
 ## Prerequisites

docs/how-to-guides/starting-feast-servers-tls-mode.md

@@ -189,3 +189,8 @@ INFO:     Waiting for application startup.
 INFO:     Application startup complete.
 INFO:     Uvicorn running on https://0.0.0.0:8888 (Press CTRL+C to quit)
 ```
+
+
+## Adding public key to CA trust store and configuring the feast to use the trust store.
+You can pass the public key for SSL verification using the `cert` parameter, however, it is sometimes difficult to maintain individual certificates and pass them individually.
+The alternative recommendation is to add the public certificate to CA trust store and set the path as an environment variable (e.g., `FEAST_CA_CERT_FILE_PATH`). Feast will use the trust store path in the  `FEAST_CA_CERT_FILE_PATH` environment variable.

examples/python-helm-demo/README.md

@@ -3,87 +3,168 @@
 
 For this tutorial, we set up Feast with Redis. 
 
-We use the Feast CLI to register and materialize features, and then retrieving via a Feast Python feature server deployed in Kubernetes
+We use the Feast CLI to register and materialize features from the current machine, and then retrieving via a 
+Feast Python feature server deployed in Kubernetes
 
 ## First, let's set up a Redis cluster
 1.  Start minikube (`minikube start`)
-2.  Use helm to install a default Redis cluster
+1.  Use helm to install a default Redis cluster
     ```bash
     helm repo add bitnami https://charts.bitnami.com/bitnami 
     helm repo update 
     helm install my-redis bitnami/redis
     ```
     ![](redis-screenshot.png)
-3. Port forward Redis so we can materialize features to it
+1. Port forward Redis so we can materialize features to it
 
     ```bash
     kubectl port-forward --namespace default svc/my-redis-master 6379:6379
     ```
-4. Get your Redis password using the command (pasted below for convenience). We'll need this to tell Feast how to communicate with the cluster.
+1. Get your Redis password using the command (pasted below for convenience). We'll need this to tell Feast how to communicate with the cluster.
 
    ```bash
     export REDIS_PASSWORD=$(kubectl get secret --namespace default my-redis -o jsonpath="{.data.redis-password}" | base64 --decode)
     echo $REDIS_PASSWORD
     ```
 
+## Then, let's set up a MinIO S3 store
+Manifests have been taken from [Deploy Minio in your project](https://ai-on-openshift.io/tools-and-applications/minio/minio/#deploy-minio-in-your-project).
+
+1. Deploy MinIO instance:
+    ```
+    kubectl apply -f minio-dev.yaml
+    ```
+
+1. Forward the UI port:
+    ```console
+    kubectl port-forward svc/minio-service 9090:9090
+    ```
+1. Login to (localhost:9090)[http://localhost:9090] as `minio`/`minio123` and create bucket called `feast-demo`.
+1. Stop previous port forwarding and forward the API port instead:
+    ```console
+    kubectl port-forward svc/minio-service 9000:9000
+    ```
+
 ## Next, we setup a local Feast repo
-1. Install Feast with Redis dependencies `pip install "feast[redis]"`
-2. Make a bucket in GCS (or S3)
-3. The feature repo is already setup here, so you just need to swap in your GCS bucket and Redis credentials.
-    We need to modify the `feature_store.yaml`, which has two fields for you to replace:
+1. Install Feast with Redis dependencies `pip install "feast[redis,aws]"`
+1. The feature repo is already setup here, so you just need to swap in your Redis credentials.
+    We need to modify the `feature_store.yaml`, which has one field for you to replace:
+    ```console
+    sed "s/_REDIS_PASSWORD_/${REDIS_PASSWORD}/" feature_repo/feature_store.yaml.template > feature_repo/feature_store.yaml
+    cat feature_repo/feature_store.yaml
+    ```
+
+    Example repo:
     ```yaml
-    registry: gs://[YOUR GCS BUCKET]/demo-repo/registry.db
+    registry: s3://localhost:9000/feast-demo/registry.db
     project: feast_python_demo
-    provider: gcp
+    provider: local
     online_store:
       type: redis
-      # Note: this would normally be using instance URL's to access Redis
-      connection_string: localhost:6379,password=[YOUR PASSWORD]
+      connection_string: localhost:6379,password=****
     offline_store:
       type: file
     entity_key_serialization_version: 2
     ```
-4. Run `feast apply` from within the `feature_repo` directory to apply your local features to the remote registry
-   - Note: you may need to authenticate to gcloud first with `gcloud auth login`
-5. Materialize features to the online store:
+1. To run `feast apply` from the current machine we need to define the AWS credentials to connect the MinIO S3 store, which
+are defined in [minio.env](./minio.env):
+    ```console
+    source minio.env
+    cd  feature_repo
+    feast apply
+    ```
+1. Let's validate the setup by running some queries
+    ```console
+    feast entities list
+    feast feature-views list
+    ```
+1. Materialize features to the online store:
     ```bash
+    cd  feature_repo
     CURRENT_TIME=$(date -u +"%Y-%m-%dT%H:%M:%S")                                    
     feast materialize-incremental $CURRENT_TIME
     ``` 
 
 ## Now let's setup the Feast Server
-1. Add the gcp-auth addon to mount GCP credentials:
-    ```bash
-   minikube addons enable gcp-auth
-   ```
-2. Add Feast's Python/Go feature server chart repo
+1. Add Feast's Python feature server chart repo
     ```bash
     helm repo add feast-charts https://feast-helm-charts.storage.googleapis.com
     helm repo update
     ```
-3. For this tutorial, because we don't have a direct hosted endpoint into Redis, we need to change `feature_store.yaml` to talk to the Kubernetes Redis service 
-   ```bash 
-   sed -i '' 's/localhost:6379/my-redis-master:6379/g' feature_store.yaml
-   ``` 
-4. Install the Feast helm chart: `helm install feast-release feast-charts/feast-feature-server --set feature_store_yaml_base64=$(base64 feature_store.yaml)`
-   > **Dev instructions**: if you're changing the java logic or chart, you can do 
-   1. `eval $(minikube docker-env)`
-   2. `make build-feature-server-dev`
-   3. `helm install feast-release ../../../infra/charts/feast-feature-server --set image.tag=dev --set feature_store_yaml_base64=$(base64 feature_store.yaml)`
-5. (Optional): check logs of the server to make sure it’s working
+1. For this tutorial, we'll use a predefined configuration where we just needs to inject the Redis service password: 
+    ```console
+    sed "s/_REDIS_PASSWORD_/$REDIS_PASSWORD/" online_feature_store.yaml.template > online_feature_store.yaml
+    cat online_feature_store.yaml
+    ```
+    As you see, the connection points to `my-redis-master:6379` instead of `localhost:6379`.
+
+1. Install the Feast helm chart: 
+    ```console
+    helm upgrade --install feast-online feast-charts/feast-feature-server \
+    --set fullnameOverride=online-server --set feast_mode=online \
+    --set feature_store_yaml_base64=$(base64 -i 'online_feature_store.yaml')
+    ```
+1. Patch the deployment to include MinIO settings:
+    ```console
+    kubectl patch deployment online-server --type='json' -p='[
+      {
+        "op": "add",
+        "path": "/spec/template/spec/containers/0/env/-",
+        "value": {
+          "name": "AWS_ACCESS_KEY_ID",
+          "value": "minio"
+        }
+      },
+      {
+        "op": "add",
+        "path": "/spec/template/spec/containers/0/env/-",
+        "value": {
+          "name": "AWS_SECRET_ACCESS_KEY",
+          "value": "minio123"
+        }
+      },
+      {
+        "op": "add",
+        "path": "/spec/template/spec/containers/0/env/-",
+        "value": {
+          "name": "AWS_DEFAULT_REGION",
+          "value": "default"
+        }
+      },
+      {
+        "op": "add",
+        "path": "/spec/template/spec/containers/0/env/-",
+        "value": {
+          "name": "FEAST_S3_ENDPOINT_URL",
+          "value": "http://minio-service:9000"
+        }
+      }
+    ]'
+    kubectl wait --for=condition=available deployment/online-server --timeout=2m
+    ```
+1. (Optional): check logs of the server to make sure it’s working
    ```bash
-   kubectl logs svc/feast-release-feast-feature-server
+   kubectl logs svc/online-server
    ```
-6. Port forward to expose the grpc endpoint:
+1. Port forward to expose the grpc endpoint:
    ```bash
-   kubectl port-forward svc/feast-release-feast-feature-server 6566:80
+   kubectl port-forward svc/online-server 6566:80
    ```
-7. Run test fetches for online features:8. 
-    - First: change back the Redis connection string to allow localhost connections to Redis
+1. Run test fetches for online features:8. 
       ```bash
-      sed -i '' 's/my-redis-master:6379/localhost:6379/g' feature_store.yaml
+      source minio.env
+      cd test
+      python test_python_fetch.py
       ```
-    - Then run the included fetch script, which fetches both via the HTTP endpoint and for comparison, via the Python SDK
-      ```bash
-        python test_python_fetch.py
+
+      Output example:
+      ```console
+      --- Online features with SDK ---
+      WARNING:root:_list_feature_views will make breaking changes. Please use _list_batch_feature_views instead. _list_feature_views will behave like _list_all_feature_views in the future.
+      conv_rate  :  [0.6799587607383728, 0.9761165976524353]
+      driver_id  :  [1001, 1002]
+
+      --- Online features with HTTP endpoint ---
+      conv_rate  :  [0.67995876 0.9761166 ]
+      driver_id  :  [1001 1002]      
       ```

examples/python-helm-demo/feature_repo/feature_store.yaml.template

@@ -0,0 +1,9 @@
+registry: s3://feast-demo/registry.db
+project: feast_python_demo
+provider: local
+online_store:
+  type: redis
+  connection_string: localhost:6379,password=_REDIS_PASSWORD_
+offline_store:
+  type: file
+entity_key_serialization_version: 2