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Fixing the Java helm charts and adding a demo tutorial on how to use them (feast-dev#2298)
* Fixing helm chart and adding java tutorial Signed-off-by: Danny Chiao <danny@tecton.ai> * fix chart version Signed-off-by: Danny Chiao <danny@tecton.ai> * test Signed-off-by: Danny Chiao <danny@tecton.ai> * Parse the overrides to not pass enabled Signed-off-by: Danny Chiao <danny@tecton.ai> * Disable secrets by default Signed-off-by: Danny Chiao <danny@tecton.ai> * Change secrets default Signed-off-by: Danny Chiao <danny@tecton.ai> * revert Signed-off-by: Danny Chiao <danny@tecton.ai> * Update README Signed-off-by: Danny Chiao <danny@tecton.ai> * finally Signed-off-by: Danny Chiao <danny@tecton.ai> * remove credentials Signed-off-by: Danny Chiao <danny@tecton.ai> * new line Signed-off-by: Danny Chiao <danny@tecton.ai> * new line Signed-off-by: Danny Chiao <danny@tecton.ai> * new line Signed-off-by: Danny Chiao <danny@tecton.ai> * Add reference from Gitbook Signed-off-by: Danny Chiao <danny@tecton.ai> * remove description Signed-off-by: Danny Chiao <danny@tecton.ai>
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‎docs/SUMMARY.md‎

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* [Load data into the online store](how-to-guides/feast-snowflake-gcp-aws/load-data-into-the-online-store.md)
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* [Read features from the online store](how-to-guides/feast-snowflake-gcp-aws/read-features-from-the-online-store.md)
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* [Running Feast in production](how-to-guides/running-feast-in-production.md)
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* [Deploying a Java feature server on Kubernetes](how-to-guides/fetching-java-features-k8s.md)
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* [Upgrading from Feast 0.9](https://docs.google.com/document/u/1/d/1AOsr\_baczuARjCpmZgVd8mCqTF4AZ49OEyU4Cn-uTT0/edit)
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* [Adding a custom provider](how-to-guides/creating-a-custom-provider.md)
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* [Adding a new online store](how-to-guides/adding-support-for-a-new-online-store.md)

‎docs/how-to-guides/fetching-java-features-k8s.md‎

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# How to set up a Java feature server
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This tutorial guides you on how to:
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* Define features and data sources in Feast using the Feast CLI
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* Materialize features to a Redis cluster deployed on Kubernetes.
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* Deploy a Feast Java feature server into a Kubernetes cluster using the Feast helm charts
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* Retrieve features using the gRPC API exposed by the Feast Java server
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Try it and let us know what you think!
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| ![](../.gitbook/assets/github-mark-32px.png)[ View guide in Github](../../examples/java-demo/README.md) |
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|:--------------------------------------------------------------------------------------------------------|
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‎examples/java-demo/README.md‎

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# Running Feast Java Server with Redis & calling with python (with registry in GCP)
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For this tutorial, we setup Feast with Redis, using the Feast CLI to register and materialize features, and then retrieving via a Feast Java server deployed in Kubernetes via a gRPC call.
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> :point_right: for tips on how to run and debug this locally without using Kubernetes, see [java/serving/README.md](https://github.com/feast-dev/feast/blob/master/java/serving/README.md)
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## First, let's setup a Redis cluster
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1. Start minikube (`minikube start`)
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2. Use helm to install a default Redis cluster
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```bash
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helm repo add bitnami https://charts.bitnami.com/bitnami
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helm repo update
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helm install my-redis bitnami/redis
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```
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![](redis-screenshot.png)
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3. Port forward Redis so we can materialize features to it
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```bash
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kubectl port-forward --namespace default svc/my-redis-master 6379:6379
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```
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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.
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```bash
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export REDIS_PASSWORD=$(kubectl get secret --namespace default my-redis -o jsonpath="{.data.redis-password}" | base64 --decode)
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echo $REDIS_PASSWORD
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```
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## Next, we setup a local Feast repo
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1. Install Feast with Redis dependencies `pip install "feast[redis]"`
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2. Make a bucket in GCS (or S3)
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3. The feature repo is already setup here, so you just need to swap in your GCS bucket and Redis credentials.
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We need to modify the `feature_store.yaml`, which has two fields for you to replace:
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```yaml
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registry: gs://[YOUR BUCKET]/demo-repo/registry.db
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project: feast_java_demo
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provider: gcp
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online_store:
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type: redis
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connection_string: localhost:6379,password=[YOUR PASSWORD]
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offline_store:
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type: file
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flags:
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alpha_features: true
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on_demand_transforms: true
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```
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4. Run `feast apply` to apply your local features to the remote registry
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5. Materialize features to the online store:
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```bash
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CURRENT_TIME=$(date -u +"%Y-%m-%dT%H:%M:%S")
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feast materialize-incremental $CURRENT_TIME
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```
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## Now let's setup the Feast Server
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1. Add the gcp-auth addon to mount GCP credentials:
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```bash
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minikube addons enable gcp-auth
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```
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3. Add Feast's Java feature server chart repo
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```bash
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helm repo add feast-charts https://feast-helm-charts.storage.googleapis.com
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helm repo update
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```
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4. Modify the application-override.yaml file to have your credentials + bucket location:
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```yaml
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feature-server:
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application-override.yaml:
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enabled: true
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feast:
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activeStore: online
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stores:
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- name: online
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type: REDIS
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config:
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host: my-redis-master
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port: 6379
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password: [YOUR PASSWORD]
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global:
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registry:
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path: gs://[YOUR BUCKET]/demo-repo/registry.db
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cache_ttl_seconds: 60
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project: feast_java_demo
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```
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5. Install the Feast helm chart: `helm install feast-release feast-charts/feast --values application-override.yaml`
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6. (Optional): check logs of the server to make sure it’s working
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```bash
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kubectl logs svc/feast-release-feature-server
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```
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7. Port forward to expose the grpc endpoint:
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```bash
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kubectl port-forward svc/feast-release-feature-server 6566:6566
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```
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8. Make a gRPC call:
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- Python example
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```bash
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python test.py
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```
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- gRPC cli:
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```bash
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grpc_cli call localhost:6566 GetOnlineFeatures '
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features {
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val: "driver_hourly_stats:conv_rate"
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val: "driver_hourly_stats:acc_rate"
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}
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entities {
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key: "driver_id"
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value {
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val {
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int64_val: 1001
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}
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val {
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int64_val: 1002
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}
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}
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}'
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```
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- Response:
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```bash
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connecting to localhost:6566
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metadata {
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feature_names {
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val: "driver_hourly_stats:conv_rate"
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val: "driver_hourly_stats:acc_rate"
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}
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}
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results {
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values {
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float_val: 0.812357187
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}
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values {
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float_val: 0.379484832
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}
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statuses: PRESENT
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statuses: PRESENT
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event_timestamps {
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seconds: 1631725200
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}
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event_timestamps {
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seconds: 1631725200
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}
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}
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results {
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values {
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float_val: 0.840873241
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}
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values {
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float_val: 0.151376978
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}
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statuses: PRESENT
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statuses: PRESENT
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event_timestamps {
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seconds: 1631725200
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}
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event_timestamps {
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seconds: 1631725200
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}
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}
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Rpc succeeded with OK status
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```

‎examples/java-demo/feature_repo/__init__.py‎

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‎examples/java-demo/feature_repo/application-override.yaml‎

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feature-server:
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application-override.yaml:
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enabled: true
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feast:
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activeStore: online
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stores:
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- name: online
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type: REDIS
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config:
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host: my-redis-master
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port: 6379
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password: [YOUR PASSWORD]
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global:
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registry:
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path: gs://[YOUR BUCKET]/demo-repo/registry.db
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cache_ttl_seconds: 60
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project: feast_java_demo

‎examples/java-demo/feature_repo/data/driver_stats_with_string.parquet‎

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‎examples/java-demo/feature_repo/driver_repo.py‎

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import pandas as pd
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from feast import Entity, Feature, FeatureView, FileSource, ValueType
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from feast.data_source import RequestDataSource
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from feast.on_demand_feature_view import on_demand_feature_view
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from feast.request_feature_view import RequestFeatureView
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from google.protobuf.duration_pb2 import Duration
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driver_hourly_stats = FileSource(
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path="data/driver_stats_with_string.parquet",
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event_timestamp_column="event_timestamp",
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created_timestamp_column="created",
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)
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driver = Entity(name="driver_id", value_type=ValueType.INT64, description="driver id",)
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driver_hourly_stats_view = FeatureView(
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name="driver_hourly_stats",
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entities=["driver_id"],
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ttl=Duration(seconds=86400000),
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features=[
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Feature(name="conv_rate", dtype=ValueType.FLOAT),
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Feature(name="acc_rate", dtype=ValueType.FLOAT),
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Feature(name="avg_daily_trips", dtype=ValueType.INT64),
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Feature(name="string_feature", dtype=ValueType.STRING),
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],
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online=True,
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batch_source=driver_hourly_stats,
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tags={},
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)
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# Define a request data source which encodes features / information only
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# available at request time (e.g. part of the user initiated HTTP request)
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input_request = RequestDataSource(
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name="vals_to_add",
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schema={"val_to_add": ValueType.INT64, "val_to_add_2": ValueType.INT64},
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)
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# Define an on demand feature view which can generate new features based on
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# existing feature views and RequestDataSource features
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@on_demand_feature_view(
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inputs={
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"driver_hourly_stats": driver_hourly_stats_view,
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"vals_to_add": input_request,
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},
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features=[
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Feature(name="conv_rate_plus_val1", dtype=ValueType.DOUBLE),
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Feature(name="conv_rate_plus_val2", dtype=ValueType.DOUBLE),
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],
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)
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def transformed_conv_rate(inputs: pd.DataFrame) -> pd.DataFrame:
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df = pd.DataFrame()
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df["conv_rate_plus_val1"] = inputs["conv_rate"] + inputs["val_to_add"]
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df["conv_rate_plus_val2"] = inputs["conv_rate"] + inputs["val_to_add_2"]
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return df
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# Define request feature view
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driver_age_request_fv = RequestFeatureView(
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name="driver_age",
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request_data_source=RequestDataSource(
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name="driver_age", schema={"driver_age": ValueType.INT64,}
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),
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)

‎examples/java-demo/feature_repo/feature_store.yaml‎

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registry: gs://[YOUR BUCKET]/demo-repo/registry.db
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project: feast_java_demo
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provider: gcp
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online_store:
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type: redis
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connection_string: localhost:6379,password=[YOUR PASSWORD]
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offline_store:
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type: file
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flags:
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alpha_features: true
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on_demand_transforms: true

‎examples/java-demo/feature_repo/test.py‎

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import grpc
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from feast.protos.feast.serving.ServingService_pb2 import (
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FeatureList,
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GetOnlineFeaturesRequest,
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)
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from feast.protos.feast.serving.ServingService_pb2_grpc import ServingServiceStub
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from feast.protos.feast.types.Value_pb2 import RepeatedValue, Value
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# Sample logic to fetch from a local gRPC java server deployed at 6566
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def fetch_java():
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channel = grpc.insecure_channel("localhost:6566")
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stub = ServingServiceStub(channel)
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feature_refs = FeatureList(val=["driver_hourly_stats:conv_rate"])
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entity_rows = {
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"driver_id": RepeatedValue(
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val=[Value(int64_val=driver_id) for driver_id in range(1001, 1003)]
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)
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}
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print(
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stub.GetOnlineFeatures(
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GetOnlineFeaturesRequest(features=feature_refs, entities=entity_rows,)
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)
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)
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if __name__ == "__main__":
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fetch_java()

‎examples/java-demo/redis-screenshot.png‎

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