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‎CHANGELOG.md‎

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# Changelog
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# [0.58.0](https://github.com/feast-dev/feast/compare/v0.57.0...v0.58.0) (2025-12-16)
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### Bug Fixes
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* Add java proto ([#5719](https://github.com/feast-dev/feast/issues/5719)) ([fc3ea20](https://github.com/feast-dev/feast/commit/fc3ea208e0d37d1083fda261fd374c87fa4da17c))
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* Add possibility to force full features names for materialize ops ([#5728](https://github.com/feast-dev/feast/issues/5728)) ([55c9c36](https://github.com/feast-dev/feast/commit/55c9c36a5cc385869d2c7671a5629da1ba282d47))
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* Fixed file registry cache sync ([09505d4](https://github.com/feast-dev/feast/commit/09505d4ae79f5ca5b452c9c5e41a56e1f7d3d136))
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* Handle hyphon in sqlite project name ([#5575](https://github.com/feast-dev/feast/issues/5575)) ([#5749](https://github.com/feast-dev/feast/issues/5749)) ([b8346ff](https://github.com/feast-dev/feast/commit/b8346ff58c0d78deff52b2a864772dc23741ea75))
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* Pinned substrait to fix protobuf issue ([d0ef4da](https://github.com/feast-dev/feast/commit/d0ef4da05dc91893b14b10b888b3c458fd389ce5))
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* Set TLS certificate annotation only on gRPC service ([#5715](https://github.com/feast-dev/feast/issues/5715)) ([75d13db](https://github.com/feast-dev/feast/commit/75d13db1ea536bd4759dbb719190a266b6466b24))
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* SQLite online store deletes tables from other projects in shared registry scenarios ([#5766](https://github.com/feast-dev/feast/issues/5766)) ([fabce76](https://github.com/feast-dev/feast/commit/fabce765804a8577ef31fb83525adc5df5748c44))
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* Validate not existing entity join keys for preventing panic ([0b93559](https://github.com/feast-dev/feast/commit/0b935595b7e296f1d51a52c42b750a470ea10ceb))
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### Features
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* Add annotations for pod templates ([534e647](https://github.com/feast-dev/feast/commit/534e647f40567a8d632fb562f1e58057b1c61c3e))
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* Add Pytorch template ([#5780](https://github.com/feast-dev/feast/issues/5780)) ([6afd353](https://github.com/feast-dev/feast/commit/6afd353425027fe7de561092306c390ea1ad0d19))
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* Add support for extra options for stream source ([#5618](https://github.com/feast-dev/feast/issues/5618)) ([18956c2](https://github.com/feast-dev/feast/commit/18956c2765ebc4c92ca8afc7e02aca8b7dfc339d))
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* Added matched_tag field search api results with fuzzy search capabilities ([#5769](https://github.com/feast-dev/feast/issues/5769)) ([4a9ffae](https://github.com/feast-dev/feast/commit/4a9ffae3d2c7449670d9dddb8b3b341ee00ffc29))
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* Added support for enabling metrics in Feast Operator ([#5317](https://github.com/feast-dev/feast/issues/5317)) ([#5748](https://github.com/feast-dev/feast/issues/5748)) ([a8498c2](https://github.com/feast-dev/feast/commit/a8498c2c43439024cd9516eae8e1733b3f69b577))
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* Configure CacheTTLSecondscache,CacheMode for file-based registry in Feast Operator([#5708](https://github.com/feast-dev/feast/issues/5708)) ([#5744](https://github.com/feast-dev/feast/issues/5744)) ([f25f83b](https://github.com/feast-dev/feast/commit/f25f83b3e4d324a9b52cd2e90036c3d25f25ee80))
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* Implemented Tiling Support for Time-Windowed Aggregations ([#5724](https://github.com/feast-dev/feast/issues/5724)) ([7a99166](https://github.com/feast-dev/feast/commit/7a991660817ffcbe3059b0141f42f15e5f7d5b04))
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* Offline Store historical features retrieval based on datetime range for spark ([#5720](https://github.com/feast-dev/feast/issues/5720)) ([27ec8ec](https://github.com/feast-dev/feast/commit/27ec8ec26b2884d09df3ee3e648b96824e1f2b4c))
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* Offline Store historical features retrieval based on datetime range in dask ([#5717](https://github.com/feast-dev/feast/issues/5717)) ([a16582a](https://github.com/feast-dev/feast/commit/a16582a82914b015095f60a61a3a722c9c82bb63))
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* Production ready feast operator with v1 apiversion ([#5771](https://github.com/feast-dev/feast/issues/5771)) ([49359c6](https://github.com/feast-dev/feast/commit/49359c6227601bc31ee08fdc4f4208fb6d101755))
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* Support for Map value data type ([#5768](https://github.com/feast-dev/feast/issues/5768)) ([#5772](https://github.com/feast-dev/feast/issues/5772)) ([b99a8a9](https://github.com/feast-dev/feast/commit/b99a8a9aeafba912a6ceea8658f5efeae90b245d))
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# [0.57.0](https://github.com/feast-dev/feast/compare/v0.56.0...v0.57.0) (2025-11-13)
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‎docs/SUMMARY.md‎

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* [Scaling Feast](how-to-guides/scaling-feast.md)
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* [Structuring Feature Repos](how-to-guides/structuring-repos.md)
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* [Running Feast in production (e.g. on Kubernetes)](how-to-guides/running-feast-in-production.md)
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* [Feast on Kubernetes](how-to-guides/feast-on-kubernetes.md)
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* [Customizing Feast](how-to-guides/customizing-feast/README.md)
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* [Adding a custom batch materialization engine](how-to-guides/customizing-feast/creating-a-custom-materialization-engine.md)
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* [Adding a new offline store](how-to-guides/customizing-feast/adding-a-new-offline-store.md)
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* [Registry server](reference/feature-servers/registry-server.md)
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* [\[Beta\] Web UI](reference/alpha-web-ui.md)
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* [\[Beta\] On demand feature view](reference/beta-on-demand-feature-view.md)
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* [\[Alpha\] Static Artifacts Loading](reference/alpha-static-artifacts.md)
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* [\[Alpha\] Vector Database](reference/alpha-vector-database.md)
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* [\[Alpha\] Data quality monitoring](reference/dqm.md)
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* [\[Alpha\] Streaming feature computation with Denormalized](reference/denormalized.md)

‎docs/getting-started/quickstart.md‎

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Let's take a look at the resulting demo repo itself. It breaks down into
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* `data/` contains raw demo parquet data
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* `example_repo.py` contains demo feature definitions
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* `feature_definitions.py` contains demo feature definitions
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* `feature_store.yaml` contains a demo setup configuring where data sources are
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* `test_workflow.py` showcases how to run all key Feast commands, including defining, retrieving, and pushing features.
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You can run this with `python test_workflow.py`.
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```
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{% endtab %}
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{% tab title="example_repo.py" %}
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{% tab title="feature_definitions.py" %}
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```python
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# This is an example feature definition file
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### Step 4: Register feature definitions and deploy your feature store
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The `apply` command scans python files in the current directory for feature view/entity definitions, registers the
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objects, and deploys infrastructure. In this example, it reads `example_repo.py` and sets up SQLite online store tables. Note that we had specified SQLite as the default online store by
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objects, and deploys infrastructure. In this example, it reads `feature_definitions.py` and sets up SQLite online store tables. Note that we had specified SQLite as the default online store by
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configuring `online_store` in `feature_store.yaml`.
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{% endtabs %}
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### Step 6: Ingest batch features into your online store
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We now serialize the latest values of features since the beginning of time to prepare for serving. Note, `materialize_incremental` serializes all new features since the last `materialize` call, or since the time provided minus the `ttl` timedelta. In this case, this will be `CURRENT_TIME - 1 day` (`ttl` was set on the `FeatureView` instances in [feature_repo/feature_repo/example_repo.py](feature_repo/feature_repo/example_repo.py)).
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We now serialize the latest values of features since the beginning of time to prepare for serving. Note, `materialize_incremental` serializes all new features since the last `materialize` call, or since the time provided minus the `ttl` timedelta. In this case, this will be `CURRENT_TIME - 1 day` (`ttl` was set on the `FeatureView` instances in [feature_repo/feature_repo/feature_definitions.py](feature_repo/feature_repo/feature_definitions.py)).
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{% tabs %}
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{% tab title="Bash (with timestamp)" %}

‎docs/how-to-guides/feast-on-kubernetes.md‎

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# Feast on Kubernetes
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This page covers deploying Feast on Kubernetes, including the Feast Operator and feature servers.
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## Overview
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Kubernetes is a common target environment for running Feast in production. You can use Kubernetes to:
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1. Run Feast feature servers for online feature retrieval.
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2. Run scheduled and ad-hoc jobs (e.g. materialization jobs) as Kubernetes Jobs.
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3. Operate Feast components using Kubernetes-native primitives.
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## Feast Operator
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To deploy Feast components on Kubernetes, use the included [feast-operator](../../infra/feast-operator).
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For first-time Operator users, it may be a good exercise to try the [Feast Operator Quickstart](../../examples/operator-quickstart). The quickstart demonstrates some of the Operator's built-in features, e.g. git repos, `feast apply` jobs, etc.
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## Deploy Feast feature servers on Kubernetes
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{% embed url="https://www.youtube.com/playlist?list=PLPzVNzik7rsAN-amQLZckd0so3cIr7blX" %}
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**Basic steps**
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1. Install [kubectl](https://kubernetes.io/docs/tasks/tools/install-kubectl/)
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2. Install the Operator
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Install the latest release:
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```sh
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kubectl apply -f https://raw.githubusercontent.com/feast-dev/feast/refs/heads/stable/infra/feast-operator/dist/install.yaml
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```
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OR, install a specific version:
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```sh
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kubectl apply -f https://raw.githubusercontent.com/feast-dev/feast/refs/tags/<version>/infra/feast-operator/dist/install.yaml
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```
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3. Deploy a Feature Store
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```sh
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kubectl apply -f https://raw.githubusercontent.com/feast-dev/feast/refs/heads/stable/infra/feast-operator/config/samples/v1_featurestore.yaml
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```
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Verify the status:
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```sh
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$ kubectl get feast
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NAME STATUS AGE
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sample Ready 2m21s
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```
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The above will install a simple [FeatureStore CR](../../infra/feast-operator/docs/api/markdown/ref.md) like the following. By default, it will run the [Online Store feature server](../reference/feature-servers/python-feature-server.md):
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```yaml
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apiVersion: feast.dev/v1
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kind: FeatureStore
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metadata:
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name: sample
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spec:
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feastProject: my_project
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```
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> _More advanced FeatureStore CR examples can be found in the feast-operator [samples directory](../../infra/feast-operator/config/samples)._
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{% hint style="success" %}
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Please check the how-to guide for some specific recommendations on [how to scale Feast](./scaling-feast.md).
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{% endhint %}

‎docs/how-to-guides/running-feast-in-production.md‎

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{% embed url="https://www.youtube.com/playlist?list=PLPzVNzik7rsAN-amQLZckd0so3cIr7blX" %}
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2. Install the Operator
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Install the latest release
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```
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```
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The above will install a simple [FeatureStore CR](../../infra/feast-operator/docs/api/markdown/ref.md) like the following. By default, it will run the [Online Store feature server](../reference/feature-servers/python-feature-server.md) -
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kind: FeatureStore
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metadata:
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name: sample
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```
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> _More advanced FeatureStore CR examples can be found in the feast-operator [samples directory](../../infra/feast-operator/config/samples)._
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For first-time Operator users, it may be a good exercise to try the [Feast Operator Quickstart](../../examples/operator-quickstart). The quickstart will demonstrate some of the Operator's built-in features, e.g. git repos, `feast apply` jobs, etc.
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{% hint style="success" %} Important note: Scaling a Feature Store Deployment should only be done if the configured data store(s) will support it.
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Please check the how-to guide for some specific recommendations on [how to scale Feast](./scaling-feast.md). {% endhint %}
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See [Feast on Kubernetes](./feast-on-kubernetes.md).
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## 5. Using environment variables in your yaml configuration
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