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Allow specifying FeatureServices in FeatureStore methods (feast-dev#1691)
* Allow specifying FeatureServices in FeatureStore methods Signed-off-by: Achal Shah <achals@gmail.com> * format and lint Signed-off-by: Achal Shah <achals@gmail.com> * features_refs -> features Signed-off-by: Achal Shah <achals@gmail.com> * remote type: ignores Signed-off-by: Achal Shah <achals@gmail.com> * implement applying and persisting feature services into registry Signed-off-by: Achal Shah <achals@gmail.com> * Fix integration test references Signed-off-by: Achal Shah <achals@gmail.com> * Add backwards compatibility Signed-off-by: Achal Shah <achals@gmail.com> * More lint Signed-off-by: Achal Shah <achals@gmail.com> * add integration tests Signed-off-by: Achal Shah <achals@gmail.com> * refactor and lint Signed-off-by: Achal Shah <achals@gmail.com> * More tests Signed-off-by: Achal Shah <achals@gmail.com> * Add cli commands Signed-off-by: Achal Shah <achals@gmail.com> * Update sdk/python/feast/feature_store.py Co-authored-by: Willem Pienaar <6728866+woop@users.noreply.github.com> Signed-off-by: Achal Shah <achals@gmail.com> * Update sdk/python/feast/feature_store.py Co-authored-by: Willem Pienaar <6728866+woop@users.noreply.github.com> Signed-off-by: Achal Shah <achals@gmail.com> * Update sdk/python/feast/feature_store.py Co-authored-by: Willem Pienaar <6728866+woop@users.noreply.github.com> Signed-off-by: Achal Shah <achals@gmail.com> * Update sdk/python/feast/feature_store.py Co-authored-by: Willem Pienaar <6728866+woop@users.noreply.github.com> Signed-off-by: Achal Shah <achals@gmail.com> * Update tests to use file offline source Signed-off-by: Achal Shah <achals@gmail.com> * Make tests integration and lint Signed-off-by: Achal Shah <achals@gmail.com> * Retrieve feature services to avoid stale references Signed-off-by: Achal Shah <achals@gmail.com> * Fix lint Signed-off-by: Achal Shah <achals@gmail.com> * Merge master into branch Signed-off-by: Achal Shah <achals@gmail.com> * fix tests Signed-off-by: Achal Shah <achals@gmail.com> * fix import Signed-off-by: Achal Shah <achals@gmail.com> * fix integ tests Signed-off-by: Achal Shah <achals@gmail.com> * CR Signed-off-by: Achal Shah <achals@gmail.com> * format Signed-off-by: Achal Shah <achals@gmail.com> * Fix integ test Signed-off-by: Achal Shah <achals@gmail.com> * Fix integ test Signed-off-by: Achal Shah <achals@gmail.com> * Rename in comments Signed-off-by: Achal Shah <achals@gmail.com> * Docstrings for Feature Services Signed-off-by: Achal Shah <achals@gmail.com> * make format Signed-off-by: Achal Shah <achals@gmail.com> * format and refactor Signed-off-by: Achal Shah <achals@gmail.com> * docs and updates Signed-off-by: Achal Shah <achals@gmail.com> * Update docs/concepts/feature-service.md Co-authored-by: Willem Pienaar <6728866+woop@users.noreply.github.com> Signed-off-by: Achal Shah <achals@gmail.com> * Update sdk/python/feast/feature_service.py Co-authored-by: Willem Pienaar <6728866+woop@users.noreply.github.com> Signed-off-by: Achal Shah <achals@gmail.com> * Update sdk/python/feast/feature_service.py Co-authored-by: Willem Pienaar <6728866+woop@users.noreply.github.com> Signed-off-by: Achal Shah <achals@gmail.com> * docs docs docs Signed-off-by: Achal Shah <achals@gmail.com> * docs docs Signed-off-by: Achal Shah <achals@gmail.com> * Fixes after merge Signed-off-by: Achal Shah <achals@gmail.com> * Remove dupe Signed-off-by: Achal Shah <achals@gmail.com> * Renames and deletions Signed-off-by: Achal Shah <achals@gmail.com> * lint Signed-off-by: Achal Shah <achals@gmail.com> * format and tests Signed-off-by: Achal Shah <achals@gmail.com> * remove unused imports Signed-off-by: Achal Shah <achals@gmail.com> * fix registry Signed-off-by: Achal Shah <achals@gmail.com> * fix docs Signed-off-by: Achal Shah <achals@gmail.com> * fix dangling print Signed-off-by: Achal Shah <achals@gmail.com> * make format and lint Signed-off-by: Achal Shah <achals@gmail.com> * list feature servces Signed-off-by: Achal Shah <achals@gmail.com> * fix tests Signed-off-by: Achal Shah <achals@gmail.com> * Remove unused file Signed-off-by: Achal Shah <achals@gmail.com> * single heading Signed-off-by: Achal Shah <achals@gmail.com> Co-authored-by: Willem Pienaar <6728866+woop@users.noreply.github.com>
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‎README.md‎

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@@ -62,7 +62,7 @@ store = FeatureStore(repo_path=".")
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training_df = store.get_historical_features(
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entity_df=entity_df,
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feature_refs = [
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features = [
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'driver_hourly_stats:conv_rate',
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'driver_hourly_stats:acc_rate',
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'driver_hourly_stats:avg_daily_trips'
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store = FeatureStore(repo_path=".")
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feature_vector = store.get_online_features(
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feature_refs=[
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features=[
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'driver_hourly_stats:conv_rate',
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'driver_hourly_stats:acc_rate',
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'driver_hourly_stats:avg_daily_trips'

‎docs/concepts/data-model-and-concepts.md‎

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@@ -16,7 +16,7 @@ Feature references are used for the retrieval of features from Feast:
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```python
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online_features = fs.get_online_features(
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feature_refs=[
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features=[
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'driver_locations:lon',
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'drivers_activity:trips_today'
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],
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```python
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training_df = store.get_historical_features(
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entity_df=entity_df,
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feature_refs = [
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features = [
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'drivers_activity:trips_today'
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'drivers_activity:rating'
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],

‎docs/concepts/feature-service.md‎

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# Feature Service
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A feature service is an object that represents a logical group of features from one or more [feature views](feature-view.md).
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Feature Services allows features from within a feature view to be used as needed by an ML model. Users can expect to create one feature service per model, allowing for tracking of the features used by models.
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{% tabs %}
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{% tab title="driver\_trips\_feature\_service.py" %}
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```python
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from driver_ratings_feature_view import driver_ratings_fv
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from driver_trips_feature_view import driver_stats_fv
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driver_stats_fs = FeatureService(
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name="driver_activity",
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features=[driver_stats_fv, driver_ratings_fv[["lifetime_rating"]]]
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)
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```
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{% endtab %}
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{% endtabs %}
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Feature services are used during
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* The generation of training datasets when querying feature views in order to find historical feature values. A single training dataset may consist of features from multiple feature views.
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* Retrieval of features from the online store. The features retrieved from the online store may also belong to multiple feature views.
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{% hint style="info" %}
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Applying a feature service does not result in an actual service being deployed.
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{% endhint %}

‎docs/feast-on-kubernetes/user-guide/getting-training-features.md‎

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@@ -8,7 +8,7 @@ Below is an example of the process required to produce a training dataset:
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```python
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# Feature references with target feature
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feature_refs = [
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features = [
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"driver_trips:average_daily_rides",
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"driver_trips:maximum_daily_rides",
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"driver_trips:rating",
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# Retrieve historical dataset from Feast.
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historical_feature_retrieval_job = client.get_historical_features(
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feature_refs=feature_refs,
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features=features,
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entity_rows=entity_source
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)
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‎docs/getting-started/build-a-training-dataset.md‎

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fs = FeatureStore(repo_path="path/to/your/feature/repo")
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training_df = fs.get_historical_features(
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feature_refs=[
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features=[
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"driver_hourly_stats:conv_rate",
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"driver_hourly_stats:acc_rate"
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],

‎docs/getting-started/read-features-from-the-online-store.md‎

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Create a list of features that you would like to retrieve. This list typically comes from the model training step and should accompany the model binary.
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```python
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feature_refs = [
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features = [
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"driver_hourly_stats:conv_rate",
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"driver_hourly_stats:acc_rate"
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]
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```python
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fs = FeatureStore(repo_path="path/to/feature/repo")
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online_features = fs.get_online_features(
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feature_refs=feature_refs,
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features=features,
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entity_rows=[
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{"driver_id": 1001},
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{"driver_id": 1002}]

‎docs/quickstart.md‎

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training_df = store.get_historical_features(
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entity_df=entity_df,
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feature_refs=[
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features=[
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"driver_hourly_stats:conv_rate",
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"driver_hourly_stats:acc_rate",
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"driver_hourly_stats:avg_daily_trips",
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store = FeatureStore(repo_path=".")
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feature_vector = store.get_online_features(
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feature_refs=[
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features=[
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"driver_hourly_stats:conv_rate",
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"driver_hourly_stats:acc_rate",
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"driver_hourly_stats:avg_daily_trips",

‎docs/user-guide/getting-training-features.md‎

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```python
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# Feature references with target feature
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feature_refs = [
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features = [
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"driver_trips:average_daily_rides",
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"driver_trips:maximum_daily_rides",
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"driver_trips:rating",
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# Retrieve historical dataset from Feast.
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historical_feature_retrieval_job = client.get_historical_features(
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feature_refs=feature_refs,
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features=features,
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entity_rows=entity_source
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)
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‎protos/feast/core/Registry.proto‎

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option go_package = "github.com/feast-dev/feast/sdk/go/protos/feast/core";
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import "feast/core/Entity.proto";
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import "feast/core/FeatureService.proto";
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import "feast/core/FeatureTable.proto";
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import "feast/core/FeatureView.proto";
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import "google/protobuf/timestamp.proto";
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repeated Entity entities = 1;
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repeated FeatureTable feature_tables = 2;
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repeated FeatureView feature_views = 6;
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repeated FeatureService feature_services = 7;
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string registry_schema_version = 3; // to support migrations; incremented when schema is changed
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string version_id = 4; // version id, random string generated on each update of the data; now used only for debugging purposes
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google.protobuf.Timestamp last_updated = 5;
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}

‎sdk/python/feast/__init__.py‎

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from .data_source import KafkaSource, KinesisSource, SourceType
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from .entity import Entity
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from .feature import Feature
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from .feature_service import FeatureService
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from .feature_store import FeatureStore
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from .feature_table import FeatureTable
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from .feature_view import FeatureView
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"KafkaSource",
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"KinesisSource",
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"Feature",
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"FeatureService",
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"FeatureStore",
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"FeatureTable",
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"FeatureView",

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