feast-ci-bot · Jan 26, 2022 · Jan 6, 2022 · Jan 26, 2022
@@ -16,6 +16,7 @@
   * [Feature service](getting-started/concepts/feature-service.md)
   * [Feature retrieval](getting-started/concepts/feature-retrieval.md)
   * [Point-in-time joins](getting-started/concepts/point-in-time-joins.md)
+  * [Dataset](getting-started/concepts/dataset.md)
 * [Architecture](getting-started/architecture-and-components/README.md)
   * [Overview](getting-started/architecture-and-components/overview.md)
   * [Feature repository](getting-started/architecture-and-components/feature-repository.md)

@@ -14,3 +14,4 @@
 
 {% page-ref page="point-in-time-joins.md" %}
 
+{% page-ref page="dataset.md" %}
@@ -0,0 +1,46 @@
+# Dataset
+
+Feast datasets allow for conveniently saving dataframes that include both features and entities to be subsequently used for data analysis and model training.
+[Data Quality Monitoring](https://docs.google.com/document/d/110F72d4NTv80p35wDSONxhhPBqWRwbZXG4f9mNEMd98) was the primary motivation for creating dataset concept.
+
+Dataset's metadata is stored in the Feast registry and raw data (features, entities, additional input keys and timestamp) is stored in the [offline store](../architecture-and-components/offline-store.md).
+
+Dataset can be created from:
+1. Results of historical retrieval
+2. [planned] Logging request (including input for [on demand transformation](../../reference/alpha-on-demand-feature-view.md)) and response during feature serving
+3. [planned] Logging features during writing to online store (from batch source or stream)
+
+
+### Creating Saved Dataset from Historical Retrieval
+
+To create a saved dataset from historical features for later retrieval or analysis, a user needs to call `get_historical_features` method first and then pass the returned retrieval job to `create_saved_dataset` method.
+`create_saved_dataset` will trigger provided retrieval job (by calling `.persist()` on it) to store the data using specified `storage`.
+Storage type must be the same as globally configured offline store (eg, it's impossible to persist data to Redshift with BigQuery source).
+`create_saved_dataset` will also create SavedDataset object with all related metadata and will write it to the registry.
+
+```python
+from feast import FeatureStore
+from feast.infra.offline_stores.bigquery_source import SavedDatasetBigQueryStorage
+
+store = FeatureStore()
+
+historical_job = store.get_historical_features(
+    features=["driver:avg_trip"],
+    entity_df=...,
+)
+
+dataset = store.create_saved_dataset(
+    from_=historical_job,
+    name='my_training_dataset',
+    storage=SavedDatasetBigQueryStorage(table_ref='<gcp-project>.<gcp-dataset>.my_training_dataset'),
+    tags={'author': 'oleksii'}
+)
+
+dataset.to_df()
+```
+
+Saved dataset can be later retrieved using `get_saved_dataset` method:
+```python
+dataset = store.get_saved_dataset('my_training_dataset')
+dataset.to_df()
+```
@@ -28,6 +28,7 @@ import "feast/core/FeatureView.proto";
 import "feast/core/InfraObject.proto";
 import "feast/core/OnDemandFeatureView.proto";
 import "feast/core/RequestFeatureView.proto";
+import "feast/core/SavedDataset.proto";
 import "google/protobuf/timestamp.proto";
 
 message Registry {
@@ -37,6 +38,7 @@ message Registry {
     repeated OnDemandFeatureView on_demand_feature_views = 8;
     repeated RequestFeatureView request_feature_views = 9;
     repeated FeatureService feature_services = 7;
+    repeated SavedDataset saved_datasets = 11;
     Infra infra = 10;
 
     string registry_schema_version = 3; // to support migrations; incremented when schema is changed

@@ -0,0 +1,76 @@
+//
+// Copyright 2021 The Feast Authors
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+
+
+syntax = "proto3";
+
+package feast.core;
+option java_package = "feast.proto.core";
+option java_outer_classname = "SavedDatasetProto";
+option go_package = "github.com/feast-dev/feast/sdk/go/protos/feast/core";
+
+import "google/protobuf/timestamp.proto";
+import "feast/core/FeatureViewProjection.proto";
+import "feast/core/DataSource.proto";
+
+message SavedDatasetSpec {
+  // Name of the dataset. Must be unique since it's possible to overwrite dataset by name
+  string name = 1;
+
+  // Name of Feast project that this Dataset belongs to.
+  string project = 2;
+
+  // list of feature references with format "<view name>:<feature name>"
+  repeated string features = 3;
+
+  // entity columns + request columns from all feature views used during retrieval
+  repeated string join_keys = 4;
+
+  // Whether full feature names are used in stored data
+  bool full_feature_names = 5;
+
+  SavedDatasetStorage storage = 6;
+
+  // User defined metadata
+  map<string, string> tags = 7;
+}
+
+message SavedDatasetStorage {
+  oneof kind {
+    DataSource.FileOptions file_storage = 4;
+    DataSource.BigQueryOptions bigquery_storage = 5;
+    DataSource.RedshiftOptions redshift_storage = 6;
+  }
+}
+
+message SavedDatasetMeta {
+  // Time when this saved dataset is created
+  google.protobuf.Timestamp created_timestamp = 1;
+
+  // Time when this saved dataset is last updated
+  google.protobuf.Timestamp last_updated_timestamp = 2;
+
+  // Min timestamp in the dataset (needed for retrieval)
+  google.protobuf.Timestamp min_event_timestamp = 3;
+
+  // Max timestamp in the dataset (needed for retrieval)
+  google.protobuf.Timestamp max_event_timestamp = 4;
+}
+
+message SavedDataset {
+  SavedDatasetSpec spec = 1;
+  SavedDatasetMeta meta = 2;
+}
@@ -74,6 +74,11 @@ def __init__(self, bucket):
         super().__init__(f"S3 bucket {bucket} for the Feast registry can't be accessed")
 
 
+class SavedDatasetNotFound(FeastObjectNotFoundException):
+    def __init__(self, name: str, project: str):
+        super().__init__(f"Saved dataset {name} does not exist in project {project}")
+
+
 class FeastProviderLoginError(Exception):
     """Error class that indicates a user has not authenticated with their provider."""
 

@@ -77,6 +77,7 @@
 from feast.repo_config import RepoConfig, load_repo_config
 from feast.repo_contents import RepoContents
 from feast.request_feature_view import RequestFeatureView
+from feast.saved_dataset import SavedDataset, SavedDatasetStorage
 from feast.type_map import python_values_to_proto_values
 from feast.usage import log_exceptions, log_exceptions_and_usage, set_usage_attribute
 from feast.value_type import ValueType
@@ -764,6 +765,93 @@ def get_historical_features(
 
         return job
 
+    @log_exceptions_and_usage
+    def create_saved_dataset(
+        self,
+        from_: RetrievalJob,
+        name: str,
+        storage: SavedDatasetStorage,
+        tags: Optional[Dict[str, str]] = None,
+    ) -> SavedDataset:
+        """
+            Execute provided retrieval job and persist its outcome in given storage.
+            Storage type (eg, BigQuery or Redshift) must be the same as globally configured offline store.
+            After data successfully persisted saved dataset object with dataset metadata is committed to the registry.
+            Name for the saved dataset should be unique within project, since it's possible to overwrite previously stored dataset
+            with the same name.
+
+            Returns:
+                SavedDataset object with attached RetrievalJob
+
+            Raises:
+                ValueError if given retrieval job doesn't have metadata
+        """
+        warnings.warn(
+            "Saving dataset is an experimental feature. "
+            "This API is unstable and it could and most probably will be changed in the future. "
+            "We do not guarantee that future changes will maintain backward compatibility.",
+            RuntimeWarning,
+        )
+
+        if not from_.metadata:
+            raise ValueError(
+                "RetrievalJob must contains metadata. "
+                "Use RetrievalJob produced by get_historical_features"
+            )
+
+        dataset = SavedDataset(
+            name=name,
+            features=from_.metadata.features,
+            join_keys=from_.metadata.keys,
+            full_feature_names=from_.full_feature_names,
+            storage=storage,
+            tags=tags,
+        )
+
+        dataset.min_event_timestamp = from_.metadata.min_event_timestamp
+        dataset.max_event_timestamp = from_.metadata.max_event_timestamp
+
+        from_.persist(storage)
+
+        self._registry.apply_saved_dataset(dataset, self.project, commit=True)
+
+        return dataset.with_retrieval_job(
+            self._get_provider().retrieve_saved_dataset(
+                config=self.config, dataset=dataset
+            )
+        )
+
+    @log_exceptions_and_usage
+    def get_saved_dataset(self, name: str) -> SavedDataset:
+        """
+        Find a saved dataset in the registry by provided name and
+        create a retrieval job to pull whole dataset from storage (offline store).
+
+        If dataset couldn't be found by provided name SavedDatasetNotFound exception will be raised.
+
+        Data will be retrieved from globally configured offline store.
+
+        Returns:
+            SavedDataset with RetrievalJob attached
+
+        Raises:
+            SavedDatasetNotFound
+        """
+        warnings.warn(
+            "Retrieving datasets is an experimental feature. "
+            "This API is unstable and it could and most probably will be changed in the future. "
+            "We do not guarantee that future changes will maintain backward compatibility.",
+            RuntimeWarning,
+        )
+
+        dataset = self._registry.get_saved_dataset(name, self.project)
+        provider = self._get_provider()
+
+        retrieval_job = provider.retrieve_saved_dataset(
+            config=self.config, dataset=dataset
+        )
+        return dataset.with_retrieval_job(retrieval_job)
+
     @log_exceptions_and_usage
     def materialize_incremental(
         self, end_date: datetime, feature_views: Optional[List[str]] = None,
Original file line number	Diff line number	Diff line change
Expand Up		@@ -14,3 +14,4 @@

		{% page-ref page="point-in-time-joins.md" %}

		{% page-ref page="dataset.md" %}