Merge branch 'the-merge-python-hsfs' into the-merge-complete

hsfs/utils/java/src/main/java/com/logicalclocks/utils/MainClass.java

@@ -118,5 +118,9 @@ public static void main(String[] args) throws Exception {
     if (op.equals("offline_fg_materialization") || op.equals("offline_fg_backfill")) {
       SparkEngine.getInstance().streamToHudiTable(streamFeatureGroup, writeOptions);
     }
+
+    LOGGER.info("Closing spark session...");
+    SparkEngine.getInstance().closeSparkSession();
+    System.exit(0);
   }
 }

hsfs/utils/python/hsfs_utils.py

@@ -292,3 +292,6 @@ def parse_isoformat_date(da: str) -> datetime:
         import_fg(job_conf)
     elif args.op == "run_feature_monitoring":
         run_feature_monitoring(job_conf)
+
+    if spark is not None:
+        spark.stop()

java/spark/src/main/java/com/logicalclocks/hsfs/spark/engine/SparkEngine.java

@@ -1097,5 +1097,10 @@ protected String makeQueryName(String queryName, FeatureGroupBase featureGroup)
     }
     return queryName;
   }
-
+
+  public void closeSparkSession() {
+    if (getSparkSession() != null) {
+      getSparkSession().stop();
+    }
+  }
 }

python/hsfs/builtin_transformations.py

@@ -0,0 +1,66 @@
+#
+#   Copyright 2024 Hopsworks AB
+#
+#   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
+#
+#       http://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.
+#
+
+import numpy as np
+import pandas as pd
+from hsfs.hopsworks_udf import udf
+from hsfs.transformation_statistics import TransformationStatistics
+
+
+feature_statistics = TransformationStatistics("feature")
+
+
+@udf(float)
+def min_max_scaler(feature: pd.Series, statistics=feature_statistics) -> pd.Series:
+    return (feature - statistics.feature.min) / (
+        statistics.feature.max - statistics.feature.min
+    )
+
+
+@udf(float)
+def standard_scaler(feature: pd.Series, statistics=feature_statistics) -> pd.Series:
+    return (feature - statistics.feature.mean) / statistics.feature.stddev
+
+
+@udf(float)
+def robust_scaler(feature: pd.Series, statistics=feature_statistics) -> pd.Series:
+    return (feature - statistics.feature.percentiles[49]) / (
+        statistics.feature.percentiles[74] - statistics.feature.percentiles[24]
+    )
+
+
+@udf(int)
+def label_encoder(feature: pd.Series, statistics=feature_statistics) -> pd.Series:
+    unique_data = sorted(
+        [value for value in statistics.feature.extended_statistics["unique_values"]]
+    )
+    value_to_index = {value: index for index, value in enumerate(unique_data)}
+    return pd.Series(
+        [value_to_index[data] if not pd.isna(data) else np.nan for data in feature]
+    )
+
+
+@udf(bool)
+def one_hot_encoder(feature: pd.Series, statistics=feature_statistics) -> pd.Series:
+    unique_data = [
+        value for value in statistics.feature.extended_statistics["unique_values"]
+    ]
+    one_hot = pd.get_dummies(feature, dtype="bool")
+    for data in unique_data:
+        if data not in one_hot:
+            one_hot[data] = False
+    # Sorting by columns so as to maintain consistency in column order.
+    return one_hot.reindex(sorted(one_hot.columns), axis=1)

python/hsfs/constructor/join.py

@@ -47,7 +47,7 @@ def __init__(
         self._on = util.parse_features(on)
         self._left_on = util.parse_features(left_on)
         self._right_on = util.parse_features(right_on)
-        self._join_type = join_type or self.INNER
+        self._join_type = join_type or self.LEFT
         self._prefix = prefix
 
     def to_dict(self) -> Dict[str, Any]:

python/hsfs/constructor/query.py

@@ -59,7 +59,7 @@ def __init__(
             fg_mod.ExternalFeatureGroup,
             fg_mod.SpineGroup,
         ],
-        left_features: List[Union[str, "Feature"]],
+        left_features: List[Union[str, "Feature", Dict]],
         feature_store_name: Optional[str] = None,
         feature_store_id: Optional[int] = None,
         left_feature_group_start_time: Optional[Union[str, int, date, datetime]] = None,
@@ -239,7 +239,7 @@ def join(
         on: Optional[List[str]] = None,
         left_on: Optional[List[str]] = None,
         right_on: Optional[List[str]] = None,
-        join_type: Optional[str] = "inner",
+        join_type: Optional[str] = "left",
         prefix: Optional[str] = None,
     ) -> "Query":
         """Join Query with another Query.
@@ -769,7 +769,7 @@ def featuregroups(
         """List of feature groups used in the query"""
         featuregroups = {self._left_feature_group}
         for join_obj in self.joins:
-            featuregroups.add(join_obj.query._left_feature_group)
+            self._fg_rec_add(join_obj, featuregroups)
         return list(featuregroups)
 
     @property
@@ -809,6 +809,18 @@ def get_feature(self, feature_name: str) -> "Feature":
         """
         return self._get_feature_by_name(feature_name)[0]
 
+    def _fg_rec_add(self, join_object, featuregroups):
+        """
+        Recursively get a feature groups from nested join and add to featuregroups list.
+
+        # Arguments
+            join_object: `Join object`.
+        """
+        if len(join_object.query.joins) > 0:
+            for nested_join in join_object.query.joins:
+                self._fg_rec_add(nested_join, featuregroups)
+        featuregroups.add(join_object.query._left_feature_group)
+
     def __getattr__(self, name: str) -> Any:
         try:
             return self.__getitem__(name)

python/hsfs/core/constants.py

@@ -1,6 +1,19 @@
 import importlib.util
 
 
+# Avro
+HAS_FAST_AVRO: bool = importlib.util.find_spec("fastavro") is not None
+HAS_AVRO: bool = importlib.util.find_spec("avro") is not None
+
+# Confluent Kafka
+HAS_CONFLUENT_KAFKA: bool = importlib.util.find_spec("confluent_kafka") is not None
+confluent_kafka_not_installed_message = (
+    "Confluent Kafka package not found. "
+    "If you want to use Kafka with Hopsworks you can install the corresponding extras "
+    """`pip install hopsworks[python]` or `pip install "hopsworks[python]"` if using zsh. """
+    "You can also install confluent-kafka directly in your environment e.g `pip install confluent-kafka`. "
+    "You will need to restart your kernel if applicable."
+)
 # Data Validation / Great Expectations
 HAS_GREAT_EXPECTATIONS: bool = (
     importlib.util.find_spec("great_expectations") is not None

python/hsfs/core/feature_view_api.py

@@ -17,12 +17,7 @@
 
 from typing import List, Optional, Union
 
-from hsfs import (
-    client,
-    feature_view,
-    training_dataset,
-    transformation_function_attached,
-)
+from hsfs import client, feature_view, training_dataset, transformation_function
 from hsfs.client.exceptions import RestAPIError
 from hsfs.constructor import query, serving_prepared_statement
 from hsfs.core import explicit_provenance, job, training_dataset_job_conf
@@ -86,13 +81,28 @@ def update(self, feature_view_obj: feature_view.FeatureView) -> None:
             data=feature_view_obj.json(),
         )
 
-    def get_by_name(self, name: str) -> feature_view.FeatureView:
+    def get_by_name(self, name: str) -> List[feature_view.FeatureView]:
+        """
+        Get a feature view from the backend using its name.
+
+        # Arguments
+            name `str`: Name of the feature view.
+
+        # Returns
+            `List[FeatureView]`: A list that contains all version of the feature view.
+
+        # Raises
+            `RestAPIError`: If the feature view cannot be found from the backend.
+            `ValueError`: If the feature group associated with the feature view cannot be found.
+        """
         path = self._base_path + [name]
         try:
             return [
                 feature_view.FeatureView.from_response_json(fv)
                 for fv in self._client._send_request(
-                    self._GET, path, {"expand": ["query", "features"]}
+                    self._GET,
+                    path,
+                    {"expand": ["query", "features", "transformationfunctions"]},
                 )["items"]
             ]
         except RestAPIError as e:
@@ -106,11 +116,27 @@ def get_by_name(self, name: str) -> feature_view.FeatureView:
                 raise e
 
     def get_by_name_version(self, name: str, version: int) -> feature_view.FeatureView:
+        """
+        Get a feature view form the backend using both name and version
+
+        # Arguments
+            name `str`: Name of feature view.
+            version `version`: Version of the feature view.
+
+        # Returns
+            `FeatureView`
+
+        # Raises
+            `RestAPIError`: If the feature view cannot be found from the backend.
+            `ValueError`: If the feature group associated with the feature view cannot be found.
+        """
         path = self._base_path + [name, self._VERSION, version]
         try:
             return feature_view.FeatureView.from_response_json(
                 self._client._send_request(
-                    self._GET, path, {"expand": ["query", "features"]}
+                    self._GET,
+                    path,
+                    {"expand": ["query", "features", "transformationfunctions"]},
                 )
             )
         except RestAPIError as e:
@@ -190,12 +216,23 @@ def get_serving_prepared_statement(
 
     def get_attached_transformation_fn(
         self, name: str, version: int
-    ) -> Union[
-        "transformation_function_attached.TransformationFunctionAttached",
-        List["transformation_function_attached.TransformationFunctionAttached"],
-    ]:
+    ) -> List["transformation_function.TransformationFunction"]:
+        """
+        Get transformation functions attached to a feature view form the backend
+
+        # Arguments
+            name `str`: Name of feature view.
+            version `ìnt`: Version of feature view.
+
+        # Returns
+            `List[TransformationFunction]` : List of transformation functions attached to the feature view.
+
+        # Raises
+            `RestAPIError`: If the feature view cannot be found from the backend.
+            `ValueError`: If the feature group associated with the feature view cannot be found.
+        """
         path = self._base_path + [name, self._VERSION, version, self._TRANSFORMATION]
-        return transformation_function_attached.TransformationFunctionAttached.from_response_json(
+        return transformation_function.TransformationFunction.from_response_json(
             self._client._send_request("GET", path)
         )