feat(MLOP-1985): optional params (#347)

* feat: optional params

Author: ralphrass

butterfree/extract/source.py

@@ -49,13 +49,22 @@ class Source(HookableComponent):
         temporary views regarding each reader and, after, will run the
         desired query and return a dataframe.
 
+        The `eager_evaluation` param forces Spark to apply the currently
+        mapped changes to the DataFrame. When this parameter is set to
+        False, Spark follows its standard behaviour of lazy evaluation.
+        Lazy evaluation can improve Spark's performance as it allows
+        Spark to build the best version of the execution plan.
+
     """
 
-    def __init__(self, readers: List[Reader], query: str) -> None:
+    def __init__(
+        self, readers: List[Reader], query: str, eager_evaluation: bool = True,
+    ) -> None:
         super().__init__()
         self.enable_pre_hooks = False
         self.readers = readers
         self.query = query
+        self.eager_evaluation = eager_evaluation
 
     def construct(
         self, client: SparkClient, start_date: str = None, end_date: str = None
@@ -87,7 +96,7 @@ def construct(
 
         dataframe = client.sql(self.query)
 
-        if not dataframe.isStreaming:
+        if not dataframe.isStreaming and self.eager_evaluation:
             dataframe.cache().count()
 
         post_hook_df = self.run_post_hooks(dataframe)

butterfree/transform/aggregated_feature_set.py

@@ -197,14 +197,23 @@ def __init__(
         keys: List[KeyFeature],
         timestamp: TimestampFeature,
         features: List[Feature],
+        deduplicate_rows: bool = True,
+        eager_evaluation: bool = True,
     ):
         self._windows: List[Any] = []
         self._pivot_column: Optional[str] = None
         self._pivot_values: Optional[List[Union[bool, float, int, str]]] = []
         self._distinct_subset: List[Any] = []
         self._distinct_keep: Optional[str] = None
         super(AggregatedFeatureSet, self).__init__(
-            name, entity, description, keys, timestamp, features,
+            name,
+            entity,
+            description,
+            keys,
+            timestamp,
+            features,
+            deduplicate_rows,
+            eager_evaluation,
         )
 
     @property
@@ -626,8 +635,10 @@ def construct(
             float("nan"), None
         )
         if not output_df.isStreaming:
-            output_df = self._filter_duplicated_rows(output_df)
-            output_df.cache().count()
+            if self.deduplicate_rows:
+                output_df = self._filter_duplicated_rows(output_df)
+            if self.eager_evaluation:
+                output_df.cache().count()
 
         post_hook_df = self.run_post_hooks(output_df)
 

butterfree/transform/feature_set.py

@@ -97,6 +97,12 @@ class FeatureSet(HookableComponent):
     values over key columns and timestamp column, we do this in order to reduce
     our dataframe (regarding the number of rows). A detailed explation of this
     method can be found at filter_duplicated_rows docstring.
+
+    The `eager_evaluation` param forces Spark to apply the currently
+    mapped changes to the DataFrame. When this parameter is set to
+    False, Spark follows its standard behaviour of lazy evaluation.
+    Lazy evaluation can improve Spark's performance as it allows
+    Spark to build the best version of the execution plan.
     """
 
     def __init__(
@@ -107,6 +113,8 @@ def __init__(
         keys: List[KeyFeature],
         timestamp: TimestampFeature,
         features: List[Feature],
+        deduplicate_rows: bool = True,
+        eager_evaluation: bool = True,
     ) -> None:
         super().__init__()
         self.name = name
@@ -116,6 +124,8 @@ def __init__(
         self.timestamp = timestamp
         self.features = features
         self.incremental_strategy = IncrementalStrategy(column=TIMESTAMP_COLUMN)
+        self.deduplicate_rows = deduplicate_rows
+        self.eager_evaluation = eager_evaluation
 
     @property
     def name(self) -> str:
@@ -426,8 +436,10 @@ def construct(
         ).select(*self.columns)
 
         if not output_df.isStreaming:
-            output_df = self._filter_duplicated_rows(output_df)
-            output_df.cache().count()
+            if self.deduplicate_rows:
+                output_df = self._filter_duplicated_rows(output_df)
+            if self.eager_evaluation:
+                output_df.cache().count()
 
         output_df = self.incremental_strategy.filter_with_incremental_strategy(
             dataframe=output_df, start_date=start_date, end_date=end_date