primary_keys -> primary_key (#394)

Co-authored-by: davitbzh <[email protected]>

docs/user_guides/fs/feature_view/batch-data.md

@@ -19,15 +19,15 @@ It is very common that ML models are deployed in a "batch" setting where ML pipe
 ## Retrieve batch data with primary keys and event time
 For certain use cases, e.g. time series models, the input data needs to be sorted according to the primary key(s) and event time combination. Or one might want to merge predictions back with the original input data for postmortem analysis.
 Primary key(s) and event time are not usually included in the feature view query as they are not features used for training.
-To retrieve the primary key(s) and/or event time when retrieving batch data for inference, you need to set the parameters `primary_keys=True` and/or `event_time=True`.
+To retrieve the primary key(s) and/or event time when retrieving batch data for inference, you need to set the parameters `primary_key=True` and/or `event_time=True`.
 
 === "Python"
     ```python
     # get batch data
     df = feature_view.get_batch_data(
     start_time = "20220620",
     end_time = "20220627",
-    primary_keys=True,
+    primary_key=True,
     event_time=True
     ) # return a dataframe with primary keys and event time
     ```

docs/user_guides/fs/feature_view/training-data.md

@@ -97,21 +97,21 @@ X_train, X_val, X_test, y_train, y_val, y_test = feature_view.get_train_validati
 ## Read training data with primary key(s) and event time
 For certain use cases, e.g. time series models, the input data needs to be sorted according to the primary key(s) and event time combination. 
 Primary key(s) and event time are not usually included in the feature view query as they are not features used for training.
-To retrieve the primary key(s) and/or event time when retrieving training data, you need to set the parameters `primary_keys=True` and/or `event_time=True`.
+To retrieve the primary key(s) and/or event time when retrieving training data, you need to set the parameters `primary_key=True` and/or `event_time=True`.
 
 
 ```python
 # get a training dataset
 X_train, X_test, y_train, y_test = feature_view.get_train_test_split(training_dataset_version=1, 
-                                                                     primary_keys=True,
+                                                                     primary_key=True,
                                                                      event_time=True)
 ```
 
 !!! note
     All primary and event time columns of all the feature groups included in the feature view will be returned. If they have the same names across feature groups and the join prefix was not provided then reading operation will fail with ambiguous column exception.
     Make sure to define the join prefix if primary key and event time columns have the same names across feature groups. 
 
-    To use primary key(s) and event time column with materialized training datasets it needs to be created with `primary_keys=True` and/or `with_event_time=True`.  
+    To use primary key(s) and event time column with materialized training datasets it needs to be created with `primary_key=True` and/or `with_event_time=True`.  
 
 ## Deletion
 To clean up unused training data, you can delete all training data or for a particular version. Note that all metadata of training data and materialised files stored in HopsFS will be deleted and cannot be recreated anymore.