Add warning for exact match approximation in high record volumes

cumulusci/tasks/bulkdata/select_utils.py

@@ -343,6 +343,12 @@ def annoy_post_process(
     threshold: T.Union[float, None],
 ) -> T.Tuple[T.List[dict], list]:
     """Processes the query results for the similarity selection strategy using Annoy algorithm for large number of records"""
+    # Add warning when threshold is 0
+    if threshold is not None and threshold == 0:
+        logger.warning(
+            "Warning: A threshold of 0 may miss exact matches in high volumes. Use a small value like 0.1 for better accuracy."
+        )
+
     selected_records = []
     insertion_candidates = []
 

docs/data.md

@@ -352,6 +352,9 @@ This parameter is **optional**; if not specified, no threshold will be applied a
 
 This feature is particularly useful during version upgrades, where records that closely match can be selected, while those that do not match sufficiently can be inserted into the target org.
 
+**Important Note:**  
+For high volumes of records, an approximation algorithm is applied to improve performance. In such cases, setting a threshold of `0` may not guarantee the selection of exact matches, as the algorithm can assign a small non-zero similarity score to exact matches. To ensure accurate selection, it is recommended to set the threshold to a small value slightly greater than `0`, such as `0.1`. This ensures both precision and efficiency in the selection process.
+
 ---
 
 #### Example