made function more robust per comments

src/sempy_labs/_helper_functions.py

@@ -1188,16 +1188,24 @@ def generate_guid():
     return str(uuid.uuid4())
 
 
-def _get_column_value(lakehouse: str, table_name: str, column_name: str = 'RunId', function: str = 'MAX') -> int:
+def _get_column_aggregate(
+    lakehouse: str,
+    table_name: str,
+    column_name: str = "RunId",
+    function: str = "max",
+    default_value: int = 0,
+) -> int:
 
     from pyspark.sql import SparkSession
 
     spark = SparkSession.builder.getOrCreate()
+    function = function.upper()
     query = f"SELECT {function}({column_name}) FROM {lakehouse}.{table_name}"
+    if "COUNT" in function and "DISTINCT" in function:
+        query = f"SELECT COUNT(DISTINCT({column_name})) FROM {lakehouse}.{table_name}"
     dfSpark = spark.sql(query)
-    max_run_id = dfSpark.collect()[0][0] or 0
 
-    return max_run_id
+    return dfSpark.collect()[0][0] or default_value
 
 
 def _make_list_unique(my_list):

src/sempy_labs/_model_bpa.py

@@ -12,7 +12,7 @@
     resolve_workspace_capacity,
     resolve_dataset_name_and_id,
     get_language_codes,
-    _get_column_value,
+    _get_column_aggregate,
     resolve_workspace_name_and_id,
 )
 from sempy_labs.lakehouse import get_lakehouse_tables, lakehouse_attached
@@ -383,7 +383,7 @@ def translate_using_spark(rule_file):
         if len(lakeT_filt) == 0:
             runId = 1
         else:
-            max_run_id = _get_column_value(
+            max_run_id = _get_column_aggregate(
                 lakehouse=lakehouse, table_name=delta_table_name
             )
             runId = max_run_id + 1

src/sempy_labs/_model_bpa_bulk.py

@@ -6,7 +6,7 @@
     save_as_delta_table,
     resolve_workspace_capacity,
     retry,
-    _get_column_value,
+    _get_column_aggregate,
 )
 from sempy_labs.lakehouse import (
     get_lakehouse_tables,
@@ -76,7 +76,7 @@ def run_model_bpa_bulk(
     if len(lakeT_filt) == 0:
         runId = 1
     else:
-        max_run_id = _get_column_value(lakehouse=lakehouse, table_name=output_table)
+        max_run_id = _get_column_aggregate(lakehouse=lakehouse, table_name=output_table)
         runId = max_run_id + 1
 
     if isinstance(workspace, str):

src/sempy_labs/_vertipaq.py

@@ -12,7 +12,7 @@
     resolve_lakehouse_name,
     save_as_delta_table,
     resolve_workspace_capacity,
-    _get_column_value,
+    _get_column_aggregate,
     resolve_workspace_name_and_id,
     resolve_dataset_name_and_id,
 )
@@ -519,7 +519,9 @@ def _style_columns_based_on_types(dataframe: pd.DataFrame, column_type_mapping):
         if len(lakeT_filt) == 0:
             runId = 1
         else:
-            max_run_id = _get_column_value(lakehouse=lakehouse, table_name=lakeTName)
+            max_run_id = _get_column_aggregate(
+                lakehouse=lakehouse, table_name=lakeTName
+            )
             runId = max_run_id + 1
 
         dfMap = {

src/sempy_labs/report/_report_bpa.py

@@ -10,7 +10,7 @@
     resolve_report_id,
     resolve_lakehouse_name,
     resolve_workspace_capacity,
-    _get_column_value,
+    _get_column_aggregate,
     resolve_workspace_name_and_id,
 )
 from sempy_labs.lakehouse import get_lakehouse_tables, lakehouse_attached
@@ -217,7 +217,7 @@ def execute_rule(row):
         if len(lakeT_filt) == 0:
             runId = 1
         else:
-            max_run_id = _get_column_value(
+            max_run_id = _get_column_aggregate(
                 lakehouse=lakehouse, table_name=delta_table_name
             )
             runId = max_run_id + 1