Fix MetaAPIHandler: allow empty result (#11932)

Author: StpMax

mindsdb/integrations/libs/api_handler.py

@@ -553,6 +553,17 @@ def meta_get_tables(self, table_names: Optional[List[str]] = None, **kwargs) ->
                 except Exception:
                     logger.exception(f"Error retrieving metadata for table {table_name}:")
 
+        if len(df.columns) == 0:
+            df = pd.DataFrame(
+                columns=[
+                    "TABLE_NAME",
+                    "TABLE_TYPE",
+                    "TABLE_SCHEMA",
+                    "TABLE_DESCRIPTION",
+                    "ROW_COUNT",
+                ]
+            )
+
         return Response(RESPONSE_TYPE.TABLE, df)
 
     def meta_get_columns(self, table_names: Optional[List[str]] = None, **kwargs) -> Response:
@@ -575,6 +586,18 @@ def meta_get_columns(self, table_names: Optional[List[str]] = None, **kwargs) ->
                 except Exception:
                     logger.exception(f"Error retrieving column metadata for table {table_name}:")
 
+        if len(df.columns) == 0:
+            df = pd.DataFrame(
+                columns=[
+                    "TABLE_NAME",
+                    "COLUMN_NAME",
+                    "DATA_TYPE",
+                    "COLUMN_DESCRIPTION",
+                    "IS_NULLABLE",
+                    "COLUMN_DEFAULT",
+                ]
+            )
+
         return Response(RESPONSE_TYPE.TABLE, df)
 
     def meta_get_column_statistics(self, table_names: Optional[List[str]] = None, **kwargs) -> Response:
@@ -597,6 +620,20 @@ def meta_get_column_statistics(self, table_names: Optional[List[str]] = None, **
                 except Exception:
                     logger.exception(f"Error retrieving column statistics for table {table_name}:")
 
+        if len(df.columns) == 0:
+            df = pd.DataFrame(
+                columns=[
+                    "TABLE_NAME",
+                    "COLUMN_NAME",
+                    "MOST_COMMON_VALUES",
+                    "MOST_COMMON_FREQUENCIES",
+                    "NULL_PERCENTAGE",
+                    "MINIMUM_VALUE",
+                    "MAXIMUM_VALUE",
+                    "DISTINCT_VALUES_COUNT",
+                ]
+            )
+
         return Response(RESPONSE_TYPE.TABLE, df)
 
     def meta_get_primary_keys(self, table_names: Optional[List[str]] = None, **kwargs) -> Response:
@@ -619,6 +656,16 @@ def meta_get_primary_keys(self, table_names: Optional[List[str]] = None, **kwarg
                 except Exception:
                     logger.exception(f"Error retrieving primary keys for table {table_name}:")
 
+        if len(df.columns) == 0:
+            df = pd.DataFrame(
+                columns=[
+                    "TABLE_NAME",
+                    "COLUMN_NAME",
+                    "ORDINAL_POSITION",
+                    "CONSTRAINT_NAME",
+                ]
+            )
+
         return Response(RESPONSE_TYPE.TABLE, df)
 
     def meta_get_foreign_keys(self, table_names: Optional[List[str]] = None, **kwargs) -> Response:
@@ -644,6 +691,17 @@ def meta_get_foreign_keys(self, table_names: Optional[List[str]] = None, **kwarg
                 except Exception:
                     logger.exception(f"Error retrieving foreign keys for table {table_name}:")
 
+        if len(df.columns) == 0:
+            df = pd.DataFrame(
+                columns=[
+                    "PARENT_TABLE_NAME",
+                    "PARENT_COLUMN_NAME",
+                    "CHILD_TABLE_NAME",
+                    "CHILD_COLUMN_NAME",
+                    "CONSTRAINT_NAME",
+                ]
+            )
+
         return Response(RESPONSE_TYPE.TABLE, df)
 
 

mindsdb/interfaces/data_catalog/data_catalog_retriever.py

@@ -173,10 +173,14 @@ def _construct_metadata_string_for_columns(
         """
         Construct a formatted string representation of the columns for a single table.
         """
-        columns_str = "\n\nColumns:"
+        columns_str = "\n\nColumns:\n"
         for _, column_row in columns_df.iterrows():
             # Ideally, there should be only one stats row per column.
-            stats_row = column_stats_df[column_stats_df["COLUMN_NAME"] == column_row["COLUMN_NAME"]].iloc[0]
+            stats_row = column_stats_df[column_stats_df["COLUMN_NAME"] == column_row["COLUMN_NAME"]]
+            if len(stats_row) == 0:
+                stats_row = pd.Series()
+            else:
+                stats_row = stats_row.iloc[0]
             columns_str += self._construct_metadata_string_for_column(
                 column_row,
                 stats_row,