mattmartin14 commented on code in PR #1534:
URL: https://github.com/apache/iceberg-python/pull/1534#discussion_r1933907086


##########
pyiceberg/table/__init__.py:
##########
@@ -1064,6 +1064,125 @@ def name_mapping(self) -> Optional[NameMapping]:
         """Return the table's field-id NameMapping."""
         return self.metadata.name_mapping()
 
+    def merge_rows(self, df: pa.Table, join_cols: list
+                    ,merge_options: dict = {'when_matched_update_all': True, 
'when_not_matched_insert_all': True}
+                ) -> Dict:
+        """
+        Shorthand API for performing an upsert/merge to an iceberg table.
+        
+        Args:
+            df: The input dataframe to merge with the table's data.
+            join_cols: The columns to join on.
+            merge_options: A dictionary of merge actions to perform. Currently 
supports these predicates >
+                when_matched_update_all: default is True
+                when_not_matched_insert_all: default is True
+
+        Returns:
+            A dictionary containing the number of rows updated and inserted.
+        """
+
+        from pyiceberg.table import merge_rows_util
+
+        try:
+            from datafusion import SessionContext
+        except ModuleNotFoundError as e:
+            raise ModuleNotFoundError("For merge_rows, DataFusion needs to be 
installed") from e
+        
+        try:
+            from pyarrow import dataset as ds
+        except ModuleNotFoundError as e:
+            raise ModuleNotFoundError("For merge_rows, PyArrow needs to be 
installed") from e
+
+        source_table_name = "source"
+        target_table_name = "target"
+
+        if merge_options is None or merge_options == {}:
+            merge_options = {'when_matched_update_all': True, 
'when_not_matched_insert_all': True}
+
+        when_matched_update_all = merge_options.get('when_matched_update_all', 
False)
+        when_not_matched_insert_all = 
merge_options.get('when_not_matched_insert_all', False)
+
+        if when_matched_update_all == False and when_not_matched_insert_all == 
False:
+            return {'rows_updated': 0, 'rows_inserted': 0, 'msg': 'no merge 
options selected...exiting'}
+
+        ctx = SessionContext()
+
+        #register both source and target tables so we can find the deltas to 
update/append
+        ctx.register_dataset(source_table_name, ds.dataset(df))
+        ctx.register_dataset(target_table_name, 
ds.dataset(self.scan().to_arrow()))
+
+        source_col_list = merge_rows_util.get_table_column_list(ctx, 
source_table_name)
+        target_col_list = merge_rows_util.get_table_column_list(ctx, 
target_table_name)
+        
+        source_col_names = set([col[0] for col in source_col_list])
+        target_col_names = set([col[0] for col in target_col_list])
+
+        source_col_types = {col[0]: col[1] for col in source_col_list}
+
+        missing_columns = 
merge_rows_util.do_join_columns_exist(source_col_names, target_col_names, 
join_cols)
+        
+        if missing_columns['source'] or missing_columns['target']:
+
+            return {'error_msgs': f"Join columns missing in tables: Source 
table columns missing: {missing_columns['source']}, Target table columns 
missing: {missing_columns['target']}"}
+
+        #check for dups on source
+        if merge_rows_util.dups_check_in_source(source_table_name, join_cols, 
ctx):
+
+            return {'error_msgs': 'Duplicate rows found in source dataset 
based on the key columns. No Merge executed'}
+
+        update_row_cnt = 0
+        insert_row_cnt = 0
+
+        txn = self.transaction()
+
+        try:
+            
+            if when_matched_update_all:
+                
+                update_recs_sql = 
merge_rows_util.get_rows_to_update_sql(source_table_name, target_table_name, 
join_cols, source_col_names, target_col_names)
+            
+                update_recs = ctx.sql(update_recs_sql).to_arrow_table()
+
+                update_row_cnt = len(update_recs)
+
+                if len(join_cols) == 1:
+                    join_col = join_cols[0]
+                    col_type = source_col_types[join_col]  
+                    values = [row[join_col] for row in update_recs.to_pylist()]
+                    # if strings are in the filter, we encapsulate with tick 
marks
+                    formatted_values = [f"'{value}'" if col_type == 'string' 
else str(value) for value in values]
+                    overwrite_filter = f"{join_col} IN ({', 
'.join(formatted_values)})"

Review Comment:
   Can you explain more on this? I thought i was building my overwrite filter 
expression correctly, given I have to provide a list of keys or composite key 
columns to scan for.



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