tscottcoombes1 commented on code in PR #1534:
URL: https://github.com/apache/iceberg-python/pull/1534#discussion_r1941734610
##########
pyiceberg/table/__init__.py:
##########
@@ -1064,6 +1067,115 @@ def name_mapping(self) -> Optional[NameMapping]:
"""Return the table's field-id NameMapping."""
return self.metadata.name_mapping()
+ @dataclass
+ class MergeResult:
+ """docstring"""
+ rows_updated: int
+ rows_inserted: int
+ info_msgs: str
+ error_msgs: str
+
+ def merge_rows(self, df: pa.Table, join_cols: list
+ , when_matched_update_all: bool = True
+ , when_not_matched_insert_all: bool = True
+ ) -> MergeResult:
+ """
+ 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.
+ when_matched_update_all: Bool indicating to update rows that are
matched but require an update due to a value in a non-key column changing
+ when_not_matched_insert_all: Bool indicating new rows to be
inserted that do not match any existing rows in the table
+
+ 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 when_matched_update_all == False and when_not_matched_insert_all ==
False:
+ return {'rows_updated': 0, 'rows_inserted': 0, 'info_msgs': 'no
merge options selected...exiting'}
+
+ missing_columns = merge_rows_util.do_join_columns_exist(df, self,
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']}"}
+
+ if merge_rows_util.dups_check_in_source(df, join_cols):
+
+ return {'error_msgs': 'Duplicate rows found in source dataset
based on the key columns. No Merge executed'}
+
+
+ source_col_list = merge_rows_util.get_table_column_list_pa(df)
+ target_col_list = merge_rows_util.get_table_column_list_iceberg(self)
Review Comment:
re: the sets, you basically don't need these two functions
```suggestion
source_col_list = df.column_names
target_col_list = self.schema().column_names
```
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