mattmartin14 commented on code in PR #1534: URL: https://github.com/apache/iceberg-python/pull/1534#discussion_r1933904478
########## 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())) Review Comment: Hi @Fokko - from our meeting yesterday, i kind of have to load all this into a datafusion table in order to identify the deltas. I'm not sure of any other way around this; if you have some examples, then i'd be glad to take a look, but i think what I'm doing is in the best spirit of what a MERGE command should do, considering it has to check on primary key matching rows as well as identify what rows are actually different based on the non-key columns. -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: issues-unsubscr...@iceberg.apache.org For queries about this service, please contact Infrastructure at: us...@infra.apache.org --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@iceberg.apache.org For additional commands, e-mail: issues-h...@iceberg.apache.org