Fokko commented on code in PR #1534:
URL: https://github.com/apache/iceberg-python/pull/1534#discussion_r1935573488
##########
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:
Happy to elaborate. The SQL based syntax is supported, mostly to make it
easier for humans. If you follow the predicate in the code, first at
`txn.overwrite(update_recs, overwrite_filter)`, and then we come to the
`.delete()` method:
```python
if isinstance(delete_filter, str):
delete_filter = _parse_row_filter(delete_filter)
```
What we do here, is we take the string, and turn it into a
`BooleanExpression`, as an example:
This converts the string into an `In('col', [1,2,3])` method. Since we don't
need to use SQL here, we can optimize this by pushing down `In(..)` directly,
and avoid conversion back and forth.
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