mattmartin14 commented on code in PR #1534:
URL: https://github.com/apache/iceberg-python/pull/1534#discussion_r1945220594
##########
pyiceberg/table/__init__.py:
##########
@@ -1064,6 +1066,97 @@ def name_mapping(self) -> Optional[NameMapping]:
"""Return the table's field-id NameMapping."""
return self.metadata.name_mapping()
+ @dataclass(frozen=True)
+ class UpsertResult:
+ """Summary the upsert operation"""
+ rows_updated: int = 0
+ rows_inserted: int = 0
+
+ def upsert(self, df: pa.Table, join_cols: list
+ , when_matched_update_all: bool = True
+ , when_not_matched_insert_all: bool = True
+ ) -> UpsertResult:
+ """
+ Shorthand API for performing an upsert to an iceberg table.
+
+ Args:
+ self: the target Iceberg table to execute the upsert on
+ df: The input dataframe to upsert with the table's data.
+ join_cols: The columns to join on. These are essentially analogous
to primary keys
+ 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
+
+ Example Use Cases:
+ Case 1: Both Parameters = True (Full Upsert)
+ Existing row found → Update it
+ New row found → Insert it
+
+ Case 2: when_matched_update_all = False,
when_not_matched_insert_all = True
+ Existing row found → Do nothing (no updates)
+ New row found → Insert it
+
+ Case 3: when_matched_update_all = True,
when_not_matched_insert_all = False
+ Existing row found → Update it
+ New row found → Do nothing (no inserts)
+
+ Case 4: Both Parameters = False (No Merge Effect)
+ Existing row found → Do nothing
+ New row found → Do nothing
+ (Function effectively does nothing)
+
+
+ Returns: a UpsertResult class (contains details of rows updated and
inserted)
+ """
+
+ from pyiceberg.table import upsert_util
+
+ if when_matched_update_all == False and when_not_matched_insert_all ==
False:
+ raise Exception('no upsert options selected...exiting')
+
+ if upsert_util.has_duplicate_rows(df, join_cols):
+
+ raise Exception('Duplicate rows found in source dataset based on
the key columns. No upsert executed')
+
+ #get list of rows that exist so we don't have to load the entire
target table
+ matched_predicate = upsert_util.create_match_filter(df, join_cols)
+ matched_iceberg_table =
self.scan(row_filter=matched_predicate).to_arrow()
+
+ update_row_cnt = 0
+ insert_row_cnt = 0
+
+ try:
+
+ with self.transaction() as txn:
+
+ if when_matched_update_all:
+
+ #function get_rows_to_update is doing a check on non-key
columns to see if any of the values have actually changed
+ rows_to_update = upsert_util.get_rows_to_update(df,
matched_iceberg_table, join_cols)
Review Comment:
@Fokko - function get_rows_to_update is slightly different than
create_match_filter; get_rows_to_update also evaulates if any non-key columns
from both tables are different. for example, if we had these 2 tables:
df
id | val
a | abc
b | edf
and iceberg table
id | val
a | abc
b | hij
and ran get_rows_to_update, it would only return row (b, edf) since val
"edf" differs from "hij" in the target.
Function create_match_filter would return both rows, but really, we only
want to update what has actually changed. Does that make sense?
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