Feature: MERGE/Upsert Support

.gitignore

@@ -50,3 +50,6 @@ htmlcov
 pyiceberg/avro/decoder_fast.c
 pyiceberg/avro/*.html
 pyiceberg/avro/*.so
+
+# node modules
+node_modules/

pyiceberg/table/__init__.py

@@ -150,6 +150,14 @@
 DOWNCAST_NS_TIMESTAMP_TO_US_ON_WRITE = "downcast-ns-timestamp-to-us-on-write"
 
 
+@dataclass()
+class UpsertResult:
+    """Summary the upsert operation."""
+
+    rows_updated: int = 0
+    rows_inserted: int = 0
+
+
 class TableProperties:
     PARQUET_ROW_GROUP_SIZE_BYTES = "write.parquet.row-group-size-bytes"
     PARQUET_ROW_GROUP_SIZE_BYTES_DEFAULT = 128 * 1024 * 1024  # 128 MB
@@ -1086,6 +1094,78 @@ def name_mapping(self) -> Optional[NameMapping]:
         """Return the table's field-id NameMapping."""
         return self.metadata.name_mapping()
 
+    def upsert(
+        self, df: pa.Table, join_cols: list[str], 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:
+            An UpsertResult class (contains details of rows updated and inserted)
+        """
+        from pyiceberg.table import upsert_util
+
+        if not when_matched_update_all and not when_not_matched_insert_all:
+            raise ValueError("no upsert options selected...exiting")
+
+        if upsert_util.has_duplicate_rows(df, join_cols):
+            raise ValueError("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
+
+        with self.transaction() as tx:
+            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
+                # we don't want to do just a blanket overwrite for matched rows if the actual non-key column data hasn't changed
+                # this extra step avoids unnecessary IO and writes
+                rows_to_update = upsert_util.get_rows_to_update(df, matched_iceberg_table, join_cols)
+
+                update_row_cnt = len(rows_to_update)
+
+                # build the match predicate filter
+                overwrite_mask_predicate = upsert_util.create_match_filter(rows_to_update, join_cols)
+
+                tx.overwrite(rows_to_update, overwrite_filter=overwrite_mask_predicate)
+
+            if when_not_matched_insert_all:
+                rows_to_insert = upsert_util.get_rows_to_insert(df, matched_iceberg_table, join_cols)
+
+                insert_row_cnt = len(rows_to_insert)
+
+                tx.append(rows_to_insert)
+
+        return UpsertResult(rows_updated=update_row_cnt, rows_inserted=insert_row_cnt)
+
     def append(self, df: pa.Table, snapshot_properties: Dict[str, str] = EMPTY_DICT) -> None:
         """
         Shorthand API for appending a PyArrow table to the table.

pyiceberg/table/upsert_util.py

@@ -0,0 +1,118 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+import functools
+import operator
+
+import pyarrow as pa
+from pyarrow import Table as pyarrow_table
+from pyarrow import compute as pc
+
+from pyiceberg.expressions import (
+    And,
+    BooleanExpression,
+    EqualTo,
+    In,
+    Or,
+)
+
+
+def create_match_filter(df: pyarrow_table, join_cols: list[str]) -> BooleanExpression:
+    unique_keys = df.select(join_cols).group_by(join_cols).aggregate([])
+
+    if len(join_cols) == 1:
+        return In(join_cols[0], unique_keys[0].to_pylist())
+    else:
+        return Or(*[And(*[EqualTo(col, row[col]) for col in join_cols]) for row in unique_keys.to_pylist()])
+
+
+def has_duplicate_rows(df: pyarrow_table, join_cols: list[str]) -> bool:
+    """Check for duplicate rows in a PyArrow table based on the join columns."""
+    return len(df.select(join_cols).group_by(join_cols).aggregate([([], "count_all")]).filter(pc.field("count_all") > 1)) > 0
+
+
+def get_rows_to_update(source_table: pa.Table, target_table: pa.Table, join_cols: list[str]) -> pa.Table:
+    """
+    Return a table with rows that need to be updated in the target table based on the join columns.
+
+    When a row is matched, an additional scan is done to evaluate the non-key columns to detect if an actual change has occurred.
+    Only matched rows that have an actual change to a non-key column value will be returned in the final output.
+    """
+    all_columns = set(source_table.column_names)
+    join_cols_set = set(join_cols)
+
+    non_key_cols = list(all_columns - join_cols_set)
+
+    match_expr = functools.reduce(operator.and_, [pc.field(col).isin(target_table.column(col).to_pylist()) for col in join_cols])
+
+    matching_source_rows = source_table.filter(match_expr)
+
+    rows_to_update = []
+
+    for index in range(matching_source_rows.num_rows):
+        source_row = matching_source_rows.slice(index, 1)
+
+        target_filter = functools.reduce(operator.and_, [pc.field(col) == source_row.column(col)[0].as_py() for col in join_cols])
+
+        matching_target_row = target_table.filter(target_filter)
+
+        if matching_target_row.num_rows > 0:
+            needs_update = False
+
+            for non_key_col in non_key_cols:
+                source_value = source_row.column(non_key_col)[0].as_py()
+                target_value = matching_target_row.column(non_key_col)[0].as_py()
+
+                if source_value != target_value:
+                    needs_update = True
+                    break
+
+            if needs_update:
+                rows_to_update.append(source_row)
+
+    if rows_to_update:
+        rows_to_update_table = pa.concat_tables(rows_to_update)
+    else:
+        rows_to_update_table = pa.Table.from_arrays([], names=source_table.column_names)
+
+    common_columns = set(source_table.column_names).intersection(set(target_table.column_names))
+    rows_to_update_table = rows_to_update_table.select(list(common_columns))
+
+    return rows_to_update_table
+
+
+def get_rows_to_insert(source_table: pa.Table, target_table: pa.Table, join_cols: list[str]) -> pa.Table:
+    source_filter_expr = pc.scalar(True)
+
+    for col in join_cols:
+        target_values = target_table.column(col).to_pylist()
+        expr = pc.field(col).isin(target_values)
+
+        if source_filter_expr is None:
+            source_filter_expr = expr
+        else:
+            source_filter_expr = source_filter_expr & expr
+
+    non_matching_expr = ~source_filter_expr
+
+    source_columns = set(source_table.column_names)
+    target_columns = set(target_table.column_names)
+
+    common_columns = source_columns.intersection(target_columns)
+
+    non_matching_rows = source_table.filter(non_matching_expr).select(common_columns)
+
+    return non_matching_rows