tscottcoombes1 commented on code in PR #1534: URL: https://github.com/apache/iceberg-python/pull/1534#discussion_r1947249998
########## pyiceberg/table/upsert_util.py: ########## @@ -0,0 +1,146 @@ +# 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. +from pyarrow import Table as pyarrow_table +import pyarrow as pa +from pyarrow import compute as pc + +from pyiceberg.expressions import ( + BooleanExpression, + And, + EqualTo, + Or, + In, +) + +def create_match_filter(df: pyarrow_table, join_cols: list) -> 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) -> bool: + """ + This function checks if there are duplicate rows in 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) -> pa.Table: + + """ + This function takes the source_table, trims it down to rows that match in both source and target. + It then does a scan for the non-key columns to see if any are mis-aligned before returning the final row set to update + """ + + all_columns = set(source_table.column_names) + join_cols_set = set(join_cols) + + non_key_cols = list(all_columns - join_cols_set) + + match_expr = None + + for col in join_cols: + target_values = target_table.column(col).to_pylist() + expr = pc.field(col).isin(target_values) + + if match_expr is None: + match_expr = expr + else: + match_expr = match_expr & expr + + 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 = None + + for col in join_cols: + target_value = source_row.column(col)[0].as_py() + if target_filter is None: + target_filter = pc.field(col) == target_value + else: + target_filter = target_filter & (pc.field(col) == target_value) + + 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: Review Comment: i think you might want to use pa.compute.equals to handle some typings I also wondered if there is a better way of doing a row comparison, perhaps doing a hash of the row, rather than looping through the column. but i couldn't see a nice way of doing that. polars has a `hash_rows` function: https://docs.pola.rs/api/python/stable/reference/dataframe/api/polars.DataFrame.hash_rows.html that would be good for the job. maybe we come back to this. -- 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
