tscottcoombes1 commented on code in PR #1534: URL: https://github.com/apache/iceberg-python/pull/1534#discussion_r1949594241
########## tests/table/test_upsert.py: ########## @@ -0,0 +1,281 @@ +# 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 tests.catalog.test_base import InMemoryCatalog +import pytest +from datafusion import SessionContext + +_TEST_NAMESPACE = "test_ns" + +def show_iceberg_table(table, ctx: SessionContext): + import pyarrow.dataset as ds + table_name = "target" + if ctx.table_exist(table_name): + ctx.deregister_table(table_name) + ctx.register_dataset(table_name, ds.dataset(table.scan().to_arrow())) + ctx.sql(f"SELECT * FROM {table_name} limit 5").show() + +def show_df(df, ctx: SessionContext): + import pyarrow.dataset as ds + ctx.register_dataset("df", ds.dataset(df)) + ctx.sql("select * from df limit 10").show() + +def gen_source_dataset(start_row: int, end_row: int, composite_key: bool, add_dup: bool, ctx: SessionContext): + + additional_columns = ", t.order_id + 1000 as order_line_id" if composite_key else "" + + dup_row = f""" + UNION ALL + ( + SELECT t.order_id {additional_columns} + , date '2021-01-01' as order_date, 'B' as order_type + from t + limit 1 + ) + """ if add_dup else "" + + + sql = f""" + with t as (SELECT unnest(range({start_row},{end_row+1})) as order_id) + SELECT t.order_id {additional_columns} + , date '2021-01-01' as order_date, 'B' as order_type + from t + {dup_row} + """ + + df = ctx.sql(sql).to_arrow_table() + + return df + +def gen_target_iceberg_table(start_row: int, end_row: int, composite_key: bool, ctx: SessionContext, catalog: InMemoryCatalog, namespace: str): + + additional_columns = ", t.order_id + 1000 as order_line_id" if composite_key else "" + + df = ctx.sql(f""" + with t as (SELECT unnest(range({start_row},{end_row+1})) as order_id) + SELECT t.order_id {additional_columns} + , date '2021-01-01' as order_date, 'A' as order_type + from t + """).to_arrow_table() + + table = catalog.create_table(f"{_TEST_NAMESPACE}.target", df.schema) + + table.append(df) + + return table + +def assert_upsert_result(res, expected_updated, expected_inserted): + assert res.rows_updated == expected_updated, f"rows updated should be {expected_updated}, but got {res.rows_updated}" + assert res.rows_inserted == expected_inserted, f"rows inserted should be {expected_inserted}, but got {res.rows_inserted}" + +@pytest.fixture(scope="session") +def catalog_conn(): + catalog = InMemoryCatalog('test') + catalog.create_namespace(namespace=_TEST_NAMESPACE) + yield catalog + +@pytest.mark.parametrize( + "join_cols, src_start_row, src_end_row, target_start_row, target_end_row, when_matched_update_all, when_not_matched_insert_all, expected_updated, expected_inserted", + [ + (["order_id"], 1, 2, 2, 3, True, True, 1, 1), # single row + (["order_id"], 5001, 15000, 1, 10000, True, True, 5000, 5000), #10k rows + (["order_id"], 501, 1500, 1, 1000, True, False, 500, 0), # update only + (["order_id"], 501, 1500, 1, 1000, False, True, 0, 500), # insert only + ] +) +def test_merge_rows(catalog_conn, join_cols, src_start_row, src_end_row, target_start_row, target_end_row + , when_matched_update_all, when_not_matched_insert_all, expected_updated, expected_inserted): + + ctx = SessionContext() + + catalog = catalog_conn + + source_df = gen_source_dataset(src_start_row, src_end_row, False, False, ctx) + ice_table = gen_target_iceberg_table(target_start_row, target_end_row, False, ctx, catalog, _TEST_NAMESPACE) + res = ice_table.upsert(df=source_df, join_cols=join_cols, when_matched_update_all=when_matched_update_all, when_not_matched_insert_all=when_not_matched_insert_all) + + assert_upsert_result(res, expected_updated, expected_inserted) + + catalog.drop_table(f"{_TEST_NAMESPACE}.target") + +def test_merge_scenario_skip_upd_row(catalog_conn): + + """ + tests a single insert and update; skips a row that does not need to be updated + """ + + ctx = SessionContext() + + df = ctx.sql(f""" + select 1 as order_id, date '2021-01-01' as order_date, 'A' as order_type + union all + select 2 as order_id, date '2021-01-01' as order_date, 'A' as order_type + """).to_arrow_table() + + catalog = catalog_conn + table = catalog.create_table(f"{_TEST_NAMESPACE}.target", df.schema) + + table.append(df) + + source_df = ctx.sql(f""" + select 1 as order_id, date '2021-01-01' as order_date, 'A' as order_type + union all + select 2 as order_id, date '2021-01-01' as order_date, 'B' as order_type + union all + select 3 as order_id, date '2021-01-01' as order_date, 'A' as order_type + """).to_arrow_table() + + res = table.upsert(df=source_df, join_cols=["order_id"]) + + expected_updated = 1 + expected_inserted = 1 + + assert_upsert_result(res, expected_updated, expected_inserted) + + catalog.drop_table(f"{_TEST_NAMESPACE}.target") + +def test_merge_scenario_date_as_key(catalog_conn): + + """ + tests a single insert and update; primary key is a date column + """ + + ctx = SessionContext() + + df = ctx.sql(f""" + select date '2021-01-01' as order_date, 'A' as order_type + union all + select date '2021-01-02' as order_date, 'A' as order_type + """).to_arrow_table() + + catalog = catalog_conn + table = catalog.create_table(f"{_TEST_NAMESPACE}.target", df.schema) + + table.append(df) + + source_df = ctx.sql(f""" + select date '2021-01-01' as order_date, 'A' as order_type + union all + select date '2021-01-02' as order_date, 'B' as order_type + union all + select date '2021-01-03' as order_date, 'A' as order_type + """).to_arrow_table() + + res = table.upsert(df=source_df, join_cols=["order_date"]) + + expected_updated = 1 + expected_inserted = 1 + + assert_upsert_result(res, expected_updated, expected_inserted) + + catalog.drop_table(f"{_TEST_NAMESPACE}.target") + +def test_merge_scenario_string_as_key(catalog_conn): + + """ + tests a single insert and update; primary key is a string column + """ + + ctx = SessionContext() + + df = ctx.sql(f""" + select 'abc' as order_id, 'A' as order_type + union all + select 'def' as order_id, 'A' as order_type + """).to_arrow_table() + + catalog = catalog_conn + table = catalog.create_table(f"{_TEST_NAMESPACE}.target", df.schema) + + table.append(df) + + source_df = ctx.sql(f""" + select 'abc' as order_id, 'A' as order_type + union all + select 'def' as order_id, 'B' as order_type + union all + select 'ghi' as order_id, 'A' as order_type + """).to_arrow_table() + + res = table.upsert(df=source_df, join_cols=["order_id"]) + + expected_updated = 1 + expected_inserted = 1 + + assert_upsert_result(res, expected_updated, expected_inserted) + + catalog.drop_table(f"{_TEST_NAMESPACE}.target") + +def test_merge_scenario_composite_key(catalog_conn): + + """ + tests merging 200 rows with a composite key + """ + + ctx = SessionContext() + + catalog = catalog_conn + table = gen_target_iceberg_table(1, 200, True, ctx, catalog, _TEST_NAMESPACE) + source_df = gen_source_dataset(101, 300, True, False, ctx) + + res = table.upsert(df=source_df, join_cols=["order_id", "order_line_id"]) + + expected_updated = 100 + expected_inserted = 100 + + assert_upsert_result(res, expected_updated, expected_inserted) + + catalog.drop_table(f"{_TEST_NAMESPACE}.target") + +def test_merge_source_dups(catalog_conn): + + """ + tests duplicate rows in source + """ + + ctx = SessionContext() + + catalog = catalog_conn + table = gen_target_iceberg_table(1, 10, False, ctx, catalog, _TEST_NAMESPACE) + source_df = gen_source_dataset(5, 15, False, True, ctx) + + with pytest.raises(Exception, match="Duplicate rows found in source dataset based on the key columns. No upsert executed"): + table.upsert(df=source_df, join_cols=["order_id"]) + + catalog.drop_table(f"{_TEST_NAMESPACE}.target") + +def test_key_cols_misaligned(catalog_conn): + Review Comment: you can do it via pre-commit, or `make lint` which runs the pre-commit as well -- 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
