kevinjqliu commented on code in PR #1539: URL: https://github.com/apache/iceberg-python/pull/1539#discussion_r1922707073
########## tests/benchmark/test_benchmark.py: ########## @@ -0,0 +1,72 @@ +# 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 statistics +import timeit +import urllib + +import pyarrow as pa +import pyarrow.parquet as pq +import pytest + +from pyiceberg.transforms import DayTransform + + +@pytest.fixture(scope="session") +def taxi_dataset(tmp_path_factory: pytest.TempPathFactory) -> pa.Table: + """Reads the Taxi dataset to disk""" + taxi_dataset = "https://d37ci6vzurychx.cloudfront.net/trip-data/yellow_tripdata_2022-01.parquet"; + taxi_dataset_dest = tmp_path_factory.mktemp("taxi_dataset") / "yellow_tripdata_2022-01.parquet" + urllib.request.urlretrieve(taxi_dataset, taxi_dataset_dest) + + return pq.read_table(taxi_dataset_dest) + + +@pytest.mark.benchmark Review Comment: CI is failing since we dont have the new marker added here https://github.com/apache/iceberg-python/blob/b15934d5a9e6bf97b047f63239cc21ba1c15cdd4/pyproject.toml#L1216-L1223 ########## tests/integration/test_writes/test_partitioned_writes.py: ########## @@ -1126,3 +1127,25 @@ def test_append_multiple_partitions( """ ) assert files_df.count() == 6 + + +@pytest.mark.integration +def test_pyarrow_overflow(session_catalog: Catalog) -> None: + """Test what happens when the offset is beyond 32 bits""" + identifier = "default.arrow_table_overflow" + try: + session_catalog.drop_table(identifier=identifier) + except NoSuchTableError: + pass + + x = pa.array([random.randint(0, 999) for _ in range(30_000)]) + ta = pa.chunked_array([x] * 10_000) + y = ["fixed_string"] * 30_000 + tb = pa.chunked_array([y] * 10_000) + # Create pa.table + arrow_table = pa.table({"a": ta, "b": tb}) Review Comment: it wasnt obv to me that this test offset is beyond 32 bits, but i ran it and `4800280000` is `>2^32`/`4294967296` ``` >>> len(arrow_table) 300000000 >>> arrow_table.get_total_buffer_size() 4800280000 ``` ########## pyiceberg/io/pyarrow.py: ########## @@ -2594,42 +2566,46 @@ def _determine_partitions(spec: PartitionSpec, schema: Schema, arrow_table: pa.T We then retrieve the partition keys by offsets. And slice the arrow table by offsets and lengths of each partition. """ - partition_columns: List[Tuple[PartitionField, NestedField]] = [ - (partition_field, schema.find_field(partition_field.source_id)) for partition_field in spec.fields - ] - partition_values_table = pa.table( - { - str(partition.field_id): partition.transform.pyarrow_transform(field.field_type)(arrow_table[field.name]) - for partition, field in partition_columns - } - ) + # Assign unique names to columns where the partition transform has been applied + # to avoid conflicts + partition_fields = [f"_partition_{field.name}" for field in spec.fields] + + for partition, name in zip(spec.fields, partition_fields): + source_field = schema.find_field(partition.source_id) + arrow_table = arrow_table.append_column( + name, partition.transform.pyarrow_transform(source_field.field_type)(arrow_table[source_field.name]) + ) + + unique_partition_fields = arrow_table.select(partition_fields).group_by(partition_fields).aggregate([]) Review Comment: from [`group_by` docs](https://arrow.apache.org/docs/python/generated/pyarrow.Table.html#pyarrow.Table.group_by) ``` use_threadsbool, default True Whether to use multithreading or not. When set to True (the default), no stable ordering of the output is guaranteed. ``` should we be concerned with stable ordering here? ########## pyiceberg/partitioning.py: ########## @@ -425,8 +426,13 @@ def _to_partition_representation(type: IcebergType, value: Any) -> Any: @_to_partition_representation.register(TimestampType) @_to_partition_representation.register(TimestamptzType) -def _(type: IcebergType, value: Optional[datetime]) -> Optional[int]: - return datetime_to_micros(value) if value is not None else None +def _(type: IcebergType, value: Optional[Union[datetime, int]]) -> Optional[int]: + if value is None: + return None + elif isinstance(value, int): + return value Review Comment: what can return int here? since this function is registered to ``` @_to_partition_representation.register(TimestampType) @_to_partition_representation.register(TimestamptzType) ``` ########## pyiceberg/io/pyarrow.py: ########## Review Comment: the docstring for `_determine_partitions` is outdated with this change https://github.com/apache/iceberg-python/pull/1539/files#diff-8d5e63f2a87ead8cebe2fd8ac5dcf2198d229f01e16bb9e06e21f7277c328abdR2547-R2568 -- This is an automated message from the Apache Git Service. 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