vibhatha commented on a change in pull request #11911:
URL: https://github.com/apache/arrow/pull/11911#discussion_r771750889
##########
File path: python/pyarrow/tests/test_dataset.py
##########
@@ -3621,6 +3621,191 @@ def compare_tables_ignoring_order(t1, t2):
assert not extra_file.exists()
+def _generate_random_int_array(size=4, min=1, max=10):
+ return np.random.randint(min, max, size)
+
+
+def _generate_data_and_columns(num_of_columns, num_of_records):
+ data = []
+ column_names = []
+ for i in range(num_of_columns):
+ data.append(_generate_random_int_array(size=num_of_records,
+ min=1,
+ max=num_of_records))
+ column_names.append("c" + str(i))
+ record_batch = pa.record_batch(data=data, names=column_names)
+ return record_batch
+
+
+def _get_num_of_files_generated(base_directory, file_format):
+ return len(list(pathlib.Path(base_directory).glob(f'**/*.{file_format}')))
+
+
+def test_write_dataset_max_rows_per_file(tempdir):
+ directory = tempdir / 'ds'
+ max_rows_per_file = 10
+ max_rows_per_group = 10
+ num_of_columns = 2
+ num_of_records = 35
+
+ record_batch = _generate_data_and_columns(num_of_columns,
+ num_of_records)
+
+ ds.write_dataset(record_batch, directory, format="parquet",
+ max_rows_per_file=max_rows_per_file,
+ max_rows_per_group=max_rows_per_group)
+
+ files_in_dir = os.listdir(directory)
+
+ # number of partitions with max_rows and the partition with the remainder
+ expected_partitions = num_of_records // max_rows_per_file + 1
+
+ # test whether the expected amount of files are written
+ assert len(files_in_dir) == expected_partitions
+
+ # compute the number of rows per each file written
+ result_row_combination = []
+ for _, f_file in enumerate(files_in_dir):
+ f_path = directory / str(f_file)
+ dataset = ds.dataset(f_path, format="parquet")
+ result_row_combination.append(dataset.to_table().shape[0])
+
+ # test whether the generated files have the expected number of rows
+ assert expected_partitions == len(result_row_combination)
+ assert num_of_records == sum(result_row_combination)
+
+
+def test_write_dataset_min_rows_per_group(tempdir):
+ directory = tempdir / 'ds'
+ min_rows_per_group = 6
+ max_rows_per_group = 8
+ num_of_columns = 2
+
+ record_sizes = [5, 5, 5, 5, 5, 4, 4, 4, 4, 4]
+
+ record_batches = [_generate_data_and_columns(num_of_columns,
+ num_of_records)
+ for num_of_records in record_sizes]
+
+ data_source = directory / "min_rows_group"
+
+ ds.write_dataset(data=record_batches, base_dir=data_source,
+ min_rows_per_group=min_rows_per_group,
+ max_rows_per_group=max_rows_per_group,
+ format="parquet")
+
+ files_in_dir = os.listdir(data_source)
+ for _, f_file in enumerate(files_in_dir):
+ f_path = data_source / str(f_file)
+ dataset = ds.dataset(f_path, format="parquet")
+ table = dataset.to_table()
+ batches = table.to_batches()
+
+ for id, batch in enumerate(batches):
+ rows_per_batch = batch.num_rows
+ if id < len(batches) - 1:
+ assert rows_per_batch >= min_rows_per_group and \
+ rows_per_batch <= max_rows_per_group
+ else:
+ assert rows_per_batch <= max_rows_per_group
+
+
+def test_write_dataset_max_rows_per_group(tempdir):
+ directory = tempdir / 'ds'
+ max_rows_per_group = 18
+ num_of_columns = 2
+ num_of_records = 30
+
+ record_batch = _generate_data_and_columns(num_of_columns,
+ num_of_records)
+
+ data_source = directory / "max_rows_group"
+
+ ds.write_dataset(data=record_batch, base_dir=data_source,
+ max_rows_per_group=max_rows_per_group,
+ format="parquet")
+
+ files_in_dir = os.listdir(data_source)
+ batched_data = []
+ for f_file in files_in_dir:
+ f_path = data_source / str(f_file)
+ dataset = ds.dataset(f_path, format="parquet")
+ table = dataset.to_table()
+ batches = table.to_batches()
+ for batch in batches:
+ batched_data.append(batch.num_rows)
+
+ assert batched_data == [18, 12]
+
+
+def test_write_dataset_max_open_files(tempdir):
+ directory = tempdir / 'ds'
+ file_format = "parquet"
+ partition_column_id = 1
+ column_names = ['c1', 'c2']
+ record_batch_1 = pa.record_batch(data=[[1, 2, 3, 4, 0, 10],
+ ['a', 'b', 'c', 'd', 'e', 'a']],
+ names=column_names)
+ record_batch_2 = pa.record_batch(data=[[5, 6, 7, 8, 0, 1],
+ ['a', 'b', 'c', 'd', 'e', 'c']],
+ names=column_names)
+ record_batch_3 = pa.record_batch(data=[[9, 10, 11, 12, 0, 1],
+ ['a', 'b', 'c', 'd', 'e', 'd']],
+ names=column_names)
+ record_batch_4 = pa.record_batch(data=[[13, 14, 15, 16, 0, 1],
+ ['a', 'b', 'c', 'd', 'e', 'b']],
+ names=column_names)
+
+ table = pa.Table.from_batches([record_batch_1, record_batch_2,
+ record_batch_3, record_batch_4])
+
+ partitioning = ds.partitioning(
+ pa.schema([(column_names[partition_column_id], pa.string())]),
+ flavor="hive")
+
+ data_source_1 = directory / "default"
+
+ ds.write_dataset(data=table, base_dir=data_source_1,
+ partitioning=partitioning, format=file_format)
+
+ # Here we consider the number of unique partitions created when
+ # partitioning column contains duplicate records.
+ # Returns: (number_of_files_generated, number_of_partitions)
+ def _get_compare_pair(data_source, record_batch, file_format, col_id):
+ num_of_files_generated = _get_num_of_files_generated(
+ base_directory=data_source, file_format=file_format)
+ number_of_partitions = len(pa.compute.unique(record_batch[col_id]))
+ return num_of_files_generated, number_of_partitions
+
+ # CASE 1: when max_open_files=default & max_open_files >= num_of_partitions
+ # In case of a writing to disk via partitioning based on a
+ # particular column (considering row labels in that column),
+ # the number of unique rows must be equal
+ # to the number of files generated
+
+ num_of_files_generated, number_of_partitions \
+ = _get_compare_pair(data_source_1, record_batch_1, file_format,
+ partition_column_id)
+ assert num_of_files_generated == number_of_partitions
+
+ # CASE 2: when max_open_files > 0 & max_open_files < num_of_partitions
+ # the number of files generated must be greater than the number of
+ # partitions
+
+ data_source_2 = directory / "max_1"
+ print(data_source_2)
Review comment:
Of course
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