paimon-python/pypaimon/tests/arrow_schema_test.py

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import glob
import os
import tempfile
import unittest
from unittest.mock import Mock, patch

import pyarrow as pa
import pyarrow.parquet as pq
from parameterized import parameterized

from pypaimon import CatalogFactory, Schema
from pypaimon.schema.arrow_schema import arrow_schemas_compatible, normalize_arrow_strings
from pypaimon.write.table_write import TableWrite
from pypaimon.execution.python.write.table_write import PythonTableWrite


class ArrowSchemaTest(unittest.TestCase):
    def test_compatibility_keeps_non_string_contracts(self):
        target = pa.schema([
            pa.field('id', pa.int32(), nullable=False),
            ('text', pa.struct([pa.field('value', pa.string(), nullable=False)])),
            ('blob', pa.large_binary()),
        ])
        source = target.set(1, pa.field('text', pa.struct([
            pa.field('value', pa.large_string(), nullable=False),
        ])))
        self.assertTrue(arrow_schemas_compatible(source, target))
        for invalid in [
            source.set(0, pa.field('id', pa.int64(), nullable=False)),
            source.set(1, pa.field('text', pa.struct([('value', pa.large_string())]))),
            source.set(2, pa.field('blob', pa.binary())),
            pa.schema(list(reversed(list(source)))),
        ]:
            self.assertFalse(arrow_schemas_compatible(invalid, target))

    @parameterized.expand([(False,), (True,)])
    def test_map_sorting_contract_is_preserved(self, sorted_keys):
        small = pa.schema([('v', pa.map_(pa.string(), pa.string(), keys_sorted=sorted_keys))])
        large = pa.schema([('v', pa.map_(pa.large_string(), pa.large_string(), keys_sorted=sorted_keys))])
        different_sorting = pa.schema([('v', pa.map_(pa.string(), pa.string(), keys_sorted=not sorted_keys))])
        self.assertTrue(arrow_schemas_compatible(small, large))
        self.assertFalse(arrow_schemas_compatible(large, different_sorting))

    @parameterized.expand([(False, False), (False, True), (True, False), (True, True)])
    def test_normalization_preserves_schema_and_other_buffers(self, record_batch, empty):
        schema = pa.schema([
            pa.field('id', pa.int64(), nullable=False),
            pa.field('text', pa.large_string(), metadata={b'description': b'label'}),
            ('blob', pa.large_binary()),
            ('bytes', pa.binary(1)),
        ], metadata={b'source': b'input'})
        values = [[], [], [], []] if empty else [[1, 2], ['中文', None], [b'blob', None], [b'x', b'y']]
        arrays = [pa.array(items, type=field.type) for items, field in zip(values, schema)]
        factory = pa.RecordBatch if record_batch else pa.Table
        source = factory.from_arrays(arrays, schema=schema)
        result = normalize_arrow_strings(source)
        self.assertIsInstance(result, factory)
        self.assertEqual(result.schema, schema.set(1, schema[1].with_type(pa.string())))
        self.assertEqual(result.schema.metadata, schema.metadata)
        self.assertEqual(result.schema[1].metadata, schema[1].metadata)
        self.assertEqual(result.to_pydict(), source.to_pydict())
        self.assertIs(normalize_arrow_strings(result), result)
        if not empty:
            blob = result.column(2) if record_batch else result.column(2).chunk(0)
            self.assertEqual(blob.buffers()[2].address, arrays[2].buffers()[2].address)

    @parameterized.expand([('table',), ('batch',), ('bucket',), ('postpone',), ('postpone_batch',)])
    def test_core_writes_canonical_strings(self, entry):
        with tempfile.TemporaryDirectory() as directory:
            source_schema = pa.schema([
                pa.field('id', pa.int64(), nullable=False), ('text', pa.large_string()),
            ])
            catalog = CatalogFactory.create({'warehouse': directory})
            catalog.create_database('default', False)
            catalog.create_table('default.strings', Schema.from_pyarrow_schema(
                source_schema, primary_keys=['id'] if entry.startswith('postpone') else [],
                options={'bucket': '-2' if entry.startswith('postpone') else '1', 'file.format': 'parquet'},
            ), False)
            table = catalog.get_table('default.strings')
            builder = (table.new_postpone_fixed_bucket_write_builder()
                       if entry.startswith('postpone') else table.new_batch_write_builder())
            writer, commit = builder.new_write(), builder.new_commit()
            expected = {'id': [1, 2], 'text': ['中文', None]}
            source = pa.Table.from_pydict(expected, schema=source_schema)
            try:
                if entry in ('batch', 'postpone_batch'):
                    writer.write_arrow_batch(source.to_batches()[0])
                elif entry == 'bucket':
                    writer.write_arrow(source.to_batches()[0], bucket=0)
                else:
                    writer.write_arrow(source)
                commit.commit(writer.prepare_commit())
            except Exception:
                writer.abort()
                raise
            finally:
                writer.close()
                commit.close()
            files = glob.glob(os.path.join(directory, '**', '*.parquet'), recursive=True)
            self.assertTrue(files)
            for path in files:
                self.assertEqual(pq.read_schema(path).field('text').type, pa.string())
            reader = table.new_read_builder()
            result = reader.new_read().to_arrow(reader.new_scan().plan().splits())
            actual = result.to_pydict()
            self.assertEqual(sorted(zip(actual['id'], actual['text'])), [(1, '中文'), (2, None)])

    @parameterized.expand([('table',), ('batch',)])
    def test_schema_only_writer_normalizes_input(self, entry):
        writer = object.__new__(TableWrite)
        writer._execution = PythonTableWrite(writer)
        writer.file_store_write = Mock(write_cols=None)
        writer.table_pyarrow_schema = pa.schema([('text', pa.string())])
        source = pa.Table.from_pydict({'text': ['中文', None]}, schema=pa.schema([('text', pa.large_string())]))
        if entry == 'batch':
            source = source.to_batches()[0]

        result = PythonTableWrite(writer)._prepare_arrow_data(source)

        self.assertIsInstance(result, type(source))
        self.assertEqual(result.schema, writer.table_pyarrow_schema)
        self.assertEqual(result.to_pydict(), source.to_pydict())
        writer.file_store_write.write.assert_not_called()

    @parameterized.expand([('table',), ('batch',)])
    def test_normalization_failure_fails_before_routing(self, entry):
        writer = object.__new__(TableWrite)
        writer._execution = PythonTableWrite(writer)
        writer.file_store_write = Mock(write_cols=None)
        writer.row_key_extractor = Mock()
        writer.table_pyarrow_schema = pa.schema([('text', pa.string())])
        source = pa.Table.from_pydict({'text': ['中文']}, schema=pa.schema([('text', pa.large_string())]))
        error = ValueError('Cannot convert large_string input to string')
        with patch('pypaimon.execution.python.write.table_write.normalize_arrow_strings', side_effect=error):
            with self.assertRaises(ValueError) as raised:
                if entry == 'batch':
                    writer.write_arrow_batch(source.to_batches()[0])
                else:
                    writer.write_arrow(source)
        self.assertIs(raised.exception, error)
        writer.row_key_extractor.extract_partition_bucket_groups.assert_not_called()
        writer.file_store_write.write.assert_not_called()

    @unittest.skipUnless(int(pa.__version__.split('.')[0]) == 6, 'Arrow 6 lacks struct cast kernels')
    def test_unsupported_nested_cast_fails_before_routing(self):
        writer = object.__new__(TableWrite)
        writer._execution = PythonTableWrite(writer)
        writer.file_store_write = Mock(write_cols=None)
        writer.row_key_extractor = Mock()
        writer.table_pyarrow_schema = pa.schema([('nested', pa.struct([('text', pa.string())]))])
        source = pa.Table.from_pydict(
            {'nested': [{'text': '中文'}]},
            schema=pa.schema([('nested', pa.struct([('text', pa.large_string())]))]),
        )
        with self.assertRaisesRegex(ValueError, 'Cannot convert large_string input to string'):
            writer.write_arrow(source)
        with self.assertRaisesRegex(ValueError, 'Cannot convert large_string input to string'):
            writer.write_arrow_batch(source.to_batches()[0])
        writer.row_key_extractor.extract_partition_bucket_groups.assert_not_called()
        writer.file_store_write.write.assert_not_called()