mirror of
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454 lines
16 KiB
Python
454 lines
16 KiB
Python
# Copyright 2025 Collate
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# Licensed under the Collate Community License, Version 1.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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# https://github.com/open-metadata/OpenMetadata/blob/main/ingestion/LICENSE
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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Test datalake utils
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"""
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import os
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from unittest import TestCase
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import pandas as pd
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from metadata.generated.schema.entity.data.table import Column, DataType
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from metadata.readers.dataframe.reader_factory import SupportedTypes
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from metadata.utils.datalake.datalake_utils import (
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DataFrameColumnParser,
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GenericDataFrameColumnParser,
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ParquetDataFrameColumnParser,
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)
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STRUCTURE = {
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"a": "w",
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"b": 4,
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"c": {
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"d": 2,
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"e": 4,
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"f": {
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"g": 9,
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"h": {"i": 6},
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"n": {
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"o": 10,
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"p": 11,
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},
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},
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"j": 7,
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"k": 8,
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},
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}
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class TestDatalakeUtils(TestCase):
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"""class for datalake utils test"""
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def test_unique_json_structure(self):
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"""test unique json structure fn"""
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sample_data = [
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{"a": "x", "b": 1, "c": {"d": 2}},
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{"a": "y", "b": 2, "c": {"e": 4, "f": {"g": 5, "h": {"i": 6}, "n": 5}}},
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{"a": "z", "b": 3, "c": {"j": 7}},
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{"a": "w", "b": 4, "c": {"k": 8, "f": {"g": 9, "n": {"o": 10, "p": 11}}}},
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]
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expected = STRUCTURE
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actual = GenericDataFrameColumnParser.unique_json_structure(sample_data)
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self.assertDictEqual(expected, actual)
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def test_construct_column(self):
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"""test construct column fn"""
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expected = [
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{
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"dataTypeDisplay": "STRING",
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"dataType": "STRING",
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"name": "a",
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"displayName": "a",
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},
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{
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"dataTypeDisplay": "INT",
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"dataType": "INT",
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"name": "b",
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"displayName": "b",
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},
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{
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"dataTypeDisplay": "JSON",
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"dataType": "JSON",
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"name": "c",
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"displayName": "c",
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"children": [
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{
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"dataTypeDisplay": "INT",
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"dataType": "INT",
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"name": "d",
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"displayName": "d",
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},
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{
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"dataTypeDisplay": "INT",
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"dataType": "INT",
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"name": "e",
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"displayName": "e",
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},
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{
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"dataTypeDisplay": "JSON",
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"dataType": "JSON",
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"name": "f",
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"displayName": "f",
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"children": [
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{
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"dataTypeDisplay": "INT",
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"dataType": "INT",
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"name": "g",
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"displayName": "g",
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},
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{
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"dataTypeDisplay": "JSON",
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"dataType": "JSON",
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"name": "h",
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"displayName": "h",
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"children": [
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{
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"dataTypeDisplay": "INT",
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"dataType": "INT",
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"name": "i",
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"displayName": "i",
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}
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],
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},
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{
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"dataTypeDisplay": "JSON",
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"dataType": "JSON",
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"name": "n",
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"displayName": "n",
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"children": [
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{
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"dataTypeDisplay": "INT",
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"dataType": "INT",
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"name": "o",
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"displayName": "o",
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},
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{
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"dataTypeDisplay": "INT",
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"dataType": "INT",
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"name": "p",
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"displayName": "p",
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},
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],
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},
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],
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},
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{
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"dataTypeDisplay": "INT",
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"dataType": "INT",
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"name": "j",
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"displayName": "j",
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},
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{
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"dataTypeDisplay": "INT",
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"dataType": "INT",
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"name": "k",
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"displayName": "k",
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},
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],
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},
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]
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actual = GenericDataFrameColumnParser.construct_json_column_children(STRUCTURE)
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for el in zip(expected, actual):
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self.assertDictEqual(el[0], el[1])
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def test_create_column_object(self):
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"""test create column object fn"""
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formatted_column = GenericDataFrameColumnParser.construct_json_column_children(
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STRUCTURE
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)
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column = {
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"dataTypeDisplay": "STRING",
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"dataType": "STRING",
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"name": "a",
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"displayName": "a",
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"children": formatted_column,
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}
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column_obj = Column(**column)
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assert len(column_obj.children) == 3
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class TestParquetDataFrameColumnParser(TestCase):
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"""Test parquet dataframe column parser"""
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@classmethod
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def setUpClass(cls) -> None:
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resources_path = os.path.join(
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os.path.dirname(os.path.dirname(__file__)), "resources"
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)
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cls.parquet_path = os.path.join(resources_path, "datalake", "example.parquet")
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cls.df = pd.read_parquet(cls.parquet_path)
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cls.parquet_parser = ParquetDataFrameColumnParser(cls.df)
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def test_parser_instantiation(self):
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"""Test the right parser is instantiated from the creator method"""
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parquet_parser = DataFrameColumnParser.create(self.df, SupportedTypes.PARQUET)
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self.assertIsInstance(parquet_parser.parser, ParquetDataFrameColumnParser)
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parquet_types = [
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SupportedTypes.PARQUET,
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SupportedTypes.PARQUET_PQ,
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SupportedTypes.PARQUET_PQT,
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SupportedTypes.PARQUET_PARQ,
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SupportedTypes.PARQUET_SNAPPY,
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]
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other_types = [typ for typ in SupportedTypes if typ not in parquet_types]
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for other_type in other_types:
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with self.subTest(other_type=other_type):
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generic_parser = DataFrameColumnParser.create(self.df, other_type)
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self.assertIsInstance(
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generic_parser.parser, GenericDataFrameColumnParser
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)
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def test_shuffle_and_sample_from_parser(self):
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"""test the shuffle and sampling logic from the parser creator method"""
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parquet_parser = DataFrameColumnParser.create(self.df, SupportedTypes.PARQUET)
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self.assertEqual(parquet_parser.parser.data_frame.shape, self.df.shape)
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parquet_parser = DataFrameColumnParser.create(
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[self.df, self.df], SupportedTypes.PARQUET
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)
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self.assertEqual(parquet_parser.parser.data_frame.shape, self.df.shape)
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parquet_parser = DataFrameColumnParser.create(
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[self.df, self.df], SupportedTypes.PARQUET, sample=False
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)
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self.assertEqual(
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parquet_parser.parser.data_frame.shape, pd.concat([self.df, self.df]).shape
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)
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def test_get_columns(self):
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"""test `get_columns` method of the parquet column parser"""
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expected = [
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Column(
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dataTypeDisplay="bool",
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dataType=DataType.BOOLEAN,
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name="a",
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displayName="a",
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), # type: ignore
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Column(
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dataTypeDisplay="int8",
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dataType=DataType.INT,
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name="b",
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displayName="b",
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), # type: ignore
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Column(
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dataTypeDisplay="int16",
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dataType=DataType.INT,
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name="c",
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displayName="c",
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), # type: ignore
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Column(
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dataTypeDisplay="int32",
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dataType=DataType.INT,
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name="d",
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displayName="d",
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), # type: ignore
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Column(
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dataTypeDisplay="int64",
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dataType=DataType.INT,
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name="e",
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displayName="e",
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), # type: ignore
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Column(
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dataTypeDisplay="uint8",
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dataType=DataType.UINT,
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name="f",
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displayName="f",
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), # type: ignore
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Column(
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dataTypeDisplay="uint16",
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dataType=DataType.UINT,
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name="g",
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displayName="g",
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), # type: ignore
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Column(
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dataTypeDisplay="uint32",
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dataType=DataType.UINT,
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name="h",
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displayName="h",
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), # type: ignore
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Column(
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dataTypeDisplay="uint64",
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dataType=DataType.UINT,
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name="i",
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displayName="i",
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), # type: ignore
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Column(
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dataTypeDisplay="float",
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dataType=DataType.FLOAT,
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name="k",
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displayName="k",
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), # type: ignore
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Column(
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dataTypeDisplay="double",
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dataType=DataType.FLOAT,
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name="l",
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displayName="l",
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), # type: ignore
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Column(
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dataTypeDisplay="time64[us]",
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dataType=DataType.DATETIME,
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name="n",
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displayName="n",
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), # type: ignore
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Column(
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dataTypeDisplay="timestamp[ns]",
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dataType=DataType.DATETIME,
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name="o",
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displayName="o",
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), # type: ignore
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Column(
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dataTypeDisplay="date32[day]",
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dataType=DataType.DATE,
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name="p",
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displayName="p",
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), # type: ignore
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Column(
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dataTypeDisplay="date32[day]",
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dataType=DataType.DATE,
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name="q",
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displayName="q",
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), # type: ignore
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Column(
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dataTypeDisplay="duration[ns]",
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dataType=DataType.INT,
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name="r",
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displayName="r",
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), # type: ignore
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Column(
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dataTypeDisplay="binary",
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dataType=DataType.BINARY,
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name="t",
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displayName="t",
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), # type: ignore
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Column(
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dataTypeDisplay="string",
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dataType=DataType.STRING,
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name="u",
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displayName="u",
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), # type: ignore
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Column(
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dataTypeDisplay="string",
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dataType=DataType.STRING,
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name="v",
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displayName="v",
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), # type: ignore
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Column(
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dataTypeDisplay="binary",
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dataType=DataType.BINARY,
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name="w",
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displayName="w",
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), # type: ignore
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Column(
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dataTypeDisplay="string",
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dataType=DataType.STRING,
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name="x",
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displayName="x",
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), # type: ignore
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Column(
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dataTypeDisplay="string",
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dataType=DataType.STRING,
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name="y",
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displayName="y",
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), # type: ignore
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Column(
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dataTypeDisplay="list<item: int64>",
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dataType=DataType.ARRAY,
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name="aa",
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displayName="aa",
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), # type: ignore
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Column(
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dataTypeDisplay="list<item: int64>",
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dataType=DataType.ARRAY,
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name="bb",
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displayName="bb",
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), # type: ignore
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Column(
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dataTypeDisplay="struct<ee: int64, ff: int64, gg: struct<hh: struct<ii: int64, jj: int64, kk: int64>>>",
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dataType=DataType.STRUCT,
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name="dd",
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displayName="dd",
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children=[
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Column(
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dataTypeDisplay="int64",
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dataType=DataType.INT,
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name="ee",
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displayName="ee",
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), # type: ignore
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Column(
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dataTypeDisplay="int64",
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dataType=DataType.INT,
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name="ff",
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displayName="ff",
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), # type: ignore
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Column(
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dataTypeDisplay="struct<hh: struct<ii: int64, jj: int64, kk: int64>>",
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dataType=DataType.STRUCT,
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name="gg",
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displayName="gg",
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children=[
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Column(
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dataTypeDisplay="struct<ii: int64, jj: int64, kk: int64>",
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dataType=DataType.STRUCT,
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name="hh",
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displayName="hh",
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children=[
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Column(
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dataTypeDisplay="int64",
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dataType=DataType.INT,
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name="ii",
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displayName="ii",
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), # type: ignore
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Column(
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dataTypeDisplay="int64",
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dataType=DataType.INT,
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name="jj",
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displayName="jj",
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), # type: ignore
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Column(
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dataTypeDisplay="int64",
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dataType=DataType.INT,
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name="kk",
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displayName="kk",
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), # type: ignore
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],
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),
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],
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),
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],
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), # type: ignore
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]
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actual = self.parquet_parser.get_columns()
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for validation in zip(expected, actual):
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with self.subTest(validation=validation):
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expected_col, actual_col = validation
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self.assertEqual(expected_col.name, actual_col.name)
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self.assertEqual(expected_col.displayName, actual_col.displayName)
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self.assertEqual(expected_col.dataType, actual_col.dataType)
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def _validate_parsed_column(self, expected, actual):
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"""validate parsed column"""
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self.assertEqual(expected.name, actual.name)
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self.assertEqual(expected.dataType, actual.dataType)
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self.assertEqual(expected.displayName, actual.displayName)
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if expected.children:
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self.assertEqual(len(expected.children), len(actual.children))
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for validation in zip(expected.children, actual.children):
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with self.subTest(validation=validation):
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expected_col, actual_col = validation
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self._validate_parsed_column(expected_col, actual_col)
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