mirror of
https://github.com/open-metadata/OpenMetadata.git
synced 2025-10-31 10:39:30 +00:00
475 lines
15 KiB
Python
475 lines
15 KiB
Python
# pylint: disable=line-too-long
|
|
# Copyright 2021 Collate
|
|
# Licensed 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.
|
|
|
|
"""
|
|
Unit tests for datalake source
|
|
"""
|
|
|
|
from copy import deepcopy
|
|
from types import SimpleNamespace
|
|
from unittest import TestCase
|
|
from unittest.mock import patch
|
|
|
|
from metadata.generated.schema.entity.data.database import Database
|
|
from metadata.generated.schema.entity.data.table import Column
|
|
from metadata.generated.schema.entity.services.databaseService import (
|
|
DatabaseConnection,
|
|
DatabaseService,
|
|
DatabaseServiceType,
|
|
)
|
|
from metadata.generated.schema.metadataIngestion.workflow import (
|
|
OpenMetadataWorkflowConfig,
|
|
)
|
|
from metadata.generated.schema.type.entityReference import EntityReference
|
|
from metadata.ingestion.source.database.datalake.metadata import DatalakeSource
|
|
from metadata.readers.dataframe.avro import AvroDataFrameReader
|
|
from metadata.readers.dataframe.json import JSONDataFrameReader
|
|
from metadata.utils.datalake.datalake_utils import get_columns
|
|
|
|
mock_datalake_config = {
|
|
"source": {
|
|
"type": "datalake",
|
|
"serviceName": "local_datalake",
|
|
"serviceConnection": {
|
|
"config": {
|
|
"type": "Datalake",
|
|
"configSource": {
|
|
"securityConfig": {
|
|
"awsAccessKeyId": "aws_access_key_id",
|
|
"awsSecretAccessKey": "aws_secret_access_key",
|
|
"awsRegion": "us-east-2",
|
|
"endPointURL": "https://endpoint.com/",
|
|
}
|
|
},
|
|
"bucketName": "bucket name",
|
|
}
|
|
},
|
|
"sourceConfig": {
|
|
"config": {
|
|
"type": "DatabaseMetadata",
|
|
"schemaFilterPattern": {
|
|
"excludes": [
|
|
"^test.*",
|
|
".*test$",
|
|
]
|
|
},
|
|
}
|
|
},
|
|
},
|
|
"sink": {"type": "metadata-rest", "config": {}},
|
|
"workflowConfig": {
|
|
"openMetadataServerConfig": {
|
|
"hostPort": "http://localhost:8585/api",
|
|
"authProvider": "openmetadata",
|
|
"securityConfig": {
|
|
"jwtToken": "eyJraWQiOiJHYjM4OWEtOWY3Ni1nZGpzLWE5MmotMDI0MmJrOTQzNTYiLCJ0eXAiOiJKV1QiLCJhbGciOiJSUzI1NiJ9.eyJzdWIiOiJhZG1pbiIsImlzQm90IjpmYWxzZSwiaXNzIjoib3Blbi1tZXRhZGF0YS5vcmciLCJpYXQiOjE2NjM5Mzg0NjIsImVtYWlsIjoiYWRtaW5Ab3Blbm1ldGFkYXRhLm9yZyJ9.tS8um_5DKu7HgzGBzS1VTA5uUjKWOCU0B_j08WXBiEC0mr0zNREkqVfwFDD-d24HlNEbrqioLsBuFRiwIWKc1m_ZlVQbG7P36RUxhuv2vbSp80FKyNM-Tj93FDzq91jsyNmsQhyNv_fNr3TXfzzSPjHt8Go0FMMP66weoKMgW2PbXlhVKwEuXUHyakLLzewm9UMeQaEiRzhiTMU3UkLXcKbYEJJvfNFcLwSl9W8JCO_l0Yj3ud-qt_nQYEZwqW6u5nfdQllN133iikV4fM5QZsMCnm8Rq1mvLR0y9bmJiD7fwM1tmJ791TUWqmKaTnP49U493VanKpUAfzIiOiIbhg"
|
|
},
|
|
}
|
|
},
|
|
}
|
|
|
|
MOCK_S3_SCHEMA = {
|
|
"Buckets": [
|
|
{"Name": "test_datalake"},
|
|
{"Name": "test_gcs"},
|
|
{"Name": "s3_test"},
|
|
{"Name": "my_bucket"},
|
|
]
|
|
}
|
|
|
|
MOCK_GCS_SCHEMA = [
|
|
SimpleNamespace(name="test_datalake"),
|
|
SimpleNamespace(name="test_gcs"),
|
|
SimpleNamespace(name="s3_test"),
|
|
SimpleNamespace(name="my_bucket"),
|
|
]
|
|
|
|
EXPECTED_SCHEMA = ["my_bucket"]
|
|
|
|
|
|
MOCK_DATABASE_SERVICE = DatabaseService(
|
|
id="85811038-099a-11ed-861d-0242ac120002",
|
|
name="local_datalake",
|
|
connection=DatabaseConnection(),
|
|
serviceType=DatabaseServiceType.Postgres,
|
|
)
|
|
|
|
MOCK_DATABASE = Database(
|
|
id="2aaa012e-099a-11ed-861d-0242ac120002",
|
|
name="118146679784",
|
|
fullyQualifiedName="local_datalake.default",
|
|
displayName="118146679784",
|
|
description="",
|
|
service=EntityReference(
|
|
id="85811038-099a-11ed-861d-0242ac120002",
|
|
type="databaseService",
|
|
),
|
|
)
|
|
|
|
EXAMPLE_JSON_TEST_1 = b"""
|
|
{"name":"John","age":16,"sex":"M"}
|
|
{"name":"Milan","age":19,"sex":"M"}
|
|
"""
|
|
|
|
EXAMPLE_JSON_TEST_2 = b"""
|
|
{"name":"John","age":16,"sex":"M"}
|
|
"""
|
|
|
|
EXAMPLE_JSON_TEST_3 = b"""
|
|
{
|
|
"name":"John",
|
|
"age":16,
|
|
"sex":"M",
|
|
"address":{
|
|
"city":"Mumbai",
|
|
"state":"Maharashtra",
|
|
"country":"India",
|
|
"coordinates":{
|
|
"lat":123,
|
|
"lon":123
|
|
}
|
|
}
|
|
}
|
|
"""
|
|
|
|
EXAMPLE_JSON_TEST_4 = b"""
|
|
[
|
|
{"name":"John","age":16,"sex":"M","address":null},
|
|
{
|
|
"name":"John",
|
|
"age":16,
|
|
"sex":"M",
|
|
"address":{
|
|
"city":"Mumbai",
|
|
"state":"Maharashtra",
|
|
"country":"India",
|
|
"coordinates":{
|
|
"lat":123,
|
|
"lon":123
|
|
}
|
|
}
|
|
}
|
|
]
|
|
"""
|
|
|
|
EXAMPLE_JSON_COL_3 = [
|
|
Column(
|
|
name="name",
|
|
dataType="STRING",
|
|
dataTypeDisplay="STRING",
|
|
displayName="name",
|
|
),
|
|
Column(
|
|
name="age",
|
|
dataType="INT",
|
|
dataTypeDisplay="INT",
|
|
displayName="age",
|
|
),
|
|
Column(
|
|
name="sex",
|
|
dataType="STRING",
|
|
dataTypeDisplay="STRING",
|
|
displayName="sex",
|
|
),
|
|
Column(
|
|
name="address",
|
|
dataType="RECORD",
|
|
dataTypeDisplay="RECORD",
|
|
displayName="address",
|
|
children=[
|
|
Column(
|
|
name="city",
|
|
dataType="STRING",
|
|
dataTypeDisplay="STRING",
|
|
),
|
|
Column(
|
|
name="state",
|
|
dataType="STRING",
|
|
dataTypeDisplay="STRING",
|
|
),
|
|
Column(
|
|
name="country",
|
|
dataType="STRING",
|
|
dataTypeDisplay="STRING",
|
|
),
|
|
Column(
|
|
name="coordinates",
|
|
dataType="RECORD",
|
|
dataTypeDisplay="RECORD",
|
|
displayName="coordinates",
|
|
children=[
|
|
Column(
|
|
name="lat",
|
|
dataType="INT",
|
|
dataTypeDisplay="INT",
|
|
),
|
|
Column(
|
|
name="lon",
|
|
dataType="INT",
|
|
dataTypeDisplay="INT",
|
|
),
|
|
],
|
|
),
|
|
],
|
|
),
|
|
]
|
|
|
|
|
|
EXAMPLE_JSON_COL_4 = deepcopy(EXAMPLE_JSON_COL_3)
|
|
EXAMPLE_JSON_COL_4[3].children[3].children = [
|
|
Column(
|
|
name="lat",
|
|
dataType="FLOAT",
|
|
dataTypeDisplay="FLOAT",
|
|
),
|
|
Column(
|
|
name="lon",
|
|
dataType="FLOAT",
|
|
dataTypeDisplay="FLOAT",
|
|
),
|
|
]
|
|
|
|
EXPECTED_AVRO_COL_1 = [
|
|
Column(
|
|
name="level",
|
|
dataType="RECORD",
|
|
children=[
|
|
Column(name="uid", dataType="INT", dataTypeDisplay="int"),
|
|
Column(name="somefield", dataType="STRING", dataTypeDisplay="string"),
|
|
Column(
|
|
name="options",
|
|
dataType="ARRAY",
|
|
dataTypeDisplay="ARRAY<record>",
|
|
arrayDataType="RECORD",
|
|
children=[
|
|
Column(
|
|
name="lvl2_record",
|
|
dataType="RECORD",
|
|
children=[
|
|
Column(
|
|
name="item1_lvl2",
|
|
dataType="STRING",
|
|
dataTypeDisplay="string",
|
|
),
|
|
Column(
|
|
name="item2_lvl2",
|
|
dataType="ARRAY",
|
|
arrayDataType="RECORD",
|
|
dataTypeDisplay="ARRAY<record>",
|
|
children=[
|
|
Column(
|
|
name="lvl3_record",
|
|
dataType="RECORD",
|
|
children=[
|
|
Column(
|
|
name="item1_lvl3",
|
|
dataType="STRING",
|
|
dataTypeDisplay="string",
|
|
),
|
|
Column(
|
|
name="item2_lvl3",
|
|
dataType="STRING",
|
|
dataTypeDisplay="string",
|
|
),
|
|
],
|
|
),
|
|
],
|
|
),
|
|
],
|
|
)
|
|
],
|
|
),
|
|
],
|
|
)
|
|
]
|
|
|
|
|
|
EXPECTED_AVRO_COL_2 = [
|
|
Column(
|
|
name="twitter_schema",
|
|
dataType="RECORD",
|
|
children=[
|
|
Column(
|
|
name="username",
|
|
dataType="STRING",
|
|
description="Name of the user account on Twitter.com",
|
|
dataTypeDisplay="string",
|
|
),
|
|
Column(
|
|
name="tweet",
|
|
dataType="STRING",
|
|
dataTypeDisplay="string",
|
|
description="The content of the user's Twitter message",
|
|
),
|
|
Column(
|
|
name="timestamp",
|
|
dataType="LONG",
|
|
dataTypeDisplay="long",
|
|
description="Unix epoch time in seconds",
|
|
),
|
|
],
|
|
)
|
|
]
|
|
|
|
AVRO_SCHEMA_FILE = b"""{
|
|
"namespace": "openmetadata.kafka",
|
|
"name": "level",
|
|
"type": "record",
|
|
"fields": [
|
|
{
|
|
"name": "uid",
|
|
"type": "int"
|
|
},
|
|
{
|
|
"name": "somefield",
|
|
"type": "string"
|
|
},
|
|
{
|
|
"name": "options",
|
|
"type": {
|
|
"type": "array",
|
|
"items": {
|
|
"type": "record",
|
|
"name": "lvl2_record",
|
|
"fields": [
|
|
{
|
|
"name": "item1_lvl2",
|
|
"type": "string"
|
|
},
|
|
{
|
|
"name": "item2_lvl2",
|
|
"type": {
|
|
"type": "array",
|
|
"items": {
|
|
"type": "record",
|
|
"name": "lvl3_record",
|
|
"fields": [
|
|
{
|
|
"name": "item1_lvl3",
|
|
"type": "string"
|
|
},
|
|
{
|
|
"name": "item2_lvl3",
|
|
"type": "string"
|
|
}
|
|
]
|
|
}
|
|
}
|
|
}
|
|
]
|
|
}
|
|
}
|
|
}
|
|
]
|
|
}"""
|
|
|
|
AVRO_DATA_FILE = b'Obj\x01\x04\x16avro.schema\xe8\x05{"type":"record","name":"twitter_schema","namespace":"com.miguno.avro","fields":[{"name":"username","type":"string","doc":"Name of the user account on Twitter.com"},{"name":"tweet","type":"string","doc":"The content of the user\'s Twitter message"},{"name":"timestamp","type":"long","doc":"Unix epoch time in seconds"}],"doc:":"A basic schema for storing Twitter messages"}\x14avro.codec\x08null\x00g\xc75)s\xef\xdf\x94\xad\xd3\x00~\x9e\xeb\xff\xae\x04\xc8\x01\x0cmigunoFRock: Nerf paper, scissors is fine.\xb2\xb8\xee\x96\n\x14BlizzardCSFWorks as intended. Terran is IMBA.\xe2\xf3\xee\x96\ng\xc75)s\xef\xdf\x94\xad\xd3\x00~\x9e\xeb\xff\xae'
|
|
|
|
|
|
def _get_str_value(data):
|
|
if data:
|
|
if isinstance(data, str):
|
|
return data
|
|
return data.value
|
|
|
|
return None
|
|
|
|
|
|
def custom_column_compare(self, other):
|
|
return (
|
|
self.name == other.name
|
|
and self.displayName == other.displayName
|
|
and self.description == other.description
|
|
and self.dataTypeDisplay == other.dataTypeDisplay
|
|
and self.children == other.children
|
|
and _get_str_value(self.arrayDataType) == _get_str_value(other.arrayDataType)
|
|
)
|
|
|
|
|
|
class DatalakeUnitTest(TestCase):
|
|
"""
|
|
Datalake Source Unit Tests
|
|
"""
|
|
|
|
@patch(
|
|
"metadata.ingestion.source.database.datalake.metadata.DatalakeSource.test_connection"
|
|
)
|
|
def __init__(self, methodName, test_connection) -> None:
|
|
super().__init__(methodName)
|
|
test_connection.return_value = False
|
|
self.config = OpenMetadataWorkflowConfig.parse_obj(mock_datalake_config)
|
|
self.datalake_source = DatalakeSource.create(
|
|
mock_datalake_config["source"],
|
|
self.config.workflowConfig.openMetadataServerConfig,
|
|
)
|
|
self.datalake_source.context.__dict__["database"] = MOCK_DATABASE
|
|
self.datalake_source.context.__dict__[
|
|
"database_service"
|
|
] = MOCK_DATABASE_SERVICE
|
|
|
|
def test_s3_schema_filer(self):
|
|
self.datalake_source.client.list_buckets = lambda: MOCK_S3_SCHEMA
|
|
assert list(self.datalake_source.fetch_s3_bucket_names()) == EXPECTED_SCHEMA
|
|
|
|
def test_gcs_schema_filer(self):
|
|
self.datalake_source.client.list_buckets = lambda: MOCK_GCS_SCHEMA
|
|
assert list(self.datalake_source.fetch_gcs_bucket_names()) == EXPECTED_SCHEMA
|
|
|
|
def test_json_file_parse(self):
|
|
"""
|
|
Test json data files
|
|
"""
|
|
import pandas as pd # pylint: disable=import-outside-toplevel
|
|
|
|
sample_dict = {"name": "John", "age": 16, "sex": "M"}
|
|
|
|
exp_df_list = pd.json_normalize(
|
|
[
|
|
{"name": "John", "age": 16, "sex": "M"},
|
|
{"name": "Milan", "age": 19, "sex": "M"},
|
|
]
|
|
)
|
|
exp_df_obj = pd.json_normalize(sample_dict)
|
|
|
|
actual_df_1 = JSONDataFrameReader.read_from_json(
|
|
key="file.json", json_text=EXAMPLE_JSON_TEST_1, decode=True
|
|
)[0]
|
|
actual_df_2 = JSONDataFrameReader.read_from_json(
|
|
key="file.json", json_text=EXAMPLE_JSON_TEST_2, decode=True
|
|
)[0]
|
|
|
|
assert actual_df_1.compare(exp_df_list).empty
|
|
assert actual_df_2.compare(exp_df_obj).empty
|
|
|
|
Column.__eq__ = custom_column_compare
|
|
|
|
actual_df_3 = JSONDataFrameReader.read_from_json(
|
|
key="file.json", json_text=EXAMPLE_JSON_TEST_3, decode=True
|
|
)[0]
|
|
actual_cols_3 = get_columns(actual_df_3)
|
|
assert actual_cols_3 == EXAMPLE_JSON_COL_3
|
|
|
|
actual_df_4 = JSONDataFrameReader.read_from_json(
|
|
key="file.json", json_text=EXAMPLE_JSON_TEST_4, decode=True
|
|
)[0]
|
|
actual_cols_4 = get_columns(actual_df_4)
|
|
assert actual_cols_4 == EXAMPLE_JSON_COL_4
|
|
|
|
def test_avro_file_parse(self):
|
|
columns = AvroDataFrameReader.read_from_avro(AVRO_SCHEMA_FILE)
|
|
Column.__eq__ = custom_column_compare
|
|
|
|
assert EXPECTED_AVRO_COL_1 == columns.columns # pylint: disable=no-member
|
|
|
|
columns = AvroDataFrameReader.read_from_avro(AVRO_DATA_FILE)
|
|
assert EXPECTED_AVRO_COL_2 == columns.columns # pylint: disable=no-member
|