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chore: import upstream snapshot with attribution
2026-07-13 13:35:45 +08:00

112 lines
3.7 KiB
Python

# Copyright 2025 Collate
# Licensed under the Collate Community License, Version 1.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# https://github.com/open-metadata/OpenMetadata/blob/main/ingestion/LICENSE
# 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.
"""
test data quality
"""
import pytest
from metadata.generated.schema.entity.services.ingestionPipelines.ingestionPipeline import (
IngestionPipeline,
PipelineState,
)
from metadata.generated.schema.tests.basic import TestCaseStatus
from metadata.generated.schema.tests.testCase import TestCase
BUCKET_NAME = "my-bucket"
class TestDataQuality:
@pytest.mark.parametrize(
"test_case_name,expected_status",
[
("first_name_includes_john", TestCaseStatus.Success),
("first_name_is_john", TestCaseStatus.Failed),
],
)
def test_data_quality(
self,
run_test_suite_workflow,
metadata,
datalake_service_name,
test_case_name,
expected_status,
ingestion_fqn,
):
table_fqn = f'{datalake_service_name}.default.{BUCKET_NAME}."users/users.csv"'
test_case: TestCase = metadata.get_by_name(
TestCase,
f"{table_fqn}.first_name.{test_case_name}",
fields=["*"],
nullable=False,
)
assert test_case.testCaseResult.testCaseStatus == expected_status
# Check the ingestion pipeline is properly created
ingestion_pipeline: IngestionPipeline = metadata.get_by_name(
entity=IngestionPipeline, fqn=ingestion_fqn, fields=["pipelineStatuses"]
)
assert ingestion_pipeline
assert ingestion_pipeline.pipelineStatuses
assert ingestion_pipeline.pipelineStatuses[0].pipelineState == PipelineState.success
@pytest.mark.parametrize(
"test_case_name,failed_rows",
[
("first_name_includes_john", None),
("first_name_is_john", 2),
],
)
def test_data_quality_with_sample(
self,
run_sampled_test_suite_workflow,
metadata,
datalake_service_name,
test_case_name,
failed_rows,
):
table_fqn = f'{datalake_service_name}.default.{BUCKET_NAME}."users/users.csv"'
test_case: TestCase = metadata.get_by_name(
TestCase,
f"{table_fqn}.first_name.{test_case_name}",
fields=["*"],
nullable=False,
)
if failed_rows:
assert test_case.testCaseResult.failedRows == failed_rows
@pytest.mark.parametrize(
"test_case_name,expected_status,failed_rows",
[
("first_name_includes_john", TestCaseStatus.Success, None),
("first_name_is_john", TestCaseStatus.Failed, 1),
],
)
def test_data_quality_with_partition(
self,
run_partitioned_test_suite_workflow,
metadata,
datalake_service_name,
test_case_name,
expected_status,
failed_rows,
):
table_fqn = f'{datalake_service_name}.default.{BUCKET_NAME}."users/users.csv"'
test_case: TestCase = metadata.get_by_name(
TestCase,
f"{table_fqn}.first_name.{test_case_name}",
fields=["*"],
nullable=False,
)
assert test_case.testCaseResult.testCaseStatus == expected_status
if failed_rows:
assert test_case.testCaseResult.failedRows == failed_rows