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import random
import string
from typing import Any, Dict
from unittest.mock import patch
from freezegun import freeze_time
from google.cloud.bigquery.table import TableListItem
from datahub.ingestion.glossary.classifier import (
ClassificationConfig,
DynamicTypedClassifierConfig,
)
from datahub.ingestion.glossary.datahub_classifier import DataHubClassifierConfig
from datahub.ingestion.source.bigquery_v2.bigquery_data_reader import BigQueryDataReader
from datahub.ingestion.source.bigquery_v2.bigquery_schema import (
BigqueryColumn,
BigqueryDataset,
BigqueryProject,
BigQuerySchemaApi,
BigqueryTable,
)
from datahub.ingestion.source.bigquery_v2.bigquery_schema_gen import (
BigQuerySchemaGenerator,
)
from tests.test_helpers import mce_helpers
from tests.test_helpers.state_helpers import run_and_get_pipeline
FROZEN_TIME = "2022-02-03 07:00:00"
def random_email():
return (
"".join(
[
random.choice(string.ascii_lowercase)
for i in range(random.randint(10, 15))
]
)
+ "@xyz.com"
)
def recipe(mcp_output_path: str, override: dict = {}) -> dict:
return {
"source": {
"type": "bigquery",
"config": {
"project_ids": ["project-id-1"],
"include_usage_statistics": False,
"include_table_lineage": False,
"include_data_platform_instance": True,
"classification": ClassificationConfig(
enabled=True,
classifiers=[
DynamicTypedClassifierConfig(
type="datahub",
config=DataHubClassifierConfig(
minimum_values_threshold=1,
),
)
],
max_workers=1,
).dict(),
},
},
"sink": {"type": "file", "config": {"filename": mcp_output_path}},
}
@freeze_time(FROZEN_TIME)
@patch.object(BigQuerySchemaApi, "get_tables_for_dataset")
@patch.object(BigQuerySchemaGenerator, "get_core_table_details")
@patch.object(BigQuerySchemaApi, "get_datasets_for_project_id")
@patch.object(BigQuerySchemaApi, "get_columns_for_dataset")
@patch.object(BigQueryDataReader, "get_sample_data_for_table")
@patch("google.cloud.bigquery.Client")
@patch("google.cloud.datacatalog_v1.PolicyTagManagerClient")
@patch("google.cloud.resourcemanager_v3.ProjectsClient")
def test_bigquery_v2_ingest(
client,
policy_tag_manager_client,
projects_client,
get_sample_data_for_table,
get_columns_for_dataset,
get_datasets_for_project_id,
get_core_table_details,
get_tables_for_dataset,
pytestconfig,
tmp_path,
):
test_resources_dir = pytestconfig.rootpath / "tests/integration/bigquery_v2"
mcp_golden_path = f"{test_resources_dir}/bigquery_mcp_golden.json"
mcp_output_path = "{}/{}".format(tmp_path, "bigquery_mcp_output.json")
get_datasets_for_project_id.return_value = [
BigqueryDataset(name="bigquery-dataset-1")
]
table_list_item = TableListItem(
{"tableReference": {"projectId": "", "datasetId": "", "tableId": ""}}
)
table_name = "table-1"
get_core_table_details.return_value = {table_name: table_list_item}
get_columns_for_dataset.return_value = {
table_name: [
BigqueryColumn(
name="age",
ordinal_position=1,
is_nullable=False,
field_path="col_1",
data_type="INT",
comment="comment",
is_partition_column=False,
cluster_column_position=None,
policy_tags=["Test Policy Tag"],
),
BigqueryColumn(
name="email",
ordinal_position=1,
is_nullable=False,
field_path="col_2",
data_type="STRING",
comment="comment",
is_partition_column=False,
cluster_column_position=None,
),
]
}
get_sample_data_for_table.return_value = {
"age": [random.randint(1, 80) for i in range(20)],
"email": [random_email() for i in range(20)],
}
bigquery_table = BigqueryTable(
name=table_name,
comment=None,
created=None,
last_altered=None,
size_in_bytes=None,
rows_count=None,
)
get_tables_for_dataset.return_value = iter([bigquery_table])
pipeline_config_dict: Dict[str, Any] = recipe(mcp_output_path=mcp_output_path)
run_and_get_pipeline(pipeline_config_dict)
mce_helpers.check_golden_file(
pytestconfig,
output_path=mcp_output_path,
golden_path=mcp_golden_path,
)
@freeze_time(FROZEN_TIME)
@patch.object(BigQuerySchemaApi, attribute="get_projects_with_labels")
@patch.object(BigQuerySchemaApi, "get_tables_for_dataset")
@patch.object(BigQuerySchemaGenerator, "get_core_table_details")
@patch.object(BigQuerySchemaApi, "get_datasets_for_project_id")
@patch.object(BigQuerySchemaApi, "get_columns_for_dataset")
@patch.object(BigQueryDataReader, "get_sample_data_for_table")
@patch("google.cloud.bigquery.Client")
@patch("google.cloud.datacatalog_v1.PolicyTagManagerClient")
@patch("google.cloud.resourcemanager_v3.ProjectsClient")
def test_bigquery_v2_project_labels_ingest(
client,
policy_tag_manager_client,
projects_client,
get_sample_data_for_table,
get_columns_for_dataset,
get_datasets_for_project_id,
get_core_table_details,
get_tables_for_dataset,
get_projects_with_labels,
pytestconfig,
tmp_path,
):
test_resources_dir = pytestconfig.rootpath / "tests/integration/bigquery_v2"
mcp_golden_path = f"{test_resources_dir}/bigquery_project_label_mcp_golden.json"
mcp_output_path = "{}/{}".format(tmp_path, "bigquery_project_label_mcp_output.json")
get_datasets_for_project_id.return_value = [
BigqueryDataset(name="bigquery-dataset-1")
]
get_projects_with_labels.return_value = [
BigqueryProject(id="dev", name="development")
]
table_list_item = TableListItem(
{"tableReference": {"projectId": "", "datasetId": "", "tableId": ""}}
)
table_name = "table-1"
get_core_table_details.return_value = {table_name: table_list_item}
get_columns_for_dataset.return_value = {
table_name: [
BigqueryColumn(
name="age",
ordinal_position=1,
is_nullable=False,
field_path="col_1",
data_type="INT",
comment="comment",
is_partition_column=False,
cluster_column_position=None,
policy_tags=["Test Policy Tag"],
),
BigqueryColumn(
name="email",
ordinal_position=1,
is_nullable=False,
field_path="col_2",
data_type="STRING",
comment="comment",
is_partition_column=False,
cluster_column_position=None,
),
]
}
get_sample_data_for_table.return_value = {
"age": [random.randint(1, 80) for i in range(20)],
"email": [random_email() for i in range(20)],
}
bigquery_table = BigqueryTable(
name=table_name,
comment=None,
created=None,
last_altered=None,
size_in_bytes=None,
rows_count=None,
)
get_tables_for_dataset.return_value = iter([bigquery_table])
pipeline_config_dict: Dict[str, Any] = recipe(mcp_output_path=mcp_output_path)
del pipeline_config_dict["source"]["config"]["project_ids"]
pipeline_config_dict["source"]["config"]["project_labels"] = [
"environment:development"
]
run_and_get_pipeline(pipeline_config_dict)
mce_helpers.check_golden_file(
pytestconfig,
output_path=mcp_output_path,
golden_path=mcp_golden_path,
)