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# Copyright 2019 Google LLC
#
# 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
#
# https://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.
import datetime
from typing import Iterator, List
from unittest import mock
import uuid
import google.auth
import pytest
from google.cloud import bigquery
@pytest.fixture(scope="session", autouse=True)
def client() -> bigquery.Client:
credentials, project = google.auth.default(
scopes=[
"https://www.googleapis.com/auth/drive",
"https://www.googleapis.com/auth/bigquery",
]
)
real_client = bigquery.Client(credentials=credentials, project=project)
mock_client = mock.create_autospec(bigquery.Client)
mock_client.return_value = real_client
bigquery.Client = mock_client # type: ignore
return real_client
@pytest.fixture
def random_table_id(dataset_id: str) -> str:
now = datetime.datetime.now()
random_table_id = "example_table_{}_{}".format(
now.strftime("%Y%m%d%H%M%S"), uuid.uuid4().hex[:8]
)
return "{}.{}".format(dataset_id, random_table_id)
@pytest.fixture
def avro_source_uris() -> List[str]:
avro_source_uris = [
"gs://cloud-samples-data/bigquery/federated-formats-reference-file-schema/a-twitter.avro",
"gs://cloud-samples-data/bigquery/federated-formats-reference-file-schema/b-twitter.avro",
"gs://cloud-samples-data/bigquery/federated-formats-reference-file-schema/c-twitter.avro",
]
return avro_source_uris
@pytest.fixture
def reference_file_schema_uri() -> str:
reference_file_schema_uri = "gs://cloud-samples-data/bigquery/federated-formats-reference-file-schema/b-twitter.avro"
return reference_file_schema_uri
@pytest.fixture
def random_dataset_id(client: bigquery.Client) -> Iterator[str]:
now = datetime.datetime.now()
random_dataset_id = "example_dataset_{}_{}".format(
now.strftime("%Y%m%d%H%M%S"), uuid.uuid4().hex[:8]
)
yield "{}.{}".format(client.project, random_dataset_id)
client.delete_dataset(random_dataset_id, delete_contents=True, not_found_ok=True)
@pytest.fixture
def random_routine_id(dataset_id: str) -> str:
now = datetime.datetime.now()
random_routine_id = "example_routine_{}_{}".format(
now.strftime("%Y%m%d%H%M%S"), uuid.uuid4().hex[:8]
)
return "{}.{}".format(dataset_id, random_routine_id)
@pytest.fixture
def dataset_id(client: bigquery.Client) -> Iterator[str]:
now = datetime.datetime.now()
dataset_id = "python_dataset_sample_{}_{}".format(
now.strftime("%Y%m%d%H%M%S"), uuid.uuid4().hex[:8]
)
dataset = client.create_dataset(dataset_id)
yield "{}.{}".format(dataset.project, dataset.dataset_id)
client.delete_dataset(dataset, delete_contents=True, not_found_ok=True)
@pytest.fixture
def table_id(client: bigquery.Client, dataset_id: str) -> Iterator[str]:
now = datetime.datetime.now()
table_id = "python_table_sample_{}_{}".format(
now.strftime("%Y%m%d%H%M%S"), uuid.uuid4().hex[:8]
)
table = bigquery.Table("{}.{}".format(dataset_id, table_id))
table = client.create_table(table)
yield "{}.{}.{}".format(table.project, table.dataset_id, table.table_id)
client.delete_table(table, not_found_ok=True)
@pytest.fixture
def table_with_schema_id(client: bigquery.Client, dataset_id: str) -> Iterator[str]:
now = datetime.datetime.now()
table_id = "python_table_with_schema_{}_{}".format(
now.strftime("%Y%m%d%H%M%S"), uuid.uuid4().hex[:8]
)
schema = [
bigquery.SchemaField("full_name", "STRING"),
bigquery.SchemaField("age", "INTEGER"),
]
table = bigquery.Table("{}.{}".format(dataset_id, table_id), schema=schema)
table = client.create_table(table)
yield "{}.{}.{}".format(table.project, table.dataset_id, table.table_id)
client.delete_table(table, not_found_ok=True)
@pytest.fixture
def table_with_data_id() -> str:
return "bigquery-public-data.samples.shakespeare"
@pytest.fixture
def routine_id(client: bigquery.Client, dataset_id: str) -> Iterator[str]:
now = datetime.datetime.now()
routine_id = "python_routine_sample_{}_{}".format(
now.strftime("%Y%m%d%H%M%S"), uuid.uuid4().hex[:8]
)
routine = bigquery.Routine("{}.{}".format(dataset_id, routine_id))
routine.type_ = "SCALAR_FUNCTION"
routine.language = "SQL"
routine.body = "x * 3"
routine.arguments = [
bigquery.RoutineArgument(
name="x",
data_type=bigquery.StandardSqlDataType(
type_kind=bigquery.StandardSqlTypeNames.INT64
),
)
]
routine = client.create_routine(routine)
yield "{}.{}.{}".format(routine.project, routine.dataset_id, routine.routine_id)
client.delete_routine(routine, not_found_ok=True)
@pytest.fixture
def model_id(client: bigquery.Client, dataset_id: str) -> str:
model_id = "{}.{}".format(dataset_id, uuid.uuid4().hex)
# The only way to create a model resource is via SQL.
# Use a very small dataset (2 points), to train a model quickly.
sql = """
CREATE MODEL `{}`
OPTIONS (
model_type='linear_reg',
max_iterations=1,
learn_rate=0.4,
learn_rate_strategy='constant'
) AS (
SELECT 'a' AS f1, 2.0 AS label
UNION ALL
SELECT 'b' AS f1, 3.8 AS label
)
""".format(
model_id
)
client.query_and_wait(sql)
return model_id
@pytest.fixture
def kms_key_name() -> str:
return "projects/cloud-samples-tests/locations/us/keyRings/test/cryptoKeys/test"
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