I\'m trying to create a set of Unit Tests to test the Google Client Library for Bigquery. I\'m struggling to make a Unittest file which will mock the client and will let me test
I also find it hard to get around the authentication part and only mock interacting with methods, so I ended up just mocked the whole library. :facepalm:
import sys
from unittest.mock import MagicMock
sys.modules["google.cloud.storage"] = MagicMock()
from your_application import make_app
def test_make_app():
make_app()
It took a fair amount of Googling, and trial and error, to figure out how to do this, and I just got it working, so I thought it was worth sharing.
unittest
provides patch
which allows you to mock a function at the point of use, ie. replace a Google API call in your code under test, and mock
, which allows you to further customise the result of accessing attributes and calling functions on that mock.
The unittest
docs explaining patching here:
https://docs.python.org/3/library/unittest.mock.html#where-to-patch
This does explain how it works, but the best explanation I found in order to understand how to do this properly is: http://alexmarandon.com/articles/python_mock_gotchas/
Here is a Python script to be tested, mocking_google.py
, containing references to Google Storage and BigQuery APIs:
from google.cloud.bigquery import Client as bigqueryClient
from google.cloud.storage import Client as storageClient
def list_blobs():
storage_client = storageClient(project='test')
blobs = storage_client.list_blobs('bucket', prefix='prefix')
return blobs
def extract_table():
bigquery_client = bigqueryClient(project='test')
job = bigquery_client.extract_table('project.dataset.table_id', destination_uris='uri')
return job
Here is the unit test:
import pytest
from unittest.mock import Mock, patch
from src.data.mocking_google import list_blobs, extract_table
@pytest.fixture
def extract_result():
'Mock extract_job result with properties needed'
er = Mock()
er.return_value = 1
return er
@pytest.fixture
def extract_job(extract_result):
'Mock extract_job with properties needed'
ej = Mock()
ej.job_id = 1
ej.result.return_value = 2
return ej
@patch("src.data.mocking_google.storageClient")
def test_list_blobs(storageClient):
storageClient().list_blobs.return_value = [1,2]
blob_list = list_blobs()
storageClient().list_blobs.assert_called_with('bucket', prefix='prefix')
assert blob_list == [1,2]
@patch("src.data.mocking_google.bigqueryClient")
def test_extract_table(bigqueryClient,extract_job):
bigqueryClient().extract_table.return_value = extract_job
job = extract_table()
bigqueryClient().extract_table.assert_called_with('project.dataset.table_id', destination_uris='uri')
assert job.job_id == 1
assert job.result() == 2
Here is the test results:
pytest -v src/tests/data/test_mocking_google.py============================================================ test session starts =============================================================
platform darwin -- Python 3.7.6, pytest-5.3.5, py-1.8.1, pluggy-0.13.1 -- /Users/gaya/.local/share/virtualenvs/autoencoder-recommendation-copy-zpYZ6J1x/bin/python3
cachedir: .pytest_cache
rootdir: /Users/gaya/Documents/GitHub/mlops-autoencoder-recommendation, inifile: tox.ini
plugins: cov-2.8.1
collected 2 items
src/tests/data/test_mocking_google.py::test_list_blobs PASSED [ 50%]
src/tests/data/test_mocking_google.py::test_extract_table PASSED [100%]
============================================================= 2 passed in 1.14s ==============================================================
Happy to explain further if how this works is not clear :)