Unit tests

Create unit tests and run tests in CI/CD pipeline

You can create a tests folder in your project folder and put unit tests in this folder. In your CI/CD pipeline, you can cd into the project’s folder and run the following command to run unit tests.

python3 -m unittest discover -s tests --failfast
python3 -m pytest tests -x

Example unit tests

End-to-end test for a pipeline:

import unittest

from mage_ai.data_preparation.models.pipeline import Pipeline


class ExamplePipelineTest(unittest.TestCase):
    def test_pipeline_execution(self):
        pipeline = Pipeline.get('example_pipeline')
        pipeline.execute_sync()

Test environment

When running unit tests, the env variable’s value in the method’s kwargs is test. You can leverage this variable to change the code logic or config used in the unit tests.

Mocking IO libraries

When writing unit tests, you may need to mock the IO libraries to verify that the pipeline is being executed properly without actually calling the database or warehouse directly. If you are using the provided Mage IO libraries, you can mock the libraries in the following way:

import pandas as pd
import unittest
from unittest.mock import patch, MagicMock, Mock

from mage_ai.data_preparation.models.pipeline import Pipeline


class ExampleIOTest(unittest.TestCase):
    def setUp(self):
        self.pipeline = Pipeline.get('load_data_to_postgres')

    def tearDown(self):
        """
        Optionally clean up the output of the pipeline/block execution.
        Make sure to only do this if you are ok losing all variable data for
        the project.
        """
        # import shutil
        # from mage_ai.settings.repo import get_variables_dir
        # shutil.rmtree(get_variables_dir())

    def test_pipeline_execution(self):
        bq_mock = MagicMock()
        bq_mock.__enter__ = Mock(return_value=bq_mock)
        bq_mock.load.return_value = pd.DataFrame(
            data=dict(
                a=[1, 2, 3],
                b=[4, 5, 6],
                c=[7, 8, 9],
            )
        )

        postgres_mock = MagicMock()
        postgres_mock.__enter__ = Mock(return_value=postgres_mock)

        with patch(
            'mage_ai.io.postgres.Postgres.with_config', return_value=postgres_mock
        ), patch('mage_ai.io.bigquery.BigQuery.with_config', return_value=bq_mock):
            self.pipeline.execute_sync(global_vars=dict(num_rows=10))

            bq_mock.load.assert_called_once()
            bq_mock.load.assert_called_with('SELECT * FROM xyz')

            postgres_mock.export.assert_called_once()
            args, kwargs = postgres_mock.export.call_args
            df = args[0]
            schema_name = args[1]
            table_name = args[2]

            self.assertEqual(df.shape, (3, 3))
            self.assertEqual(schema_name, 'public')
            self.assertEqual(table_name, 'test_table_name')
            self.assertEqual(kwargs['index'], False)
            self.assertEqual(kwargs['if_exists'], 'replace')

    def test_block_execution(self):
        block1 = self.pipeline.get_block('primal_dawn')
        block2 = self.pipeline.get_block('little_glitter')

        bq_mock = MagicMock()
        bq_mock.load.return_value = pd.DataFrame(
            data=dict(
                a=[1, 2, 3],
                b=[4, 5, 6],
                c=[7, 8, 9],
            )
        )

        with patch('mage_ai.io.bigquery.BigQuery.with_config', return_value=bq_mock):
            block1.execute_sync()

            bq_mock.load.assert_called_once()
            bq_mock.load.assert_called_with('SELECT * FROM xyz')

        postgres_mock = MagicMock()
        postgres_mock.__enter__ = Mock(return_value=postgres_mock)

        with patch(
            'mage_ai.io.postgres.Postgres.with_config', return_value=postgres_mock
        ):
            block2.execute_sync()

            postgres_mock.export.assert_called_once()
            args, kwargs = postgres_mock.export.call_args
            df = args[0]
            schema_name = args[1]
            table_name = args[2]

            self.assertEqual(df.shape, (3, 3))
            self.assertEqual(schema_name, 'public')
            self.assertEqual(table_name, 'test_table_name')
            self.assertEqual(kwargs['index'], False)
            self.assertEqual(kwargs['if_exists'], 'replace')

In the above example, we are mocking the BigQuery IO class in the data loader, and the Postgres IO class in the data exporter.

Monitoring

Alerting

Logging

Testing

Create unit tests and run tests in CI/CD pipeline

Example unit tests

Test environment

Mocking IO libraries

​Create unit tests and run tests in CI/CD pipeline

​Example unit tests

​Test environment

​Mocking IO libraries

Create unit tests and run tests in CI/CD pipeline

Example unit tests

Test environment

Mocking IO libraries