Adding or reusing tests

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Overview
This guide will go over:
1.
how Feast tests are setup
2.
how to extend the test suite to test new functionality
3.
how to use the existing test suite to test a new custom offline / online store.
Test suite overview
Let's inspect the test setup as is:
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$ tree
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​
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.
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├── e2e
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│   └── test_universal_e2e.py
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├── feature_repos
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│   ├── repo_configuration.py
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│   └── universal
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│       ├── data_source_creator.py
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│       ├── data_sources
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│       │   ├── bigquery.py
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│       │   ├── file.py
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│       │   └── redshift.py
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│       ├── entities.py
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│       └── feature_views.py
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├── offline_store
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│   ├── test_s3_custom_endpoint.py
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│   └── test_universal_historical_retrieval.py
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├── online_store
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│   ├── test_e2e_local.py
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│   ├── test_feature_service_read.py
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│   ├── test_online_retrieval.py
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│   └── test_universal_online.py
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├── registration
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│   ├── test_cli.py
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│   ├── test_cli_apply_duplicated_featureview_names.py
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│   ├── test_cli_chdir.py
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│   ├── test_feature_service_apply.py
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│   ├── test_feature_store.py
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│   ├── test_inference.py
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│   ├── test_registry.py
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│   ├── test_universal_odfv_feature_inference.py
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│   └── test_universal_types.py
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└── scaffolding
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    ├── test_init.py
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    ├── test_partial_apply.py
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    ├── test_repo_config.py
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    └── test_repo_operations.py
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​
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8 directories, 27 files
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feature_repos has setup files for most tests in the test suite and sets up pytest fixtures for other tests. Crucially, this parametrizes on different offline stores, different online stores, etc and abstracts away store specific implementations so tests don't need to rewrite e.g. uploading dataframes to a specific store for setup.
Understanding an example test
Let's look at a sample test using the universal repo:
Python
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@pytest.mark.integration
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@pytest.mark.parametrize("full_feature_names", [True, False], ids=lambda v: str(v))
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def test_historical_features(environment, universal_data_sources, full_feature_names):
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    store = environment.feature_store
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​
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    (entities, datasets, data_sources) = universal_data_sources
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    feature_views = construct_universal_feature_views(data_sources)
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​
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    customer_df, driver_df, orders_df, global_df, entity_df = (
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        datasets["customer"],
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        datasets["driver"],
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        datasets["orders"],
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        datasets["global"],
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        datasets["entity"],
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    )
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​
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    # ... more test code
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​
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    customer_fv, driver_fv, driver_odfv, order_fv, global_fv = (
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        feature_views["customer"],
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        feature_views["driver"],
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        feature_views["driver_odfv"],
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        feature_views["order"],
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        feature_views["global"],
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    )
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​
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    feature_service = FeatureService(
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        "convrate_plus100",
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        features=[
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            feature_views["driver"][["conv_rate"]], 
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            feature_views["driver_odfv"]
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        ],
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    )
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​
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    feast_objects = []
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    feast_objects.extend(
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        [
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            customer_fv,
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            driver_fv,
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            driver_odfv,
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            order_fv,
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            global_fv,
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            driver(),
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            customer(),
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            feature_service,
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        ]
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    )
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    store.apply(feast_objects)
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​
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    # ... more test code
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​
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    job_from_df = store.get_historical_features(
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        entity_df=entity_df_with_request_data,
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        features=[
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            "driver_stats:conv_rate",
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            "driver_stats:avg_daily_trips",
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            "customer_profile:current_balance",
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            "customer_profile:avg_passenger_count",
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            "customer_profile:lifetime_trip_count",
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            "conv_rate_plus_100:conv_rate_plus_100",
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            "conv_rate_plus_100:conv_rate_plus_val_to_add",
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            "order:order_is_success",
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            "global_stats:num_rides",
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            "global_stats:avg_ride_length",
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        ],
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        full_feature_names=full_feature_names,
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    )
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    actual_df_from_df_entities = job_from_df.to_df()
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​
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    # ... more test code
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​
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    assert_frame_equal(
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        expected_df, actual_df_from_df_entities, check_dtype=False,
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    )
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​
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    # ... more test code
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The key fixtures are the environment and universal_data_sources fixtures, which are defined in the feature_repos directories. This by default pulls in a standard dataset with driver and customer entities, certain feature views, and feature values. By including the environment as a parameter, the test automatically parametrizes across other offline / online store combinations.
Writing a new test or reusing existing tests
To:
Include a new offline store: 
extend data_source_creator.py for your offline store
in repo_configuration.py add a newIntegrationTestRepoConfig or two (depending on how many online stores you want to test)
Run the full test suite with make test-python-integration
See more guidelines at https://github.com/feast-dev/feast/issues/1892​
Include a new online store:
in repo_configuration.py add a new config that maps to a serialized version of configuration you need in feature_store.yaml to setup the online store.
in repo_configuration.py, add newIntegrationTestRepoConfig for offline stores you want to test
Run the full test suite with make test-python-integration
See more guidelines at https://github.com/feast-dev/feast/issues/1892​
Add a new test to an existing test file:
Use the same function signatures as an existing test (e.g. have environment as an argument) to include the relevant test fixtures. 
We prefer to expand what an individual test covers due to the cost of standing up offline / online stores
Using custom data in a new test:
This is used in several places such as test_universal_types.py 
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@pytest.mark.integration
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def your_test(environment: Environment):
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    df = #...#
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    data_source = environment.data_source_creator.create_data_source(
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        df,
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        destination_name=environment.feature_store.project
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    )
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    your_fv = driver_feature_view(data_source)
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    entity = driver(value_type=ValueType.UNKNOWN)
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    fs.apply([fv, entity])
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​
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    # ... run test
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​
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    environment.data_source_creator.teardown()
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How-to Guides - Previous
Adding a new offline store
Next - Reference
Data sources
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Contents
Overview
Test suite overview
Understanding an example test
Writing a new test or reusing existing tests