Pytest Microservices Skill

Pytest Development

Workflow

1. Identify behavior - Target a single function/behavior per test
2. Write the test - Plain function (never a class) with AAA structure
3. Run and iterate - Refine for readability and isolation

Best Practices

• Write plain functions, no test classes
• Name tests test_<function>_<behavior> — be specific about inputs and outcomes
• One test per distinct behavior; multiple assertions are fine when verifying one operation's effects
• Use parametrization when testing the same logic with different inputs (e.g., True/False flags)
• Skip trivial tests that verify unchanged/default behavior (e.g., "False stays False")
• Use HTTPStatus.OK not 200 for status assertions
• Test both success and error paths
• Verify database state after operations
• Use parametrization for input/output matrices, not loops
• Keep fixtures in app-specific conftest.py files
• Use pytestmark for module-level markers
• Aim for meaningful coverage, not just high percentages
• Keep imports at the top of the file, never inside test functions
• Use blank lines to separate AAA sections, no comments needed
• Use modern type hints: dict[str, Any], list[int], str | None (not Dict, List, Optional)
• Consolidate assertions: Verify status code, response body, and side effects (DB state) in a single test function. Do not split these into separate tests.
• Avoid implementation details: Do not test logging calls, private methods, path construction strings, or simple wrapper delegation.
• Dataclasses for Parametrization: If a parametrized test needs more than 2 arguments, use a dataclass to structure the test cases.

Project Structure

Always organize tests using src layout with unit/integration split:

my-service/
├── src/
│   └── my_service/
├── tests/
│   ├── conftest.py          # Global fixtures
│   ├── mocks/               # Shared mock objects (optional)
│   │   └── fake_s3.py
│   ├── unit/
│   │   ├── conftest.py      # Unit fixtures (mocks)
│   │   └── test_api.py
│   └── integration/
│       ├── conftest.py      # Integration fixtures (containers)
│       └── test_database.py
└── pyproject.toml

• Place unit tests in tests/unit/
• Place integration tests in tests/integration/
• Create the directories if they don't exist

Unit tests: Mock everything, run in milliseconds, no external services.

Integration tests: Real dependencies via testcontainers.

Anti-Patterns (Do Not Generate)

• Test Classes: Do not use class TestFoo: to group tests. Use plain functions with descriptive names like test_foo_does_x(). Test classes add unnecessary indentation and self parameters.
• Logging Verification: Do not test that logger.error was called. Exception raising is sufficient contract verification.
• Wrapper Tests: Do not test methods that simply call another method (delegation). Test the underlying logic or the full chain.
• Path Construction: Do not write tests solely to verify a URL string is built correctly; this is covered by the actual API call test.
• Atomic Fragmentation: Do not write separate tests for status_code, response_data, and db_state. Combine them into one behavioral test.

Test Naming

Good names — specific about function and behavior:

def test_user_creation_with_valid_data(): ...
def test_login_fails_with_invalid_password(): ...
def test_api_returns_404_for_missing_resource(): ...
def test_order_total_includes_tax_for_california(): ...

Bad names — vague or meaningless:

def test_1(): ...           # Not descriptive
def test_user(): ...        # Too vague
def test_it_works(): ...    # What works?

Test Structure (AAA Pattern)

Use blank lines to separate Arrange/Act/Assert — no comments needed:

from http import HTTPStatus

# Good — plain function
def test_create_user_returns_201_with_valid_data(client, make_user_payload):
    payload = make_user_payload(email="new@example.com")

    response = client.post("/users", json=payload)

    assert response.status_code == HTTPStatus.CREATED
    assert response.json()["email"] == "new@example.com"

# Bad — test class adds unnecessary structure
class TestCreateUser:  # Don't do this
    def test_returns_201(self, client, make_user_payload):
        payload = make_user_payload(email="new@example.com")
        response = client.post("/users", json=payload)
        assert response.status_code == HTTPStatus.CREATED

Test both success and error paths:

def test_create_user_returns_400_with_invalid_email(client, make_user_payload):
    payload = make_user_payload(email="invalid-email")

    response = client.post("/users", json=payload)

    assert response.status_code == HTTPStatus.BAD_REQUEST
    assert "email" in response.json()["errors"]

Verify database state after operations:

def test_delete_user_removes_from_database(client, db_session, make_user):
    user = make_user(email="delete@example.com")
    db_session.add(user)
    db_session.commit()
    user_id = user.id

    response = client.delete(f"/users/{user_id}")

    assert response.status_code == HTTPStatus.NO_CONTENT
    assert db_session.query(User).filter_by(id=user_id).first() is None

Testing exceptions:

def test_withdraw_raises_on_insufficient_funds(make_account):
    account = make_account(balance=50.00)

    with pytest.raises(InsufficientFundsError) as exc_info:
        account.withdraw(100.00)

    assert exc_info.value.available == 50.00
    assert exc_info.value.requested == 100.00

Test Consolidation

Combine when one operation affects multiple fields:

# Good: One test verifies all effects of mark_completed()
def test_mark_order_completed_updates_status_and_timestamp(db_session, make_order):
    order = make_order(status="pending", completed_at=None)

    mark_completed(order.id)

    result = db_session.query(Order).filter_by(id=order.id).first()
    assert result.status == "completed"
    assert result.completed_at is not None

# Bad: Separate tests for each field
def test_mark_order_completed_sets_status(): ...
def test_mark_order_completed_sets_timestamp(): ...

Parametrize for input variations:

# Good: One parametrized test
@pytest.mark.parametrize("mark_error", [
    pytest.param(False, id="without_error"),
    pytest.param(True, id="with_error"),
])
def test_create_job_status(db_session, mark_error):
    create_job_status("run-123", mark_error=mark_error)
    result = db_session.query(JobStatus).first()
    assert result.has_error is mark_error

# Bad: Separate tests for True and False
def test_create_job_status_without_error(): ...
def test_create_job_status_with_error(): ...

Skip trivial tests:

# Skip: Testing that False stays False adds no value
def test_error_stays_false_when_not_marked(): ...  # Don't write this

Consolidate Initialization Tests: Instead of testing every service property separately, verify the container in one go.

# Good: Comprehensive Container Test
def test_service_container_initializes_all_services(mock_client):
    container = ServiceContainer(mock_client)

    # Verify all services exist and share the client
    assert isinstance(container.users, UserService)
    assert isinstance(container.orders, OrderService)
    assert container.users.client is mock_client
    assert container.orders.client is mock_client

Fixtures

Factory Pattern

Use make_* naming with inner factory functions:

@pytest.fixture
def make_chat_session():
    def _make(user, **kwargs):
        defaults = {"session_id": uuid4()}
        defaults.update(kwargs)
        return ChatSession.objects.create(user=user, **defaults)
    return _make

# Usage
def test_session_behavior(admin_user, make_chat_session):
    session = make_chat_session(admin_user, status="active")
    assert session.user == admin_user

Full example with factory fixture:

@pytest.fixture
def make_order():
    def _make(user, items=None, **kwargs):
        defaults = {"status": "pending", "items": items or []}
        defaults.update(kwargs)
        return Order(user=user, **defaults)
    return _make

def test_order_total_calculates_with_tax(make_order, make_user):
    user = make_user(state="CA")
    order = make_order(user=user, items=[{"name": "Widget", "price": 100.00, "quantity": 2}])

    total = order.calculate_total()

    assert total == 214.50  # 200 + 7.25% tax

Conftest Hierarchy

tests/conftest.py — Global fixtures:

@pytest.fixture(scope="session")
def app_settings():
    return {"debug": True, "db_url": "sqlite:///:memory:"}

tests/unit/conftest.py — Unit-specific mocks:

@pytest.fixture
def mock_db_session(mocker):
    return mocker.MagicMock()

tests/integration/conftest.py — Real resources:

@pytest.fixture(scope="module")
def db_session(postgres_container):
    engine = create_engine(postgres_container.get_connection_url())
    with Session(engine) as session:
        yield session
        session.rollback()

Fixture Scopes

Teardown

Use yield for cleanup:

@pytest.fixture
def temp_file():
    path = Path("/tmp/test_file.txt")
    path.write_text("test")
    yield path
    path.unlink()  # Cleanup after test

Mocking

pytest-mock

Use the mocker fixture (not unittest.mock directly):

def test_api_call(mocker):
    mock_client = mocker.patch("myservice.api.external_client")
    mock_client.return_value.fetch.return_value = {"data": "test"}

    result = call_external_api()

    assert result == {"data": "test"}
    mock_client.return_value.fetch.assert_called_once()

Mock assertions:

mock_client.assert_called_once()
mock_client.assert_awaited_once_with(user=admin_user, project=project)
mock_client.assert_not_called()
assert mock_client.call_count == 1
assert mock_client.call_args.kwargs["id"] == "test_123"

Patching patterns:

# Patch where it's used, not where it's defined
mocker.patch("myservice.handlers.requests.get")

# Patch object attribute
mocker.patch.object(MyClass, "method", return_value="mocked")

# Patch with side effect
mocker.patch("module.func", side_effect=ValueError("error"))

Shared Mocks

Inline mocks — For simple, single-test mocks:

def test_payment(mocker):
    mock_stripe = mocker.patch("myservice.payments.stripe")
    mock_stripe.charge.return_value = {"id": "ch_123"}
    # test logic

Shared mocks — For complex fakes reused across multiple tests, create tests/mocks/ module:

# tests/mocks/fake_s3.py
class FakeS3Client:
    def __init__(self):
        self.storage = {}

    def put_object(self, Bucket, Key, Body):
        self.storage[f"{Bucket}/{Key}"] = Body

    def get_object(self, Bucket, Key):
        return {"Body": self.storage[f"{Bucket}/{Key}"]}

# tests/conftest.py
from tests.mocks.fake_s3 import FakeS3Client

@pytest.fixture
def fake_s3():
    return FakeS3Client()

freezegun (Time-Based Testing)

from freezegun import freeze_time

def test_session_expiry(session_service, admin_user):
    with freeze_time("2023-01-01 12:00:00"):
        session = session_service.create_session(user=admin_user)

    with freeze_time("2023-01-01 13:00:00"):  # 1 hour later
        assert session_service.is_expired(session) is True

testcontainers (Integration Tests)

PostgreSQL:

from testcontainers.postgres import PostgresContainer

@pytest.fixture(scope="module")
def postgres_container():
    with PostgresContainer("postgres:15") as postgres:
        yield postgres

@pytest.fixture
def db_session(postgres_container):
    engine = create_engine(postgres_container.get_connection_url())
    Base.metadata.create_all(engine)
    with Session(engine) as session:
        yield session
        session.rollback()

Redis:

from testcontainers.redis import RedisContainer

@pytest.fixture(scope="module")
def redis_container():
    with RedisContainer("redis:7") as redis:
        yield redis

Place container fixtures in tests/integration/conftest.py with scope="module" or scope="session".

Parametrization

Rule: Use pytest.mark.parametrize for inputs. If the test case requires more than 2 arguments, define a dataclass at module level.

Dataclass Placement

Define parametrization dataclasses after imports, before tests:

from dataclasses import dataclass, field
from typing import Any

import pytest

pytestmark = pytest.mark.unit


@dataclass
class ApiRequestCase:
    method: str
    path: str
    status_code: int
    response_data: dict[str, Any]
    request_kwargs: dict[str, Any] = field(default_factory=dict)
    assertion_checks: dict[str, Any] = field(default_factory=dict)


def test_api_client_request_successful_scenarios(case):
    ...

Complex Parametrization (Dataclass Pattern)

Use dataclasses for 3+ parameters with modern type hints:

from dataclasses import dataclass, field
from typing import Any

@dataclass
class ApiCase:
    payload: dict[str, Any]
    expected_status: int
    expected_error: str | None = None
    headers: dict[str, str] = field(default_factory=dict)

@pytest.mark.parametrize("case", [
    ApiCase(
        payload={"email": "bad-format", "name": "Test"},
        expected_status=400,
        expected_error="Invalid email"
    ),
    ApiCase(
        payload={"email": "good@test.com"},
        expected_status=400,
        expected_error="Field 'name' required"
    ),
], ids=lambda c: f"status_{c.expected_status}")
def test_create_user_validation(client, case):
    response = client.post("/users", json=case.payload, headers=case.headers)

    assert response.status_code == case.expected_status
    if case.expected_error:
        assert case.expected_error in response.json()["detail"]

ID Generation Tips:

• Use lambda to generate IDs from dataclass fields
• Keep IDs concise but meaningful
• Do not add fields solely for test IDs (like description)
• Examples:
  • ids=lambda c: c.method.lower()
  • ids=lambda c: f"{c.status}_{c.type}"
  • ids=lambda c: f"{c.method.lower()}_{c.status_code}"
  • ids=lambda c: "guaranteed_pass" if c.guaranteed_pass else "guaranteed_fail"

Type Hints:

• Use lowercase: dict[str, Any], list[int], not Dict, List
• Use pipe syntax: str | None, not Optional[str]
• Only import Any from typing when needed

Simple Parametrization (Tuple Pattern)

For 1 or 2 arguments, tuples are acceptable:

@pytest.mark.parametrize("status_code, should_retry", [
    (500, True),
    (400, False),
], ids=["server_error", "client_error"])
def test_retry_logic(status_code, should_retry):
    assert should_retry_request(status_code) == should_retry

Markers

import pytest
pytestmark = pytest.mark.integration  # Module-level

@pytest.mark.slow
def test_heavy_computation(): ...

@pytest.mark.skip(reason="Not implemented yet")
def test_future_feature(): ...

@pytest.mark.skipif(sys.platform == "win32", reason="Unix only")
def test_unix_specific(): ...

@pytest.mark.xfail(reason="Known bug #123")
def test_known_bug(): ...

Run by marker: pytest -m "not slow" or pytest -m integration

Running Tests

uv run pytest                                      # All tests
uv run pytest -m unit                              # Unit tests only
uv run pytest -m integration                       # Integration tests only
uv run pytest tests/unit/test_module.py::test_name # Specific test
uv run pytest -v -s                                # Verbose with print output
uv run pytest --cov=src --cov-report=term-missing  # Coverage report
uv run pytest --cov-fail-under=80                  # Enforce 80% coverage
uv run pytest -k "user"                            # Match test names

Configuration

# pyproject.toml
[tool.pytest.ini_options]
testpaths = ["tests"]
python_files = ["test_*.py"]
python_functions = ["test_*"]
addopts = "-v --tb=short"
markers = [
    "unit: fast running unit tests",
    "integration: slow running integration tests",
]

[tool.pytest_env]
# Environment variables for tests (requires pytest-env)
DATABASE_URL = "postgresql://test:test@localhost:5432/test_db"
REDIS_URL = "redis://localhost:6379/0"
ENV = "test"
DEBUG = "false"

Checklist

Before finishing, verify:

• Tests placed in tests/unit/ or tests/integration/
• Imports at top of file, not inside functions
• Modern type hints: dict[str, Any], list[int], str | None
• Dataclasses defined at module level (after imports, before tests)
• Plain functions, no test classes
• Names follow test_<function>_<behavior>
• Blank lines separate AAA sections (no comments)
• One test per behavior (combine assertions for one operation's effects)
• Parametrization used for input variations (True/False, different values)
• Dataclasses used for 3+ parametrization arguments
• Lambda-based test IDs generated from dataclass fields
• No trivial tests (e.g., "False stays False")
• Using HTTPStatus enum, not raw status codes
• Testing both success and error paths
• Factory fixtures use make_* naming
• Mocks use mocker fixture, not unittest.mock
• Patching where used, not where defined
• Time tests use freezegun
• Integration tests use testcontainers