Test containers with Docker in PyTest - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of pytest test using Docker container fixture

What is the output of this pytest test when using the Docker container fixture that starts a Redis container and sets a key?

PyTest

import pytest
import redis

@pytest.fixture(scope='module')
def redis_container(docker_ip, docker_services):
    port = docker_services.port_for('redis', 6379)
    client = redis.Redis(host=docker_ip, port=port)
    docker_services.wait_until_responsive(
        timeout=30.0, pause=0.1,
        check=lambda: client.ping())
    return client

def test_redis_set_get(redis_container):
    redis_container.set('testkey', 'value')
    result = redis_container.get('testkey')
    assert result == b'value'
    print(result)

A b'value' printed and test passes

B b'value' printed but test fails due to assertion error

C Test raises a ConnectionError because Redis is not reachable

D Test raises a SyntaxError due to fixture definition

💡 Hint

The fixture waits until Redis is responsive before returning the client.

The fixture starts a Redis container and waits until it responds to ping. The test sets a key and retrieves it, expecting the byte string b'value'. The assertion passes and the value is printed.

❓ assertion

intermediate

Correct assertion for checking HTTP response in Dockerized test

You run a test against a web service inside a Docker container. Which assertion correctly verifies the HTTP status code is 200?

PyTest

response = client.get('http://localhost:8080/api/data')

A assert response.status == 200

B assert response.status_code == 200

C assert response.code == 200

D assert response.status_code is 200

💡 Hint

Check the attribute name for status code in the response object.

The correct attribute for HTTP status code in most Python HTTP clients is 'status_code'. Using 'is' for integer comparison is not recommended.

🔧 Debug

advanced

Debugging Docker container test failure due to port conflict

A pytest test using a Docker container fails with an error indicating the port 5432 is already in use. What is the most likely cause?

A The Docker container image is missing the PostgreSQL service

B The test code has a syntax error in the port mapping

C Another process on the host machine is using port 5432, causing conflict

D The pytest fixture is not starting the container

💡 Hint

Port conflicts happen when two services try to use the same port on the host.

Port 5432 is the default PostgreSQL port. If another process uses it, Docker cannot bind the container port to the host port, causing failure.

❓ framework

advanced

Choosing the best pytest plugin for Docker container management

Which pytest plugin is designed specifically to manage Docker containers lifecycle during tests?

A pytest-docker

B pytest-mock

C pytest-cov

D pytest-xdist

💡 Hint

Look for a plugin name that includes 'docker'.

pytest-docker helps start and stop Docker containers during pytest runs, making integration testing easier.

🧠 Conceptual

expert

Why use test containers in integration testing?

What is the main advantage of using test containers with Docker in integration testing?

A They automatically generate test data for database tests

B They speed up unit tests by mocking dependencies

C They eliminate the need for writing assertions in tests

D They provide isolated, reproducible environments matching production services

💡 Hint

Think about environment consistency and isolation.

Test containers run real service instances in isolated Docker containers, ensuring tests run in environments similar to production, improving reliability.

Practice

1. What is the main benefit of using test containers with Docker in pytest?

easy

A. They make tests run faster by skipping setup steps.

B. They replace the need for writing any test code.

C. They automatically fix bugs in the application code.

D. They provide real service environments during tests for better reliability.

Solution

1. Step 1: Understand test containers purpose

   Test containers run real services like databases inside Docker during tests.

2. Step 2: Identify benefit in pytest context

   This makes tests more reliable and realistic by using actual service environments.

3. Final Answer:

   They provide real service environments during tests for better reliability. -> Option D

4. Quick Check:

   Real service environment = Better test reliability [OK]

Hint: Remember: test containers run real services inside Docker [OK]

Common Mistakes:

• Thinking test containers replace writing tests
• Believing they fix code bugs automatically
• Assuming tests run faster by skipping setup

2. Which pytest fixture code correctly starts a Docker container for testing?

easy

A. def container():
container = docker.run('redis')
yield container
container.stop()

B. def container():
client = docker.from_env()
container = client.run('redis')
yield container
container.stop()

C. def container():
client = docker.from_env()
container = client.containers.run('redis', detach=True)
yield container
container.stop()

D. def container():
client = docker.from_env()
container = client.containers.run('redis')
container.start()
yield container
container.stop()

Solution

1. Step 1: Check correct Docker client usage

   Use docker.from_env() to get client, then client.containers.run() with detach=True to start container.

2. Step 2: Verify fixture lifecycle management

   Yield container for test, then stop container after test finishes.

3. Final Answer:

   def container(): client = docker.from_env() container = client.containers.run('redis', detach=True) yield container container.stop() -> Option C

4. Quick Check:

   Use client.containers.run with detach=True [OK]

Hint: Use client.containers.run with detach=True to start container [OK]

Common Mistakes:

• Calling client.run instead of client.containers.run
• Missing detach=True causing blocking call
• Not stopping container after test

3. Given this pytest fixture, what will be printed when running the test?

import pytest
import docker

@pytest.fixture
 def redis_container():
    client = docker.from_env()
    container = client.containers.run('redis:alpine', detach=True)
    yield container
    container.stop()

def test_redis_running(redis_container):
    print(redis_container.status)

medium

A. running

B. created

C. exited

D. paused

Solution

1. Step 1: Understand container lifecycle in fixture

   Container is started with detach=True, so status should be 'running' during test.

2. Step 2: Check printed status in test

   redis_container.status returns current container status, expected 'running' while test runs.

3. Final Answer:

   running -> Option A

4. Quick Check:

   Container started = status 'running' [OK]

Hint: Container status is 'running' while test uses it [OK]

Common Mistakes:

• Expecting 'created' before container starts
• Assuming container is 'exited' during test
• Confusing status with container image tag

4. Identify the error in this pytest fixture that manages a Docker container:

@pytest.fixture
def redis_container():
    client = docker.from_env()
    container = client.containers.run('redis', detach=True)
    yield container
    container.remove()

medium

A. Missing container.stop() before container.remove() to stop container properly.

B. Using container.remove() instead of container.delete() which is invalid.

C. Not specifying environment variables for Redis container causes failure.

D. Yielding container before starting it causes runtime error.

Solution

1. Step 1: Check container cleanup steps

   Container must be stopped before removal to avoid errors.

2. Step 2: Identify missing stop call

   Fixture calls container.remove() but misses container.stop() before it.

3. Final Answer:

   Missing container.stop() before container.remove() to stop container properly. -> Option A

4. Quick Check:

   Stop container before remove to clean up [OK]

Hint: Always stop container before removing it [OK]

Common Mistakes:

• Calling remove without stopping container
• Confusing remove() with non-existent delete()
• Assuming environment vars are mandatory for container start

5. You want to write a pytest fixture that starts a PostgreSQL container with Docker, waits until it is ready to accept connections, and then yields it for tests. Which approach correctly combines container management and readiness check?

hard

A. Start container without detach, yield immediately, and rely on test to wait for readiness.

B. Start container with detach=True, then poll container logs until 'database system is ready' appears before yielding.

C. Start container with detach=True and yield immediately without any readiness check.

D. Start container with detach=True, sleep fixed 1 second, then yield container.

Solution

1. Step 1: Manage container lifecycle properly

   Start PostgreSQL container detached to run in background during tests.

2. Step 2: Implement readiness check before yielding

   Poll container logs for 'database system is ready' message to ensure service is ready.

3. Step 3: Yield container after readiness confirmed

   This ensures tests run only after PostgreSQL is ready to accept connections.

4. Final Answer:

   Start container with detach=True, then poll container logs until 'database system is ready' appears before yielding. -> Option B

5. Quick Check:

   Wait for readiness log before yielding container [OK]

Hint: Wait for readiness log, don't guess with sleep [OK]

Common Mistakes:

• Yielding container before it is ready
• Using fixed sleep instead of log polling
• Starting container without detach causing blocking