Worker distribution strategies in PyTest - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is a worker distribution strategy in pytest?

It is a method to decide how test cases are divided and assigned to multiple workers to run tests in parallel efficiently.

beginner

Name two common worker distribution strategies in pytest.

Load-based distribution (balancing tests by estimated duration) and round-robin distribution (assigning tests evenly in order).

intermediate

Why is load-based distribution useful in pytest parallel testing?

Because it assigns tests to workers based on their estimated run time, helping to balance the workload and reduce total test time.

intermediate

How does pytest-xdist implement worker distribution?

It uses a round-robin strategy by default but can be customized with plugins or hooks to implement load-based or other strategies.

intermediate

What is a potential downside of simple round-robin worker distribution?

It may cause some workers to finish early while others run longer tests, leading to inefficient use of resources.

Which worker distribution strategy assigns tests based on their estimated run time?

A Random distribution

B Round-robin distribution

C Load-based distribution

D Sequential distribution

Load-based distribution balances tests by their estimated duration to optimize worker usage.

What is the default worker distribution strategy used by pytest-xdist?

A Round-robin distribution

B Priority-based distribution

C Load-based distribution

D Random distribution

pytest-xdist uses round-robin distribution by default to assign tests evenly to workers.

Why might round-robin distribution be less efficient than load-based distribution?

A It ignores test run time differences

B It assigns tests randomly

C It requires manual configuration

D It runs tests sequentially

Round-robin does not consider test duration, so some workers may finish earlier than others.

Which of these is NOT a benefit of using worker distribution strategies?

A Faster test execution

B Balanced workload among workers

C Better resource utilization

D Increased test flakiness

Proper worker distribution reduces test time and balances workload without increasing flakiness.

How can you customize worker distribution in pytest?

A By changing test names

B Using pytest-xdist plugins or hooks

C By running tests sequentially

D By disabling parallel testing

Plugins and hooks allow customizing how tests are assigned to workers.

Explain the difference between round-robin and load-based worker distribution strategies in pytest.

Think about how tests are assigned and how that affects worker usage.

Describe how worker distribution strategies impact the total test execution time in parallel testing.

Consider what happens if some workers finish early while others run longer.

Practice

1. What does the --dist=loadscope option do in pytest-xdist worker distribution?

easy

A. It distributes tests randomly to all workers.

B. It runs all tests sequentially on a single worker.

C. It groups tests by their scope and distributes them to workers.

D. It groups tests by file size before distribution.

Solution

1. Step 1: Understand the meaning of loadscope

   The loadscope mode groups tests by their scope, such as class or module, so related tests run together.

2. Step 2: Compare with other distribution modes

   Unlike random or file-based grouping, loadscope keeps related tests together for better caching and setup reuse.

3. Final Answer:

   It groups tests by their scope and distributes them to workers. -> Option C

4. Quick Check:

   loadscope = group by scope [OK]

Hint: Loadscope groups tests by scope like class or module [OK]

Common Mistakes:

• Confusing loadscope with random distribution
• Thinking loadscope groups by file size
• Assuming loadscope runs tests sequentially

2. Which of the following is the correct pytest command to run tests with 4 workers using file-based distribution?

easy

A. pytest -n 4 --dist=loadfile

B. pytest --dist=loadfile -n four

C. pytest -n=4 --dist=loadscope

D. pytest -n 4 --dist=loadgroup

Solution

1. Step 1: Identify correct syntax for number of workers

   The correct syntax is -n 4 to specify 4 workers; spelling out 'four' is invalid.

2. Step 2: Match distribution mode to file-based

   The file-based distribution mode is loadfile, so --dist=loadfile is correct.

3. Final Answer:

   pytest -n 4 --dist=loadfile -> Option A

4. Quick Check:

   -n 4 and --dist=loadfile correct syntax [OK]

Hint: Use -n number and --dist=loadfile for file grouping [OK]

Common Mistakes:

• Using spelled-out numbers like 'four'
• Mixing distribution modes incorrectly
• Using equals sign with -n option

3. Given this pytest command: pytest -n 3 --dist=loadfile, and three test files test_a.py, test_b.py, test_c.py, how will tests be distributed?

medium

A. Tests run sequentially on a single worker.

B. All workers run tests from all files randomly.

C. Tests are grouped by class across files.

D. Each worker runs tests from one file exclusively.

Solution

1. Step 1: Understand loadfile distribution

   Loadfile mode assigns tests grouped by file to different workers, so each worker gets whole files.

2. Step 2: Match number of workers to files

   With 3 workers and 3 files, each worker will get one file's tests exclusively.

3. Final Answer:

   Each worker runs tests from one file exclusively. -> Option D

4. Quick Check:

   loadfile = group by file [OK]

Hint: Loadfile means one file per worker [OK]

Common Mistakes:

• Thinking tests are split randomly
• Confusing loadfile with loadscope
• Assuming tests run sequentially

4. You run pytest -n 2 --dist=loadscope but notice tests from the same class run on different workers. What is the likely cause?

medium

A. Tests are not properly grouped because the class scope is not detected.

B. The -n option must be set to 1 for loadscope.

C. The --dist option is ignored when using multiple workers.

D. Tests are always distributed randomly regardless of options.

Solution

1. Step 1: Understand loadscope grouping behavior

   Loadscope groups tests by scope like class or module, so tests in the same class should run together.

2. Step 2: Identify why grouping fails

   If tests from the same class run on different workers, pytest likely failed to detect the class scope properly, causing wrong grouping.

3. Final Answer:

   Tests are not properly grouped because the class scope is not detected. -> Option A

4. Quick Check:

   Undetected scope breaks loadscope grouping [OK]

Hint: Undetected scope causes loadscope to fail grouping [OK]

Common Mistakes:

• Thinking -n must be 1 for loadscope
• Believing --dist is ignored with multiple workers
• Assuming distribution is always random

5. You want to run tests in custom groups using pytest-xdist. Which command and option combination allows you to define and use custom test groups for worker distribution?

hard

A. pytest -n 3 --dist=loadgroup --tx group1 --tx group2 --tx group3

B. pytest -n 3 --dist=loadgroup

C. pytest -n 3 --dist=loadfile --group=custom

D. pytest -n 3 --dist=loadscope --group=custom

Solution

1. Step 1: Identify the distribution mode for custom groups

   The loadgroup mode is designed for custom grouping of tests for distribution.

2. Step 2: Understand correct command usage

   Using --dist=loadgroup with -n 3 enables pytest-xdist to distribute tests based on user-defined groups configured elsewhere (e.g., in pytest hooks).

3. Final Answer:

   pytest -n 3 --dist=loadgroup -> Option B

4. Quick Check:

   loadgroup enables custom test groups [OK]

Hint: Use --dist=loadgroup to enable custom test groups [OK]

Common Mistakes:

• Adding invalid --group option
• Using --tx incorrectly for grouping
• Confusing loadgroup with loadfile or loadscope