Answer

History of the issue:

Parallel execution of tests has become relevant with the growth of CI/CD practices and the transition to DevOps. Teams are now striving to run thousands of tests in just a few minutes to receive quick feedback and reduce Time To Market. Parallelization has become possible thanks to support for parallel execution in testing frameworks (JUnit5, TestNG, Pytest-xdist, etc.) and cloud execution platforms (Selenium Grid, BrowserStack, SauceLabs).

Problem:

The main difficulties include:

not all tests can be parallelized (for example, those that use the same resources or data)
race conditions and data collisions
false positives/false negatives due to test conflicts
difficulty in localizing the causes of failures
need for expensive infrastructure

Solution:

For safe and productive parallelization, it is necessary to:

isolate test data for each test (see previous question)
use idempotent tests that do not alter global state
categorize tests: which can be executed in parallel and which can only be run one at a time
utilize container-based execution (Docker, Kubernetes pods)
centralize log collection and analysis

Example of setting up parallelism for Pytest (Python):

pytest -n auto  # automatically determines the number of threads

Key features:

significant acceleration of feedback
necessity for proper environment isolation
challenges with analyzing results

Tricky questions.

Can all tests be run in parallel and considered a best practice?

No. Not all tests are independent: some use shared state or resources. It's essential to analyze dependencies and impacts.

Is parallel execution a panacea for speeding up tests?

No. Sometimes it can lead to more errors and instability if the environment is not ready or tests are not isolated.

Should environments always be duplicated for each test?

Often — yes, but isolating costly or slow services can be done differently (for example, using mocks or stubs), or such tests can be run separately.

Typical mistakes and anti-patterns

Parallel execution of tests that modify the same data (race condition)
Insufficient analysis of test dependencies
Ignoring the collection and analysis of logs from concurrently running threads

Real-life example

Negative case

In an ecommerce project, the team switched all UI tests to parallel execution without preparation. The test time decreased, but the number of "flaky" failures increased. It turned out that many tests were working with the same orders in the database.

Pros:

Faster test results

Cons:

High percentage of unstable test failures
Debugging took up to 70% of the team's time

Positive case

In a fintech team, they audited the tests, categorized them, automated data isolation, and set up separate environments in Docker containers. They only applied parallel execution to independent tests.