Are Python Lists Thread-Safe in Multithreaded Environments?

Thread Safety of Lists in Multithreaded Environments

In the context of multithreaded programming, the question arises whether lists are thread-safe. The answer to this question is both yes and no.

Yes, lists themselves are thread-safe. In the case of CPython, the Global Interpreter Lock (GIL) ensures that only one thread can execute Python code at any given time, preventing concurrent access to lists and data corruption. Other Python implementations may employ fine-grained locks or synchronized data structures to achieve the same effect.

However, the thread safety of lists is not absolute. While the list structure itself remains intact, its contents may not be protected. Consider the following operation:

L[0] += 1

This operation is not guaranteed to increment L[0] by one in a multithreaded environment. If multiple threads attempt to perform this operation concurrently, race conditions can arise, leading to incorrect results.

The reason for this is that = is not an atomic operation in Python. Atomic operations are those that are indivisible and cannot be interrupted by other threads. Most Python operations, including arithmetic assignments, are not atomic, as they may involve intermediate Python code execution that could be preempted by another thread.

To mitigate this issue, it is recommended to use Queues in multithreaded environments instead of unprotected lists. Queues provide atomic operations for getting and deleting items, ensuring that the correct item is retrieved or deleted, even in the presence of concurrent access.

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