_thread — Low-level threading API

This module provides low-level primitives for managing threads (also called light-weight processes or tasks) — multiple threads of control sharing their global data space. For synchronization, simple locks (also called mutexes or binary semaphores) are provided.

Only a subset of CPython’s _thread module is supported, for instance creation of threads is not supported.

Constants and functions:


This is the type of lock objects.


Raises the SystemExit exception. When not caught, this will cause the thread to exit silently.


Return a new lock object. Methods of locks are described below. The lock is initially unlocked.


Return the ‘thread identifier’ of the current thread. This is a nonzero integer. Its value has no direct meaning; it is intended as a magic cookie to be used e.g. to index a dictionary of thread-specific data.


Not currently supported.

Lock objects have the following methods:

lock.acquire(waitflag=1, timeout=-1)

Without any optional argument, this method acquires the lock unconditionally, if necessary waiting until it is released by another thread (only one thread at a time can acquire a lock — that’s their reason for existence).

If the integer waitflag argument is present, the action depends on its value: if it is zero, the lock is only acquired if it can be acquired immediately without waiting, while if it is nonzero, the lock is acquired unconditionally as above.

If the floating-point timeout argument is present and positive, it specifies the maximum wait time in seconds before returning. A negative timeout argument specifies an unbounded wait. Unbounded waits are limited to 10 minutes at most, at which point TimeoutError will be raised.

The return value is True if the lock is acquired successfully, False if not.


Releases the lock. The lock must have been acquired earlier, but not necessarily by the same thread.


Return the status of the lock: True if it has been acquired by some thread, False if not.

In addition to these methods, lock objects can also be used via the with statement. This is the preferred means as Python will make sure that the code block is not run unless the lock was acquired and that it is always released - even if an exception occurs.


import _thread

a_lock = _thread.allocate_lock()

with a_lock:
    print("a_lock is locked while this executes")