Thread Safety Guarantees

This page documents thread-safety guarantees for built-in types in Python’s free-threaded build. The guarantees described here apply when using Python with the GIL disabled (free-threaded mode). When the GIL is enabled, most operations are implicitly serialized.

For general guidance on writing thread-safe code in free-threaded Python, see Python support for free threading.

Thread safety for list objects

Reading a single element from a list is atomic:

lst[i]   # list.__getitem__

The following methods traverse the list and use atomic reads of each item to perform their function. That means that they may return results affected by concurrent modifications:

item in lst
lst.index(item)
lst.count(item)

All of the above operations avoid acquiring per-object locks. They do not block concurrent modifications. Other operations that hold a lock will not block these from observing intermediate states.

All other operations from here on block using the per-object lock.

Writing a single item via lst[i] = x is safe to call from multiple threads and will not corrupt the list.

The following operations return new objects and appear atomic to other threads:

lst1 + lst2    # concatenates two lists into a new list
x * lst        # repeats lst x times into a new list
lst.copy()     # returns a shallow copy of the list

The following methods that only operate on a single element with no shifting required are atomic:

lst.append(x)  # append to the end of the list, no shifting required
lst.pop()      # pop element from the end of the list, no shifting required

The clear() method is also atomic. Other threads cannot observe elements being removed.

The sort() method is not atomic. Other threads cannot observe intermediate states during sorting, but the list appears empty for the duration of the sort.

The following operations may allow lock-free operations to observe intermediate states since they modify multiple elements in place:

lst.insert(idx, item)  # shifts elements
lst.pop(idx)           # idx not at the end of the list, shifts elements
lst *= x               # copies elements in place

The remove() method may allow concurrent modifications since element comparison may execute arbitrary Python code (via __eq__()).

extend() is safe to call from multiple threads. However, its guarantees depend on the iterable passed to it. If it is a list, a tuple, a set, a frozenset, a dict or a dictionary view object (but not their subclasses), the extend operation is safe from concurrent modifications to the iterable. Otherwise, an iterator is created which can be concurrently modified by another thread. The same applies to inplace concatenation of a list with other iterables when using lst += iterable.

Similarly, assigning to a list slice with lst[i:j] = iterable is safe to call from multiple threads, but iterable is only locked when it is also a list (but not its subclasses).

Operations that involve multiple accesses, as well as iteration, are never atomic. For example:

# NOT atomic: read-modify-write
lst[i] = lst[i] + 1

# NOT atomic: check-then-act
if lst:
   item = lst.pop()

# NOT thread-safe: iteration while modifying
for item in lst:
   process(item)  # another thread may modify lst

Consider external synchronization when sharing list instances across threads.

Thread safety for dict objects

Creating a dictionary with the dict constructor is atomic when the argument to it is a dict or a tuple. When using the dict.fromkeys() method, dictionary creation is atomic when the argument is a dict, tuple, set or frozenset.

The following operations and functions are lock-free and atomic.

d[key]       # dict.__getitem__
d.get(key)   # dict.get
key in d     # dict.__contains__
len(d)       # dict.__len__

All other operations from here on hold the per-object lock.

Writing or removing a single item is safe to call from multiple threads and will not corrupt the dictionary:

d[key] = value        # write
del d[key]            # delete
d.pop(key)            # remove and return
d.popitem()           # remove and return last item
d.setdefault(key, v)  # insert if missing

These operations may compare keys using __eq__(), which can execute arbitrary Python code. During such comparisons, the dictionary may be modified by another thread. For built-in types like str, int, and float, that implement __eq__() in C, the underlying lock is not released during comparisons and this is not a concern.

The following operations return new objects and hold the per-object lock for the duration of the operation:

d.copy()      # returns a shallow copy of the dictionary
d | other     # merges two dicts into a new dict
d.keys()      # returns a new dict_keys view object
d.values()    # returns a new dict_values view object
d.items()     # returns a new dict_items view object

The clear() method holds the lock for its duration. Other threads cannot observe elements being removed.

The following operations lock both dictionaries. For update() and |=, this applies only when the other operand is a dict that uses the standard dict iterator (but not subclasses that override iteration). For equality comparison, this applies to dict and its subclasses:

d.update(other_dict)  # both locked when other_dict is a dict
d |= other_dict       # both locked when other_dict is a dict
d == other_dict       # both locked for dict and subclasses

All comparison operations also compare values using __eq__(), so for non-built-in types the lock may be released during comparison.

fromkeys() locks both the new dictionary and the iterable when the iterable is exactly a dict, set, or frozenset (not subclasses):

dict.fromkeys(a_dict)      # locks both
dict.fromkeys(a_set)       # locks both
dict.fromkeys(a_frozenset) # locks both

When updating from a non-dict iterable, only the target dictionary is locked. The iterable may be concurrently modified by another thread:

d.update(iterable)        # iterable is not a dict: only d locked
d |= iterable             # iterable is not a dict: only d locked
dict.fromkeys(iterable)   # iterable is not a dict/set/frozenset: only result locked

Operations that involve multiple accesses, as well as iteration, are never atomic:

# NOT atomic: read-modify-write
d[key] = d[key] + 1

# NOT atomic: check-then-act (TOCTOU)
if key in d:
   del d[key]

# NOT thread-safe: iteration while modifying
for key, value in d.items():
   process(key)  # another thread may modify d

To avoid time-of-check to time-of-use (TOCTOU) issues, use atomic operations or handle exceptions:

# Use pop() with default instead of check-then-delete
d.pop(key, None)

# Or handle the exception
try:
   del d[key]
except KeyError:
   pass

To safely iterate over a dictionary that may be modified by another thread, iterate over a copy:

# Make a copy to iterate safely
for key, value in d.copy().items():
   process(key)

Consider external synchronization when sharing dict instances across threads.