Use a threading.Lock when threads update a counter, and a synchronized multiprocessing.Value (with its lock held around the entire increment) when processes do. A bare counter += 1 is a read-modify-write sequence, so it can lose updates even when the counter is a shared multiprocessing value.
Choose the counter for your concurrency model
First identify whether the workers are threads in one process or independent processes. The memory model determines which counter and synchronization primitive are appropriate.
| Workers | Recommended counter | How to make an increment safe | Best fit |
|---|---|---|---|
| Threads | A normal integer plus one shared threading.Lock |
Hold the lock while reading, adding, and writing | Small state shared inside one process |
| Processes | multiprocessing.Value or multiprocessing.Array |
Use with counter.get_lock(): around the complete read-modify-write |
One scalar or a fixed-size set of values |
| Processes needing Python containers | multiprocessing.Manager proxies |
Use a manager-provided lock (or another process-safe lock) around compound updates | Shared dictionaries, lists, and other richer objects |
| Processes needing a custom memory layout | multiprocessing.shared_memory.SharedMemory |
Define the byte layout and provide synchronization yourself | Direct access to a named memory block |
Why counter += 1 can lose increments
An increment consists of three logical operations:
- Read the current value.
- Add one (or another increment) to the local value.
- Write the result back.
Two workers can read the same old value, calculate the same new value, and then overwrite each other. For example, if both read 10, both calculate 11, and both write 11, the counter advances once instead of twice.
The synchronization must cover the entire read-modify-write sequence. Protecting only the read or only the assignment does not make the operation atomic. Python’s multiprocessing documentation explicitly warns that operations such as += involving a read and a write are not atomic.
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Safely incrementing a counter from threads
Threads share the process’s memory, so they can update one ordinary integer. They still need a lock to define a critical section and prevent interleaving updates.
Basic thread example
import threading
counter = 0
counter_lock = threading.Lock()
def worker(increments: int) -> None:
global counter
for _ in range(increments):
with counter_lock:
counter += 1
threads = [threading.Thread(target=worker, args=(10_000,))
for _ in range(4)]
for thread in threads:
thread.start()
for thread in threads:
thread.join()
print(counter) # 40_000The lock and the counter must be the same shared objects for every worker thread. If a worker creates its own lock, the workers are not coordinating and updates can still collide.
Keep the critical section narrow
Do work that does not touch the counter outside the with block. A short critical section reduces contention while preserving correctness:
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for item in items:
result = process_item(item) # no counter access here
with counter_lock:
counter += 1Do not rely on the CPython GIL as a counter-safety mechanism. Interpreter-lock behavior is not the documented contract for a compound update, and free-threaded Python builds make that assumption even less reliable. Use an explicit lock.
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Safely incrementing a counter from processes with multiprocessing.Value
Processes have separate address spaces. A normal global integer is therefore private to each process. multiprocessing.Value creates a shared, synchronized scalar that processes can access.
Correct increment pattern
import multiprocessing as mp
def worker(counter, increments: int) -> None:
for _ in range(increments):
with counter.get_lock():
counter.value += 1
if __name__ == "__main__":
counter = mp.Value("i", 0)
processes = [mp.Process(target=worker, args=(counter, 10_000))
for _ in range(4)]
for process in processes:
process.start()
for process in processes:
process.join()
print(counter.value) # 40_000Value supplies a synchronization lock by default. That lock protects individual access to value, but the increment still has two accesses, so acquire the lock explicitly around the whole expression with counter.get_lock().
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When a separate lock is useful
You can disable the automatically created lock and coordinate the value with a lock shared by several fields, but then every access must use that lock consistently:
counter = mp.Value("i", 0, lock=False)
counter_lock = mp.Lock()
# In every process:
with counter_lock:
counter.value += 1A shared lock is especially useful when an update must keep multiple counters or a counter and another shared value consistent as one transaction.
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Array is suitable for a fixed-size collection of shared numeric values. Like Value, it is synchronized by default, but a compound update still needs the associated lock.
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import multiprocessing as mp
def worker(counts, index: int, increments: int) -> None:
for _ in range(increments):
with counts.get_lock():
counts[index] += 1
if __name__ == "__main__":
counts = mp.Array("i", [0, 0, 0])
processes = [mp.Process(target=worker, args=(counts, i, 5_000))
for i in range(3)]
for process in processes:
process.start()
for process in processes:
process.join()
print(list(counts))The array’s one lock serializes updates to all elements. If independent counters do not need a consistent combined snapshot, separate values and locks can allow more concurrency.
When a multiprocessing.Manager is the better fit
A manager runs a server process and gives workers proxy objects such as dictionaries, lists, locks, values, and arrays. It is convenient when the shared state is richer than one scalar or a fixed numeric array.
Manager-backed scalar with an explicit lock
import multiprocessing as mp
def worker(counter, counter_lock, increments: int) -> None:
for _ in range(increments):
with counter_lock:
counter.value += 1
if __name__ == "__main__":
with mp.Manager() as manager:
counter = manager.Value("i", 0)
counter_lock = manager.Lock()
processes = [mp.Process(target=worker,
args=(counter, counter_lock, 10_000))
for _ in range(4)]
for process in processes:
process.start()
for process in processes:
process.join()
print(counter.value)The lock is explicit because the important guarantee is that the read and write happen under one process-safe critical section. Manager operations cross a server-process boundary, so they generally carry more overhead than direct synchronized shared memory. Choose a manager for flexibility, not for the lowest-latency counter.
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Manager containers need the same rule
with manager.dict({"completed": 0}) as totals:
totals_lock = manager.Lock()
# In each worker:
with totals_lock:
totals["completed"] = totals["completed"] + 1Proxy methods are separate calls. A sequence such as “get a value, add one, set the value” is still a compound operation and needs a lock.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Using multiprocessing.shared_memory for direct access
SharedMemory creates or attaches to a named block of bytes that multiple processes can access directly. It does not provide a counter abstraction or automatic atomic increments: your program must define the layout, encode and decode values, and synchronize updates.
Eight-byte counter with a process lock
import multiprocessing as mp
import struct
from multiprocessing import shared_memory
def worker(shm_name: str, counter_lock, increments: int) -> None:
shm = shared_memory.SharedMemory(name=shm_name)
try:
for _ in range(increments):
with counter_lock:
current = struct.unpack_from("q", shm.buf, 0)[0]
struct.pack_into("q", shm.buf, 0, current + 1)
finally:
shm.close()
if __name__ == "__main__":
shm = shared_memory.SharedMemory(create=True, size=8)
counter_lock = mp.Lock()
try:
struct.pack_into("q", shm.buf, 0, 0)
processes = [mp.Process(target=worker,
args=(shm.name, counter_lock, 10_000))
for _ in range(4)]
for process in processes:
process.start()
for process in processes:
process.join()
print(struct.unpack_from("q", shm.buf, 0)[0])
finally:
shm.close()
shm.unlink()Every process closes its own handle with close(). The creator (or another single coordinator) calls unlink() once, after all users have finished, to remove the named block. If a process exits without cleanup, the block can remain until the platform’s shared-memory cleanup mechanisms reclaim it.
Shared memory is useful when you need a defined binary layout, large arrays, or direct access without proxy calls. It also increases your responsibility for alignment, data types, bounds, synchronization, and cleanup.
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- Using a normal global with processes: each process updates its own copy. Use
Value,Array, a manager proxy, or shared memory. - Writing
counter.value += 1without a lock: the individual property accesses may be synchronized, but the complete read-modify-write is not atomic. Holdcounter.get_lock(). - Locking only one worker: all workers that update the state must use the same lock object.
- Creating a lock inside each worker: those locks are unrelated. Create the lock before starting workers and pass or inherit it.
- Assuming a manager makes compound operations atomic: proxy calls are separate operations. Use a manager lock around the sequence.
- Forgetting to join processes: read the final value only after every worker has completed and been joined.
- Leaking shared memory: call
close()in each process andunlink()once after the final user is done. - Relying on start-method accidents: write worker functions at module scope and put process creation under
if __name__ == "__main__":, so the program also works with start methods that import the main module in a fresh interpreter.
A practical decision checklist
- If all workers are threads, use one ordinary counter and one
threading.Lock. - If workers are processes and the state is one numeric value, use
multiprocessing.Valueand holdget_lock()around each increment. - If the state is a fixed numeric collection, use
multiprocessing.Arraywith its lock, or design separate locks when independent updates can proceed independently. - If workers need dictionaries, lists, or other proxy-supported objects, use a
multiprocessing.Managerand protect multi-step updates with a shared lock. - If you need direct access to a custom byte layout, use
SharedMemory, add your own synchronization, and document ownership and cleanup. - Test with enough workers and increments to expose lost updates, then verify the final value only after all workers have joined.
Bottom line
Safe counter increments require synchronizing the read-modify-write operation, not merely choosing a container that is described as “shared.” Use an explicit thread lock for threads, Value or Array plus their lock for ordinary process-shared numbers, a manager for flexible proxy state, and shared memory only when you need direct, custom-managed storage.
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