BrokenProcessPool: process pool is broken

The BrokenProcessPool error occurs when using Python’s concurrent.futures.ProcessPoolExecutor or multiprocessing.Pool(). It indicates that the worker processes in the pool have failed, usually due to crashes, timeouts, or pickling issues.

---

1. Common Causes and Fixes

A. Unpicklable Objects Passed to Process Pool

The ProcessPoolExecutor serializes data using pickle. If a function or argument cannot be pickled, the pool breaks.

Example (Causing the Error)

from concurrent.futures import ProcessPoolExecutor

class MyClass:
    def my_method(self, x):
        return x * x

obj = MyClass()

with ProcessPoolExecutor() as executor:
    executor.map(obj.my_method, [1, 2, 3])  # Causes BrokenProcessPool error

Fix: Use a top-level function instead of an instance method

def square(x):
    return x * x

with ProcessPoolExecutor() as executor:
    results = executor.map(square, [1, 2, 3])

---

B. Using lambda or Local Functions

Lambda functions and local functions are not picklable.

Example (Causing the Error)

with ProcessPoolExecutor() as executor:
    executor.map(lambda x: x + 1, [1, 2, 3])  # Causes BrokenProcessPool error

Fix: Use a named function instead

def add_one(x):
    return x + 1

with ProcessPoolExecutor() as executor:
    executor.map(add_one, [1, 2, 3])

---

C. Running Out of System Resources (Memory/CPU)

If the system lacks enough memory or CPU, processes can crash.

Fix:

• Reduce the number of worker processes: with ProcessPoolExecutor(max_workers=2) as executor:
• Monitor resource usage: top # Linux/macOS Task Manager # Windows

---

D. Handling Large Data Objects

Passing large data structures between processes can break the pool.

Fix: Use shared memory (multiprocessing.Manager)

from multiprocessing import Manager, Pool

def worker(shared_dict, key):
    return shared_dict[key] * 2

with Manager() as manager:
    shared_dict = manager.dict({'a': 10, 'b': 20})
    with Pool(2) as pool:
        results = pool.starmap(worker, [(shared_dict, 'a'), (shared_dict, 'b')])
        print(results)

---

E. Worker Processes Crashing

If a worker process encounters an exception, the pool breaks.

Fix: Add Error Handling in Worker Function

def safe_function(x):
    try:
        return 1 / x  # This can raise ZeroDivisionError
    except ZeroDivisionError:
        return 0  # Handle the error safely

with ProcessPoolExecutor() as executor:
    results = executor.map(safe_function, [1, 2, 0, 3])  # No crash

---

F. Exceeding Timeout in executor.submit()

If a function takes too long, the worker process may crash.

Fix: Set a timeout

from concurrent.futures import TimeoutError

def long_task(x):
    import time
    time.sleep(x)
    return x

with ProcessPoolExecutor() as executor:
    future = executor.submit(long_task, 5)
    try:
        result = future.result(timeout=2)  # Timeout in 2 seconds
    except TimeoutError:
        print("Task timed out!")

---

2. Summary of Fixes

Cause	Fix
Unpicklable objects (methods, lambdas)	Use top-level functions
Running out of resources	Reduce max_workers, check system resources
Large data structures	Use multiprocessing.Manager
Worker process crashes	Add error handling
Function taking too long	Set a timeout