In Python, comparing dictionaries across multiple processes requires careful attention to how data is shared between processes. One common approach is using the `multiprocessing` module along with the `Manager` class, which allows for shared objects. Here’s an example of how to compare dictionaries across multiple processes.
import multiprocessing
def compare_dicts(dict1, dict2, return_dict):
return_dict['result'] = dict1 == dict2
if __name__ == '__main__':
manager = multiprocessing.Manager()
return_dict = manager.dict()
dict_a = {'key1': 'value1', 'key2': 'value2'}
dict_b = {'key1': 'value1', 'key2': 'value2'}
process = multiprocessing.Process(target=compare_dicts, args=(dict_a, dict_b, return_dict))
process.start()
process.join()
print("Dictionaries are equal:", return_dict['result'])
How do I avoid rehashing overhead with std::set in multithreaded code?
How do I find elements with custom comparators with std::set for embedded targets?
How do I erase elements while iterating with std::set for embedded targets?
How do I provide stable iteration order with std::unordered_map for large datasets?
How do I reserve capacity ahead of time with std::unordered_map for large datasets?
How do I erase elements while iterating with std::unordered_map in multithreaded code?
How do I provide stable iteration order with std::map for embedded targets?
How do I provide stable iteration order with std::map in multithreaded code?
How do I avoid rehashing overhead with std::map in performance-sensitive code?
How do I merge two containers efficiently with std::map for embedded targets?