Learn how to hash dictionaries in Python across multiple processes using the built-in libraries.
Python, hashing, dictionaries, multiprocessing, performance.
import hashlib
import multiprocessing
import json
def hash_dict(d):
# Convert dict to JSON string and encode to bytes
dict_string = json.dumps(d, sort_keys=True).encode('utf-8')
# Create a SHA-256 hash of the dict
return hashlib.sha256(dict_string).hexdigest()
if __name__ == '__main__':
# Sample dictionaries to hash
dicts_to_hash = [{'a': 1, 'b': 2}, {'b': 2, 'a': 1}, {'c': 3, 'a': 1}]
# Create a pool of processes
with multiprocessing.Pool(processes=4) as pool:
hashed_results = pool.map(hash_dict, dicts_to_hash)
print(hashed_results)
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?