Python, reduce dicts, multiprocessing, parallel processing, optimization
This document provides an example of how to reduce dictionaries in Python across multiple processes, improving efficiency and performance in data processing tasks.
# Example in Python
import multiprocessing
# Function to reduce dictionaries
def reduce_dicts(dicts):
result = {}
for d in dicts:
for key, value in d.items():
if key in result:
result[key] += value
else:
result[key] = value
return result
if __name__ == "__main__":
# Sample data
dict_list = [
{'a': 1, 'b': 2},
{'a': 2, 'b': 3},
{'a': 3, 'c': 4}
]
# Create a pool of processes
with multiprocessing.Pool(processes=4) as pool:
# Split dicts into chunks and process in parallel
chunk_size = len(dict_list) // 4
chunks = [dict_list[i:i + chunk_size] for i in range(0, len(dict_list), chunk_size)]
# Map reduce function to the chunks
results = pool.map(reduce_dicts, chunks)
# Combine the results
final_result = reduce_dicts(results)
print(final_result) # Output: {'a': 6, 'b': 5, 'c': 4}
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