import time

import datetime

import random

import json

import pickle

import os

def measure_metadata_performance(num_iterations=100):

"""

Measures the performance of different metadata storage/retrieval methods.

"""

data = []

for _ in range(num_iterations):

# Generate sample data

data.append({"id": random.randint(1, 1000), "value": random.random(), "timestamp": datetime.datetime.now()})

# 1. JSON serialization/deserialization

start_time = time.time()

json_data = json.dumps(data)

json_data = json.loads(json_data)

json_time = time.time() - start_time

print(f"JSON Time: {json_time:.4f} seconds")

# 2. Pickle serialization/deserialization

start_time = time.time()

with open("temp.pkl", "wb") as f: # Use a temporary file

pickle.dump(data, f)

with open("temp.pkl", "rb") as f:

pickled_data = pickle.load(f)

os.remove("temp.pkl") # Clean up

pickle_time = time.time() - start_time

print(f"Pickle Time: {pickle_time:.4f} seconds")

# 3. Simple dictionary storage/retrieval (for comparison)

start_time = time.time()

# Simulate a dictionary-like structure

dict_data = {i: d for i, d in enumerate(data)} # Use enumerate for key

retrieved_data = [dict_data[i] for i in range(len(data))]

dict_time = time.time() - start_time

print(f"Dictionary Time: {dict_time:.4f} seconds")

if __name__ == "__main__":

measure_metadata_performance()