"""
Example 4: Memory Leak Detection
Real-world scenario: A long-running web service is slowly consuming more memory.
You need to identify where the memory is being allocated.
Use case: Detect memory leaks with allocation velocity metrics.
"""
from typing import List, Dict
def simulate_memory_leak():
"""Simulates a memory leak pattern."""
cache = {} # Global cache that grows without bound
def add_to_cache(key: str, data: str):
"""This function has a memory leak - cache never gets cleared."""
cache[key] = data * 1000 # Store large string
# Simulate request processing (like in a web service)
for i in range(1000):
request_id = f"request_{i}"
request_data = f"Data for request {i}"
add_to_cache(request_id, request_data)
return cache
def healthy_memory_usage():
"""Proper memory management with cleanup."""
results = {}
def process_request(request_id: int):
"""Process request and clean up after."""
# Store data temporarily
request_data = {"id": request_id, "data": f"Request {request_id}" * 100}
results[request_id] = request_data
return request_data
# Process requests
for i in range(1000):
data = process_request(i)
# Explicitly clean up old entries
if i > 100 and (i - 100) in results:
del results[i - 100]
return results
def main():
"""Run both patterns so profiler can see the difference."""
print("="*60)
print("Memory Leak Detection Example")
print("="*60)
print("\nRunning healthy memory usage pattern...")
healthy_result = healthy_memory_usage()
print(f" Healthy: {len(healthy_result)} items in results")
print("\nRunning memory leak pattern...")
leak_result = simulate_memory_leak()
print(f" Leak: {len(leak_result)} items in cache")
print("\n" + "="*60)
print("When you profile this with Scalene-MCP:")
print(" - Look at memory allocation per line")
print(" - Use get_memory_leaks() to find allocation velocity")
print(" - Lines with high velocity = potential leaks")
print("="*60)
if __name__ == "__main__":
main()
