Day 47:Ignoring memory leaks in long-running apps

 

Got it — you want the same format as earlier days: Mistake → Correct Way, clearly shown.

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🐍 Python Mistakes Everyone Makes ❌

Day 47: Ignoring Memory Leaks in Long-Running Apps

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❌ The Mistake

Keeping references alive forever in a long-running process.

# Memory leak example
cache = []

def process(data):
  cache.append(data) #  ❌ cache grows forever 
while True:

    process("some large data")

❌ What’s wrong here?

• cache is global

• It keeps growing

• Garbage collector cannot free memory

• Memory usage increases endlessly

In servers, workers, or background jobs, this will eventually crash the app.

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✅ The Correct Way

Use bounded data structures or explicitly clean up memory.

✔ Option 1: Limit cache size

from collections import deque

cache = deque(maxlen=1000) # ✅ fixed size

def process(data):
    cache.append(data)

while True:

      process("some large data")

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✔ Option 2: Explicit cleanup

def process(data): 

      temp = data.upper() 

     # do work

       del temp # ✅ remove reference

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✔ Option 3: Use weak references (advanced)

import weakref

class Data:
     pass 
cache = weakref.WeakSet()

d = Data() 

cache.add(d)


Objects are removed automatically when no strong references exist.

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❌ Why This Fails? (Main Points)

• Python only frees memory when references disappear

• Global variables live forever

• Unbounded caches slowly eat RAM

• Garbage collection timing is unpredictable

• Long-running apps amplify small leaks

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🧠 Simple Rule to Remember

🧠 If something is referenced, it stays in memory
🧠 Long-running apps must manage memory explicitly
🧠 Always limit caches and clean resources

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🚀 Final Takeaway

Python won’t save you from memory leaks.

Short scripts finish fast.
Servers don’t.

If your app runs forever — your mistakes will show up eventually.