Excellent.
DAY 26 is one of the most misunderstood topics in Python. Today we kill myths and replace them with clear mental models.

If you understand this day properly, you will:

• Stop misusing threads
• Choose the right concurrency model
• Answer GIL questions confidently in interviews
• Design scalable Python systems

🧠 DAY 26 — Python Concurrency Model

(GIL, threading, multiprocessing & asyncio)

🔑 CORE IDEA OF DAY 26

Python concurrency is limited by the GIL for CPU-bound work,
but excels at I/O-bound concurrency.

Everything today revolves around choosing the right model.

1️⃣ What Is Concurrency vs Parallelism

• Concurrency: multiple tasks in progress
• Parallelism: multiple tasks executing simultaneously

Python supports:

• Concurrency very well
• Parallelism only via multiprocessing (not threads)

2️⃣ The GIL (Global Interpreter Lock) — No Myths

What the GIL is:

• A mutex inside CPython
• Allows only one thread to execute Python bytecode at a time
• Protects Python memory management (refcounting)

What the GIL is NOT:

❌ A Python language feature
❌ Present in all Python implementations
❌ A bug

3️⃣ Why the GIL Exists (Design Reality)

CPython uses:

• Reference counting for GC
• Non-thread-safe memory operations

Without GIL:

• Every object access needs a lock
• Python would become much slower

Design choice:

Make single-threaded code fast and simple

4️⃣ Threads in Python (threading)

Key facts:

• OS-level threads
• Share memory
• Subject to GIL

Consequence:

• ❌ No CPU parallelism for Python code
• ✅ Excellent for I/O-bound tasks

Example:

import threading

def task():
    download_file()

t1 = threading.Thread(target=task)
t2 = threading.Thread(target=task)

Useful for:

• Network calls
• Disk I/O
• Waiting on external systems

5️⃣ Why Threads Still Matter (IMPORTANT)

While one thread:

• Waits on I/O
• Releases GIL

Another thread:

• Executes Python code

So threads do overlap I/O latency.

6️⃣ Multiprocessing (multiprocessing)

What it does:

• Spawns separate processes
• Each has its own GIL
• True parallelism on multiple cores

from multiprocessing import Process

def cpu_task():
    heavy_compute()

p = Process(target=cpu_task)
p.start()

Tradeoffs:

• High memory usage
• Inter-process communication (IPC) cost
• Serialization (pickle) overhead

Use for:

• CPU-bound workloads
• Data processing
• Parallel computation

7️⃣ Threads vs Processes (Decision Table)

8️⃣ Asyncio — Cooperative Concurrency

Async is:

• Single-threaded
• Single-process
• Event-loop based

async def fetch():
    await network_call()

Key idea:

• await gives control back to event loop
• No blocking allowed

9️⃣ Asyncio Mental Model

• One thread
• Many coroutines
• Cooperative scheduling
• No preemption

If a coroutine blocks → everything blocks.

🔟 When Asyncio Is Perfect

Use asyncio when:

• Thousands of concurrent I/O tasks
• Network-heavy systems
• APIs, microservices, web servers

Avoid asyncio for:

• CPU-heavy computation
• Blocking libraries
• Simple scripts

11️⃣ Why Python Threads Don’t Speed Up CPU Work (Interview Gold)

Because:

• GIL allows only one thread to execute Python bytecode
• Threads context-switch but don’t run in parallel
• CPU-bound threads fight over GIL

Correct interview answer:

“Threads improve concurrency, not parallelism, in CPython.”

12️⃣ Releasing the GIL (Advanced Insight)

Some C extensions:

• NumPy
• Pandas
• SciPy

Release the GIL during heavy computation → real parallelism.

This is why Python works well in data science.

🔥 INTERVIEW TRAPS (DAY 26)

Q1

Does Python support multithreading?

✔ Yes (but GIL-limited)

Q2

Why multiprocessing is slower to start?

✔ Process creation
✔ Memory duplication
✔ IPC overhead

Q3

Can asyncio use multiple cores?

❌ No
✔ One event loop per thread/process

Q4

Which model for web server handling 10k connections?

✔ Asyncio

🧠 DAY 26 MENTAL CHECKLIST

Before adding concurrency:

1. CPU-bound or I/O-bound?
2. Shared memory needed?
3. Task count?
4. Blocking calls?
5. Debuggability concerns?

📝 DAY 26 ASSIGNMENT (MANDATORY)

1️⃣ Predict behavior (NO RUNNING):

import threading

x = 0
def f():
    global x
    for _ in range(1_000_000):
        x += 1

t1 = threading.Thread(target=f)
t2 = threading.Thread(target=f)
t1.start(); t2.start()
t1.join(); t2.join()

print(x)

Will it be exactly 2_000_000? Why/why not?

2️⃣ Explain clearly:

• Why Python threads don’t scale CPU-bound tasks
• Difference between threading and asyncio

3️⃣ Design question:

How would you design a Python service that downloads 50k URLs and then performs heavy CPU parsing on results?

(Hint: hybrid model)

🔜 DAY 27 PREVIEW

DAY 27 — Performance & Optimization
(Profiling, time complexity, memory tricks & real tuning)

You’ll learn:

• How to find real bottlenecks
• Profiling tools
• Micro-optimizations that matter
• Optimizations that don’t

When ready, say 👉 “START DAY 27”