🧵 Python Multithreading – Create, Sync, and Communicate Threads

🧲 Introduction – Why Use Multithreading in Python?

Python is known for its simplicity and readability, but did you know it also supports multithreading?

Multithreading is a powerful way to:

• Run multiple tasks concurrently
• Optimize I/O-bound operations
• Build responsive applications (e.g., GUI, network services)

Even with Python’s Global Interpreter Lock (GIL), multithreading is ideal for I/O-bound tasks like file I/O, network calls, or database operations.

🎯 In this guide, you’ll learn:

• Thread lifecycle in Python
• Creating, starting, joining threads
• Main thread, daemon threads, thread priorities
• Thread pools for scalable parallelism
• Synchronization, deadlocks, and inter-thread communication

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🔁 Thread Lifecycle in Python

A Python thread goes through several states:

State	Description
New	Thread is created but not started yet
Runnable	Thread is ready to run (started)
Running	Actively executing instructions
Blocked	Waiting for I/O or a lock
Terminated	Execution completed or stopped

🧠 Python threads are managed by the OS scheduler; Python doesn’t expose all states explicitly.

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🛠️ Create / Start / Join Threads

✅ Creating a Thread with threading.Thread

import threading

def task():
    print("Thread is running")

t = threading.Thread(target=task)
t.start()   # Starts the thread
t.join()    # Waits for the thread to finish

💡 Use join() to synchronize the main thread with the child thread.

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✅ Thread with Arguments

def greet(name):
    print(f"Hello {name}")

t = threading.Thread(target=greet, args=("Alice",))
t.start()
t.join()

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✅ Using Subclassing

class MyThread(threading.Thread):
    def run(self):
        print("Thread via subclass")

t = MyThread()
t.start()
t.join()

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🧵 Thread Pools with concurrent.futures

Managing many threads manually is hard. Thread pools offer a higher-level abstraction.

from concurrent.futures import ThreadPoolExecutor

def square(n):
    return n * n

with ThreadPoolExecutor(max_workers=3) as executor:
    results = executor.map(square, [1, 2, 3, 4])
    print(list(results))

✅ Output:

[1, 4, 9, 16]

💡 Ideal for batch processing, parallel tasks, and web scraping.

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🧭 Main Thread / Daemon / Priority

✅ Main Thread

import threading

print("Main thread:", threading.current_thread().name)

🧠 Python automatically starts a main thread when your program begins.

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🔥 Daemon Threads

Daemon threads run in the background and automatically terminate when the main program exits.

def background_task():
    while True:
        print("Running in background")

t = threading.Thread(target=background_task, daemon=True)
t.start()

💡 Daemons are great for logging, heartbeat monitors, and cleanups.

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⏫ Thread Priority

Python’s threading module does not support priority control directly. Thread scheduling is left to the OS.

📌 If you need more control, consider multiprocessing or external libraries like psutil.

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🔐 Synchronization and Deadlocks

Without synchronization, race conditions can occur when multiple threads modify shared data.

✅ Using threading.Lock

lock = threading.Lock()
counter = 0

def safe_increment():
    global counter
    for _ in range(100000):
        with lock:
            counter += 1

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⚠️ Deadlock Example

lock1 = threading.Lock()
lock2 = threading.Lock()

def thread1():
    with lock1:
        with lock2:
            print("Thread1 acquired both locks")

def thread2():
    with lock2:
        with lock1:
            print("Thread2 acquired both locks")

⚠️ These two threads could deadlock by waiting for each other’s lock.

💡 Avoid nested locks or use threading.RLock() when needed.

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📬 Inter-thread Communication

Python threads share memory. Common communication tools include:

✅ Shared Variables with Lock

queue = []

def producer():
    with lock:
        queue.append("data")

def consumer():
    with lock:
        if queue:
            print(queue.pop(0))

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✅ queue.Queue for Thread-safe Messaging

import queue

q = queue.Queue()

def producer():
    q.put("message")

def consumer():
    msg = q.get()
    print("Received:", msg)

threading.Thread(target=producer).start()
threading.Thread(target=consumer).start()

✅ queue.Queue is thread-safe and handles blocking automatically.

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📘 Best Practices for Python Multithreading

✅ Do This	❌ Avoid This
Use threading.Lock() for shared data	Modifying shared variables without a lock
Use ThreadPoolExecutor for parallel tasks	Creating hundreds of threads manually
Use join() to wait for thread completion	Ignoring thread synchronization
Use daemon=True only for background tasks	Relying on daemons for important work

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📌 Summary – Recap & Next Steps

Python multithreading lets you build concurrent, responsive, and efficient applications—especially for I/O-bound tasks.

🔍 Key Takeaways:

• ✅ Use threading.Thread or ThreadPoolExecutor for concurrent tasks
• ✅ Understand the thread lifecycle: create → start → run → terminate
• ✅ Use locks and queues for synchronization and communication
• ✅ Daemon threads die with the main program; regular threads don’t
• ⚠️ Deadlocks can occur—always design thread-safe logic

⚙️ Real-World Relevance:
Used in web scrapers, network services, chat apps, logging systems, and asynchronous I/O operations.

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❓ FAQ – Python Multithreading

❓ Can Python use multiple threads for CPU-bound tasks?

❌ No. Due to the GIL, use multiprocessing for CPU-bound operations.

❓ What is the difference between threading and multiprocessing?

• threading = shared memory, ideal for I/O-bound tasks
• multiprocessing = separate memory, ideal for CPU-bound tasks

❓ What is a daemon thread?

✅ A background thread that automatically terminates when the main program exits.

❓ Can threads share data?

✅ Yes. Threads share memory, but access must be synchronized.

❓ What is the safest way to communicate between threads?

✅ Use queue.Queue – it’s thread-safe and handles locking internally.

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