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📬 Python Inter-thread Communication – Coordinate Threads Effectively

🧲 Introduction – Why Threads Need to Communicate

In multithreaded Python applications, threads often share work, signal each other, or transfer data. This requires inter-thread communication—the ability for threads to exchange information safely and efficiently.

Without proper communication:

🔁 Threads may race to modify shared variables
❌ Threads may wait forever
⚠️ Bugs may appear due to lack of coordination

Python offers several built-in tools to coordinate and exchange data between threads.

🎯 In this guide, you’ll learn:

Safe ways to share data between threads
How to use queue.Queue, threading.Event, Condition, and shared variables
Real-world examples of communication patterns
Best practices and common pitfalls

✅ What Is Inter-thread Communication?

Inter-thread communication refers to the process where two or more threads exchange information or signal events to each other during execution.

Python threads share memory space, but direct access must be synchronized using tools from the threading and queue modules.

📦 Method 1: Using `queue.Queue` – Thread-safe Data Sharing

import threading, queue

q = queue.Queue()

def producer():
    for i in range(5):
        q.put(i)
        print(f"Produced: {i}")

def consumer():
    while True:
        item = q.get()
        print(f"Consumed: {item}")
        q.task_done()

threading.Thread(target=producer).start()
threading.Thread(target=consumer, daemon=True).start()

✅ queue.Queue is:

Thread-safe
Blocking by default (waits for data)
Supports .put(), .get(), .task_done()

🔔 Method 2: Using `threading.Event` – Thread Signaling

import threading, time

event = threading.Event()

def waiter():
    print("Waiting for signal...")
    event.wait()
    print("Signal received!")

def signaler():
    time.sleep(2)
    event.set()

threading.Thread(target=waiter).start()
threading.Thread(target=signaler).start()

✅ Use .wait() to block until another thread calls .set().

💡 Great for synchronization, flags, or pause/resume behavior.

🧩 Method 3: Using `threading.Condition` – Advanced Coordination

import threading

condition = threading.Condition()
data = []

def producer():
    with condition:
        data.append("item")
        print("Produced item")
        condition.notify()

def consumer():
    with condition:
        print("Waiting for item...")
        condition.wait()
        print(f"Consumed: {data.pop()}")

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

✅ Condition allows threads to:

Wait for a specific state
Notify one or all waiting threads
Coordinate via shared resource conditions

🔀 Method 4: Shared Variables + Lock

lock = threading.Lock()
flag = False

def setter():
    global flag
    with lock:
        flag = True

def checker():
    global flag
    with lock:
        if flag:
            print("Flag is True")

# Use setter/checker in threads

⚠️ Use this only for simple boolean flags. Prefer Event or Queue for robustness.

🧠 Choosing the Right Communication Tool

Scenario	Recommended Tool
Passing data between threads	`queue.Queue()`
Signaling an event or condition	`threading.Event()`
Wait for condition, then act	`threading.Condition()`
Shared boolean flag	Variable + `Lock`

🧰 Real-World Example – Logging System

log_queue = queue.Queue()

def log_writer():
    while True:
        msg = log_queue.get()
        print(f"Log: {msg}")
        log_queue.task_done()

def log_event(event_type):
    log_queue.put(f"Event: {event_type}")

threading.Thread(target=log_writer, daemon=True).start()

log_event("START")
log_event("PROCESS")
log_event("END")

✅ Decouples logging from business logic with a producer-consumer pattern.

📘 Best Practices

✅ Do This	❌ Avoid This
Use `Queue` or `Event` for safe comms	Relying on global variables alone
Use `.join()` or `.task_done()` properly	Forgetting to block or release threads
Use daemon threads only for background logic	Never use daemon for critical tasks
Avoid busy-wait loops	Use blocking `.get()` or `.wait()`

📌 Summary – Recap & Next Steps

Inter-thread communication in Python allows safe collaboration between threads. Whether you’re signaling an event or passing data, Python offers flexible tools to get the job done safely and efficiently.

🔍 Key Takeaways:

✅ Use queue.Queue() for data transfer
✅ Use threading.Event() for signaling
✅ Use threading.Condition() for state-based waiting
✅ Shared memory must be protected using Lock

⚙️ Real-World Relevance:
Used in logging systems, web crawlers, GUI apps, game engines, and worker queues.

❓ FAQ – Python Inter-thread Communication

❓ What is the safest way to share data between threads?

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

❓ How do I pause a thread until another thread signals?

✅ Use threading.Event() with .wait() and .set().

❓ Can I use global variables for communication?

⚠️ Yes, but it’s risky. You must use a lock to prevent race conditions.

❓ What’s the difference between `Event` and `Condition`?

Event: One-time flag signaling
Condition: Wait for a complex state, notify other threads

❓ How do I make threads wait until a task is complete?

✅ Use .join() on threads or .task_done() + .join() on queues.

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