{"id":100,"date":"2025-10-25T07:09:41","date_gmt":"2025-10-25T07:09:41","guid":{"rendered":"https:\/\/codetypingpro.com\/?p=100"},"modified":"2025-12-17T07:49:36","modified_gmt":"2025-12-17T07:49:36","slug":"lesson-26-multithreading-and-multiprocessing-in-python","status":"publish","type":"post","link":"https:\/\/codetypingpro.com\/?p=100","title":{"rendered":"Lesson 26: Multithreading and Multiprocessing in Python"},"content":{"rendered":"\n

<\/h3>\n\n\n\n

\ud83c\udfaf Lesson Objective<\/strong><\/h3>\n\n\n\n

To understand how to execute multiple tasks concurrently<\/strong> or in parallel<\/strong> in Python, using threads<\/strong> and processes<\/strong>.
You\u2019ll learn the difference between multithreading<\/strong> and multiprocessing<\/strong>, when to use each, and how to build efficient, scalable programs.<\/p>\n\n\n\n

\n\n\n\n

\ud83e\udde9 1. What Are Threads and Processes?<\/strong><\/h3>\n\n\n\n
Concept<\/th>Description<\/th>Example<\/th><\/tr><\/thead>
Thread<\/strong><\/td>A lightweight unit of a process that shares the same memory space.<\/td>Running background tasks like updating UI or downloading files.<\/td><\/tr>
Process<\/strong><\/td>An independent program with its own memory and resources.<\/td>Running multiple Python scripts at once or CPU-heavy tasks.<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

In simple terms:<\/strong><\/p>\n\n\n\n

    \n
  • Threading<\/strong> = Multiple tasks sharing the same memory<\/strong> (good for I\/O tasks)<\/li>\n\n\n\n
  • Multiprocessing<\/strong> = Multiple Python instances<\/strong> (good for CPU-heavy tasks)<\/li>\n<\/ul>\n\n\n\n
    \n\n\n\n

    \ud83e\udde0 2. Why Use Concurrency?<\/strong><\/h3>\n\n\n\n

    When your program performs multiple tasks such as:<\/p>\n\n\n\n

      \n
    • Downloading files from the internet<\/li>\n\n\n\n
    • Reading and writing files<\/li>\n\n\n\n
    • Performing data analysis on large datasets<\/li>\n\n\n\n
    • Handling multiple client requests on a server<\/li>\n<\/ul>\n\n\n\n

      you don\u2019t want one task to block others.
      That\u2019s where multithreading<\/strong> and multiprocessing<\/strong> help by running tasks concurrently.<\/p>\n\n\n\n

      \n\n\n\n

      \u2699\ufe0f 3. The Global Interpreter Lock (GIL)<\/strong><\/h3>\n\n\n\n

      Python\u2019s GIL (Global Interpreter Lock)<\/strong> allows only one thread to execute Python bytecode at a time within a single process.
      This means:<\/p>\n\n\n\n

        \n
      • Multithreading in Python doesn\u2019t achieve true<\/em> parallelism for CPU-bound tasks.<\/li>\n\n\n\n
      • But it\u2019s perfect for I\/O-bound tasks<\/strong>, like waiting for network responses or reading from files.<\/li>\n<\/ul>\n\n\n\n

        For CPU-bound<\/strong> tasks (e.g., complex calculations), use multiprocessing<\/strong>.<\/p>\n\n\n\n

        \n\n\n\n

        \ud83e\uddf5 4. Multithreading in Python<\/strong><\/h3>\n\n\n\n

        Importing the Module<\/h4>\n\n\n\n
        import threading\nimport time\n<\/code><\/pre>\n\n\n\n
        Example 1 \u2014 Basic Multithreading<\/h4>\n\n\n\n
        def print_numbers():\n    for i in range(5):\n        print(f\"Number: {i}\")\n        time.sleep(1)\n\ndef print_letters():\n    for ch in \"ABCDE\":\n        print(f\"Letter: {ch}\")\n        time.sleep(1)\n\nt1 = threading.Thread(target=print_numbers)\nt2 = threading.Thread(target=print_letters)\n\nt1.start()\nt2.start()\n\nt1.join()\nt2.join()\n\nprint(\"Both threads completed.\")\n<\/code><\/pre>\n\n\n\n
        \ud83e\uddfe Output:<\/strong><\/p>\n\n\n\n
        Number: 0\nLetter: A\nNumber: 1\nLetter: B\n...\nBoth threads completed.\n<\/code><\/pre>\n\n\n\n
        \u2705 Both tasks run concurrently<\/em>, sharing the same CPU core.<\/p>\n\n\n\n
        \n\n\n\n
        Example 2 \u2014 Threads with Arguments<\/h3>\n\n\n\n
        def greet(name):\n    print(f\"Hello, {name}!\")\n    time.sleep(2)\n    print(f\"Goodbye, {name}!\")\n\nthread1 = threading.Thread(target=greet, args=(\"Sameer\",))\nthread2 = threading.Thread(target=greet, args=(\"Ali\",))\n\nthread1.start()\nthread2.start()\nthread1.join()\nthread2.join()\n<\/code><\/pre>\n\n\n\n
        \ud83e\uddfe Output (interleaved):<\/strong><\/p>\n\n\n\n
        Hello, Sameer!\nHello, Ali!\nGoodbye, Sameer!\nGoodbye, Ali!\n<\/code><\/pre>\n\n\n\n
        \n\n\n\n
        Example 3 \u2014 Thread Synchronization (Lock)<\/h4>\n\n\n\n
        When multiple threads access shared data, race conditions can occur. Use a Lock<\/strong> to prevent this.<\/p>\n\n\n\n
        lock = threading.Lock()\nshared_counter = 0\n\ndef increment():\n    global shared_counter\n    for _ in range(100000):\n        with lock:\n            shared_counter += 1\n\nthreads = [threading.Thread(target=increment) for _ in range(5)]\n\nfor t in threads: t.start()\nfor t in threads: t.join()\n\nprint(\"Final counter:\", shared_counter)\n<\/code><\/pre>\n\n\n\n
        \u2705 Without the lock, the result would be unpredictable.
        \u2705 With the lock, shared_counter<\/code> becomes exactly 500000<\/code>.<\/p>\n\n\n\n
        \n\n\n\n
        \ud83d\udd04 5. Multiprocessing in Python<\/strong><\/h3>\n\n\n\n
        Importing the Module<\/h4>\n\n\n\n
        from multiprocessing import Process\nimport os, time\n<\/code><\/pre>\n\n\n\n
        Example 1 \u2014 Basic Multiprocessing<\/h4>\n\n\n\n
        def worker(task_id):\n    print(f\"Task {task_id} running on process {os.getpid()}\")\n    time.sleep(2)\n    print(f\"Task {task_id} completed\")\n\nif __name__ == \"__main__\":\n    processes = []\n    for i in range(4):\n        p = Process(target=worker, args=(i,))\n        processes.append(p)\n        p.start()\n\n    for p in processes:\n        p.join()\n\n    print(\"All processes finished.\")\n<\/code><\/pre>\n\n\n\n
        \ud83e\uddfe Output:<\/strong><\/p>\n\n\n\n
        Task 0 running on process 23456\nTask 1 running on process 23457\n...\nAll processes finished.\n<\/code><\/pre>\n\n\n\n
        \u2705 Each task runs in its own process<\/strong>, truly in parallel on multiple CPU cores.<\/p>\n\n\n\n
        \n\n\n\n
        Example 2 \u2014 Using a Process Pool<\/h3>\n\n\n\n
        The multiprocessing.Pool<\/code> class helps manage multiple processes efficiently.<\/p>\n\n\n\n
        from multiprocessing import Pool\nimport time\n\ndef square(n):\n    time.sleep(1)\n    return n * n\n\nif __name__ == \"__main__\":\n    numbers = [1, 2, 3, 4, 5]\n    with Pool(processes=3) as pool:\n        results = pool.map(square, numbers)\n    print(\"Squares:\", results)\n<\/code><\/pre>\n\n\n\n
        \ud83e\uddfe Output:<\/strong><\/p>\n\n\n\n
        Squares: [1, 4, 9, 16, 25]\n<\/code><\/pre>\n\n\n\n
        \u2705 Tasks are distributed among 3 worker processes.<\/p>\n\n\n\n
        \n\n\n\n
        Example 3 \u2014 Sharing Data Between Processes<\/h4>\n\n\n\n
        from multiprocessing import Value, Lock, Process\n\ndef add(lock, counter):\n    for _ in range(1000):\n        with lock:\n            counter.value += 1\n\nif __name__ == \"__main__\":\n    lock = Lock()\n    counter = Value('i', 0)\n    processes = [Process(target=add, args=(lock, counter)) for _ in range(5)]\n\n    for p in processes: p.start()\n    for p in processes: p.join()\n\n    print(\"Final Counter:\", counter.value)\n<\/code><\/pre>\n\n\n\n
        \u2705 Uses shared memory variable (Value<\/code>) and a Lock<\/code> to synchronize updates.<\/p>\n\n\n\n
        \n\n\n\n
        \u26a1 6. Comparing Threading vs. Multiprocessing<\/strong><\/h3>\n\n\n\n
        Feature<\/th>Multithreading<\/th>Multiprocessing<\/th><\/tr><\/thead>
        Type of Task<\/strong><\/td>I\/O-bound<\/td>CPU-bound<\/td><\/tr>
        Execution Model<\/strong><\/td>Concurrent<\/td>Parallel<\/td><\/tr>
        Memory<\/strong><\/td>Shared memory space<\/td>Separate memory for each process<\/td><\/tr>
        Overhead<\/strong><\/td>Low<\/td>High<\/td><\/tr>
        Performance<\/strong><\/td>Limited by GIL<\/td>True multi-core utilization<\/td><\/tr>
        Example<\/strong><\/td>File downloads, web requests<\/td>Image processing, math computations<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
        \n\n\n\n
        \ud83e\udde0 7. Example \u2014 File Download Automation (Multithreading)<\/strong><\/h3>\n\n\n\n
        import threading, requests, time\n\nurls = [\n    \"https:\/\/example.com\/file1.jpg\",\n    \"https:\/\/example.com\/file2.jpg\",\n    \"https:\/\/example.com\/file3.jpg\"\n]\n\ndef download_file(url):\n    print(f\"Downloading {url}\")\n    response = requests.get(url)\n    filename = url.split(\"\/\")[-1]\n    with open(filename, \"wb\") as f:\n        f.write(response.content)\n    print(f\"Completed: {filename}\")\n\nstart = time.time()\nthreads = [threading.Thread(target=download_file, args=(url,)) for url in urls]\n\nfor t in threads: t.start()\nfor t in threads: t.join()\n\nprint(\"All downloads finished in\", round(time.time() - start, 2), \"seconds\")\n<\/code><\/pre>\n\n\n\n
        \u2705 Multiple files download concurrently<\/strong>, saving significant time.<\/p>\n\n\n\n
        \n\n\n\n
        \ud83e\uddee 8. Example \u2014 Parallel Image Resizing (Multiprocessing)<\/strong><\/h3>\n\n\n\n
        from multiprocessing import Pool\nfrom PIL import Image\nimport os\n\ndef resize_image(filename):\n    img = Image.open(filename)\n    img = img.resize((300, 300))\n    img.save(f\"resized_{filename}\")\n    return filename\n\nif __name__ == \"__main__\":\n    images = [f for f in os.listdir() if f.endswith(\".jpg\")]\n    with Pool(processes=4) as pool:\n        pool.map(resize_image, images)\n    print(\"All images resized.\")\n<\/code><\/pre>\n\n\n\n
        \u2705 Each image is processed in parallel across 4 CPU cores.<\/p>\n\n\n\n
        \n\n\n\n
        \ud83e\uddea 9. Practical Use Cases<\/strong><\/h3>\n\n\n\n
        Use Case<\/th>Technique<\/th>Example<\/th><\/tr><\/thead>
        Web scraping multiple pages<\/td>Threading<\/td>Use requests<\/code> + threading<\/code><\/td><\/tr>
        Large data computation<\/td>Multiprocessing<\/td>Use Pool.map()<\/code><\/td><\/tr>
        File backup & organization<\/td>Threading<\/td>Handle multiple file operations<\/td><\/tr>
        Video rendering<\/td>Multiprocessing<\/td>Use all CPU cores<\/td><\/tr>
        Server handling multiple clients<\/td>Threading<\/td>Each client connection in a thread<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
        \n\n\n\n
        \ud83d\udca1 10. Key Tips<\/strong><\/h3>\n\n\n\n
          \n
        • Use threading<\/strong> when your tasks are mostly waiting (I\/O-bound).<\/li>\n\n\n\n
        • Use multiprocessing<\/strong> for computation-heavy tasks.<\/li>\n\n\n\n
        • Always use .join()<\/code> to ensure threads\/processes finish before the program exits.<\/li>\n\n\n\n
        • Avoid shared mutable data between threads unless you use locks or queues.<\/li>\n\n\n\n
        • Use concurrent.futures<\/code> for a simpler API: from concurrent.futures import ThreadPoolExecutor<\/code><\/li>\n<\/ul>\n\n\n\n
          \n\n\n\n
          <\/h2>\n","protected":false},"excerpt":{"rendered":"
          \ud83c\udfaf Lesson Objective To understand how to execute multiple tasks concurrently or in parallel in Python, using threads and processes.You\u2019ll learn the difference between multithreading and multiprocessing, when to use each, and how to build efficient, scalable programs. \ud83e\udde9 1. What Are Threads and Processes? Concept Description Example Thread A lightweight unit of a process […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[6,1],"tags":[],"class_list":["post-100","post","type-post","status-publish","format-standard","hentry","category-python-easy-course-outline","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/codetypingpro.com\/index.php?rest_route=\/wp\/v2\/posts\/100","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/codetypingpro.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/codetypingpro.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/codetypingpro.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/codetypingpro.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=100"}],"version-history":[{"count":1,"href":"https:\/\/codetypingpro.com\/index.php?rest_route=\/wp\/v2\/posts\/100\/revisions"}],"predecessor-version":[{"id":101,"href":"https:\/\/codetypingpro.com\/index.php?rest_route=\/wp\/v2\/posts\/100\/revisions\/101"}],"wp:attachment":[{"href":"https:\/\/codetypingpro.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=100"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/codetypingpro.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=100"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/codetypingpro.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=100"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}