feat: added examples_tutorials

py/examples_tutorials/concurrency_threading.py

@@ -0,0 +1,70 @@
+#!/usr/bin/env python3
+
+# Imports
+import threading
+import multiprocessing
+import concurrent.futures
+import time
+
+def task(name):
+    """
+    Dummy function which pretends to do some work
+    """
+    print(f"Thread {name}: Starting...")
+    time.sleep(1)
+    print(f"Thread {name}: Finishing.")
+
+def threading_example():
+    """
+    threading: not truly concurrent as GIL (Global Interpreter Lock) limits 1 process for each bytecode execution.
+    it does allow the process to do more work while other threads are not busy.
+    threading is relatively light-weight
+    """
+    threads = []
+    for i in range(3):
+        threads.append(threading.Thread(target=task, args=(i,)))
+
+    for i in threads:
+        i.start()
+
+    for i in threads:
+        i.join()
+
+    print("threading_example: All threads completed.")
+
+def multiprocessing_example():
+    """
+    multiprocessing: True parallel execution on multiple CPU cores. Tasks are ran on independent processes.
+    More resource expensive compared to threading
+    """
+    mp = []
+    for i in range(3):
+        mp.append(multiprocessing.Process(target=task, args=(i,)))
+
+    for i in mp:
+        i.start()
+
+    for i in mp:
+        i.join()
+
+    print("multiprocessing_example: Done with all calculations!")
+
+def concurrent_futures_example():
+    """
+    high-level implementation of threading. facilitate result consolidation
+    for high-level implemetnation of multiprocessing, use ProcessPoolExecutor
+    """
+    with concurrent.futures.ThreadPoolExecutor(max_workers=3) as executor:
+        for i in range(3):
+            executor.submit(task, i)
+    print("concurrent_futures_example: All threads completed.")
+
+# Main function
+def main() -> None:
+    threading_example()
+    multiprocessing_example()
+    concurrent_futures_example()
+
+# Call main function
+if __name__ == '__main__':
+    main()