It provides a much higher-level abstraction. You don't have to manually instantiate threads, call .start(), or manually set up thread-safe Queue objects to retrieve return values or exceptions. You just instantiate an Executor, pass it a function, and it handles the entire lifecycle for you.
executor.map()blocks the iterator until the next result in the original sequence is ready. It strictly preserves the order of the input list.futures.as_completed()yields results the exact millisecond they finish, completely out of order. This is highly preferred for building responsive UI applications because you don't have to wait for the slowest task to finish just to see the fastest task's result.
If a worker thread raises an exception, the thread will terminate, but the main Python process will not crash. Instead, the Executor catches the exception and stores it inside the Future object corresponding to that task. The exception is only re-raised in the main thread if you explicitly call future.result(). If you never call .result(), the exception is silently swallowed.
The context manager protocol (__enter__ and __exit__) in Executors is designed as a safety net. The __exit__ method automatically calls executor.shutdown(wait=True). This completely blocks the main thread from exiting until all running worker threads have successfully finished their tasks. Without this with block, your main script could finish and exit while background threads are still writing to a database, resulting in data corruption.