Cooking concurrently with Python

Concurrency refers to the ability of a program to manage multiple tasks seemingly at the same time. While a single CPU core can only execute one instruction at a time, concurrency allows programs to juggle multiple tasks by rapidly switching between them. This creates the illusion of parallelism, enhancing responsiveness and improving performance for I/O bound workloads.

There are two ways one can achieve concurrency in Python:

1. Multithreading
2. Asynchronous Programming (Asyncio)

Let’s explore them one by one. This time we’re running a restraunt, we’re extending the example used in article. We’re going to have 3 chefs this time.

1. Multithreading

Threads are lightweight units of execution within a single process. They share the same memory space and resources but execute instructions independently.
Multiple threads can be running concurrently, but due to the Global Interpreter Lock (GIL) in Python’s traditional CPython implementation, only one thread can execute Python bytecode at a time. There are other Python implementations that one can choose from, depending on the use case.

1. Let’s redefine ordered_dishes to also print a message when an order is made.

   def ordered_dishes(): for _ in range(no_of_customers): order = random.choice(menu) print(f"I- Ordered {order}") yield order
   

2. Let’s define a function prepare_dish wrapping our food pipeline logic.

   def prepare_dish(dish): for op in serve(cook(get_ingredients(dish))): print(item) print(f"O- Served {dish}") return f"Served {dish}"
   

3. Let’s create a thread pool with 3 workers and map prepare_dish with the dishes being ordered.

   from concurrent.futures import ThreadPoolExecutor with ThreadPoolExecutor(max_workers=3) as executor: future_dishes = executor.map(prepare_dish, ordered_dishes()) for dish in future_dishes: print(dish)
   

4. Full script

   from concurrent.futures import ThreadPoolExecutor
   import random ingredients = { "Pizza": ["Flour", "Yeast", "Tomato Sauce", "Mozzarella Cheese", "Pepperoni"], "Spaghetti": ["Pasta", "Ground Beef", "Tomato Sauce", "Onion", "Garlic"], "Chicken Stir Fry": ["Chicken", "Broccoli", "Peppers", "Soy Sauce", "Ginger", "Rice"], "Chicken Biryani": ["Chicken", "Onion", "Garlic", "Rice", "Yogurt", "Spices"], "Salad": ["Mixed Greens", "Tomatoes", "Cucumbers", "Carrots", "Dressing"]
   } menu = list(ingredients.keys()) no_of_customers = random.randint(1, 10) def ordered_dishes(): for _ in range(no_of_customers): order = random.choice(menu) print(f"I- Ordered {order}") yield order def get_ingredients(dish): print(f"Get ingredients for {dish}...") yield from (ingredient for ingredient in ingredients[dish]) def cook(dish_ingredients): for ing in dish_ingredients: print(f"Cooking ingredient {ing}...") yield f"Cooked {ing}" def serve(cooked_items): for item in cooked_items: print(f"Seasoning {item}...") yield f"Served {item} with a pinch of magic" def prepare_dish(dish): for op in serve(cook(get_ingredients(dish))): print(op) print(f"O- Served {dish}") return f"Served {dish}" with ThreadPoolExecutor(max_workers=3) as executor: future_dishes = executor.map(prepare_dish, ordered_dishes()) for dish in future_dishes: print(dish)
   

2. Asynchronous Programming (Asyncio)

Asynchronous programming is a programming paradigm that allows for non-blocking operations. Instead of waiting for a task to complete before moving on to the next one, asynchronous programming enables tasks to run in the background, freeing up resources and potentially improving performance.
Python’s asyncio module provides a way to write concurrent code using the async and await syntax. It is particularly useful for I/O-bound and high-level structured network code.

1. Let’s make prepare_dish an asynchronous function by prefixing the definition with async keyword.

   async def prepare_dish(dish): ...
   

2. Let’s create a pool with 3 workers and call prepare_dish with the dishes being ordered, similar to what we did with threads. To attain this, we’re going to use a Semaphore. async and await can only be used inside an async function.

   semaphore = asyncio.Semaphore(3) async def chef_task(dish): async with semaphore: return await prepare_dish(dish)
   

3. Wrap it all in an async main function and run it with asyncio.run. We use asyncio.create_task to run tasks concurrently. asyncio.gather aggregates the results of every coroutine passed to it.

   async def main(): future_dishes = [] for dish in ordered_dishes(): task = asyncio.create_task(chef_task(dish)) future_dishes.append(task) served_dishes = await asyncio.gather(*future_dishes) for dish in served_dishes: print(dish) asyncio.run(main())
   

4. Full script

   import asyncio
   import random ingredients = { "Pizza": ["Flour", "Yeast", "Tomato Sauce", "Mozzarella Cheese", "Pepperoni"], "Spaghetti": ["Pasta", "Ground Beef", "Tomato Sauce", "Onion", "Garlic"], "Chicken Stir Fry": ["Chicken", "Broccoli", "Peppers", "Soy Sauce", "Ginger", "Rice"], "Chicken Biryani": ["Chicken", "Onion", "Garlic", "Rice", "Yogurt", "Spices"], "Salad": ["Mixed Greens", "Tomatoes", "Cucumbers", "Carrots", "Dressing"]
   } menu = list(ingredients.keys()) no_of_customers = random.randint(1, 10) def ordered_dishes(): for _ in range(no_of_customers): order = random.choice(menu) print(f"I- Ordered {order}") yield order def get_ingredients(dish): print(f"Get ingredients for {dish}...") yield from (ingredient for ingredient in ingredients[dish]) def cook(dish_ingredients): for ing in dish_ingredients: print(f"Cooking ingredient {ing}...") yield f"Cooked {ing}" def serve(cooked_items): for item in cooked_items: print(f"Seasoning {item}...") yield f"Served {item} with a pinch of magic" async def prepare_dish(dish): for op in serve(cook(get_ingredients(dish))): print(op) print(f"O- Served {dish}") return f"Served {dish}" semaphore = asyncio.Semaphore(3) async def chef_task(dish): async with semaphore: return await prepare_dish(dish) async def main(): future_dishes = [] for dish in ordered_dishes(): task = asyncio.create_task(chef_task(dish)) future_dishes.append(task) served_dishes = await asyncio.gather(*future_dishes) for dish in served_dishes: print(dish) asyncio.run(main())
   

Pros & Cons

Moving forward

If you’re not using CPython (e.g., Jython, IronPython), multithreading can potentially utilize multiple cores for CPU-bound tasks more effectively, depending on the implementation.
For highly CPU-bound tasks in CPython, consider libraries like multiprocessing which can leverage multiple cores by spawning separate processes instead of threads (processes don’t share the GIL). We’ll look deeper into multiprocessing in a different post.
Till then here are a few good resources to go through: