Switching from JavaScript to Python feels like moving to a city with the same street signs but different traffic laws. T

Switching from JavaScript to Python feels like moving to a city with the same street signs but different traffic laws. The syntax looks friendly and familiar. You see async and await sitting right there in the grammar, so you assume the mental model ports over cleanly. It does not. One habitual pattern from JavaScript silently tanks your Python performance without crashing, without logging an error, and without showing up on a quick code review.

How JavaScript Teaches You to Start and Forget

In JavaScript, an async function call is eager. The moment you invoke it, the engine creates a Promise and the work begins immediately. The event loop is already off to the races. That is why JavaScript developers naturally write code like this:

const userPromise = fetchUser(id);
const ordersPromise = fetchOrders(id);
const user = await userPromise;
const orders = await ordersPromise;

Both network requests are in flight before either await is reached. The first await suspends the current function until fetchUser resolves, but fetchOrders has been humming along in the background since the previous line. By the time you need the orders variable, the second request might already be done. This pattern feels so natural in JavaScript that many developers do not even think of it as a concurrency trick. It is just how async works.

The Python Surprise: A Cold Coroutine

Python uses a different contract. When you call an async def function in Python, you do not start any work. You receive a coroutine object. Think of it as a recipe written on paper. The ingredients are listed, the steps are clear, but nothing is in the oven. Until something explicitly drives that coroutine through the event loop, it remains inert.

Here is the trap. A JavaScript engineer who needs a user and their orders might write this in Python:

user_coro = fetch_user(id)
orders_coro = fetch_orders(id)
user = await user_coro
orders = await orders_coro

It looks concurrent. It smells concurrent. It is entirely sequential.

The first line assigns a dormant coroutine to user_coro. The second line assigns another dormant coroutine to orders_coro. When execution hits await user_coro, Python finally starts the first task and runs it to completion. Only after fetch_user finishes does the interpreter reach await orders_coro and start the second task. Your total execution time is the sum of both I/O operations, not the longest one. You did not run them in parallel. You ran them one after another with extra steps.

Why This Bug Is Invisible

This is the kind of performance regression that survives for months. The code is valid Python. It passes type checkers. It returns the correct results. It just runs at half speed, or worse. Because there is no stack trace and no warning, engineering teams often look everywhere else first. They add Redis caches, upgrade database tiers, or switch hosting regions. The real culprit is a subtle mismatch in expectations about what await actually does.

Three Ways to Make Python Actually Run Things Concurrently

To fix this, you must tell Python’s event loop to schedule the work immediately. You need something more active than a raw coroutine. You need a Task.

1. asyncio.create_task

The most direct translation of the JavaScript pattern is to wrap your coroutine in a Task. A Task is scheduled on the event loop as soon as you create it. It is the closest Python equivalent to a JavaScript Promise in motion.

user_task = asyncio.create_task(fetch_user(id))
orders_task = asyncio.create_task(fetch_orders(id))

user = await user_task
orders = await_orders_task

Now both fetch_user and fetch_orders are in flight before the first await. When you reach await user_task, you pause only until that specific Task completes, but the other Task keeps running. If fetch_orders finishes first, its result simply waits inside orders_task until you ask for it.

Be careful, though. If you create a Task and never await it, Python will emit an error about a destroyed pending task. You must still collect your results.

2. asyncio.gather

If you have several coroutines that all need to finish before you move on, asyncio.gather handles the boilerplate for you. It schedules each coroutine as a Task internally and awaits them together.

user, orders = await asyncio.gather(fetch_user(id), fetch_orders(id))

This is concise and readable. It shines when the operations are independent and you want a single line that expresses "run all of these, then give me every result." It also preserves the order of arguments in the returned list or tuple, even if the underlying tasks complete in a different order.

3. asyncio.TaskGroup

Python 3.11 introduced TaskGroup, which brings structured concurrency to the standard library. Instead of creating tasks by hand, you use a context manager that ensures every spawned task finishes properly. If one task raises an exception, the others are cancelled automatically.

async with asyncio.TaskGroup() as tg:
    user_task = tg.create_task(fetch_user(id))
    orders_task = tg.create_task(fetch_orders(id))

user = user_task.result()
orders = orders_task.result()

This pattern is excellent for complex workflows. It removes the risk of orphaning a Task, and it groups the lifecycle of related operations under one logical umbrella. If your codebase runs on Python 3.11 or newer, this is often the cleanest architecture for fan-out concurrency.

The Mental Model: await Means "Run This Now"

The core lesson is linguistic. In JavaScript, you can read await as "meanwhile." You kick off work, do other things, and pause only when you need the value. In Python, await means "drive this coroutine to its next suspension point or completion." If the coroutine has not been scheduled yet, await is what schedules it. That is why you cannot start two raw coroutines and then await them later. You have not given the event loop anything to do in the meantime.

Think of Python coroutines like generator functions. Calling the generator does not iterate it. You need to loop over it, call next(), or pass it to a consumer. Async works the same way. asyncio.create_task is the consumer that says "put this on the event loop right now." The subsequent await simply waits for the finished signal.

One concrete habit that helps: whenever you assign an async function call to a variable without an await, ask yourself whether you have scheduled it. If the right-hand side is not wrapped in create_task, gather, or TaskGroup, it is not running. It is just a recipe sitting on the counter.

Takeaway

Python’s async runtime is powerful, but it demands explicit intent. The language does not start background work simply because you called a function. If you are coming from JavaScript, audit every place where you store a coroutine in a variable and await it later. Unless you promoted it to a Task first, you wrote sequential code dressed in async clothing. Start the work with a Task, then wait for the results. That is how you turn Python async from a silent bottleneck into a genuine concurrency tool.