Concurrent task execution in FastAPI - Step-by-Step Execution

Concept Flow - Concurrent task execution

Start API Request

↓

Define async tasks

↓

Run tasks concurrently

↓

Wait for all tasks to finish

↓

Collect results

↓

Send response

This flow shows how FastAPI handles multiple async tasks at the same time, waits for all to finish, then sends back the combined results.

Execution Sample

FastAPI

from fastapi import FastAPI
import asyncio

app = FastAPI()

@app.get('/tasks')
async def run_tasks():
    async def task(id):
        await asyncio.sleep(1)
        return f'Task {id} done'
    results = await asyncio.gather(task(1), task(2), task(3))
    return {'results': results}

This FastAPI endpoint runs three async tasks concurrently, waits for all to complete, then returns their results.

Execution Table

Step	Action	Task 1 State	Task 2 State	Task 3 State	Results Collected
1	Start run_tasks endpoint	Not started	Not started	Not started	[]
2	Call asyncio.gather with 3 tasks	Started (sleeping)	Started (sleeping)	Started (sleeping)	[]
3	After 1 second, Task 1 completes	Completed	Sleeping	Sleeping	[]
4	After 1 second, Task 2 completes	Completed	Completed	Sleeping	[]
5	After 1 second, Task 3 completes	Completed	Completed	Completed	[]
6	Gather returns all results	Completed	Completed	Completed	['Task 1 done', 'Task 2 done', 'Task 3 done']
7	Return response with results	Completed	Completed	Completed	['Task 1 done', 'Task 2 done', 'Task 3 done']

💡 All tasks complete after 1 second, gather returns results, endpoint returns response.

Variable Tracker

Variable	Start	After Step 2	After Step 6	Final
results	undefined	undefined	['Task 1 done', 'Task 2 done', 'Task 3 done']	['Task 1 done', 'Task 2 done', 'Task 3 done']
Task 1 State	Not started	Started (sleeping)	Completed	Completed
Task 2 State	Not started	Started (sleeping)	Completed	Completed
Task 3 State	Not started	Started (sleeping)	Completed	Completed

Key Moments - 3 Insights

Why do all tasks complete after the same 1 second wait instead of 3 seconds total?

Because asyncio.gather runs tasks concurrently, all tasks sleep at the same time, so total wait is just 1 second, not the sum of each.

What happens if one task raises an error during asyncio.gather?

asyncio.gather will raise that error immediately and stop waiting for other tasks, so the endpoint will return an error response.

Why do we use 'await' before asyncio.gather?

Because asyncio.gather returns a coroutine, 'await' pauses the function until all tasks finish and results are ready.

Visual Quiz - 3 Questions

Look at the execution table, what is the state of Task 2 at Step 4?

A Sleeping

B Not started

C Completed

D Error

💡 Hint

Check the 'Task 2 State' column at Step 4 in the execution_table.

At which step does the 'results' variable get its final list of task outputs?

A Step 6

B Step 4

C Step 2

D Step 7

💡 Hint

Look at the 'Results Collected' column in execution_table rows.

If we remove 'await' before asyncio.gather, what changes in variable_tracker?

A 'results' immediately has all task outputs

B 'results' becomes a coroutine, not a list

C Tasks run sequentially instead of concurrently

D No change, code runs the same

💡 Hint

Recall that without 'await', asyncio.gather returns a coroutine object, not results.

Concept Snapshot

FastAPI concurrent tasks use async def and asyncio.gather.
Multiple async tasks run at the same time.
Use 'await' to pause until all finish.
Results come back as a list in order.
This speeds up waiting for many tasks.

Full Transcript

This example shows how FastAPI runs multiple asynchronous tasks concurrently using asyncio.gather. When the endpoint is called, it starts three tasks that each wait for one second. Because they run at the same time, the total wait is just one second, not three. After all tasks finish, their results are collected into a list and returned as the response. The key is using 'await' with asyncio.gather to pause until all tasks complete. If one task fails, the whole gather call raises an error. This method helps handle many tasks efficiently without blocking the server.

Practice

1. What is the main benefit of using asyncio.create_task() in FastAPI?

easy

A. It stops all tasks from running until manually restarted.

B. It makes the code run slower by adding delays.

C. It converts synchronous code into synchronous code.

D. It allows running multiple tasks at the same time without waiting for each to finish.

Solution

1. Step 1: Understand asyncio.create_task() purpose

   This function schedules a coroutine to run concurrently as a separate task without blocking the main program.

2. Step 2: Identify the benefit in FastAPI context

   Using it lets FastAPI handle multiple operations at once, improving responsiveness and efficiency.

3. Final Answer:

   It allows running multiple tasks at the same time without waiting for each to finish. -> Option D

4. Quick Check:

   Concurrent task execution = run tasks simultaneously [OK]

Hint: Remember: create_task starts tasks without waiting [OK]

Common Mistakes:

• Thinking create_task slows code down
• Confusing async with sync code
• Believing tasks stop until restarted

2. Which of the following is the correct way to run two async functions concurrently and wait for both to finish in FastAPI?

easy

A. func1(); func2()

B. await asyncio.create_task(func1()); await asyncio.create_task(func2())

C. await asyncio.gather(func1(), func2())

D. asyncio.run(func1(), func2())

Solution

1. Step 1: Understand how to run and wait for multiple async tasks

   asyncio.gather() runs multiple coroutines concurrently and waits for all to complete.

2. Step 2: Check each option's correctness

   await asyncio.gather(func1(), func2()) correctly uses await asyncio.gather(func1(), func2()). await asyncio.create_task(func1()); await asyncio.create_task(func2()) starts tasks but awaits them separately, not concurrently. func1(); func2() runs functions synchronously. asyncio.run(func1(), func2()) is invalid syntax.

3. Final Answer:

   await asyncio.gather(func1(), func2()) -> Option C

4. Quick Check:

   Use gather to run and wait multiple tasks [OK]

Hint: Use gather to wait all tasks together [OK]

Common Mistakes:

• Awaiting tasks separately losing concurrency
• Calling async functions without await
• Using asyncio.run inside async code

3. Given this FastAPI async endpoint code, what will be the output?

import asyncio
from fastapi import FastAPI
app = FastAPI()

async def task1():
    await asyncio.sleep(1)
    return "Task 1 done"

async def task2():
    await asyncio.sleep(2)
    return "Task 2 done"

@app.get("/run")
async def run_tasks():
    results = await asyncio.gather(task1(), task2())
    return {"results": results}

medium

A. {"results": ["Task 1 done", "Task 2 done"]}

B. {"results": ["Task 2 done", "Task 1 done"]}

C. {"results": ["Task 1 done"]}

D. TimeoutError

Solution

1. Step 1: Analyze the async tasks and their sleep durations

   task1 sleeps 1 second, task2 sleeps 2 seconds. Both run concurrently with gather.

2. Step 2: Understand gather result order

   asyncio.gather returns results in the order of the input tasks, regardless of completion time.

3. Final Answer:

   {"results": ["Task 1 done", "Task 2 done"]} -> Option A

4. Quick Check:

   Gather preserves input order in results [OK]

Hint: Gather returns results in input order, not finish order [OK]

Common Mistakes:

• Assuming results order matches finish order
• Expecting partial results if one task is slower
• Confusing gather with wait

4. What is wrong with this FastAPI code snippet for running tasks concurrently?

import asyncio
from fastapi import FastAPI
app = FastAPI()

async def task():
    await asyncio.sleep(1)
    return "Done"

@app.get("/test")
async def test():
    task1 = asyncio.create_task(task())
    task2 = asyncio.create_task(task())
    return {"result": task1.result() + task2.result()}

medium

A. Calling task1.result() before task completion causes an error.

B. Using create_task is not allowed in FastAPI endpoints.

C. The tasks should not be async functions.

D. The endpoint should not be async.

Solution

1. Step 1: Identify how task results are accessed

   Calling task1.result() immediately after creating the task tries to get the result before the task finishes, causing an error.

2. Step 2: Understand correct way to wait for task completion

   You must await the task or use await asyncio.gather(task1, task2) before accessing results.

3. Final Answer:

   Calling task1.result() before task completion causes an error. -> Option A

4. Quick Check:

   Access task result only after await [OK]

Hint: Always await tasks before accessing results [OK]

Common Mistakes:

• Accessing .result() too early
• Thinking create_task is forbidden
• Making endpoint synchronous

5. You want to run three independent async tasks in a FastAPI endpoint and return their results as soon as all finish. Which approach correctly handles errors in any task without stopping others and returns all results or error messages?

hard

A. Use asyncio.create_task() for each task but do not await them before returning.

B. Use asyncio.gather(task1(), task2(), task3(), return_exceptions=True) and process results for exceptions.

C. Use await task1(); await task2(); await task3() sequentially and catch exceptions globally.

D. Run tasks synchronously in a loop and catch exceptions inside each.

Solution

1. Step 1: Understand error handling in concurrent tasks

   Using asyncio.gather() with return_exceptions=True lets all tasks run concurrently and collects exceptions as results without stopping others.

2. Step 2: Compare options for correct concurrency and error handling

   Use asyncio.gather(task1(), task2(), task3(), return_exceptions=True) and process results for exceptions. correctly runs all tasks concurrently and handles errors individually. Use await task1(); await task2(); await task3() sequentially and catch exceptions globally. runs tasks sequentially, losing concurrency. Use asyncio.create_task() for each task but do not await them before returning. does not await tasks, so results are unavailable. Run tasks synchronously in a loop and catch exceptions inside each. runs synchronously, blocking concurrency.

3. Final Answer:

   Use asyncio.gather(task1(), task2(), task3(), return_exceptions=True) and process results for exceptions. -> Option B

4. Quick Check:

   Gather with return_exceptions handles errors concurrently [OK]

Hint: Use gather with return_exceptions=True for safe concurrency [OK]

Common Mistakes:

• Awaiting tasks sequentially losing concurrency
• Not awaiting tasks before returning
• Ignoring exceptions stopping all tasks