Bulk operations in FastAPI - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to define a FastAPI POST endpoint for bulk creating items.

FastAPI

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()

class Item(BaseModel):
    name: str
    price: float

@app.post("/items/bulk")
async def create_items(items: [1]):
    return {"created": len(items)}

Drag options to blanks, or click blank then click option'

Adict

BItem

Clist[Item]

Dstr

Attempts:

3 left

💡 Hint

Common Mistakes

Using a single Item instead of a list for bulk operations.

Using dict or str which won't validate the input properly.

The endpoint expects a list of Item objects for bulk creation, so the parameter type should be list[Item].

2fill in blank

medium

Complete the code to process each item in the bulk request asynchronously.

FastAPI

from fastapi import FastAPI
from pydantic import BaseModel
import asyncio

app = FastAPI()

class Item(BaseModel):
    name: str
    price: float

async def save_item(item: Item):
    await asyncio.sleep(0.1)  # simulate saving

@app.post("/items/bulk")
async def create_items(items: list[Item]):
    await asyncio.gather(*[[1] for item in items])
    return {"created": len(items)}

Drag options to blanks, or click blank then click option'

Asave_item(item)

Bitem.save()

Csave_item()

Ditem()

Attempts:

3 left

💡 Hint

Common Mistakes

Calling save_item without arguments.

Trying to call a method on item that doesn't exist.

We need to call the async function save_item with each item, so save_item(item) is correct.

3fill in blank

hard

Fix the error in the bulk update endpoint to accept a list of items.

FastAPI

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()

class ItemUpdate(BaseModel):
    id: int
    price: float

@app.put("/items/bulk")
async def update_items(items: [1]):
    updated = len(items)
    return {"updated": updated}

Drag options to blanks, or click blank then click option'

Adict[int, float]

Btuple[ItemUpdate]

CItemUpdate

Dlist[ItemUpdate]

Attempts:

3 left

💡 Hint

Common Mistakes

Using a single ItemUpdate instead of a list.

Using dict or tuple which won't validate the input properly.

For bulk update, the endpoint should accept a list of ItemUpdate objects, so list[ItemUpdate] is correct.

4fill in blank

hard

Fill both blanks to create a bulk delete endpoint accepting a list of IDs.

FastAPI

from fastapi import FastAPI
from typing import [1]

app = FastAPI()

@app.delete("/items/bulk")
async def delete_items(ids: [2]):
    deleted_count = len(ids)
    return {"deleted": deleted_count}

Drag options to blanks, or click blank then click option'

AList

Blist[int]

CSet

Dtuple[int]

Attempts:

3 left

💡 Hint

Common Mistakes

Not importing List from typing.

Using Set or tuple instead of list for the parameter.

We import List from typing and use list[int] as the parameter type to accept a list of integers.

5fill in blank

hard

Fill all three blanks to create a bulk response with item names and their updated prices.

FastAPI

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()

class ItemUpdate(BaseModel):
    name: str
    price: float

@app.put("/items/bulk")
async def update_items(items: list[ItemUpdate]):
    result = [1]([2]: [3] for item in items)
    return {"updated_items": result}

Drag options to blanks, or click blank then click option'

Adict

Bitem.name

Citem.price

Dlist

Attempts:

3 left

💡 Hint

Common Mistakes

Using list instead of dict for the comprehension.

Swapping key and value in the comprehension.

We create a dictionary comprehension mapping item names to their updated prices.

Practice

1. What is the main benefit of using bulk operations in FastAPI?

easy

A. They limit the number of items processed to one per request.

B. They automatically generate HTML forms for data input.

C. They encrypt data before sending it to the client.

D. They allow handling many items in a single request, reducing server calls.

Solution

1. Step 1: Understand bulk operations purpose

   Bulk operations let you send many items at once instead of one by one.

2. Step 2: Identify the main benefit

   This reduces the number of server calls and speeds up processing.

3. Final Answer:

   They allow handling many items in a single request, reducing server calls. -> Option D

4. Quick Check:

   Bulk operations = fewer server calls [OK]

Hint: Bulk means many items at once, not one by one [OK]

Common Mistakes:

• Thinking bulk operations encrypt data
• Confusing bulk with form generation
• Assuming bulk limits to one item

2. Which of the following is the correct way to accept multiple items in a FastAPI endpoint using Pydantic models?

easy

A. def create_items(items: ItemModel[]):

B. def create_items(item: ItemModel):

C. def create_items(items: List[ItemModel]):

D. def create_items(items: Dict[ItemModel]):

Solution

1. Step 1: Recall FastAPI typing for multiple items

   FastAPI uses Python typing List[Model] to accept multiple objects.

2. Step 2: Check each option's syntax

   def create_items(items: List[ItemModel]): uses List[ItemModel], which is correct. def create_items(item: ItemModel): accepts one item only. def create_items(items: ItemModel[]): uses invalid syntax. def create_items(items: Dict[ItemModel]): uses Dict which is not for lists.

3. Final Answer:

   def create_items(items: List[ItemModel]): -> Option C

4. Quick Check:

   Multiple items = List[Model] [OK]

Hint: Use List[Model] to accept many items in FastAPI [OK]

Common Mistakes:

• Using ItemModel[] which is invalid syntax
• Using Dict instead of List for multiple items
• Forgetting to import List from typing

3. Given this FastAPI endpoint:

from fastapi import FastAPI
from pydantic import BaseModel
from typing import List

app = FastAPI()

class Item(BaseModel):
    name: str
    price: float

@app.post('/items/')
async def create_items(items: List[Item]):
    return {'count': len(items), 'names': [item.name for item in items]}

What will be the response if the client sends this JSON body?

[{"name": "apple", "price": 1.2}, {"name": "banana", "price": 0.5}]

medium

A. Validation error due to missing fields

B. {"count": 2, "names": ["apple", "banana"]}

C. {"count": 1, "names": ["apple"]}

D. {"count": 0, "names": []}

Solution

1. Step 1: Understand input and endpoint behavior

   The endpoint accepts a list of Item objects and returns count and names list.

2. Step 2: Analyze input JSON

   The input has two items with names 'apple' and 'banana'.

3. Step 3: Predict output

   Count is 2, names list contains both names.

4. Final Answer:

   {"count": 2, "names": ["apple", "banana"]} -> Option B

5. Quick Check:

   List length = 2, names extracted correctly [OK]

Hint: Count list length and extract names from each item [OK]

Common Mistakes:

• Assuming only one item processed
• Expecting validation error without reason
• Returning empty list mistakenly

4. Identify the error in this FastAPI bulk operation endpoint:

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()

class User(BaseModel):
    username: str
    age: int

@app.post('/users/')
async def create_users(users: User):
    return {'usernames': [user.username for user in users]}

medium

A. The parameter 'users' should be a list type like List[User].

B. The return statement should not use list comprehension.

C. The User model is missing required fields.

D. The endpoint path should be '/user/' not '/users/'.

Solution

1. Step 1: Check parameter type for bulk input

   The parameter 'users' is typed as User, which accepts one object, not multiple.

2. Step 2: Identify correct typing for multiple users

   To accept many users, it should be List[User] from typing module.

3. Final Answer:

   The parameter 'users' should be a list type like List[User]. -> Option A

4. Quick Check:

   Bulk input needs List[Model] type [OK]

Hint: Bulk input must be List[Model], not single Model [OK]

Common Mistakes:

• Using single model instead of list for bulk
• Thinking list comprehension causes error
• Confusing endpoint path naming

5. You want to create a FastAPI endpoint that accepts a bulk list of products but only saves those with price greater than 10. Which code snippet correctly implements this filtering inside the endpoint?

hard

A. async def create_products(products: List[Product]):
filtered = [p for p in products if p.price > 10]
return {'saved': len(filtered)}

B. async def create_products(products: List[Product]):
filtered = [p for p in products if p.price < 10]
return {'saved': len(filtered)}

C. async def create_products(products: Product):
filtered = [p for p in products if p.price > 10]
return {'saved': len(filtered)}

D. async def create_products(products: List[Product]):
filtered = filter(lambda p: p.price < 10, products)
return {'saved': len(filtered)}

Solution

1. Step 1: Confirm parameter type for bulk products

   Parameter must be List[Product] to accept multiple products.

2. Step 2: Check filtering condition

   We want products with price greater than 10, so filter with p.price > 10.

3. Step 3: Validate return value

   Return count of filtered list using len(filtered).

4. Final Answer:

   async def create_products(products: List[Product]): filtered = [p for p in products if p.price > 10] return {'saved': len(filtered)} -> Option A

5. Quick Check:

   Filter price > 10 and count saved [OK]

Hint: Filter with list comprehension and correct condition [OK]

Common Mistakes:

• Filtering with wrong condition (price < 10)
• Using single Product instead of List[Product]
• Using filter but forgetting to convert to list before len()