Bulk operations in FastAPI - Mini Project: Build & Apply

Bulk Operations with FastAPI

📖 Scenario: You are building a simple FastAPI app to manage a list of products. You want to add a feature that allows clients to add multiple products at once in a single request.

🎯 Goal: Create a FastAPI app that accepts a bulk list of products via a POST request and stores them in memory.

📋 What You'll Learn

Create a list called products to store product dictionaries

Define a Pydantic model called Product with fields id (int) and name (str)

Create a POST endpoint /add-products that accepts a list of Product items

Add the received products to the products list

💡 Why This Matters

🌍 Real World

Bulk operations are common in APIs to efficiently add or update many items at once, such as uploading multiple products to an online store.

💼 Career

Understanding how to handle bulk data input and output is essential for backend developers building scalable and user-friendly APIs.

1

Data Setup: Create the products list

Create a list called products initialized as an empty list to store product dictionaries.

FastAPI

# Create an empty list called products
# Your code here

Hint

Use products = [] to create an empty list.

2

Configuration: Define the Product model

Define a Pydantic model called Product with two fields: id of type int and name of type str.

FastAPI

from pydantic import BaseModel

products = []

# Define Product model here
# Your code here

Hint

Use class Product(BaseModel): and define the fields inside.

3

Core Logic: Create the FastAPI app and POST endpoint

Create a FastAPI app instance called app. Then create a POST endpoint /add-products that accepts a list of Product items as the request body parameter named new_products. Inside the endpoint, add each product from new_products to the products list as a dictionary using .dict().

FastAPI

from fastapi import FastAPI
from pydantic import BaseModel

products = []

class Product(BaseModel):
    id: int
    name: str

# Create FastAPI app instance
app = FastAPI()

# Create POST endpoint /add-products
@app.post("/add-products")
async def add_products(new_products: list[Product]):
    # Add each product to products list
    # Your code here

Hint

Use for product in new_products: and append product.dict() to products.

4

Completion: Add a GET endpoint to view all products

Add a GET endpoint /products that returns the current products list.

FastAPI

from fastapi import FastAPI
from pydantic import BaseModel

products = []

class Product(BaseModel):
    id: int
    name: str

app = FastAPI()

@app.post("/add-products")
async def add_products(new_products: list[Product]):
    for product in new_products:
        products.append(product.dict())

# Add GET endpoint /products to return products list
# Your code here

Hint

Define a GET endpoint that returns the products list.

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()