Pagination patterns in FastAPI

Introduction

Pagination helps split large lists into smaller parts. This makes data easier to handle and faster to load.

Showing search results in small groups instead of all at once.

Displaying user comments page by page on a blog.

Loading product lists in an online store gradually.

Fetching database records in chunks to save memory.

Syntax

FastAPI

from fastapi import FastAPI, Query
from typing import List

app = FastAPI()

@app.get("/items/")
async def read_items(skip: int = 0, limit: int = 10):
    items = [f"item{i}" for i in range(100)]
    return items[skip : skip + limit]

skip tells how many items to skip from the start.

limit controls how many items to return after skipping.

Examples

Returns 5 items starting from the position given by skip.

FastAPI

async def read_items(skip: int = 0, limit: int = 5):
    items = [f"item{i}" for i in range(20)]
    return items[skip : skip + limit]

Uses page number and page size to calculate which items to return.

FastAPI

async def read_items(page: int = 1, size: int = 10):
    items = [f"item{i}" for i in range(50)]
    start = (page - 1) * size
    end = start + size
    return items[start:end]

Sample Program

This FastAPI app returns items in pages. You can ask for a page number and size. It returns that slice of items plus info about total items.

FastAPI

from fastapi import FastAPI, Query
from typing import List

app = FastAPI()

items = [f"item{i}" for i in range(1, 101)]  # 100 items

@app.get("/items/")
async def get_items(page: int = Query(1, ge=1), size: int = Query(10, ge=1, le=50)):
    start = (page - 1) * size
    end = start + size
    return {
        "page": page,
        "size": size,
        "total_items": len(items),
        "items": items[start:end]
    }

Important Notes

Use Query to add validation like minimum and maximum values.

Keep page size reasonable to avoid slow responses.

Always return total count if possible to help clients know how many pages exist.

Summary

Pagination splits data into smaller, manageable parts.

Use skip and limit or page and size to control data slices.

Validate inputs and return helpful info like total items.

Practice

1. What is the main purpose of pagination in a FastAPI application?

easy

A. To split large data sets into smaller, manageable parts

B. To speed up the server hardware

C. To encrypt data before sending it

D. To change the database schema automatically

Solution

1. Step 1: Understand pagination concept

   Pagination divides large data into smaller chunks to avoid overwhelming users or servers.

2. Step 2: Identify the main goal in FastAPI

   FastAPI uses pagination to send only a part of data per request, improving performance and usability.

3. Final Answer:

   To split large data sets into smaller, manageable parts -> Option A

4. Quick Check:

   Pagination = split data [OK]

Hint: Pagination means breaking data into pages [OK]

Common Mistakes:

• Thinking pagination speeds up hardware
• Confusing pagination with encryption
• Assuming pagination changes database structure

2. Which of the following is the correct way to declare pagination parameters in a FastAPI endpoint using query parameters?

easy

A. def get_items(offset: str = '0', count: str = '10'):

B. def get_items(page: str = '1', size: str = '10'):

C. def get_items(skip: int = 0, limit: int = 10):

D. def get_items(start: int, end: int):

Solution

1. Step 1: Check parameter types for pagination

   Pagination parameters should be integers with default values for skip and limit.

2. Step 2: Validate correct syntax in FastAPI

   Using skip and limit as integers with defaults is standard and valid for query parameters.

3. Final Answer:

   def get_items(skip: int = 0, limit: int = 10): -> Option C

4. Quick Check:

   skip and limit as int with defaults [OK]

Hint: Use int with defaults for skip and limit [OK]

Common Mistakes:

• Using strings instead of integers for pagination
• Missing default values for query parameters
• Using unclear parameter names like start/end

3. Given this FastAPI endpoint snippet, what will be the output if called with ?skip=2&limit=3 on a list [10, 20, 30, 40, 50, 60]?

items = [10, 20, 30, 40, 50, 60]

@app.get('/items')
def read_items(skip: int = 0, limit: int = 5):
    return items[skip : skip + limit]

medium

A. [40, 50, 60]

B. [20, 30, 40]

C. [10, 20, 30]

D. [30, 40, 50]

Solution

1. Step 1: Understand slicing with skip and limit

   With skip=2 and limit=3, slice is items[2:5].

2. Step 2: Extract slice from list

   items[2:5] = [30, 40, 50] from the list.

3. Final Answer:

   [30, 40, 50] -> Option D

4. Quick Check:

   items[2:5] = [30, 40, 50] [OK]

Hint: Slice list from skip to skip+limit [OK]

Common Mistakes:

• Confusing skip with page number
• Using limit as end index instead of length
• Off-by-one errors in slicing

4. Identify the error in this FastAPI pagination endpoint:

@app.get('/users')
def get_users(page: int = 1, size: int = 10):
    skip = page * size
    users = get_all_users()
    return users[skip : skip + size]

medium

A. The calculation of skip should be (page - 1) * size

B. The function should use skip and limit parameters instead of page and size

C. The slice should be users[skip : size] instead of users[skip : skip + size]

D. The default size should be 1, not 10

Solution

1. Step 1: Analyze skip calculation

   Skip should start from zero on page 1, so skip = (page - 1) * size.

2. Step 2: Check slicing correctness

   Current skip calculation skips too many items, causing wrong slice.

3. Final Answer:

   The calculation of skip should be (page - 1) * size -> Option A

4. Quick Check:

   skip = (page - 1) * size [OK]

Hint: Page 1 means skip zero items [OK]

Common Mistakes:

• Using page * size instead of (page - 1) * size
• Confusing slice end index
• Ignoring zero-based indexing

5. You want to create a FastAPI endpoint that returns paginated results along with total count of items. Which of the following code snippets correctly implements this pattern?

hard

A. def get_paginated(page: int = 0, size: int = 10):
data = get_all_data()
skip = (page - 1) * size
return {"total": len(data), "items": data[skip:skip+size]}

B. def get_paginated(skip: int = 0, limit: int = 10):
data = get_all_data()
return {"total": len(data), "items": data[skip:skip+limit]}

C. def get_paginated(offset: int = 0, count: int = 10):
data = get_all_data()
return {"total": len(data), "items": data[offset:count]}

D. def get_paginated(page: int = 1, size: int = 10):
data = get_all_data()
skip = page * size
return {"count": len(data), "results": data[skip:skip+size]}

Solution

1. Step 1: Check pagination calculation

   def get_paginated(skip: int = 0, limit: int = 10): data = get_all_data() return {"total": len(data), "items": data[skip:skip+limit]} correctly uses skip and limit with default values and slices data properly.

2. Step 2: Verify total count and returned keys

   def get_paginated(skip: int = 0, limit: int = 10): data = get_all_data() return {"total": len(data), "items": data[skip:skip+limit]} returns total count and paginated items with clear keys "total" and "items".

3. Final Answer:

   def get_paginated(skip: int = 0, limit: int = 10):\n data = get_all_data()\n return {"total": len(data), "items": data[skip:skip+limit]} -> Option B

4. Quick Check:

   Skip/limit with total count returned [OK]

Hint: Return total count and slice with skip/limit [OK]

Common Mistakes:

• Wrong skip calculation with page * size
• Incorrect slice indices causing empty results
• Using page=0 instead of page=1 for pagination