Why Pagination patterns (cursor, offset) in HLD? - Purpose & Use Cases

The Big Idea

Discover how smart pagination saves your app from slow loading and confused users!

The Scenario

Imagine you have a huge list of items, like thousands of photos or messages, and you want to show them page by page on a website.

Without a smart way to split this list, you try to load all items at once or manually pick which ones to show on each page.

The Problem

Loading everything at once is slow and crashes the app.

Manually tracking which items belong to which page is confusing and causes mistakes, like showing duplicates or missing items.

Users get frustrated waiting or seeing wrong data.

The Solution

Pagination patterns like cursor and offset help by breaking the list into small, manageable pages.

Offset uses page numbers and counts to jump to a spot, while cursor remembers a position marker to fetch the next set smoothly.

This makes loading fast, accurate, and easy to handle.

Before vs After

✗ Before

SELECT * FROM items LIMIT 1000; -- load all items at once

✓ After

SELECT * FROM items WHERE id > last_seen_id ORDER BY id ASC LIMIT 20; -- cursor pagination

What It Enables

It enables fast, smooth browsing through large data without overwhelming servers or users.

Real Life Example

Social media apps use cursor pagination to load new posts as you scroll, so you never wait long or see repeated content.

Key Takeaways

Manual loading of large lists is slow and error-prone.

Pagination patterns split data into easy pages.

Cursor and offset each have strengths for different needs.

Practice

1. What is the main advantage of cursor pagination over offset pagination in system design?

easy

A. Cursor pagination is faster and more reliable for large datasets.

B. Cursor pagination uses page numbers to navigate pages.

C. Cursor pagination requires less memory than offset pagination.

D. Cursor pagination always returns the total number of pages.

Solution

1. Step 1: Understand offset pagination limitations

   Offset pagination uses page numbers and offsets, which can be slow and inconsistent on large or changing datasets.

2. Step 2: Analyze cursor pagination benefits

   Cursor pagination uses a unique cursor (like an ID) to fetch the next set of results, making it faster and more reliable for large data.

3. Final Answer:

   Cursor pagination is faster and more reliable for large datasets. -> Option A

4. Quick Check:

   Cursor pagination = faster, reliable [OK]

Hint: Cursor uses unique IDs, offset uses page numbers [OK]

Common Mistakes:

• Confusing cursor with page number
• Thinking cursor pagination returns total pages
• Assuming offset is always faster

2. Which of the following is the correct way to represent an offset pagination request in an API call?

easy

A. /items?start=abc123&count=10

B. /items?cursor=abc123&limit=10

C. /items?page=2&limit=10

D. /items?offset=abc123&limit=10

Solution

1. Step 1: Identify offset pagination syntax

   Offset pagination typically uses page numbers and limit, e.g., page=2 and limit=10.

2. Step 2: Check options for correct syntax

   /items?page=2&limit=10 uses page=2 and limit=10, which matches offset pagination style.

3. Final Answer:

   /items?page=2&limit=10 -> Option C

4. Quick Check:

   Offset pagination = page + limit [OK]

Hint: Offset uses page and limit parameters [OK]

Common Mistakes:

• Using cursor parameter for offset pagination
• Confusing offset with cursor values
• Using invalid parameter names like start or offset with strings

3. Given the following cursor pagination code snippet, what will be the next cursor value returned?

items = [
  {"id": 101, "name": "A"},
  {"id": 102, "name": "B"},
  {"id": 103, "name": "C"}
]

last_cursor = 101
page_size = 2

next_page = [item for item in items if item["id"] > last_cursor][:page_size]
next_cursor = next_page[-1]["id"] if next_page else None
print(next_cursor)

medium

A. 102

B. 101

C. None

D. 103

Solution

1. Step 1: Filter items with id greater than last_cursor

   Items with id > 101 are 102 and 103.

2. Step 2: Take first 2 items as page size is 2

   Next page items are [{"id": 102, "name": "B"}, {"id": 103, "name": "C"}].

3. Step 3: Get last item's id as next cursor

   Last item id is 103, so next_cursor = 103.

4. Final Answer:

   103 -> Option D

5. Quick Check:

   Next cursor = last id in next page = 103 [OK]

Hint: Next cursor = last item's unique id in current page [OK]

Common Mistakes:

• Choosing last_cursor as next cursor
• Selecting first item id instead of last
• Ignoring page size limit

4. A developer implemented offset pagination but users report inconsistent results when data changes rapidly. What is the likely cause and fix?

medium

A. Cause: Using offset with changing data causes duplicates or skips; Fix: Switch to cursor pagination.

B. Cause: Using cursor pagination causes slow queries; Fix: Use offset pagination instead.

C. Cause: Not caching pages; Fix: Add caching layer for offset pages.

D. Cause: Using limit too high; Fix: Reduce limit to 1.

Solution

1. Step 1: Understand offset pagination issues with changing data

   Offset pagination can skip or repeat items if data changes between requests because offset is position-based.

2. Step 2: Identify solution with cursor pagination

   Cursor pagination uses unique IDs, avoiding duplicates or skips even if data changes.

3. Final Answer:

   Cause: Using offset with changing data causes duplicates or skips; Fix: Switch to cursor pagination. -> Option A

4. Quick Check:

   Offset pagination = inconsistent with changing data [OK]

Hint: Offset breaks with data changes; cursor stays consistent [OK]

Common Mistakes:

• Thinking caching fixes offset pagination issues
• Confusing cursor and offset pagination roles
• Reducing limit does not solve data inconsistency

5. You need to design a scalable API for a social media feed with millions of posts. Which pagination pattern should you choose and why?

hard

A. Offset pagination, because it is simple and supports jumping to any page.

B. Cursor pagination, because it handles large, changing datasets efficiently and avoids duplicates.

C. Offset pagination, because it requires less backend logic and is faster for large datasets.

D. Cursor pagination, because it returns total page count quickly.

Solution

1. Step 1: Analyze requirements for large, changing datasets

   Social media feeds have millions of posts and frequent updates, so pagination must be efficient and consistent.

2. Step 2: Compare offset and cursor pagination suitability

   Offset pagination is simple but slow and inconsistent with large, changing data. Cursor pagination efficiently handles these issues by using unique cursors.

3. Final Answer:

   Cursor pagination, because it handles large, changing datasets efficiently and avoids duplicates. -> Option B

4. Quick Check:

   Large dynamic data = cursor pagination best [OK]

Hint: For big changing data, use cursor pagination [OK]

Common Mistakes:

• Choosing offset for large dynamic datasets
• Assuming offset is faster for big data
• Thinking cursor returns total pages