Pagination patterns (cursor, offset) in HLD - Scalability & System Analysis

Scalability Analysis - Pagination patterns (cursor, offset)

Growth Table: Pagination Patterns (Cursor vs Offset)

Users / Requests	Offset Pagination	Cursor Pagination
100 users	Simple queries, small data scans, fast response	Simple cursor tokens, fast and efficient queries
10,000 users	Offset queries slow down due to large OFFSET scans	Cursor queries remain efficient, use indexed columns
1,000,000 users	Offset queries cause high DB CPU and IO, slow response	Cursor queries scale well, minimal DB load, stable latency
100,000,000 users	Offset pagination becomes impractical, DB overload	Cursor pagination requires careful token management, still scalable

First Bottleneck

The database query performance is the first bottleneck. Offset pagination requires scanning and skipping many rows as OFFSET grows, causing high CPU and IO load. This slows down response times and increases resource use. Cursor pagination avoids large skips by using indexed positions, reducing DB load and improving latency.

Scaling Solutions

• Use Cursor Pagination: Replace offset with cursor to avoid large skips and improve query efficiency.
• Indexing: Ensure columns used for cursor are indexed for fast lookups.
• Cache Results: Cache popular pages or query results to reduce DB hits.
• Read Replicas: Use DB replicas to distribute read load.
• Limit Page Size: Keep page sizes small to reduce data transfer and processing.
• Token Management: For cursor, manage tokens securely and efficiently to avoid state issues.

Back-of-Envelope Cost Analysis

Assuming 10,000 requests per second (RPS):

• Offset pagination at high OFFSET causes DB CPU spikes, increasing server costs.
• Cursor pagination keeps DB CPU stable, reducing need for extra servers.
• Storage impact is minimal for pagination itself but indexing adds overhead (~10-20% storage).
• Bandwidth depends on page size; smaller pages reduce network load.

Interview Tip

Start by explaining the difference between offset and cursor pagination. Discuss how offset causes performance issues at scale due to large skips. Then explain how cursor pagination uses indexed positions to improve efficiency. Finally, mention practical scaling techniques like indexing, caching, and read replicas. Use real numbers to show understanding.

Self Check

Your database handles 1000 QPS with offset pagination. Traffic grows 10x. What do you do first?

Answer: Switch to cursor pagination to reduce DB load caused by large OFFSET scans. This improves query efficiency and supports higher traffic without immediate hardware upgrades.

Key Result

Offset pagination works well at small scale but causes database performance issues as data grows. Cursor pagination scales better by using indexed positions, reducing DB load and improving response times.

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