Mastering API Pagination: Methods, Scenarios, and Best Practices

APIs often return data in bulk—be it products, transactions, logs, or posts. However, returning all records in one go can be disastrous for performance and usability. That's where API Pagination comes in.

In this blog, we’ll explore what pagination is, why it matters, the common strategies (with pros, cons, and examples) and best practices for implementation.

🚨 The Problem: Large Data Sets

Imagine an e-commerce app retrieving 1 million products or a social platform loading 10 years of user activity at once. Returning such massive datasets:

• Increases memory and CPU usage on the server
• Slows down API response time
• Causes timeouts or crashes on the client
• Eats up network bandwidth unnecessarily

Pagination helps mitigate this by allowing the client to fetch data in manageable chunks.

📦 Common Use Cases

• Infinite scrolling (e.g., Twitter feed)
• Search results (e.g., e-commerce filters)
• Log viewers (e.g., admin dashboards)
• Reports and analytics over large time spans

🧭 How Clients Specify Data Chunks

Clients typically specify:

• Page size: Number of items per page (e.g., 10, 50, 100)
• Page or offset: Which chunk of data they want (e.g., page 3 or offset 100)
• Cursor or key: A unique pointer to continue from where the last call ended

⚙️ Pagination Methods: Pros, Cons & Complexity

1. Offset & Limit Pagination

Description:

Client provides a numeric offset (start index) and limit (number of records).

Example:
GET /products?offset=100&limit=20

Pros:

• Simple to implement
• Easy for clients to understand

Cons:

• Expensive for large offsets (e.g., offset=100000)
• Data inconsistencies if new records are added or removed during paging

Complexity:

🔹 Low (but O(N) DB scan for large offsets)

2. Page-Based Pagination

Description:

Uses page and pageSize instead of numeric offsets.

Example:

GET /products?page=5&pageSize=50

Pros:

• Human-readable page numbers
• Works well for small, stable datasets

Cons:

• Suffers from same issues as offset-based (performance & consistency)

Complexity:

🔹 Low

3. Cursor-Based Pagination (a.k.a. Seek Pagination)

Description:

Uses a unique, immutable field (e.g., id, createdAt) to fetch the next set.

Example:

GET /messages?cursor=8756&limit=20

Pros:

• Efficient for large datasets
• Consistent even with concurrent updates

Cons:

• Harder to implement and debug
• Requires indexed and sortable fields

Complexity:

🔸 Medium to High

4. Time-Based Pagination

Description:

Fetches records created before/after a certain timestamp.

Example:

GET /logs?start=2025-01-01T00:00:00Z&end=2025-01-02T00:00:00Z

Pros:

• Great for time-series data (logs, events)
• Easy to cache and archive

Cons:

• Can miss or duplicate records with identical timestamps

Complexity:

🔸 Medium

5. Keyset Pagination

Description:

Uses sorted, indexed keys (e.g., id) to get the next page.

Example:

GET /orders?after_id=1050&limit=20

Pros:

• High performance for deep pagination
• Resilient to data changes

Cons:

• Doesn’t allow jumping to arbitrary pages
• Can’t handle unsorted or composite keys easily

Complexity:

🔸 Medium

6. Hybrid Pagination

Description:

Combines techniques like offset + cursor or cursor + timestamp.

Example:
GET /comments?after=1000&createdBefore=2025-01-01&limit=50

Pros:

• Flexible and powerful
• Can balance performance with UX needs

Cons:

• Most complex to implement
• Can confuse API consumers

Complexity:

🔶 High

🛠 Best Practices for Implementing Pagination

1. Use default limits: Always cap the number of records returned (limit=100 max).
2. Return metadata: Include fields like totalCount, nextPage, hasMore, or nextCursor in the response.
3. Document your API: Clearly explain supported pagination styles in your API docs.
4. Index your sort fields: Especially for cursor or keyset pagination.
5. Avoid offset for large data: Cursor/keyset is better for scale and consistency.
6. Consistent sorting: Always sort by a unique, stable column (e.g., id, createdAt) to prevent duplicates or missing data.
7. Make pagination optional: But enforce it with sane defaults if not specified.

🧠 Conclusion

Pagination isn’t just about breaking data into pages—it’s about balancing performance, usability, and consistency.

Choose wisely based on your use case, and always test performance at scale.