Which pagination approach is most scalable for large address datasets when supplying filtering and sorting?

Which pagination approach is most scalable for large address datasets when supplying filtering and sorting?

• A. Limit/Offset pagination is simplest but less scalable at scale.
• B. Page-based pagination with page numbers is best for scalability.
• C. Cursor-based pagination with a stable sort key is commonly adopted for large datasets.
• D. Cursor-based pagination using a forward-only token (server-issued cursor) provides scalability and consistent results.

Answer: (D)

When you paginate large address datasets while supporting filtering and sorting, the goal is to avoid having the database skip many rows just to reach the next page. Offset-based pagination does this: asking for a page after a large offset forces the database to scan through a lot of rows, which gets slow and can produce inconsistent results if data changes between requests. Page numbers suffer from the same issue because jumping to a high page often requires similar work and can break when the dataset is updated or filtered differently.

The strongest approach is using a cursor with a forward-only token issued by the server. After delivering the first page, the server returns an opaque cursor that encodes your position in the sorted (and filtered) result set. For the next page, you pass this token back to the server, which uses it to resume exactly where you left off. Because you’re moving forward from a known position, the database can leverage indexes on the sort keys efficiently, avoiding large skips and scans. This also keeps results consistent: the next page is guaranteed to follow the previous one in the same order, even as new data arrives or filters change, as long as you maintain the same filter and sort criteria. In practice, this pattern scales well for large datasets and is widely adopted for APIs that require reliable, high-performance pagination with filtering and sorting.