Why You Should Avoid LIMIT OFFSET for Pagination in PostgreSQL (And What to Use Instead)

When developing a web interface, it’s common to deal with large volumes of data. One of the ways to optimize data retrieval is through pagination, loading records incrementally, for instance, as the user scrolls down the page. With each request, a fixed number of items are fetched (limit), starting from a specific position in the list (offset).

Unset

SELECT
     *
FROM
     <table>
OFFSET
     <Y>
LIMIT
     <X>;

The simplest, and most widely used, solution is to add LIMIT and OFFSET clauses to your SQL queries. However, this approach quickly becomes problematic as data volumes increase. In this article, I’ll explain why this method isn’t optimal in PostgreSQL, and how to replace it with a more efficient index-based pagination strategy.

The Problem with LIMIT OFFSET

Using LIMIT 10 OFFSET 1000 to fetch page 101 might seem harmless, but PostgreSQL is doing much more behind the scenes than you think:

• Unnecessary scanning: PostgreSQL has to read and sort the first 1,010 rows, discard the first 1,000, and return the 10 requested rows. This is expensive in terms of disk reads and CPU.
• Increased latency: The higher the offset, the slower the query. As pagination progresses, response times degrade significantly..
• Inconsistent results: If the data changes while the user is paging (inserts, deletes), some records may be duplicated or skipped. (e.g., I’m on page 10, a record from page 1 is deleted, then when I go to page 11, the first record from page 11, which has now shifted to the end of page 10, is never seen).

Keyset Pagination: The Better Alternative

Instead of using an arbitrary offset, we can paginate using a known value from an index, typically an auto-incremented ID or a timestamp (created_at). This is commonly referred to as keyset pagination.

Example

OFFSET-based pagination (not recommended)

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SELECT
     *
FROM
     users
WHERE
     id > 12345
ORDER BY
     id
LIMIT
     10;

Keyset pagination (recommended)

Unset

SELECT
     *
FROM
     users
WHERE
     id > 12345
ORDER BY
     id
LIMIT
     10;

The idea is to use the last value from the previous page (e.g., id = 12345) to load the next one. This query:

• Fully leverages the index on id
• Skips over preceding rows without scanning them
• Is significantly faster and more scalable

Benchmarks

To be more factual, let’s use an example with a table of about 2 million rows. We'll try to retrieve rows from 1,000,000 to 1,000,010.

OFFSET-Based Query (not recommended)

Unset

SELECT
     *
FROM
     "client_data"."ean"
ORDER BY
     id
OFFSET
     1000000
LIMIT
     10;

Execution Plan Analysis:

The execution plan shows an Index Scan on ean_pkey, but without any filtering condition. PostgreSQL reads the entire table, sorts all rows, skips the first 1,000,000, and returns the 10 following rows.

➡️ This leads to a very high total cost (~548k) and makes deep navigation extremely inefficient.

Keyset-Based Query (recommended)

Unset

SELECT
     *
FROM
     "client_data"."ean"
WHERE
     id > '2609901045790'
ORDER BY
     id
LIMIT 10;

Execution Plan Analysis:

Thanks to the condition id > ..., PostgreSQL triggers an Index Scan on ean_pkey. Execution starts right after the specified key and reads only the 10 relevant rows. The plan shows a minimal cost (0.56 to 7.40), indicating efficient use of the index. There is no need to scan all the index, making the query highly performant and stable, even at large scale.

Advantages

• ✅ Constant performance, regardless of page depth
• ✅ Optimal use of indexes
• ✅ Stable results, even with concurrent modifications
• ✅ Better user experience, especially on mobile or infinite scroll

Limitations

This method comes with a few constraints:

• Requires a column that is both ordered and unique (usually id)
• Requires slightly more logic for backward pagination (e.g., reversing sort order and using the first ID as reference), but it’s straightforward to implement.
• No built-in tracking of current page number

However, in most cases, these trade-offs are minor compared to the performance and reliability gains.

Real-World Implementation: EagleAI

To illustrate a practical use case, here's how we implemented keyset pagination at EagleAI, using Python FastAPI for the backend and Next.js for the frontend.

The backend returns:

• The first index in the list (used to go to the previous page)
• The last index (to go to the next page)
• The current page number (which we could previously compute using offset + limit)
• A boolean indicating if we're on the last page (for frontend display)

The frontend, to change pages, sends the reference ID (last_seen_id) (either the first or last item in the list, depending on direction) back to the backend, along with the direction ("next" or "prev"). Here's the backend logic:

Python

def get_page(
    db_session: Session,
    limit: int,
    last_seen_id: str | None = None,
    direction: Literal["next", "prev"] = "next",
):
    """Get a page of products."""
    is_next = direction == "next"
    order = order_by.asc() if is_next else order_by.desc()

    query = select(Product)

    if last_seen_id:
        comparator = Product.id > last_seen_id if is_next else
Product.id < last_seen_id
        query = query.where(comparator)

    query = query.order_by(order).limit(limit)
    products = db_session.execute(query).scalars().all()

    return products if is_next else list(reversed(products))

Conclusion

If your database contains more than a few thousand rows, or if you care about delivering a fast, smooth user experience, it's time to abandon LIMIT OFFSET.

Switching to index-based pagination is simple, and it’s a change that pays off in the long run.