FastAPI Backend Development Bootcamp · Lección

Paginación por cursor frente a paginación por offset a gran escala

Implemente paginación por keyset o cursor para listar de forma estable y eficaz conjuntos de datos grandes y cambiantes.

Paginación por cursor frente a paginación por offset a gran escala es una lección gratuita de FastAPI Backend Development Bootcamp en CoddyKit. Esta es la lección 2 de 4. Puedes leer la lección completa abajo gratuitamente — luego la practicas en el navegador con un editor de código integrado y un tutor de IA 24/7. Forma parte de la ruta de aprendizaje de FastAPI Backend Development Bootcamp, y tu progreso se sincroniza en la web y la app de CoddyKit. El curso de FastAPI Backend Development Bootcamp incluye 4 lecciones en total.

Partes de esta lección aún no han sido traducidas y se muestran en inglés.

Why Pagination Strategy Matters

When a list endpoint returns thousands or millions of rows, you must page the results. The two dominant strategies are offset pagination (LIMIT/OFFSET or ?page=3) and cursor/keyset pagination (?after=<token>).

Offset is simple and supports jumping to arbitrary pages.
Cursor is stable and fast on large, frequently changing datasets.

This lesson shows why offset breaks down at scale and how to implement keyset pagination correctly in a FastAPI service.

How Offset Pagination Works

Offset pagination tells the database to skip N rows and return the next page-sized chunk. A request for page 3 with page size 20 becomes OFFSET 40 LIMIT 20.

The endpoint is trivial to write and lets clients jump directly to any page. Here is a minimal in-memory simulation of how offset slicing behaves.

def offset_page(rows, page, size):
    start = (page - 1) * size
    end = start + size
    return rows[start:end]

items = [f"item-{i}" for i in range(1, 101)]
print("page 1:", offset_page(items, 1, 5))
print("page 3:", offset_page(items, 3, 5))
print("page 20:", offset_page(items, 20, 5))

The Hidden Cost of OFFSET

Databases do not magically jump to row 1,000,000. To satisfy OFFSET 1000000 LIMIT 20, the engine must scan and discard the first one million rows before returning 20. The deeper the page, the slower the query.

Page 1 is instant; page 50,000 can take seconds.
Work grows linearly with the offset (O(offset)).
Indexes help ordering but cannot skip the discarded rows for free.

This makes deep offset pagination a common cause of slow list endpoints and database load spikes.

The Drift Problem

Offset pages are computed against a moving target. If rows are inserted or deleted between requests, the offsets shift underneath the client.

A new row is inserted at the top while the user reads page 1.
On page 2, the last item of page 1 reappears (duplicate).
Or a deletion causes an item to be skipped entirely.

On busy feeds and dashboards this produces missing and repeated items — unacceptable for infinite scroll.

rows = list(range(1, 11))  # ids 1..10, newest last
size = 3

page1 = rows[0:3]            # [1, 2, 3]
rows.insert(0, 0)           # a new row 0 arrives at the top
page2 = rows[3:6]           # shifted by the insert
print("page1:", page1)
print("page2:", page2)
print("id 3 repeated?", 3 in page2)

Enter Keyset (Cursor) Pagination

Keyset pagination does not count rows to skip. Instead it remembers the last seen key and asks for rows strictly after it: WHERE id < :last_id ORDER BY id DESC LIMIT :size.

The query uses an index seek, not a scan — constant time regardless of depth.
Inserts and deletes before the cursor do not shift the window, so no duplicates or skips.

The trade-off: you can only move next/previous relative to a cursor — you cannot jump to "page 4,217".

Keyset Logic in Plain Python

Before touching SQL, it helps to see the keyset rule in isolation. Given a sorted list and the last id from the previous page, return the next chunk of items whose id is below that cursor.

Notice the work depends only on the page size, not on how deep we are.

def keyset_page(rows, after_id, size):
    # rows sorted by id DESC; return items strictly after the cursor
    result = [r for r in rows if r["id"] < after_id]
    return result[:size]

rows = [{"id": i, "name": f"u{i}"} for i in range(10, 0, -1)]
first = rows[:3]
print("first page:", [r["id"] for r in first])
cursor = first[-1]["id"]
next_page = keyset_page(rows, cursor, 3)
print("next page:", [r["id"] for r in next_page])

Designing a Stable Sort Key

Keyset pagination needs a total ordering — the sort key must be unique. Paginating by created_at alone is unsafe because many rows can share the same timestamp; rows on the boundary may be lost or repeated.

Use a strictly increasing key like the primary key id, or
Use a composite key such as (created_at, id) so ties are broken by the unique id.

Always include the unique column as the final tiebreaker, and make sure a matching index exists on the same column order.

Encoding the Cursor as an Opaque Token

Never expose raw internal keys directly. Wrap them in an opaque, URL-safe token (typically base64). Clients treat it as a black box and simply echo it back, which lets you change the underlying key format later without breaking the contract.

For composite keys, encode all parts together inside the token.

import base64, json

def encode_cursor(created_at, last_id):
    raw = json.dumps({"ts": created_at, "id": last_id}).encode()
    return base64.urlsafe_b64encode(raw).decode()

def decode_cursor(token):
    raw = base64.urlsafe_b64decode(token.encode())
    return json.loads(raw)

token = encode_cursor("2026-06-10T12:00:00Z", 8842)
print("cursor:", token)
print("decoded:", decode_cursor(token))

A FastAPI Cursor Endpoint

Now wire it into FastAPI. The endpoint accepts an optional cursor and a limit, decodes the cursor to a last_id, and queries one page. A common trick: fetch limit + 1 rows to detect whether a next page exists.

This is framework code that depends on a database session, so it is illustrative rather than self-contained.

from fastapi import FastAPI, Query, Depends
from sqlalchemy import select

app = FastAPI()

@app.get("/users")
async def list_users(
    cursor: str | None = Query(default=None),
    limit: int = Query(default=20, le=100),
    db=Depends(get_session),
):
    last_id = decode_cursor(cursor)["id"] if cursor else None
    stmt = select(User).order_by(User.id.desc()).limit(limit + 1)
    if last_id is not None:
        stmt = stmt.where(User.id < last_id)
    rows = (await db.execute(stmt)).scalars().all()
    has_more = len(rows) > limit
    rows = rows[:limit]
    next_cursor = encode_cursor(rows[-1].id) if has_more else None
    return {"items": rows, "next_cursor": next_cursor}

Shaping the Response Contract

A clean cursor API returns the page plus navigation metadata — never a total page count (computing it would reintroduce a full scan). A typical envelope:

items: the current page of records.
next_cursor: token for the following page, or null when the list is exhausted.
optionally has_more: a boolean convenience flag.

Clients loop by passing the returned next_cursor back as ?cursor= until it is null.

def build_page(rows, limit, get_id):
    has_more = len(rows) > limit
    page = rows[:limit]
    next_cursor = get_id(page[-1]) if has_more and page else None
    return {"items": page, "next_cursor": next_cursor, "has_more": has_more}

fetched = list(range(20, 9, -1))  # asked for 10, got 11
result = build_page(fetched, limit=10, get_id=lambda x: x)
print("items:", result["items"])
print("next_cursor:", result["next_cursor"])
print("has_more:", result["has_more"])

Composite Keyset and Indexing in SQL

For sorting by recency, compare the tuple (created_at, id) so ties never break the contract. In Postgres you can use row-value comparison:

WHERE (created_at, id) < (:ts, :id) ORDER BY created_at DESC, id DESC LIMIT :n
Back it with CREATE INDEX ON items (created_at DESC, id DESC).

The index lets the planner seek directly to the cursor position, giving stable performance whether the user is on page 1 or page 10,000.

Quick Check

Test your understanding of when to choose each strategy.

Recap: Cursor vs Offset at Scale

You learned to choose and build the right pagination strategy:

Offset is simple and supports page jumping, but is O(offset) slow on deep pages and drifts (duplicates/skips) when data changes.
Keyset/cursor seeks by the last-seen key, giving constant-time deep pages and stable results on busy datasets.
Use a unique total ordering — a primary key or composite (created_at, id) — and back it with a matching index.
Expose an opaque cursor token and return { items, next_cursor } instead of page numbers and totals.

Rule of thumb: pick offset for small, admin-style tables that need page jumping; pick cursor for large, frequently changing, infinitely scrolled lists.

Preguntas frecuentes

¿La lección «Paginación por cursor frente a paginación por offset a gran escala» es gratis?

Sí — el texto completo de «Paginación por cursor frente a paginación por offset a gran escala» es gratis para leer aquí en la web. Para practicarla de forma interactiva (editor de código integrado y tutor de IA 24/7) y desbloquear el resto del curso de FastAPI Backend Development Bootcamp, actualiza a CoddyKit PRO. El curso de FastAPI Backend Development Bootcamp incluye 4 lecciones en total.

¿Qué aprenderé en «Paginación por cursor frente a paginación por offset a gran escala»?

Implemente paginación por keyset o cursor para listar de forma estable y eficaz conjuntos de datos grandes y cambiantes. Practicas FastAPI Backend Development Bootcamp con código real que ejecutas directamente en el navegador, y un tutor de IA 24/7 responde tus preguntas mientras trabajas en la lección.

¿Necesito experiencia previa para empezar FastAPI Backend Development Bootcamp?

No se requiere experiencia previa. FastAPI Backend Development Bootcamp en CoddyKit está estructurado para principiantes hasta estudiantes avanzados, así que puedes empezar aquí o desde el inicio y avanzar a tu ritmo. Esta es la lección 2 de 4.

¿Cuánto tiempo toma la lección «Paginación por cursor frente a paginación por offset a gran escala»?

La mayoría de las lecciones de CoddyKit toman alrededor de 5–10 minutos. Cada una es compacta e interactiva, así que avanzas constantemente y retomas exactamente por donde dejaste en la web y la app.

¿Puedo escribir y ejecutar código en esta lección de FastAPI Backend Development Bootcamp?

Sí. Cada lección de FastAPI Backend Development Bootcamp incluye un editor de código integrado, así que escribes y ejecutas código real directamente en tu navegador y obtienes retroalimentación instantánea de IA — sin configuración local necesaria.

Todas las lecciones de este curso

Versionado mediante URL, headers y tipos de medios
Paginación por cursor frente a paginación por offset a gran escala
Parámetros dinámicos de filtrado y ordenación
Diseño de envelopes de respuesta estables

← Volver a FastAPI Backend Development Bootcamp