Flask API Pagination and Filtering for Large Datasets

 As your Flask application grows, dealing with large datasets becomes inevitable. Whether you're building an e-commerce platform, a blogging site, or a data dashboard, returning thousands of records in a single API response is inefficient and can drastically degrade performance. That’s where pagination and filtering come in — they help deliver data in manageable chunks and give users control over what they want to see.

In this blog, we’ll explore how to implement pagination and filtering in a Flask API, making your endpoints more scalable and user-friendly.

🚀 Why Pagination and Filtering Matter

Performance: Reduces the load on both the server and the client.

User Experience: Enables faster UI rendering and better navigation.

Scalability: Helps the backend handle large datasets efficiently.

Control: Empowers users to search and narrow down results.

🔧 Setting Up the Flask API

Let’s assume you have a simple Flask app with a SQLAlchemy model:

python

from flask import Flask, request, jsonify

from flask_sqlalchemy import SQLAlchemy

app = Flask(__name__)

app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///products.db'

db = SQLAlchemy(app)

class Product(db.Model):

    id = db.Column(db.Integer, primary_key=True)

    name = db.Column(db.String(100))

    category = db.Column(db.String(50))

    price = db.Column(db.Float)

📄 Implementing Pagination

Pagination involves fetching only a specific number of records per request using limit and offset.

python

@app.route('/products', methods=['GET'])

def get_products():

    page = request.args.get('page', 1, type=int)

    per_page = request.args.get('per_page', 10, type=int)

    products = Product.query.paginate(page=page, per_page=per_page, error_out=False)

    data = [{

        'id': p.id,

        'name': p.name,

        'category': p.category,

        'price': p.price

    } for p in products.items]

    return jsonify({

        'total': products.total,

        'pages': products.pages,

        'current_page': products.page,

        'per_page': products.per_page,

        'data': data

    })

This endpoint:

Fetches products in pages.

Returns total items, total pages, and current page details.

Keeps responses light and easy to load.

🔍 Adding Filtering Options

Let’s extend the same endpoint to support filtering by category and price:

python

@app.route('/products', methods=['GET'])

def get_filtered_products():

    page = request.args.get('page', 1, type=int)

    per_page = request.args.get('per_page', 10, type=int)

    category = request.args.get('category')

    min_price = request.args.get('min_price', type=float)

    max_price = request.args.get('max_price', type=float)

    query = Product.query

    if category:

        query = query.filter_by(category=category)

    if min_price is not None:

        query = query.filter(Product.price >= min_price)

    if max_price is not None:

        query = query.filter(Product.price <= max_price)

    products = query.paginate(page=page, per_page=per_page, error_out=False)

    data = [{

        'id': p.id,

        'name': p.name,

        'category': p.category,

        'price': p.price

    } for p in products.items]

    return jsonify({

        'total': products.total,

        'pages': products.pages,

        'current_page': products.page,

        'per_page': products.per_page,

        'data': data

    })

Now users can:

Paginate results (?page=2&per_page=5)

Filter by category (?category=Electronics)

Filter by price range (?min_price=100&max_price=500)

✅ Best Practices

Set sensible defaults for pagination to avoid overwhelming your database.

Limit per_page to prevent abuse (e.g., max 100 records per page).

Combine pagination with sorting to maintain consistent results.

Document your filters in the API docs so frontend developers can use them effectively.

🧠 Final Thoughts

Implementing pagination and filtering in your Flask APIs is a smart way to handle large datasets efficiently. It improves performance, scalability, and usability—essential for any professional-grade backend system.

Start small, implement basic pagination, and gradually add filters based on your users’ needs. Combined with proper indexing and query optimization, these techniques will make your APIs faster and more user-centric.

Learn FullStack Python Training

Read More : Implementing Rate Limiting in Flask APIs with Flask-Limiter

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