Does Flask Work With MongoDB?

Fully CompatibleLast verified: 2026-02-20

Flask and MongoDB work together seamlessly with minimal friction, making it a popular choice for building flexible, scalable web applications.

Quick Facts

Compatibility
full
Setup Difficulty
Easy
Official Integration
No — community maintained
Confidence
high
Minimum Versions
Flask: 1.0.0
MongoDB: 3.6

How Flask Works With MongoDB

Flask and MongoDB integrate beautifully through community libraries like PyMongo (official MongoDB driver) and MongoEngine (ODM layer). PyMongo provides direct database access with full control over queries, while MongoEngine adds schema validation and ORM-like convenience. Neither requires tight coupling—you simply instantiate a MongoDB client in your Flask app and use it in route handlers or service layers. The document-oriented nature of MongoDB aligns well with Flask's lightweight philosophy: no migrations, flexible schemas that evolve with your application, and JSON-native serialization that pairs naturally with Flask's JSON responses. Most developers use PyMongo for direct control or MongoEngine for schema management, depending on whether they value flexibility or structure. The main architectural consideration is connection pooling: Flask apps should reuse a single MongoDB client instance across requests rather than creating new connections, which you typically handle via Flask's application context or a singleton pattern.

Best Use Cases

Real-time content management systems with dynamic content types and unpredictable data structures
Rapid prototyping and MVP development where schema evolution matters more than strict contracts
IoT data collection platforms ingesting heterogeneous sensor data with varying schemas
Social media applications with user-generated content, comments, and feeds requiring flexible document structures

Quick Setup

bash
pip install flask pymongo
python
from flask import Flask, jsonify
from pymongo import MongoClient
from bson.objectid import ObjectId

app = Flask(__name__)
client = MongoClient('mongodb://localhost:27017/')
db = client['my_app']

@app.route('/users', methods=['GET'])
def get_users():
    users = list(db.users.find().limit(10))
    for user in users:
        user['_id'] = str(user['_id'])
    return jsonify(users)

@app.route('/users/<user_id>', methods=['GET'])
def get_user(user_id):
    user = db.users.find_one({'_id': ObjectId(user_id)})
    if user:
        user['_id'] = str(user['_id'])
        return jsonify(user)
    return {'error': 'Not found'}, 404

if __name__ == '__main__':
    app.run(debug=True)

Known Issues & Gotchas

critical

Connection pooling exhaustion under high concurrency if connections aren't properly managed

Fix: Store MongoDB client in Flask app context using app.config or a singleton; let PyMongo handle connection pooling automatically rather than creating multiple clients

warning

ObjectId serialization fails when converting MongoDB documents directly to JSON responses

Fix: Implement a custom JSON encoder or convert ObjectId to string: json.dumps(doc, default=str) or use a helper function in your response layer

warning

No built-in transaction support in older MongoDB versions, leading to consistency issues in multi-document updates

Fix: Use MongoDB 4.0+ for multi-document ACID transactions, or redesign schema to keep related data in single documents

warning

N+1 query problems when iterating over documents and fetching related data in loops

Fix: Use MongoDB aggregation pipeline or denormalization strategies; with MongoEngine, use .select_related() equivalent patterns

Alternatives

  • Django with MongoDB (django-mongodb-engine) - heavier framework with more built-in features
  • FastAPI with Motor (async MongoDB driver) - modern async alternative for high-performance APIs
  • Flask with PostgreSQL - relational structure with mature ORM ecosystem (SQLAlchemy)

Resources

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