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How to Connect a Django App to MongoDB with PyMongo
MongoDB’s document-based storage model paired with Django’s ORM-free approach creates a powerful combination for building scalable applications that need flexible schema management. While Django traditionally works with relational databases through its ORM, PyMongo provides a direct bridge to MongoDB’s rich document operations, enabling developers to leverage both Django’s web framework capabilities and MongoDB’s horizontal scaling features. This guide walks through the complete setup process, from initial connection configuration to production deployment strategies, along with common gotchas that can trip up even experienced developers.
Understanding PyMongo vs Django ORM Integration
Before diving into implementation, it’s worth understanding how PyMongo fits into Django’s architecture. Unlike traditional Django database backends, PyMongo bypasses Django’s ORM entirely, giving you direct access to MongoDB’s native query language and aggregation framework.
| Feature | Django ORM (Traditional) | PyMongo Integration |
|---|---|---|
| Schema Management | Fixed schema with migrations | Dynamic schema, no migrations needed |
| Query Language | Django QuerySet API | MongoDB native queries |
| Relationships | Foreign keys, automatic joins | Manual reference handling |
| Performance | N+1 query issues common | Efficient aggregation pipelines |
| Admin Interface | Built-in Django admin | Custom admin implementation required |
Installation and Basic Setup
Start by installing the required packages. PyMongo is MongoDB’s official Python driver, while dnspython handles MongoDB Atlas connection strings if you’re using a cloud deployment.
pip install pymongo dnspython
Create a database configuration file to centralize your MongoDB connection settings. This approach separates database logic from your Django settings and makes testing easier.
# db_config.py
import os
from pymongo import MongoClient
from pymongo.errors import ConnectionFailure
class MongoDBConnection:
_instance = None
_client = None
_database = None
def __new__(cls):
if cls._instance is None:
cls._instance = super().__new__(cls)
return cls._instance
def __init__(self):
if self._client is None:
self.connect()
def connect(self):
try:
# Local MongoDB
if os.getenv('MONGODB_LOCAL', 'False').lower() == 'true':
self._client = MongoClient('mongodb://localhost:27017/')
else:
# MongoDB Atlas or remote instance
connection_string = os.getenv('MONGODB_URI',
'mongodb://localhost:27017/')
self._client = MongoClient(connection_string)
# Test the connection
self._client.admin.command('ismaster')
self._database = self._client[os.getenv('MONGODB_DATABASE', 'myapp')]
print("MongoDB connection established successfully")
except ConnectionFailure as e:
print(f"Failed to connect to MongoDB: {e}")
raise
def get_database(self):
return self._database
def get_collection(self, collection_name):
return self._database[collection_name]
# Global instance
mongodb = MongoDBConnection()
Add the MongoDB configuration to your Django settings file. This keeps environment-specific settings separate from your connection logic.
# settings.py
import os
from dotenv import load_dotenv
load_dotenv()
# Your existing Django settings...
# MongoDB Configuration
MONGODB_SETTINGS = {
'URI': os.getenv('MONGODB_URI', 'mongodb://localhost:27017/'),
'DATABASE': os.getenv('MONGODB_DATABASE', 'myapp'),
'OPTIONS': {
'connectTimeoutMS': 30000,
'socketTimeoutMS': 30000,
'serverSelectionTimeoutMS': 30000,
'maxPoolSize': 50,
'minPoolSize': 5,
}
}
Creating MongoDB Models with PyMongo
Since you’re not using Django’s ORM, you’ll need to create your own model abstraction. This approach gives you the flexibility of MongoDB while maintaining some structure in your application.
# models/base.py
from bson import ObjectId
from datetime import datetime
from typing import Dict, Any, Optional
from db_config import mongodb
class MongoModel:
collection_name = None
def __init__(self, **kwargs):
self.data = kwargs
if '_id' not in self.data:
self.data['_id'] = None
if 'created_at' not in self.data:
self.data['created_at'] = datetime.utcnow()
self.data['updated_at'] = datetime.utcnow()
@classmethod
def get_collection(cls):
if not cls.collection_name:
raise ValueError("collection_name must be defined")
return mongodb.get_collection(cls.collection_name)
def save(self):
collection = self.get_collection()
self.data['updated_at'] = datetime.utcnow()
if self.data['_id']:
# Update existing document
result = collection.update_one(
{'_id': self.data['_id']},
{'$set': self.data}
)
return result.modified_count > 0
else:
# Insert new document
result = collection.insert_one(self.data)
self.data['_id'] = result.inserted_id
return True
def delete(self):
if not self.data.get('_id'):
return False
collection = self.get_collection()
result = collection.delete_one({'_id': self.data['_id']})
return result.deleted_count > 0
@classmethod
def find_by_id(cls, object_id):
collection = cls.get_collection()
if isinstance(object_id, str):
object_id = ObjectId(object_id)
result = collection.find_one({'_id': object_id})
return cls(**result) if result else None
@classmethod
def find(cls, query: Dict[str, Any] = None, **kwargs):
collection = cls.get_collection()
query = query or {}
cursor = collection.find(query, **kwargs)
return [cls(**doc) for doc in cursor]
def to_dict(self):
return self.data.copy()
Now create specific models for your application. This example shows a user model with common operations you’d need in a real application.
# models/user.py
from .base import MongoModel
from werkzeug.security import generate_password_hash, check_password_hash
from bson import ObjectId
class User(MongoModel):
collection_name = 'users'
def __init__(self, **kwargs):
super().__init__(**kwargs)
if 'password' in kwargs and not kwargs['password'].startswith('pbkdf2:'):
self.set_password(kwargs['password'])
def set_password(self, password):
self.data['password'] = generate_password_hash(password)
def check_password(self, password):
return check_password_hash(self.data.get('password', ''), password)
@classmethod
def find_by_email(cls, email):
collection = cls.get_collection()
result = collection.find_one({'email': email})
return cls(**result) if result else None
@classmethod
def create_user(cls, email, password, **extra_fields):
existing_user = cls.find_by_email(email)
if existing_user:
raise ValueError("User with this email already exists")
user_data = {
'email': email,
'password': password,
'is_active': True,
**extra_fields
}
user = cls(**user_data)
user.save()
return user
def get_posts(self):
from .post import Post
return Post.find({'author_id': self.data['_id']})
Integrating with Django Views
Here’s how to use your MongoDB models within Django views. This approach maintains Django’s request/response pattern while leveraging MongoDB’s document capabilities.
# views.py
from django.http import JsonResponse
from django.views.decorators.csrf import csrf_exempt
from django.views.decorators.http import require_http_methods
import json
from bson import ObjectId
from models.user import User
from models.post import Post
class JSONEncoder(json.JSONEncoder):
def default(self, obj):
if isinstance(obj, ObjectId):
return str(obj)
if isinstance(obj, datetime):
return obj.isoformat()
return super().default(obj)
@csrf_exempt
@require_http_methods(["POST"])
def create_user(request):
try:
data = json.loads(request.body)
user = User.create_user(
email=data['email'],
password=data['password'],
first_name=data.get('first_name', ''),
last_name=data.get('last_name', '')
)
return JsonResponse({
'success': True,
'user_id': str(user.data['_id']),
'message': 'User created successfully'
})
except ValueError as e:
return JsonResponse({
'success': False,
'error': str(e)
}, status=400)
except Exception as e:
return JsonResponse({
'success': False,
'error': 'Internal server error'
}, status=500)
def get_user(request, user_id):
try:
user = User.find_by_id(user_id)
if not user:
return JsonResponse({
'success': False,
'error': 'User not found'
}, status=404)
user_data = user.to_dict()
# Remove sensitive information
user_data.pop('password', None)
return JsonResponse({
'success': True,
'user': user_data
}, cls=JSONEncoder)
except Exception as e:
return JsonResponse({
'success': False,
'error': 'Internal server error'
}, status=500)
def user_dashboard(request, user_id):
try:
user = User.find_by_id(user_id)
if not user:
return JsonResponse({'error': 'User not found'}, status=404)
# Get user's posts with aggregation
from db_config import mongodb
pipeline = [
{'$match': {'author_id': ObjectId(user_id)}},
{'$lookup': {
'from': 'comments',
'localField': '_id',
'foreignField': 'post_id',
'as': 'comments'
}},
{'$addFields': {
'comment_count': {'$size': '$comments'}
}},
{'$project': {
'title': 1,
'content': 1,
'created_at': 1,
'comment_count': 1
}},
{'$sort': {'created_at': -1}}
]
posts = list(mongodb.get_collection('posts').aggregate(pipeline))
return JsonResponse({
'user': user.to_dict(),
'posts': posts,
'total_posts': len(posts)
}, cls=JSONEncoder)
except Exception as e:
return JsonResponse({'error': str(e)}, status=500)
Advanced Query Patterns and Aggregation
MongoDB’s aggregation framework is one of its strongest features. Here are some practical examples that showcase capabilities you can’t easily replicate with traditional SQL databases.
# utils/queries.py
from db_config import mongodb
from bson import ObjectId
from datetime import datetime, timedelta
class AdvancedQueries:
@staticmethod
def get_user_analytics(user_id, days=30):
"""Get comprehensive user analytics using aggregation pipeline"""
pipeline = [
{'$match': {
'author_id': ObjectId(user_id),
'created_at': {'$gte': datetime.utcnow() - timedelta(days=days)}
}},
{'$lookup': {
'from': 'comments',
'localField': '_id',
'foreignField': 'post_id',
'as': 'comments'
}},
{'$lookup': {
'from': 'likes',
'localField': '_id',
'foreignField': 'post_id',
'as': 'likes'
}},
{'$group': {
'_id': None,
'total_posts': {'$sum': 1},
'total_comments': {'$sum': {'$size': '$comments'}},
'total_likes': {'$sum': {'$size': '$likes'}},
'avg_comments_per_post': {'$avg': {'$size': '$comments'}},
'posts_by_day': {
'$push': {
'date': {'$dateToString': {
'format': '%Y-%m-%d',
'date': '$created_at'
}},
'title': '$title'
}
}
}},
{'$project': {
'_id': 0,
'total_posts': 1,
'total_comments': 1,
'total_likes': 1,
'avg_comments_per_post': {'$round': ['$avg_comments_per_post', 2]},
'engagement_rate': {
'$round': [
{'$divide': [
{'$add': ['$total_comments', '$total_likes']},
'$total_posts'
]}, 2
]
}
}}
]
result = list(mongodb.get_collection('posts').aggregate(pipeline))
return result[0] if result else {}
@staticmethod
def search_posts(query, limit=20, skip=0):
"""Full-text search with relevance scoring"""
pipeline = [
{'$match': {'$text': {'$search': query}}},
{'$addFields': {
'score': {'$meta': 'textScore'}
}},
{'$lookup': {
'from': 'users',
'localField': 'author_id',
'foreignField': '_id',
'as': 'author'
}},
{'$unwind': '$author'},
{'$project': {
'title': 1,
'content': 1,
'created_at': 1,
'score': 1,
'author': {
'name': {'$concat': ['$author.first_name', ' ', '$author.last_name']},
'email': '$author.email'
}
}},
{'$sort': {'score': {'$meta': 'textScore'}}},
{'$skip': skip},
{'$limit': limit}
]
return list(mongodb.get_collection('posts').aggregate(pipeline))
@staticmethod
def get_trending_topics(hours=24, limit=10):
"""Find trending topics based on recent activity"""
pipeline = [
{'$match': {
'created_at': {'$gte': datetime.utcnow() - timedelta(hours=hours)}
}},
{'$unwind': '$tags'},
{'$group': {
'_id': '$tags',
'count': {'$sum': 1},
'recent_posts': {'$push': {
'title': '$title',
'created_at': '$created_at'
}}
}},
{'$sort': {'count': -1}},
{'$limit': limit},
{'$project': {
'tag': '$_id',
'count': 1,
'sample_posts': {'$slice': ['$recent_posts', 3]},
'_id': 0
}}
]
return list(mongodb.get_collection('posts').aggregate(pipeline))
Performance Optimization and Indexing
Proper indexing is crucial for MongoDB performance. Here’s how to create and manage indexes programmatically from your Django application.
# management/commands/create_indexes.py
from django.core.management.base import BaseCommand
from db_config import mongodb
import pymongo
class Command(BaseCommand):
help = 'Create MongoDB indexes for optimal performance'
def handle(self, *args, **options):
db = mongodb.get_database()
# Users collection indexes
users = db.users
users.create_index('email', unique=True)
users.create_index([('first_name', pymongo.TEXT), ('last_name', pymongo.TEXT)])
users.create_index('created_at')
# Posts collection indexes
posts = db.posts
posts.create_index('author_id')
posts.create_index([('title', pymongo.TEXT), ('content', pymongo.TEXT)])
posts.create_index([('created_at', -1)]) # Descending for recent posts
posts.create_index('tags') # For array field indexing
posts.create_index([('author_id', 1), ('created_at', -1)]) # Compound index
# Comments collection indexes
comments = db.comments
comments.create_index('post_id')
comments.create_index([('post_id', 1), ('created_at', -1)])
# Geospatial index example (if you have location data)
# posts.create_index([('location', pymongo.GEOSPHERE)])
self.stdout.write(
self.style.SUCCESS('Successfully created all indexes')
)
# Show index usage statistics
self.show_index_stats()
def show_index_stats(self):
db = mongodb.get_database()
for collection_name in ['users', 'posts', 'comments']:
collection = db[collection_name]
indexes = collection.list_indexes()
self.stdout.write(f"\n{collection_name.upper()} INDEXES:")
for index in indexes:
self.stdout.write(f" - {index['name']}: {index.get('key', {})}")
Monitor query performance with this utility class that helps identify slow operations:
# utils/performance.py
import time
from functools import wraps
from db_config import mongodb
import logging
logger = logging.getLogger(__name__)
class QueryProfiler:
def __init__(self, slow_threshold=100): # milliseconds
self.slow_threshold = slow_threshold
def profile_query(self, operation_name="Unknown"):
def decorator(func):
@wraps(func)
def wrapper(*args, **kwargs):
start_time = time.time()
result = func(*args, **kwargs)
execution_time = (time.time() - start_time) * 1000
if execution_time > self.slow_threshold:
logger.warning(
f"Slow query detected: {operation_name} "
f"took {execution_time:.2f}ms"
)
return result
return wrapper
return decorator
@staticmethod
def explain_query(collection_name, query, operation="find"):
"""Analyze query execution plan"""
collection = mongodb.get_collection(collection_name)
if operation == "find":
explanation = collection.find(query).explain()
elif operation == "aggregate":
explanation = collection.aggregate(query, explain=True)
return {
'execution_stats': explanation.get('executionStats', {}),
'index_used': explanation.get('queryPlanner', {}).get('winningPlan', {}),
'total_docs_examined': explanation.get('executionStats', {}).get('totalDocsExamined', 0),
'total_docs_returned': explanation.get('executionStats', {}).get('totalDocsReturned', 0)
}
# Usage example
profiler = QueryProfiler(slow_threshold=50)
@profiler.profile_query("User search")
def search_users(email_pattern):
return User.find({'email': {'$regex': email_pattern, '$options': 'i'}})
Error Handling and Connection Management
Robust error handling is essential when working with MongoDB, especially in production environments. Here’s a comprehensive approach to handle common issues:
# utils/error_handling.py
from pymongo.errors import (
ConnectionFailure, ServerSelectionTimeoutError,
DuplicateKeyError, BulkWriteError,
NetworkTimeout, ExecutionTimeout
)
from functools import wraps
import logging
import time
logger = logging.getLogger(__name__)
class MongoErrorHandler:
@staticmethod
def retry_on_failure(max_retries=3, delay=1):
def decorator(func):
@wraps(func)
def wrapper(*args, **kwargs):
last_exception = None
for attempt in range(max_retries):
try:
return func(*args, **kwargs)
except (ConnectionFailure, ServerSelectionTimeoutError, NetworkTimeout) as e:
last_exception = e
if attempt < max_retries - 1:
logger.warning(
f"MongoDB connection failed (attempt {attempt + 1}): {e}. "
f"Retrying in {delay} seconds..."
)
time.sleep(delay)
delay *= 2 # Exponential backoff
continue
except DuplicateKeyError as e:
logger.error(f"Duplicate key error: {e}")
raise ValueError("Record with this data already exists")
except ExecutionTimeout as e:
logger.error(f"Query timeout: {e}")
raise TimeoutError("Database operation timed out")
# If we get here, all retries failed
logger.error(f"All {max_retries} attempts failed. Last error: {last_exception}")
raise last_exception
return wrapper
return decorator
@staticmethod
def handle_bulk_errors(bulk_result):
"""Process bulk operation results and handle partial failures"""
if bulk_result.bulk_api_result.get('writeErrors'):
errors = bulk_result.bulk_api_result['writeErrors']
logger.error(f"Bulk operation had {len(errors)} errors:")
for error in errors:
logger.error(f" Index {error['index']}: {error['errmsg']}")
return {
'inserted': bulk_result.inserted_count,
'modified': bulk_result.modified_count,
'deleted': bulk_result.deleted_count,
'errors': len(bulk_result.bulk_api_result.get('writeErrors', []))
}
# Enhanced model with error handling
class RobustUser(User):
@MongoErrorHandler.retry_on_failure(max_retries=3)
def save(self):
return super().save()
@classmethod
@MongoErrorHandler.retry_on_failure(max_retries=2)
def bulk_create_users(cls, user_data_list):
"""Create multiple users with error handling"""
from pymongo import InsertOne
collection = cls.get_collection()
operations = []
for user_data in user_data_list:
user = cls(**user_data)
operations.append(InsertOne(user.to_dict()))
try:
result = collection.bulk_write(operations, ordered=False)
return MongoErrorHandler.handle_bulk_errors(result)
except BulkWriteError as bwe:
logger.error(f"Bulk write error: {bwe.details}")
return {
'inserted': bwe.details.get('nInserted', 0),
'errors': len(bwe.details.get('writeErrors', []))
}
Production Deployment Considerations
When deploying to production, especially on VPS or dedicated servers, you'll need to consider several MongoDB-specific configurations:
# production_settings.py
import os
# Production MongoDB settings
MONGODB_PRODUCTION_CONFIG = {
'URI': os.getenv('MONGODB_URI'),
'DATABASE': os.getenv('MONGODB_DATABASE'),
'OPTIONS': {
# Connection pool settings
'maxPoolSize': 100,
'minPoolSize': 10,
'maxIdleTimeMS': 30000,
'waitQueueTimeoutMS': 5000,
# Timeout settings
'connectTimeoutMS': 20000,
'socketTimeoutMS': 20000,
'serverSelectionTimeoutMS': 30000,
# Write concern for data consistency
'w': 'majority',
'wtimeout': 5000,
'j': True, # Journal acknowledgment
# Read preference for replica sets
'readPreference': 'secondaryPreferred',
'readConcernLevel': 'majority',
# SSL/TLS settings
'ssl': True,
'ssl_cert_reqs': 'CERT_REQUIRED',
'ssl_ca_certs': '/path/to/ca-certificates.crt',
# Authentication
'authSource': 'admin',
'authMechanism': 'SCRAM-SHA-256',
}
}
# Health check endpoint
def mongodb_health_check():
"""Check MongoDB connection health for monitoring"""
try:
from db_config import mongodb
db = mongodb.get_database()
# Ping the database
ping_result = db.command('ping')
# Get basic stats
stats = db.command('dbstats')
return {
'status': 'healthy',
'ping': ping_result,
'database': stats['db'],
'collections': stats['collections'],
'data_size': stats['dataSize'],
'storage_size': stats['storageSize'],
'connection_count': db.command('serverStatus')['connections']['current']
}
except Exception as e:
return {
'status': 'unhealthy',
'error': str(e)
}
Common Pitfalls and Troubleshooting
Here are the most frequent issues developers encounter when integrating MongoDB with Django, along with solutions:
- ObjectId JSON Serialization: Django's default JSON encoder can't handle MongoDB's ObjectId. Always use a custom encoder or convert to strings explicitly.
- Connection Pooling Issues: Not configuring proper connection limits can lead to "too many connections" errors under load. Set maxPoolSize appropriately for your server capacity.
- Index Forgetting: Unlike Django migrations, MongoDB indexes aren't automatically created. Implement a management command to ensure indexes exist in all environments.
- Schema Validation Absence: Without Django's model validation, bad data can easily enter your database. Implement validation at the application level.
- Transaction Confusion: MongoDB transactions work differently than SQL. They're only available on replica sets and have specific limitations around multi-document operations.
Here's a debugging utility to help diagnose common issues:
# utils/debug.py
from db_config import mongodb
from bson import ObjectId
import json
class MongoDebugger:
@staticmethod
def diagnose_connection():
"""Comprehensive connection diagnosis"""
try:
db = mongodb.get_database()
# Test basic connectivity
ping = db.command('ping')
# Get server info
server_info = db.command('buildInfo')
# Check current operations
current_ops = db.command('currentOp')
# Get database stats
stats = db.command('dbStats')
return {
'connection': 'OK',
'server_version': server_info['version'],
'active_operations': len(current_ops.get('inprog', [])),
'database_size_mb': round(stats['dataSize'] / 1024 / 1024, 2),
'collections': stats['collections']
}
except Exception as e:
return {'connection': 'FAILED', 'error': str(e)}
@staticmethod
def analyze_slow_queries(threshold_ms=100):
"""Find operations slower than threshold"""
try:
db = mongodb.get_database()
# Enable profiling for slow operations
db.set_profiling_level(1, slow_ms=threshold_ms)
# Get slow queries from profiler collection
slow_queries = list(db.system.profile.find().sort('ts', -1).limit(10))
return {
'slow_queries_found': len(slow_queries),
'queries': [
{
'duration_ms': query.get('millis', 0),
'operation': query.get('op', 'unknown'),
'collection': query.get('ns', '').split('.')[-1],
'command': str(query.get('command', {}))[:100]
}
for query in slow_queries
]
}
except Exception as e:
return {'error': str(e)}
@staticmethod
def validate_indexes(collection_name):
"""Check if proper indexes exist for a collection"""
try:
collection = mongodb.get_collection(collection_name)
indexes = list(collection.list_indexes())
index_info = []
for index in indexes:
stats = collection.index_information()[index['name']]
index_info.append({
'name': index['name'],
'keys': index['key'],
'unique': index.get('unique', False),
'sparse': index.get('sparse', False)
})
return {
'collection': collection_name,
'total_indexes': len(index_info),
'indexes': index_info
}
except Exception as e:
return {'error': str(e)}
This integration approach gives you the best of both worlds: Django's robust web framework capabilities with MongoDB's flexible document storage and powerful aggregation features. The key to success is proper error handling, thoughtful indexing, and maintaining clear separation between your database logic and Django's framework components. Remember to monitor query performance regularly and adjust your indexing strategy based on actual usage patterns in production.
For additional reference, check out the official PyMongo documentation and MongoDB Manual for detailed information about advanced features and best practices.
This article incorporates information and material from various online sources. We acknowledge and appreciate the work of all original authors, publishers, and websites. While every effort has been made to appropriately credit the source material, any unintentional oversight or omission does not constitute a copyright infringement. All trademarks, logos, and images mentioned are the property of their respective owners. If you believe that any content used in this article infringes upon your copyright, please contact us immediately for review and prompt action.
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