pip install bson: Your Complete Python BSON Guide

1. MongoDB Database Operations

The most common use case - connecting to MongoDB and performing CRUD operations:

from pymongo import MongoClient

# Connect to MongoDB
client = MongoClient('mongodb://localhost:27017/')
db = client['myapp']
users = db['users']

# Insert document (automatically converted to BSON)
user = {
    "name": "Alice",
    "email": "[email protected]",
    "age": 28
}
result = users.insert_one(user)

# Query documents (BSON returned as Python dicts)
user = users.find_one({"name": "Alice"})
print(user)

2. File Storage and Serialization

Save Python objects to disk in BSON format for efficient storage:

from bson import encode, decode

# Save to file
with open('data.bson', 'wb') as f:
    bson_data = encode({"key": "value", "number": 42})
    f.write(bson_data)

# Load from file
with open('data.bson', 'rb') as f:
    bson_data = f.read()
    data = decode(bson_data)
    print(data)

3. API Data Exchange

Send and receive binary BSON data in APIs for better performance:

import requests
from bson import encode, decode

# Send BSON data
data = {"user": "john", "action": "login"}
response = requests.post(
    'https://api.example.com/data',
    data=encode(data),
    headers={'Content-Type': 'application/bson'}
)

# Receive and decode BSON response
result = decode(response.content)
print(result)

1. Use BSON for Large Data Transfers

BSON is more compact than JSON for binary data and can be 20-30% smaller for typical documents.

2. Batch Operations

When inserting multiple documents, use batch operations:

# Efficient - batch insert
collection.insert_many([doc1, doc2, doc3])

# Inefficient - multiple single inserts
collection.insert_one(doc1)
collection.insert_one(doc2)
collection.insert_one(doc3)

3. Limit Field Projections

Only retrieve the fields you need to reduce BSON decoding overhead and network transfer size.

Q: Should I use "pip install bson" or "pip install pymongo"?

A: Always use pip install pymongo. The standalone bson package is outdated and can cause conflicts. PyMongo includes all BSON functionality you need.

Q: Do I need MongoDB installed to use BSON in Python?

A: No! You can use PyMongo's BSON encoding/decoding features without a MongoDB server. Just from bson import encode, decode and you're ready to work with BSON data.

Q: Can I use BSON with Flask or Django?

A: Absolutely! PyMongo works great with Flask and Django. Many developers use MongoDB as their database backend. Check out Flask-PyMongo or Djongo for Django integration.

Q: How do I convert JSON to BSON in Python?

A: Use the json_util module from PyMongo:

from bson import json_util

json_str = '{"name": "John", "age": 30}'
bson_data = json_util.loads(json_str)

Or use our online JSON to BSON converter for quick conversions.

Q: What Python version do I need for PyMongo?

A: PyMongo 4.x requires Python 3.7 or later. If you're still on Python 2.7 (not recommended!), you'll need to use PyMongo 3.x. Always use the latest Python 3.x version for best results.

Q: Is BSON faster than JSON in Python?

A: For MongoDB operations, yes! BSON encoding/decoding is optimized for database operations and supports more data types natively. However, for web APIs, JSON is usually preferred because it's human-readable. Learn more in our BSON vs JSON comparison.

Q: How do I handle BSON files in Python?

A: Read and write BSON files using binary mode:

from bson import encode, decode

# Write
with open('data.bson', 'wb') as f:
    f.write(encode(data))

# Read
with open('data.bson', 'rb') as f:
    data = decode(f.read())