Sling Academy
Home/Python/PyMongo bson.errors.InvalidDocument: Cannot encode object

PyMongo bson.errors.InvalidDocument: Cannot encode object

Last updated: February 10, 2024

The Problem

When working with PyMongo to perform operations in MongoDB databases, one common error encountered is bson.errors.InvalidDocument: Cannot encode object. This error occurs when PyMongo attempts to serialize an object that is not natively supported by BSON (Binary JSON), the format used for storing documents in MongoDB. This guide will walk through several solutions to overcome this error, allowing for effective database interactions.

Solution 1: Use Built-in BSON Types

The simplest solution is to ensure all the objects being inserted or updated in MongoDB are of types natively supported by BSON. Common BSON types include strings, integers, arrays, dictionaries (but with keys as strings), and others.

  • Step 1: Inspect the data structure to identify non-BSON types.
  • Step 2: Replace or convert these types into BSON-supported types.
  • Step 3: Retry the database operation.

Code example:

from datetime import datetime
document = {
  'title': 'Example',
  'creation_date': datetime.utcnow(),
  'tags': ['mongodb', 'pymongo'],
  'metadata': {
    'views': 100
  }
}
db.collection.insert_one(document)

Output:

None (Object is successfully inserted without errors)

Notes: This method is straightforward but may not always be feasible for complex objects or third-party types that do not have a direct BSON equivalent.

Solution 2: Custom Encoding

For objects that cannot be easily converted to BSON types, implementing a custom encoder that translates these objects to BSON-compliant structures before insertion can resolve the issue.

  • Step 1: Define a custom encoder function that handles non-BSON types.
  • Step 2: Use this function to transform your data before sending it to MongoDB.
  • Step 3: Perform the database operation.

Code example:

import json
from bson import json_util
def custom_encoder(my_object):
  if isinstance(my_object, CustomType):
    return {'_custom_type': True, 'value': my_object.value}
  return json_util.default(my_object)

document = {'custom_object': CustomType('example')}
db.collection.insert_one(json.loads(json.dumps(document, default=custom_encoder)))

Output:

None (Custom object inserted successfully)

Notes: While this solution offers flexibility, it introduces complexity and the potential for serialization errors if not implemented correctly. It is optimal for cases where the data structure cannot be easily simplified or replaced.

Solution 3: Use Third-party Libraries

Certain Python libraries are designed to help serialize complex Python objects to BSON. These can offer a more streamlined approach to handling incompatible types.

  • Step 1: Find and install a suitable third-party library that supports your object types.
  • Step 2: Utilize this library to serialize your data before interacting with the database.
  • Step 3: Proceed with the database operation.

Not applicable. Libraries will have their own usage documentation, and developers should refer to this for specific implementation details.

Notes: Leveraging third-party libraries can greatly reduce manual efforts in data serialization but adds external dependencies to your project, which may affect its maintainability and compatibility.

In summary, resolving the bson.errors.InvalidDocument: Cannot encode object error in PyMongo involves correctly identifying the cause of the serialization issue and applying suitable methods to ensure compatibility with BSON. Depending on the specific scenario, developers may choose to adjust data types, implement custom encoding, or leverage third-party libraries to facilitate seamless database operations.

Next Article: PyMongo: Using $and, $or, $not, $nor operators (6 examples)

Previous Article: PyMongo: How to create compound indexes

Series: Data Persistence in Python – Tutorials & Examples

Python

You May Also Like

  • Introduction to yfinance: Fetching Historical Stock Data in Python
  • Monitoring Volatility and Daily Averages Using cryptocompare
  • Advanced DOM Interactions: XPath and CSS Selectors in Playwright (Python)
  • Automating Strategy Updates and Version Control in freqtrade
  • Setting Up a freqtrade Dashboard for Real-Time Monitoring
  • Deploying freqtrade on a Cloud Server or Docker Environment
  • Optimizing Strategy Parameters with freqtrade’s Hyperopt
  • Risk Management: Setting Stop Loss, Trailing Stops, and ROI in freqtrade
  • Integrating freqtrade with TA-Lib and pandas-ta Indicators
  • Handling Multiple Pairs and Portfolios with freqtrade
  • Using freqtrade’s Backtesting and Hyperopt Modules
  • Developing Custom Trading Strategies for freqtrade
  • Debugging Common freqtrade Errors: Exchange Connectivity and More
  • Configuring freqtrade Bot Settings and Strategy Parameters
  • Installing freqtrade for Automated Crypto Trading in Python
  • Scaling cryptofeed for High-Frequency Trading Environments
  • Building a Real-Time Market Dashboard Using cryptofeed in Python
  • Customizing cryptofeed Callbacks for Advanced Market Insights
  • Integrating cryptofeed into Automated Trading Bots