MongoDB is a document-oriented NoSQL database used for high volume data storage. Instead of using tables and rows as in traditional relational databases, MongoDB uses collections and documents with a JSON-like structure.
MongoDB Anti-Patterns Overview
MongoDB, despite its flexibility and scalability, has several common anti-patterns that can lead to performance issues, maintenance problems, and data inconsistency. Here are the most important anti-patterns to avoid when working with MongoDB.
Not Using Proper Indexing
Not using proper indexes leads to full collection scans and poor query performance. Create indexes based on your query patterns and use explain()
to verify that your queries are using indexes efficiently.
Using Deeply Nested Documents
Deeply nested documents are difficult to query and update. Limit nesting to 2-3 levels and consider flattening your document structure for better performance and maintainability.
Using Massive Arrays
Massive arrays in documents can lead to performance issues and hit the 16MB document size limit. Use separate collections for one-to-many relationships with high cardinality.
Not Using Aggregation Framework
Using multiple queries and processing data in application code is inefficient. Use MongoDB’s aggregation framework to process data on the server side, reducing network traffic and improving performance.
Not Using Transactions When Needed
Not using transactions for operations that need to be atomic can lead to data inconsistency. Use transactions when you need to ensure that multiple operations succeed or fail as a unit.
Not Using Schema Validation
Not using schema validation can lead to inconsistent data. Use MongoDB’s schema validation to enforce data integrity and structure.
Using $where Queries
Using $where
queries is slow and can be a security risk (potential for injection attacks). Use standard MongoDB query operators instead.
Not Using Proper Connection Pooling
Creating new connections for each operation is inefficient. Use connection pooling to reuse connections and improve performance.
Not Handling Write Concerns Properly
Not specifying write concerns can lead to data loss in case of failures. Use appropriate write concerns based on your application’s durability requirements.
Not Using Projection in Queries
Retrieving entire documents when only a few fields are needed wastes bandwidth and memory. Use projection to retrieve only the fields you need.
Not Using Change Streams for Real-time Updates
Polling for changes is inefficient and can miss updates. Use change streams for real-time notifications of database changes.
Not Using Appropriate Data Types
Using inappropriate data types makes queries and aggregations more complex and less efficient. Use the appropriate BSON data types for your data.