Collections & Documents Best Practices in MongoDB

MongoDB is a flexible NoSQL database designed for high performance, scalability, and ease of development. However, to get the best performance and maintainability out of MongoDB, it’s important to follow best practices when designing collections and documents. Below are some key best practices related to collections and documents.

1. Design Schema According to Application Needs

MongoDB is schema-less, meaning you don’t have to define a schema before storing data. However, designing a logical schema that suits your application’s needs is essential for optimal performance and easier data management.

• Embedded Documents vs. References:
  • Embedded Documents: When related data is often queried together, embedding documents is a good choice. It reduces the number of queries and improves performance.
    • Example: A blog post document with embedded comments.
  • References: When the related data changes frequently or is too large to be embedded, references are preferred. References help maintain data consistency.
    • Example: A product collection with references to the manufacturer.

2. Limit the Use of Arrays for Large Data

Arrays in MongoDB allow you to store multiple values within a single field. While arrays can be useful, they should be used wisely, especially for large data sets.

• Limit Array Size: MongoDB has a maximum document size of 16MB, so storing large arrays can quickly lead to oversized documents. If you expect a large number of items to be stored, consider using a separate collection or breaking the data into smaller, more manageable pieces.
• Use Sparse Arrays: If you only expect some documents to have array elements, use sparse arrays or the $exists operator to query only those documents that contain the array.

3. Indexing Strategies

Indexes are crucial in MongoDB for optimizing queries, but they come with a trade-off: they consume additional disk space and can slow down write operations. Therefore, indexing should be done carefully.

• Indexing Common Query Fields: For fields that are frequently queried, it’s important to create indexes to speed up search operations.
  • Example: Index fields used in find() queries or range-based searches.
• Compound Indexes: If your queries involve multiple fields, create compound indexes to improve query performance.
  • Example: For a query db.users.find({ name: "John", age: 25 }), a compound index on name and age would be beneficial.
• Ensure Indexes on Foreign Keys: For reference-based documents, ensure that foreign keys (referenced fields) are indexed to speed up lookups.

4. Avoid Storing Large Binary Data in Documents

While MongoDB allows you to store binary data, such as images and videos, directly within documents (using BinData type), it’s often better to store large binary objects elsewhere.

• Use GridFS for Large Files: MongoDB’s GridFS is a specification for storing and retrieving large files that exceed the 16MB limit of a single document. If you’re storing large files, use GridFS to split them into smaller chunks and store them in separate collections.

5. Maintain Consistent Document Structure

Even though MongoDB is schema-less, it is important to maintain a consistent structure for documents within a collection. This helps ensure that your queries are efficient and consistent.

• Consistency in Field Names: Avoid using inconsistent or misspelled field names within a collection. This ensures easier querying and better data integrity.
• Data Types: Make sure that fields in your documents have consistent data types. For example, if a field stores dates, ensure all entries are stored as Date objects rather than strings.

6. Use Document Size Efficiently

MongoDB has a maximum document size of 16MB. While this is a large limit, it’s essential to design your documents to be as small and efficient as possible to avoid performance bottlenecks.

• Avoid Storing Excessive Data: If you’re storing large documents, evaluate if you can break them down into smaller documents or use references instead of embedding everything in a single document.
• Use the projection Query Operator: To reduce the amount of data returned by queries, use projection to only return the fields you need.

7. Design for High Availability and Sharding

If you’re designing a system that requires horizontal scaling, sharding and high availability are critical aspects of your collection design.

• Sharding: Design your collections for sharding by choosing a shard key that ensures an even distribution of data across all shards.
  • Sharding Key Considerations: Choose a shard key that is frequently used in queries to maximize performance. Avoid high cardinality (fields with too many unique values) as shard keys unless it’s required.
  • Example: Using a user’s region as the shard key in a multi-region application.
• Replica Sets: Ensure high availability by setting up MongoDB replica sets. This ensures that data is replicated across multiple servers, improving data redundancy and fault tolerance.

8. Use Data Validation and Schema Enforcement

Although MongoDB is schema-less, you can define validation rules to enforce a structure and ensure data consistency. MongoDB 3.2 and above allows for document validation rules.

• JSON Schema Validation: Use MongoDB’s built-in JSON Schema validation to enforce rules for the data structure in a collection. bashCopyEditdb.createCollection("users", { validator: { $jsonSchema: { bsonType: "object", required: ["name", "email"], properties: { name: { bsonType: "string" }, email: { bsonType: "string" } } } } })

9. Use Proper Naming Conventions

Use descriptive and consistent naming conventions for your collections and document fields. This enhances readability and maintainability.

• Collection Names: Name collections based on the type of data they store (e.g., users, orders, products).
• Field Names: Use camelCase for field names (e.g., firstName, lastName) or snake_case if required (e.g., first_name, last_name).

10. Be Cautious with Aggregations on Large Datasets

MongoDB’s aggregation framework is powerful, but for large datasets, aggregation queries can be resource-intensive.

• Indexing Before Aggregations: Ensure that the fields used in aggregation queries are indexed to optimize performance.
• Limit the Pipeline: Always apply filters as early as possible in the aggregation pipeline to limit the number of documents processed.
• Avoid Large Sorting: Sorting large datasets can be slow. Use pagination or limit the results before performing sorting.

11. Monitoring and Backup

• Monitor Collections: Regularly monitor the size of your collections and indexes. MongoDB provides built-in tools such as db.stats() to check the size of collections and other metrics.
• Backup Strategies: Implement regular backups for critical data using mongodump or other automated solutions. Also, use mongorestore for restoring data when needed.

12. Document References in Embedding Relationships

Sometimes embedding can create issues with large documents or deeply nested relationships. When this happens, use document references instead of embedding entire documents.

• Document Reference Example: If a blog post references an author, store just the author’s _id in the blog post document rather than embedding the entire author object. jsonCopyEdit{ "_id": ObjectId("123"), "title": "Sample Blog Post", "author_id": ObjectId("456") }

Conclusion

Following these best practices when designing collections and documents in MongoDB can help you ensure your application remains scalable, efficient, and maintainable. MongoDB’s flexibility allows for rapid development, but careful attention to schema design, indexing, and data management practices is necessary to prevent issues as your application grows.