Foreign keys in mongo?

sql

data-modeling

mongodb

database-design

byAlex Kataev·Aug 27, 2024

MongoDB does not have foreign keys per se. Instead, it uses manual references to establish relationships between documents across collections.

A typical example involves storing another document's ObjectID as a reference:

// User document in "users" collection
{ "_id": ObjectId("user123"), "name": "John Doe" }

// Order document in "orders" collection, featuring our friend John Doe
{ "product": "Widget", "quantity": 1, "user_id": ObjectId("user123") }

To simulate SQL-style joins, MongoDB offers $lookup:

db.orders.aggregate([
  { $lookup: {
    from: "users", 
    localField: "user_id", // John Doe's ID in users collection
    foreignField: "_id",  // John Doe's ID in orders collection
    as: "userDetails"     // The last puzzle piece
  }}
])

This technique permits relationship simulation but doesn't offer referential integrity. That's all on your application's shoulders, buddy.

In-depth data modeling strategies

When structuring your data model in MongoDB, you should consider several principles:

Denormalization: Your guide to drastically improving read performance and query efficiency since it involves embedding related information.
Manual references: For that special schema freedom, choose to manually reference documents using an array of ObjectIDs.
Denormalization vs. Referencing: It's a cat-and-mouse game! For one-to-few relationships, go for embedding documents. For one-to-many, manual referencing plays the role. Facing one-to-squillions relationships? Turn to parent referencing.
Write Operations: Without elaborating cascading updates or deletes, MongoDB hands these over to the application layer. Brace for some thrilling write-heavy environment adventures!
Schema Design: Pay heed to MongoDB's "6 Rules of Thumb" for a successful schema design strategy. The main takeaway: "Embedding is the new black, unless referencing is a more compelling choice."

ORM in the spotlight

ORMs (Object-Relational Mappers) like Mongoid and MongoMapper or ODM (Object Document Mappers) like Mongoose work wonders in simplifying relationships setup:

In Mongoid:
- embeds_many and embedded_in manifest as embedded documents.
- The defined relations belongs_to and has_many in SQL are the same in Mongoid.
In Mongoose:
- The Populate constitutes the key to automating the replacement of specified paths in the document(s).
- The Virtuals can act like big brother, ensuring proper linking with additional info or computations.

Protecting data integrity at the application level

Given MongoDB's absence of constraint enforcement traditional to SQL, it relies on the application to maintain data integrity:

Data Cascades: Deletion of a document does not impact its related documents. Thus, your application needs to accommodate these "orphaned" documents.
Consistency: Develop helper functions or middleware in your application to ensure consistency across data updates.
Dead Links: Shape your application to dispose of or repurpose "dead links" once linked documents no longer exist. Kinda like spring cleaning.

Considering scalability

Manifesting a relationship between a large volume of data involves juggling denormalization and referencing:

Reference Efficiency: With a mammoth dataset, resolving references can cause performance bottlenecks due to extra queries.
Denormalization Space Usage: While denormalized data comes with faster read times, it may pose challenges with storage and maintaining consistency across updates.

Advanced referencing conventions

Aside from using ObjectIDs for manual referencing, MongoDB furnishes other methods:

DBRef: A systematic way of forming references, including "$ref", "$id", and "$db".
Manual DBRef-like references: If you want the flexibility, mimic DBRef without the actual DBRef type.
Aggregation Framework: For the complex ones, use the aggregation framework to devise document graphs or execute operations on referenced data.

Dealing with data updates and deletions

In MongoDB's world, without in-built cascading, here are some strategies for managing data:

Batch Updates: Write functions that find and update every related document whenever you update one of them. It's like inviting everyone to the party, nobody gets missed out.
Transactional Logic: MongoDB's transaction features may come in handy when updating multiple collections. All or nothing, baby!
Hooks and Middleware: Think of them as your little elves in the background, helping you automate updates and delete operations across related documents.

Performance and optimization

To streamline your queries with references in MongoDB:

Use Indexes: Indexes are your best friends when it comes to speeding up query execution, so index the fields used in references or $lookup stages.
Sharding Keys: If you're scaling horizontally (sharding), choose sharding keys wisely to maintain reference locality and reduce cross-shard queries.
Read and Write Patterns: Be mindful of your application's reading and writing tendencies. Design your schema in a way that complements these patterns.