MongoDB embedding and referencing
In MongoDB, embedding and referencing are two fundamental strategies for modeling relationships between data. The choice between these two approaches significantly impacts performance, scalability, and data management in your application. Here's an in-depth comparison:
1. Embedding in MongoDB
Embedding is the practice of storing related data directly within a single document. This is MongoDB’s preferred method for handling relationships when data is commonly accessed together.
a) How Embedding Works
In embedding, fields in a document can themselves be objects (subdocuments) or arrays of objects. All related data is stored within the parent document.
Example:
Consider a blog post and its comments:
{
"_id": ObjectId("..."),
"title": "My First Post",
"content": "This is the body of the post.",
"comments": [
{ "author": "Alice", "text": "Great post!" },
{ "author": "Bob", "text": "Thanks for the info!" }
]
}
In this case, the comments are embedded in the blog post document.
b) Advantages of Embedding
Fast Reads:
- Since all related data is stored together in one document, there is no need to perform multiple queries to retrieve related data. This results in faster reads.
Atomic Operations:
- MongoDB treats individual documents as atomic. This means updates to embedded data are atomic, simplifying concurrency management.
Simplified Querying:
- You can retrieve the entire document (and its related data) in a single query.
Denormalization:
- Embedding allows for denormalized data storage, reducing the complexity of joins and simplifying your application logic.
c) Drawbacks of Embedding
Document Size Limit:
- MongoDB has a document size limit of 16MB. Embedding large or growing data structures (e.g., an ever-growing list of comments) could exceed this limit and degrade performance.
Data Duplication:
- Embedding can lead to data duplication when the same subdocument is embedded in multiple documents (e.g., user details in multiple posts). Updating this data requires updating multiple documents, leading to complexity and potential inconsistencies.
Unbounded Growth:
- If an embedded array (such as comments) grows indefinitely, it can lead to performance degradation. This is especially true for large, unbounded arrays of embedded data.
d) When to Use Embedding
- 1:1 and 1relationships where the data is accessed together frequently.
- When data does not grow indefinitely and will remain relatively small.
- Fast reads are more critical than data update complexity.
Examples:
- A user profile document with an embedded address.
- A blog post document with a small number of embedded comments.
2. Referencing in MongoDB
Referencing is the practice of storing relationships between documents by using references (such as foreign keys in relational databases). Instead of embedding all related data, you store related information in separate collections and use references to link them.
a) How Referencing Works
In referencing, one document contains a reference (usually the _id
field) to another document stored in a different collection.
Example:
Instead of embedding comments, we can store them in a separate collection and reference them:
// In the posts collection
{
"_id": ObjectId("..."),
"title": "My First Post",
"content": "This is the body of the post.",
"commentIds": [ ObjectId("..."), ObjectId("...") ]
}
// In the comments collection
{
"_id": ObjectId("..."),
"postId": ObjectId("..."),
"author": "Alice",
"text": "Great post!"
}
In this example, the commentIds
field in the post document stores references to comment documents in a separate comments
collection.
b) Advantages of Referencing
Avoids Document Size Limit:
- Since related data is stored in separate documents, you avoid hitting the 16MB document size limit. This makes referencing ideal for large data sets and relationships with unbounded growth (e.g., many comments on a blog post).
Data Reusability:
- Referenced data (such as a user profile or a category) can be shared across multiple documents. This reduces duplication and ensures consistency—updating the user profile in one place updates all references to it.
Flexible Data Structures:
- Referencing supports more complex relationships like many-to-many relationships (e.g., users and roles, or students and courses). It allows you to efficiently manage relationships across collections.
c) Drawbacks of Referencing
Slower Reads:
- To retrieve all related data, multiple queries or
$lookup
operations (joins) are required. This can be slower than embedding since MongoDB has to access multiple collections.
- To retrieve all related data, multiple queries or
Complexity in Queries:
- Queries that require data from multiple collections become more complex. You may need to perform joins using aggregation pipelines or make multiple round-trip queries to the database.
Consistency Challenges:
- Ensuring that referenced data remains consistent can be tricky. MongoDB doesn’t enforce referential integrity (like foreign key constraints in relational databases), so you have to manage consistency at the application level.
d) When to Use Referencing
- Many-to-many relationships where the data is shared across multiple documents (e.g., users and posts, students and courses).
- When data is large or grows indefinitely (e.g., a post with thousands of comments).
- Write-heavy applications where updating individual pieces of data separately is critical.
Examples:
- Users and posts, where a user can author many posts.
- Orders and products, where products are stored separately and referenced in order documents.
3. Choosing Between Embedding and Referencing
a) Use Embedding When:
- The relationship is 1:1 or 1, and data is frequently read together.
- You want to optimize read performance and reduce the number of queries.
- Data will not grow unbounded (e.g., a fixed number of embedded subdocuments).
- You need atomic operations on the document, where changes to the embedded data need to be made in a single operation.
b) Use Referencing When:
- The relationship is many-to-many or the related data is frequently accessed separately.
- The embedded data is large or grows indefinitely (e.g., a product with thousands of reviews).
- You need to avoid data duplication, such as shared data that appears in multiple documents (e.g., user details shared across posts).
- You are working with a write-heavy application, where embedding would result in frequent updates to multiple documents.
4. Hybrid Approach: Embedding with Referencing
Sometimes, you may want to use a hybrid approach that combines embedding and referencing. This balances the trade-offs between read and write performance.
Example:
- You could embed a subset of frequently accessed data (e.g., recent comments) in a document and reference the rest of the data (e.g., older comments in a separate collection).
{
"_id": ObjectId("..."),
"title": "My First Post",
"recentComments": [
{ "author": "Alice", "text": "Great post!" }
],
"commentIds": [ ObjectId("..."), ObjectId("...") ] // References to older comments
}
This way, your application can quickly access recent data through embedding, while still maintaining flexibility with referencing for larger or less frequently accessed data.