Joins vs embedding decision in MongoDB - Trade-offs & Expert Analysis

Overview - Joins vs embedding decision

What is it?

In MongoDB, data can be organized in two main ways: embedding documents inside other documents or linking documents using references, which is similar to joins in relational databases. Embedding means storing related data together in one document, while referencing means storing related data separately and connecting them when needed. Choosing between embedding and referencing affects how you store, retrieve, and update your data.

Why it matters

This decision impacts how fast and efficient your database queries are, how easy it is to keep data consistent, and how well your database scales as it grows. Without understanding when to embed or join, your application might run slowly, use too much storage, or become hard to maintain. Good design here makes your app faster and more reliable.

Where it fits

Before learning this, you should understand basic MongoDB documents and collections. After this, you can learn about advanced data modeling, indexing strategies, and performance tuning in MongoDB.

Mental Model

Core Idea

Embedding stores related data together inside one document for fast access, while referencing links separate documents to avoid duplication and keep data consistent.

Think of it like...

Imagine a filing cabinet: embedding is like putting all papers about one project in a single folder, while referencing is like keeping separate folders for each topic and using an index card to find related folders.

┌───────────────┐       ┌───────────────┐
│   Document A  │       │   Document B  │
│ ┌───────────┐ │       │ ┌───────────┐ │
│ │ Embedded  │ │       │ │ Referenced │ │
│ │ Document  │ │       │ │ Document  │ │
│ └───────────┘ │       │ └───────────┘ │
└──────┬────────┘       └──────┬────────┘
       │                       │
       │ Embedding             │ Referencing
       │                       │
       ▼                       ▼
  Fast single read       Requires multiple reads
  Larger document        Smaller documents
  Possible duplication   Data consistency easier

Build-Up - 7 Steps

1

Foundation

Understanding MongoDB Documents

Concept: Learn what a MongoDB document is and how data is stored in JSON-like format.

MongoDB stores data as documents, which are like JSON objects. Each document has fields with values, and these documents are grouped into collections. Documents can contain simple data like strings and numbers, or complex data like arrays and nested documents.

Result

You can store structured data in flexible documents that can vary in shape.

Understanding documents is essential because embedding and referencing decisions depend on how data is organized inside these documents.

2

Foundation

What is Embedding in MongoDB?

Concept: Embedding means putting related data inside the same document as nested objects or arrays.

For example, a blog post document might embed comments as an array inside it. This keeps all related data together, so reading the post also reads its comments in one go.

Result

One document contains all related data, making reads fast and simple.

Embedding reduces the need for multiple queries, which can speed up data retrieval when related data is always accessed together.

3

Intermediate

What is Referencing (Joins) in MongoDB?

Concept: Referencing means storing related data in separate documents and linking them using IDs.

Instead of embedding comments inside a blog post, you store comments in their own collection and save their IDs in the post document. To get comments, you query both collections and join the data in your application or with MongoDB's $lookup.

Result

Data is stored separately, avoiding duplication but requiring multiple queries or joins.

Referencing helps keep data consistent and avoids large documents, especially when related data grows independently.

4

Intermediate

When to Choose Embedding vs Referencing

🤔Before reading on: do you think embedding is always better for performance? Commit to your answer.

Concept: Learn the criteria to decide between embedding and referencing based on data access patterns and size.

Embed when related data is frequently accessed together and does not grow without limit. Use referencing when related data is large, changes independently, or is shared across many documents. Embedding can cause large documents that slow writes, while referencing can slow reads due to multiple queries.

Result

You can design your data model to balance performance and data consistency.

Knowing when to embed or reference prevents common performance and maintenance problems in MongoDB applications.

5

Advanced

Using $lookup for Joins in MongoDB

🤔Before reading on: do you think MongoDB can perform joins like SQL databases? Commit to yes or no.

Concept: MongoDB supports joins using the $lookup aggregation stage to combine data from multiple collections.

The $lookup stage lets you join documents from two collections by matching fields, similar to SQL joins. This is useful when you use referencing but want to get related data in one query. However, $lookup can be slower than embedding because it requires extra processing.

Result

You can perform join-like queries in MongoDB to combine related data from separate collections.

Understanding $lookup helps you use referencing without losing the ability to get combined data efficiently.

6

Advanced

Impact of Embedding on Document Size Limits

Concept: MongoDB documents have a size limit, so embedding too much data can cause errors.

MongoDB limits documents to 16MB. If you embed large or growing arrays, you risk exceeding this limit, causing write failures. Referencing avoids this by keeping documents small and linking them.

Result

You avoid errors and design scalable data models by respecting document size limits.

Knowing document size limits guides embedding decisions to prevent application crashes.

7

Expert

Balancing Consistency and Performance in Embedding vs Referencing

🤔Before reading on: do you think embedding always makes data consistency easier? Commit to your answer.

Concept: Embedding can simplify consistency but complicate updates; referencing can ease updates but require careful transaction management.

Embedding keeps related data in one place, so updates are atomic and consistent. But if embedded data changes often, you rewrite the whole document, which can be inefficient. Referencing allows updating related data independently but may require multi-document transactions to keep data consistent, which can impact performance.

Result

You understand trade-offs between consistency and performance when choosing data models.

Balancing consistency and performance is key to designing robust MongoDB applications that scale well.

Under the Hood

MongoDB stores each document as a BSON object on disk. Embedded documents are stored inside the parent document's BSON, making reads a single disk operation. Referenced documents are stored separately, requiring multiple disk reads and network calls. The $lookup aggregation stage performs a server-side join by scanning and matching documents across collections, which is more resource-intensive than reading a single embedded document.

Why designed this way?

MongoDB was designed for flexibility and scalability. Embedding supports fast reads for related data accessed together, while referencing supports normalized data and independent updates. The 16MB document size limit enforces practical boundaries to prevent performance degradation. $lookup was added later to provide join capabilities without sacrificing MongoDB's flexible schema.

┌───────────────┐
│ Parent Document│
│ ┌───────────┐ │
│ │ Embedded  │ │
│ │ Document  │ │
│ └───────────┘ │
└──────┬────────┘
       │
       ▼
  Single disk read

Separate Collections:
┌───────────────┐   ┌───────────────┐
│ Collection A  │   │ Collection B  │
│ Document A1   │   │ Document B1   │
│ References B1 │   │               │
└──────┬────────┘   └───────────────┘
       │
       ▼
Multiple reads + $lookup join

Myth Busters - 4 Common Misconceptions

Quick: Does embedding always improve query speed? Commit yes or no.

Common Belief: Embedding always makes queries faster because all data is in one document.

Reality: Embedding can slow down writes and cause large documents that hurt performance if embedded data grows too large or changes frequently.

Why it matters: Ignoring this can lead to slow writes, increased memory use, and hitting document size limits.

Quick: Can MongoDB perform joins like SQL databases? Commit yes or no.

Common Belief: MongoDB cannot do joins, so referencing is useless for combining data.

Reality: MongoDB supports joins using the $lookup aggregation stage, allowing combining data from multiple collections.

Why it matters: Not knowing this limits design options and can lead to inefficient data duplication.

Quick: Is referencing always better for data consistency? Commit yes or no.

Common Belief: Referencing always ensures better data consistency because data is stored separately.

Reality: Referencing can require multi-document transactions to maintain consistency, which adds complexity and can reduce performance.

Why it matters: Assuming referencing is always better can cause unexpected bugs and performance issues.

Quick: Does embedding mean data duplication? Commit yes or no.

Common Belief: Embedding never duplicates data because it's all in one place.

Reality: Embedding can duplicate data if the same embedded data is repeated in many documents, leading to update challenges.

Why it matters: Overlooking this can cause data inconsistency and harder maintenance.

Expert Zone

1

Embedding is optimal when related data is accessed together and changes rarely, but even small changes require rewriting the whole document, which can impact write throughput.

2

Referencing with $lookup can be efficient if indexes are well designed, but excessive use of $lookup in large collections can cause performance bottlenecks.

3

MongoDB's document size limit forces a natural boundary on embedding, but clever use of arrays and subdocuments can maximize data locality without hitting limits.

When NOT to use

Avoid embedding when related data grows without bound or changes frequently; instead, use referencing with careful indexing. Avoid referencing when you need ultra-fast reads of tightly coupled data. For highly relational data with complex joins, consider using a relational database instead.

Production Patterns

In production, embedding is common for user profiles with small, fixed related data. Referencing is used for comments, orders, or logs that grow independently. $lookup is often used sparingly for reporting or admin queries, not in high-traffic user-facing queries.

Connections

Normalization in Relational Databases

Referencing in MongoDB is similar to normalization, separating data to reduce duplication.

Understanding normalization helps grasp why referencing avoids data duplication and maintains consistency.

Caching Strategies in Web Development

Embedding is like caching related data together for fast access, while referencing is like fetching fresh data on demand.

Knowing caching trade-offs clarifies why embedding improves read speed but can cause stale or duplicated data.

File System Organization

Embedding resembles storing all files of a project in one folder, referencing resembles storing files in separate folders with shortcuts.

This connection shows how organizing data affects access speed and maintenance complexity.

Common Pitfalls

#1 Embedding large, growing arrays causing document size limit errors.

Wrong approach:

{
_id: 1,
name: "User",
comments: [ /* thousands of comments embedded here */ ]
}

Correct approach:

{
_id: 1,
name: "User",
comment_ids: [ /* array of comment IDs */ ]
}
// Comments stored in separate collection

Root cause:

Misunderstanding that embedding unlimited growing data can exceed MongoDB's 16MB document size limit.

#2 Using referencing without indexes causing slow joins.

Wrong approach:

db.posts.aggregate([
{ $lookup: {
from: "comments",
localField: "comment_ids",
foreignField: "_id",
as: "comments"
}}
])
// No indexes on comment_ids or _id

Correct approach:

db.comments.createIndex({ _id: 1 })
// Then run the same $lookup query

Root cause:

Ignoring the need for indexes on join fields leads to slow query performance.

#3 Embedding data that changes frequently causing inefficient writes.

Wrong approach:

{
_id: 1,
product: "Book",
stock: { quantity: 100, last_updated: "2024-01-01" }
}
// Stock changes often but is embedded

Correct approach:

{
_id: 1,
product: "Book",
stock_id: ObjectId("...")
}
// Stock stored in separate collection updated independently

Root cause:

Not realizing that frequent updates to embedded data rewrite the whole document, reducing write efficiency.

Key Takeaways

Embedding stores related data together inside one document, making reads fast but risking large document sizes and inefficient writes if data grows or changes often.

Referencing stores related data separately and links them, avoiding duplication and large documents but requiring multiple queries or joins that can slow reads.

MongoDB supports joins using the $lookup aggregation stage, allowing referencing without losing the ability to combine data in queries.

Choosing between embedding and referencing depends on data access patterns, size, update frequency, and consistency needs.

Understanding these trade-offs helps design efficient, scalable, and maintainable MongoDB data models.