Data Modeling
Data Modeling
General Tips:
Think about how your application will use the data. Focus on the queries and operations you'll perform, not just the structure of the raw data.
Ask yourself: What are the most common queries, updates, inserts, and aggregations you'll perform?
For one-to-few relationships where the related data is often accessed alongside the parent document, embed data in a single document.
Example: A blog post and its tags/comments can be modeled as an embedded array inside the blog post document.
Benefits:
One atomic query to get related data.
Simplifies updates by reducing joins.
Tip: Keep embedded documents small (< 16MB BSON limit) to avoid performance issues.
For one-to-many (e.g., users and orders) or many-to-many (e.g., students and courses) relationships, use normalized references by storing related data in separate collections and linking them using unique identifiers.
Example: user_id in the Orders collection references a _id in the Users collection.
When to choose referencing:
If related data is large or frequently updated (e.g., customer and order histories).
If related data isn't always needed.
Design backward: Optimize your schema for the queries you need to support, not just the structure of the data.
Use indexes to support queries.
Avoid querying across collections (i.e., reduce the number of $lookup operations).
Keep frequently accessed fields close (embedded or indexed).
Ask yourself: Can you retrieve most (or all) of your data in a single query?
Avoid deeply nested documents because:
They become harder to query and maintain.
Query engines might need to traverse long paths, impacting performance.
Best Practice:
Flatten structures when querying the data or manipulating nested elements becomes cumbersome.
Proper indexing is critical for good performance.
Single-field Index: Useful for simple queries.
Compound Index: Useful for queries with multiple conditions; ensure the order of fields matches your query patterns.
Wildcard Index: Use for collections with varying fields.
Avoid too many or unnecessary indexes (can slow writes and increase storage).
Tip: Use MongoDB's Explain (.explain()) to analyze query plans and optimize indexes.
MongoDB allows dynamic document structures, so different documents in the same collection can have different schemas if necessary. Use this to handle variations in your data without overloading a single large schema.
Example:
A product catalog with different attributes for each type of product (e.g., TV, Laptop, Sofa) can live in the same collection with distinct fields for each.
Tip: Don't overuse this flexibility; have a general schema pattern to maintain consistency and readability.
Arrays are great for storing ordered or related data lists (e.g., tags, categories, related items).
Use $elemMatch and $arrayElemAt to query or manipulate array data efficiently.
Tip: Arrays should remain moderate in size; very large arrays can lead to performance problems.
Write-heavy workloads:
Avoid frequent updates to the same document.
Consider splitting writes into multiple collections to reduce contention.
Read-heavy workloads:
Include all the necessary fields (denormalize) for faster reads.
Use indexes to minimize query execution time.
Tip: Use replication (read from secondaries) to offload read-heavy workloads.
Use MongoDB's powerful Aggregation Pipeline for complex queries, transformations, and calculations.
Example: $group, $match, $unwind, $lookup, etc.
Pre-process data at the database layer to reduce app-layer processing.
Sharding allows you to scale MongoDB horizontally by distributing data across multiple nodes.
Select a shard key that ensures even distribution of data and allows efficient queries.
Tips for Shard Key Selection:
Avoid monotonically increasing fields (e.g., timestamps or sequential IDs).
Use fields with high cardinality.
Test with real-world workloads.
MongoDB enforces a 16MB limit on a single document's size.
For large data:
Split data into chunks across multiple documents.
Consider GridFS for storing large files like images or videos.
While denormalization is a MongoDB strength, normalize data when:
Relationships are complex and frequently changing.
You need to ensure data consistency across records.
Use TTL indexes to auto-expire or delete time-sensitive data (e.g., session tokens, logs).
Example: Create a TTL index on a createdAt field to delete documents after a specific duration.
Simulate real-world traffic and query patterns to validate your schema before going to production.
Use tools like MongoDB Atlas Performance Advisor or Profiler to identify bottlenecks.
MongoDB 3.6+ supports schema validation with JSON schema. Use this to maintain schema discipline in collections that require strict structures.
Example:
db.createCollection("users", {
validator: {
$jsonSchema: {
bsonType: "object",
required: ["name", "email"],
properties: {
name: { bsonType: "string" },
email: { bsonType: "string", pattern: "^.+@.+\..+$" }
}
}
}
});
Track database performance over time using:
Query performance stats (db.currentOp(), Explain plans).
MongoDB Atlas monitoring tools.
Logging slow queries to uncover inefficient patterns.
Data modeling works best when database architects and application developers collaborate closely. Ensure the schema aligns with both back-end and front-end requirements.
Anticipate changes in your application. Design a schema that's flexible enough to accommodate new fields or updates without requiring extensive migrations.
MongoDB University (Free courses on data modeling, performance tuning, aggregation, etc.).
MongoDB documentation and pattern guides.