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Data modeling patterns and anti-patterns for MongoDB, maintained by MongoDB. Bad schema is the root cause of most MongoDB performance and cost issues—queries and indexes cannot fix a fundamentally wrong model.
When to Apply
Reference these guidelines when:
Designing a new MongoDB schema from scratch
Migrating from SQL/relational databases to MongoDB
Reviewing existing data models for performance issues
Troubleshooting slow queries or growing document sizes
antipattern-excessive-lookups - When encountering overly normalized collections that reference each other or frequent and possibly slow $lookup operations, consult this reference to validate whether this is problematic and how to fix it.
antipattern-unnecessary-indexes - Consult this reference when indexes overlap or are not used by queries, to identify and remove unnecessary indexes that add overhead without benefit.
2. Schema Fundamentals - 4 rules
fundamental-embed-vs-reference - Consult this reference for approaches to modeling different types of relationships (1:1, 1:few, 1:many, many:many, tree/hierarchical data) and how to decide between embedding and referencing based on access patterns.
fundamental-document-model - Fundamentals of the document model. Consult this reference when migrating from SQL or other normalized data to a document database like MongoDB.
fundamental-schema-validation - Consult this reference when creating new collections, or adding validation to existing collections, for example in response to finding inconsistent document structures or data quality issues.
fundamental-document-size - Consult this reference when documents hit the hard 16MB limit, or when accesses are slower than expected as a result of large documents.
pattern-outlier - Handle collections in which a small subset of documents are much larger than the rest, to prevent outliers from dominating memory and index costs
pattern-polymorphic - Store different types of entities in the same collection, often when they are different types of the same base entity (e.g. different types of users or different types of products)
pattern-schema-versioning - Schema evolution, preventing drift, and safe online migrations. Consult when encountering inconsistent document structures, or when planning a schema change that cannot be applied atomically.
"Data that is accessed together should be stored together."
This is MongoDB's core philosophy. Embedding related data eliminates joins, reduces round trips, and enables atomic updates. Reference only when you must.
A core way to implement this philosophy is the fact that MongoDB exposes flexible schemas. This means you can have different fields in different documents, and even different structures. This allows you to model data in the way that best fits your access patterns, without being constrained by a rigid schema. For example, if different documents have different sets of fields, that is perfectly fine as long as it serves your application's needs. You can also use schema validation to enforce certain rules while still allowing for flexibility.
Another implication of the key principle is that information about the expected read and write workload becomes very relevant to schema design. If pieces of information from different entities are often queried or updated together, that means that prioritizing co-location of that data in the same document can lead to significant performance benefits. On the other hand, if certain pieces of information are rarely accessed together, it may make sense to store them separately to avoid loading more data than necessary.
Schema Fundamentals Summary
Embed vs Reference: Choose embedding or referencing based on access patterns: embed when data is always accessed together (1:1, 1:few, bounded arrays, atomic updates needed); reference when data is accessed independently, relationships are many-to-many, or arrays can grow without bound.
Data accessed together stored together: MongoDB's core principle: design schemas around queries, not entities. Embed related data to eliminate cross-collection joins and reduce round trips. Identify your API endpoints/pages, list the data each returns, then shape documents to match those queries.
Embrace the document model: Don't recreate SQL tables 1:1 as MongoDB collections. Instead, denormalize joined tables into rich documents for single-query reads and atomic updates. When migrating from SQL, identify tables that are always joined together and merge them into single documents.
Schema validation: Use MongoDB's built-in $jsonSchema validator to catch invalid data at the database level (type checks, required fields, enum constraints, array size limits). Start with validationLevel: "moderate" and validationAction: "warn" on existing collections, then tighten to strict/error.
16MB document limit: MongoDB documents cannot exceed 16MB—this is a hard limit, not a guideline. Common causes: unbounded arrays, large embedded binaries, deeply nested objects. Mitigate by moving unbounded data to separate collections and monitoring document sizes with $bsonSize.
Embed/Reference Decision Framework
Relationship
Cardinality
Access Pattern
Recommendation
One-to-One
1:1
Always together
Embed
One-to-Few
1:N (N < 100)
Usually together
Embed array
One-to-Many
1:N (N > 100)
Often separate
Reference
Many-to-Many
M:N
Varies
Two-way reference
This is a rough guideline, and whether to embed or reference depends on your specific access patterns, data size, and read/write frequencies. Always verify with your actual workload.
How to Use
Each reference file listed above contains detailed explanations and code examples. Use the descriptions in the Quick Reference to identify which files are relevant to your current task.
If the MCP server is running and connected, I can automatically run verification commands to check your actual schema, document sizes, array lengths, index usage, and more. This allows me to provide tailored recommendations based on your real data, not just code patterns.
⚠️ Security: Use --readOnly for safety. Remove only if you need write operations.
When connected, I can automatically:
Infer schema via mcp__mongodb__collection-schema
Measure document/array sizes via mcp__mongodb__aggregate
Check collection statistics via mcp__mongodb__db-stats
⚠️ Action Policy
I will NEVER execute write operations without your explicit approval.
Before any write or destructive operation via MCP, I will: (1) summarize the exact operation (collection, index/validator, estimated number of docs affected), and (2) ask for explicit confirmation (yes/no). I will not proceed on partial or ambiguous approvals.
