Mongodb Schema Design

MongoDBOfficialPublished by MongoDB

Skill
Guidelines
Data and AI
Apache-2.0
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From the author

MongoDB schema design patterns and anti-patterns. Use when designing data models, reviewing schemas, migrating from SQL, or troubleshooting performance issues caused by schema problems. Triggers on "design schema", "embed vs reference", "MongoDB data model", "schema review", "unbounded arrays", "one-to-many", "tree structure", "16MB limit", "schema validation", "JSON Schema", "time series", "schema migration", "polymorphic", "TTL", "data lifecycle", "archive", "index explosion", "unnecessary indexes", "approximation pattern", "document versioning".

— the description by MongoDB

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides a comprehensive framework for designing, reviewing, and optimizing MongoDB data models. It emphasizes the core philosophy that data accessed together should be stored together, offering actionable patterns for embedding versus referencing, handling time-series data, and avoiding common pitfalls like unbounded arrays or excessive collection splitting. It includes diagnostic workflows to analyze access patterns and performance, ensuring schema decisions are driven by real-world workload requirements rather than theoretical assumptions.

What it does

Provides patterns for common modeling challenges like tree structures, polymorphic data, and time-series.
Identifies anti-patterns such as unnecessary collections, excessive lookups, and index bloat.
Offers a decision framework for choosing between embedding and referencing based on access patterns.
Guides users through performance analysis using query statistics and slow query logs.
Includes best practices for schema validation and managing document size limits.
Supports integration with MongoDB MCP for automated schema verification and diagnostic tasks.

When to use it

When designing a new MongoDB data model from scratch.
When migrating existing data from a relational SQL database to MongoDB.
When troubleshooting performance issues like slow queries or high index overhead.
When documents are hitting the 16MB size limit or exhibiting inconsistent structures.
When evaluating whether to denormalize data or use $lookup operations.

Advantages

Reduces performance bottlenecks by aligning schema design with actual application access patterns.
Prevents common pitfalls that lead to high storage costs and inefficient query execution.
Provides a structured, evidence-based approach to schema evolution and migration.
Enables data-driven decision making by combining design patterns with real-time diagnostic data.

Usage examples

“How should I model a one-to-many relationship between users and their orders?”: The agent will analyze your access patterns to determine if you should embed the orders within the user document or store them in a separate collection, based on the frequency of access and document size.
“My queries are slow and I see a lot of $lookup stages in my logs. How can I fix this?”: The agent will guide you through identifying if your data is over-normalized and suggest specific denormalization or embedding strategies to reduce join overhead.
“I am migrating from SQL to MongoDB. How do I handle my normalized tables?”: The agent will help you identify tables that are frequently joined and demonstrate how to merge them into rich, denormalized documents for better performance.
“How can I prevent my documents from growing indefinitely?”: The agent will explain patterns like the bucket pattern or archiving to manage unbounded data and ensure your documents stay within the 16MB limit.

Install

Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/mongodb/mongodb-schema-design/download

Unzip it into the cross-client skills folder of your project:

mkdir -p .agents/skills && unzip mongodb-schema-design.zip -d .agents/skills

Documentation by MongoDB

MongoDB Schema Design

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
Deciding between embedding and referencing
Modeling relationships (one-to-one, one-to-many, many-to-many)
Implementing tree/hierarchical structures
Seeing Atlas Schema Suggestions or Performance Advisor warnings
Hitting the 16MB document limit
Adding schema validation to existing collections

Quick Reference

1. Schema Anti-Patterns - 3 rules

antipattern-unnecessary-collections - Splitting homogeneous data into multiple collections is often an anti-pattern; consult this reference to validate whether this is the case.
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.

3. Design Patterns - 11 rules

pattern-approximation - Use approximate values for high-frequency counters
pattern-archive - Move historical data to separate/cold storage for performance
pattern-attribute - Collapse many optional fields into key-value attributes
pattern-bucket - Group time-series or IoT data into buckets
pattern-computed - Pre-calculate expensive aggregations
pattern-document-versioning - Track document changes to enable historical queries and audit trails
pattern-extended-reference - Cache frequently-accessed data from related entities
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.
pattern-time-series-collections - Use native time series collections for high-frequency time series data

Access Pattern Analysis

Do not immediately recommend a pattern or schema change without understanding the broader context. Together with the user, analyze access patterns to identify pain points and opportunities for optimization.

Workflow

Step 1: Assess the environment Ask the user:

Is this a new design or is there a production database with existing access patterns to analyze?
If there is production data, is it on Atlas? If yes, what tier? (M0/M2/M5 vs M10+)

Step 2: Determine workload type Is the workload read-heavy, write-heavy, or balanced? This will influence which diagnostic sources are most relevant. Ask the user:

What's the primary workload for these collections — read-heavy (analytics, reports, searches), write-heavy (logging, IoT ingestion, frequent updates), or balanced?

Verify with db.serverStatus().opcounters.

Step 3: Work with the user to choose the best source(s) Recommend the best source(s) for their situation, explaining the tradeoffs. For schema design decisions, we often need to combine multiple sources for a complete picture.

Step 4: Proceed with analysis Only after source selection, fetch data or guide the user through analysis.

Sources

Query statistics - Returns runtime statistics for recorded queries showing query shapes and frequency. Limitation: Currently only captures read operations (pair with other sources for write patterns). Requires Atlas M10+ tier.
Atlas Slow Query Logs - Review slow queries (actual queries, not shapes) to identify performance bottlenecks. Captures all reads and writes. Requires Atlas M10+ tier.
Codebase - Examine actual queries in application code to understand access patterns, especially for new applications or with changing workloads. Can be used in conjunction with query stats for a more complete picture.
Natural language input - Ask the user to describe their typical queries and access patterns in natural language. Can be used as the only source or to supplement and validate other sources - the user might have contextual knowledge that is not reflected in the data or codebase.

Combining Query Stats and Slow Query Logs:

Use both together for comprehensive analysis:

Query Stats → identify frequent access patterns (which queries run most often)
Slow Query Logs → identify performance bottlenecks (which queries are slow)
Focus schema optimization on queries that are both frequent AND slow (highest impact)

Key Principle

"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.

Each reference file contains:

Brief explanation of why it matters
Incorrect code example with explanation
Correct code example with explanation
"When NOT to use" exceptions
Performance impact and metrics
Verification diagnostics

How These Rules Work

MongoDB MCP Integration

For automatic verification, connect the MongoDB MCP Server.

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, slow query logs, 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.

Operation Type	MCP Tools	Action
Read (Safe)	`find`, `aggregate`, `collection-schema`, `db-stats`, `count`	I may run automatically to verify
Write (Requires Approval)	`update-many`, `insert-many`, `create-collection`	I will show the command and wait for your "yes"
Destructive (Requires Approval)	`delete-many`, `drop-collection`, `drop-database`	I will warn you and require explicit confirmation

When I recommend schema changes or data modifications:

I'll explain what I want to do and why
I'll show you the exact command
I'll wait for your approval before executing
If you say "go ahead" or "yes", only then will I run it

Your database, your decision. I'm here to advise, not to act unilaterally.

Working Together

If you're not sure about a recommendation:

Run the verification commands I provide
Share the output with me
I'll adjust my recommendation based on your actual data

We're a team—let's get this right together.

Files

ATTRIBUTION.mdAdded by AutoGineer845 B
LICENSELicence file added by AutoGineer11.1 KiB
SKILL.md13.3 KiB
references/antipattern-excessive-lookups.md3.5 KiB
references/antipattern-unnecessary-collections.md4.1 KiB
references/antipattern-unnecessary-indexes.md3.7 KiB
references/fundamental-document-model.md3.5 KiB
references/fundamental-document-size.md9.6 KiB
references/fundamental-embed-vs-reference.md14.9 KiB
references/fundamental-schema-validation.md4.3 KiB
references/pattern-approximation.md3.2 KiB
references/pattern-archive.md5.3 KiB
references/pattern-attribute.md2.4 KiB
references/pattern-bucket.md4.2 KiB
references/pattern-computed.md6.0 KiB
references/pattern-document-versioning.md5.0 KiB
references/pattern-extended-reference.md3.3 KiB
references/pattern-outlier.md5.6 KiB
references/pattern-polymorphic.md6.0 KiB
references/pattern-schema-versioning.md8.2 KiB
references/pattern-time-series-collections.md6.5 KiB
references/source-query-stats.md3.4 KiB
references/source-slow-query-logs.md2.3 KiB

references/antipattern-unnecessary-collections.md

title: Reduce Unnecessary Collections impact: CRITICAL impactDescription: "Reduces avoidable joins when related data is repeatedly queried together" tags: schema, collections, anti-pattern, embedding, normalization, atlas-suggestion

Reduce Unnecessary Collections

Collection count alone is not the anti-pattern. The anti-pattern is using collections as a substitute for indexes — creating one collection per category, time period, or partition key instead of indexing a single collection. Every collection carries a default _id index that consumes storage and strains the replica set, and cross-collection queries require $lookup or $unionWith, adding complexity and overhead.

Incorrect (one collection per day as partitioning strategy):

Creating one collection per time period (e.g. temperatures_2024_05_10, temperatures_2024_05_11, …) means each collection carries its own default _id index (365 collections/year = 365 extra indexes), cross-day queries require $unionWith across many collections, schema validation / indexes / TTL must be duplicated on every collection, and application code must dynamically resolve the collection name for each query.

Correct (single collection with an index):

// All readings in one collection — the index does the partitioning work
{ _id: ObjectId(), timestamp: ISODate("2024-05-10T10:00:00Z"), temperature: 60 }
{ _id: ObjectId(), timestamp: ISODate("2024-05-10T11:00:00Z"), temperature: 61 }
{ _id: ObjectId(), timestamp: ISODate("2024-05-11T10:00:00Z"), temperature: 68 }

db.temperatures.createIndex({ timestamp: 1 })

// Efficient range query — one collection, one index
db.temperatures.find({
  timestamp: { $gte: ISODate("2024-05-10"), $lt: ISODate("2024-05-11") }
})

// Optional TTL for automatic expiry (e.g. 90 days)
db.temperatures.createIndex({ timestamp: 1 }, { expireAfterSeconds: 7776000 })

Even better (bucket pattern or time series collection):

For high-volume time-stamped data, group readings into buckets or use a native time series collection, which is optimized for this workload:

// Bucket pattern — one document per day
{
  _id: ISODate("2024-05-10T00:00:00Z"),
  readings: [
    { timestamp: ISODate("2024-05-10T10:00:00Z"), temperature: 60 },
    { timestamp: ISODate("2024-05-10T11:00:00Z"), temperature: 61 },
    { timestamp: ISODate("2024-05-10T12:00:00Z"), temperature: 64 }
  ]
}

// In this particular case, a native time series collection
// is also a good option to consider
db.createCollection("temperatures", {
  timeseries: { timeField: "timestamp", granularity: "hours" }
})

When to use separate collections:

Scenario	Separate Collection	Why
Data accessed independently	Yes	Different query patterns
Unbounded relationships	Yes	Prevents document growth
Many-to-many	Yes	Students ↔ Courses
1:1 always together	No (embed)	User and profile

When NOT to use this pattern:

Data is genuinely independent: Products exist separately from orders; don't embed full product catalog in every order.
Frequent independent updates: If customer email changes shouldn't update all historical orders (it shouldn't).
Data is accessed in different contexts: Same address entity used for shipping, billing, user profile—keep it separate.
Regulatory requirements: Some industries require normalized data for audit trails.

Verify with

// Count your collections
for (const d of db.adminCommand({ listDatabases: 1 }).databases) {
  const colls = db.getSiblingDB(d.name).getCollectionNames().length
  print(`${d.name}: ${colls} collections`)
}
// Count alone is not sufficient: combine with access and index/storage evidence

Check if collections are always accessed together.

For Atlas M10+ use $queryStats. See Query Stats Use codebase if available, ask the user.

Atlas Schema Suggestions flags: "Reduce number of collections"

Reference: Reduce the Number of Collections

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs an MCP server

Security

No issues found by our static scan· No issues found

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Attribution and licence

Published by

MongoDB

Publisher

MongoDB (Official)

Source

mongodb/agent-skills · skills/mongodb-schema-design · commit 155bb9c

Licence

Apache License 2.0 (redistribution allowed)

LICENSE (added by AutoGineer from LICENSE)
ATTRIBUTION.md (written by AutoGineer)

Synced

2026-11-08

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