Skill

documentdb-data-modeling

Name: documentdb-data-modeling
Author: Azure (Microsoft)

design Azure DocumentDB data models

Published by Azure (Microsoft) Updated Jul 12

Covers Azure MongoDB Database Data Modeling

Description

Data modeling patterns for Azure DocumentDB — embed vs reference, 16 MB document limit, denormalization for read-heavy workloads, schema versioning. Use when designing new schemas, reviewing existing data models, migrating from SQL, deciding between embedding and referencing, modeling one-to-one / one-to-many / many-to-many relationships, or troubleshooting document-size and query-performance problems that stem from the data model.

SKILL.md

Data Modeling — Azure DocumentDB

Guiding principle: "Data that is accessed together should be stored together."

Each rule follows the same shape — why it matters → incorrect example → correct example → references.

Rules

model-embed-vs-reference — Embed data accessed together; reference unbounded N-sides.
model-16mb-limit — Stay well under the 16 MB BSON document limit; plan for steady-state growth.
model-denormalize-reads — Denormalize for read-heavy workloads; pre-compute aggregates to avoid $lookup.
model-schema-versioning — Add a schemaVersion field and migrate documents lazily.

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 or junction collection

See each rule file for the full reasoning and code examples.

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