The Tool Desk
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Begin with access patterns
MongoDB’s schema-design guidance tells you to identify the application workload, map the relationships, and then apply patterns. Microsoft’s Cosmos DB guide frames the same idea: model for the operations you perform. List each important read and write, the data each needs, how often it runs, and how latency-sensitive it is. Only then decide where related data lives.
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MongoDB’s relationship map distinguishes one-to-one, one-to-many, and many-to-many cases, and says embed-or-reference decisions should follow your common and critical queries.
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| Situation | Starting point | Why, and the caveat |
|---|---|---|
| Child data is small, bounded, usually read with its parent, and rarely changes on its own | Embed | Fewer read operations. In MongoDB, one document updates atomically. Keep size and growth bounded. |
| Child set can grow without a clear bound, or is queried independently | Separate child records that reference the parent | Avoids unbounded arrays and allows direct child queries, at the cost of extra reads or a database-supported join. |
| A related value changes often and should have one current copy | Reference it, or use a read-optimized projection | Avoids rewriting many duplicated copies. The right choice depends on read frequency and consistency needs. |
| Many-to-many or complex graph/hierarchy | Explicit references or the database’s own relationship pattern | A generic embedded array can duplicate data or grow awkwardly. |
| Long history where users mostly need recent items | Subset pattern | Keep hot items in the parent, move the rest elsewhere. Better working-set performance, more complexity. |
Questions to compare options
- Which reads are most frequent and latency-sensitive?
- Are parent and child returned together?
- How often does each field change?
- What are the cardinality and growth bounds?
- Is duplication acceptable, and how will consistency be maintained?
- What are the document or item size limits and the index implications?
- Must the related records be queried on their own?
The vendor guides support these dimensions but publish no benchmarks or percentages showing one model beating another, so treat “faster” as a design argument to verify with your own workload.
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MongoDB
Embedding
MongoDB names embedding and references as its two main ways to link data. Its manual says: “Embedded data models let applications query related pieces of information in the same database record.” Embedded subdocuments come back in one operation and update atomically with their containing document. The hard limit is that a document must be smaller than 16 mebibytes, which is another reason to avoid arrays that grow indefinitely.
References
References suit complex many-to-many relationships, large hierarchies, data queried independently, and cases where duplication would not pay for its read benefit. A manual reference usually stores another document’s _id, and the application resolves it. The aggregation $lookup stage can join an unsharded collection in the same database, and $graphLookup handles recursive traversals. DBRefs are a more structured reference format, but they are not the default for every relationship.
MongoDB’s one-to-many example uses publishers and books: each book stores a publisher reference rather than the publisher holding a growing list of books.
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Subset pattern
MongoDB’s EF Core provider documentation describes keeping the most frequently accessed items in the main document and moving the remainder to a separate collection. Treat it as a design pattern you implement, not a feature every database provides.
Azure Cosmos DB for NoSQL
Microsoft recommends embedding for contained or one-to-few relationships when the data changes infrequently, does not grow without bound, and is queried together. It recommends normalized, reference-based models for one-to-many and many-to-many relationships, frequently changing related data, or potentially unbounded referenced data. As in MongoDB’s example, the publisher reference lives in each book so the publisher never holds an unbounded list.
Integrity is your job. If the application must ensure referenced data exists, Microsoft says you implement that check in application logic or with server-side triggers or stored procedures.
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Microsoft also sets expectations: “Azure Cosmos DB isn’t designed for complex relationships like those in relational databases, but simple links between items are possible and can be helpful.” Where a relational design would join, remodel around access patterns: embed, reference, or build a read-optimized projection.
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Amazon DynamoDB
AWS Prescriptive Guidance, in its DynamoDB data-modeling guide, names the adjacency-list design pattern for managing one-to-many and many-to-many relationships. It is one documented option, not a universal one. For large items, AWS advises storing only metadata in the DynamoDB table, putting the blob in Amazon S3, and keeping a reference to it in DynamoDB. Key design and query details are in that guide; check them against your access patterns before implementing.
Many-to-many: do not copy the join table blindly
Many-to-many modeling is the clearest case where databases diverge. Document stores may use arrays of references or a purpose-built linking collection. DynamoDB uses adjacency lists. Reproducing a relational join table without checking how your target database reads and writes it often yields extra round trips for little benefit.
What “NoSQL” does and does not mean for relationships
Relationships are not impossible. MongoDB offers lookup facilities, and Cosmos DB supports simple links. But you cannot assume foreign-key enforcement, general joins, or the same atomicity boundary across products. Check each one: in MongoDB the single-document update is the atomic unit that embedding exploits, while in Cosmos DB reference validation is up to you.
Practical workflow
- List entities and their relationships, with cardinality and a realistic upper bound for each.
- List the top reads and writes, with frequency and latency needs.
- Embed bounded data read together with its parent and rarely changed alone.
- Reference unbounded, independently queried, or frequently updated data.
- Where you duplicate data for reads, decide who updates the copies and how stale they may be.
- Check size limits and index impact, then test with representative data.
MongoDB’s data-modeling overview names MongoDB Compass, Hackolade, and Studio 3T as tools for designing and visualizing schemas. They are optional aids; none is required to apply these ideas.
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