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Clear out junk files and repair common Windows errorsFree Scan →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Model relationships in a NoSQL database around the reads and writes your application actually performs. Embed related data when it is small, bounded, usually fetched with its parent, and rarely changes on its own. Keep it in separate records and link to it when it grows, changes frequently, or must be queried independently. The right choice depends on the database: MongoDB, Azure Cosmos DB for NoSQL, and Amazon DynamoDB do not share one universal relationship model.
Start with the application’s access patterns
Before choosing a storage shape, list the important operations: what the application reads, creates, changes, and deletes, and which data each operation needs. Then map the relationships among the entities involved. MongoDB’s schema-design guidance likewise starts with workload and relationship mapping, then applies patterns to common and critical queries: MongoDB data modeling and mapping schema relationships.
For each relationship, answer these questions:
- Are the parent and related data usually returned together?
- Does the related data have a clear maximum size, or can it grow without bound?
- Does it change independently or need to be queried directly?
- Would copying it into multiple records be acceptable, and how would copies stay consistent?
- What are the database’s document or item size limits and indexing implications?
- Which reads are most frequent or latency-sensitive, and what extra reads or writes can they tolerate?
These answers determine the trade-off: embedding can improve read locality, while references can make independent access and updates easier. Neither choice is inherently faster or better in every workload.
Choose between embedding and references
| Situation | Likely starting point | Trade-off to check |
|---|---|---|
| Related data is small, bounded, usually read with its parent, and rarely changes independently | Embed it in the parent | Fewer separate reads; keep growth within product limits. In MongoDB, updates to one document are atomic. |
| Related records are independently queried, frequently updated, or potentially unbounded | Store them separately and reference the parent or related record | Avoid unlimited child arrays and repeated updates to duplicated copies; resolving a link may require extra reads or a supported join. |
| A related value changes often but readers need one current version | Reference the value, or maintain a read-optimized projection | Balance read frequency against the effort of keeping any projection current. |
| The relationship is many-to-many or a complex hierarchy | Use explicit references or a database-specific pattern | A generic embedded list may duplicate data or grow awkwardly; confirm how the target database supports the required queries. |
| A large history is rarely read in full | Consider a subset pattern | Keep frequently accessed entries with the parent and move the rest to separate records; this adds modeling and query complexity. |
Embedding: keep closely used, bounded data together
Embedding stores related fields or child objects inside a parent record. It fits when the application commonly needs the related data alongside its parent and the embedded set has a practical bound. MongoDB says embedded models let applications query related information in the same record; in MongoDB, an update to that document is atomic. See MongoDB’s embedding guidance.
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Embedding also couples the data’s lifecycle to the parent. If a child grows into a large history, is queried by itself, or changes independently, keeping every child inside the parent can become costly or awkward. MongoDB documents have a maximum size of 16 mebibytes, so a design must leave room for growth rather than treating the limit as a target.
References: keep independently useful records separate
A reference stores a link to another record instead of copying its full contents into the parent. This is a useful starting point for large or unbounded child sets, frequently changing related data, and records that users query independently. It avoids updating many duplicated copies when a shared value changes, but the application may need additional reads and may need to maintain relationship integrity itself. MongoDB’s guidance discusses these cases in reference data in a schema.
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References do not automatically guarantee that the target exists. In Azure Cosmos DB for NoSQL, Microsoft says applications must implement that check in application logic or with server-side triggers or stored procedures when it is required. A reference is therefore a modeling choice, not a promise of relational-style foreign-key enforcement.
Model common relationship shapes without assuming a universal pattern
One-to-one
If the two pieces are almost always read and changed together, embedding is often a reasonable starting point in a document database. If they have separate lifecycles or are accessed independently, separate records with a reference may fit better. Decide based on actual operations rather than the relationship’s label alone.
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One-to-many
A bounded set of small children commonly read with the parent can be embedded. For a potentially unbounded set, store children separately and link them to the parent; this keeps the parent from accumulating an indefinitely growing array and allows direct child queries. In Microsoft’s Azure Cosmos DB for NoSQL example, each book contains a publisher reference instead of the publisher holding an unbounded list of books.
Many-to-many and hierarchies
Many-to-many relationships often call for explicit links or a product-specific pattern. Duplicating one side into arrays on the other can increase update work or produce unbounded records. AWS documents an adjacency-list pattern for managing one-to-many and many-to-many relationships in DynamoDB. That is a DynamoDB design option, not a rule for every NoSQL database; choose keys and access paths for the queries your application needs.
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For large objects in DynamoDB, the same AWS guidance recommends storing metadata in the table, placing the blob in Amazon S3, and keeping a reference in DynamoDB. This separates large payloads from the records used to find and manage them.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What differs across databases
MongoDB
MongoDB presents embedding and references as its two main approaches. An embedded subdocument can be returned with its parent in one database operation, and updates to the containing document are atomic. For manual references, MongoDB commonly stores another document’s _id; application code resolves the link. Aggregation’s $lookup can join an unsharded collection in the same database, while DBRefs provide a more structured reference format. These facilities do not make DBRefs the default choice for every relationship. Details are in MongoDB’s database references documentation.
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For a large one-to-many set where users mainly need recent entries, the MongoDB EF Core provider documentation describes a subset pattern: retain the most frequently accessed items in the main document and store the rest in a separate collection. This is a pattern in that provider’s guidance, not a guarantee that other databases implement it identically: MongoDB EF Core embedded relationships and manual references.
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 references for one-to-many and many-to-many cases, frequently changing related data, or potentially unbounded data. Cosmos DB for NoSQL is not designed for complex relationships like those in relational databases; simple links can still help, and relational-style joins may call for embedding, references, or read-optimized projections around the application’s access patterns. See Microsoft’s Cosmos DB for NoSQL data-modeling guidance.
Amazon DynamoDB
DynamoDB has its own key and access-pattern design considerations. AWS’s adjacency-list pattern is one documented approach for one-to-many and many-to-many relationships; it should not be copied mechanically into another product or treated as a drop-in relational join table. For implementation, use the linked AWS guidance to work through key design and the queries the application must serve.
Quick Recap
Common modeling mistakes to avoid
- Starting with a relational schema. Reproducing tables and join tables without checking NoSQL access paths can create extra reads or awkward queries.
- Embedding an unbounded collection. A list that grows indefinitely can make parent records unwieldy and, in MongoDB, risks the 16-mebibyte document limit.
- Duplicating frequently changing data without a consistency plan. Copies can save reads, but every copy that must remain current creates update work.
- Assuming a reference enforces integrity. Confirm whether the product enforces the constraint; in Cosmos DB for NoSQL, Microsoft places existence checks in application logic or server-side code.
- Assuming NoSQL means no joins or that all products join alike. MongoDB provides aggregation lookup facilities, while Cosmos DB for NoSQL advises modeling complex relational-style needs around access patterns.
A practical modeling workflow
- List operations: Write down the application’s important reads, writes, and deletes, including which related fields each needs.
- Map relationships: Identify one-to-one, one-to-many, and many-to-many links, along with which records are queried independently.
- Estimate growth and change: Mark collections as bounded or unbounded, and note how often parents and related records change separately.
- Choose a starting shape: Embed small, bounded, co-read data; reference data with independent access, frequent changes, or unbounded growth; evaluate database-specific patterns for complex relationships.
- Check product constraints: Review atomicity boundaries, size limits, query/join facilities, indexing, and integrity behavior for the specific database and edition.
- Walk through critical queries: Confirm each common operation can retrieve the needed data with acceptable reads and updates, and decide how any duplicated or projected data stays current.
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