mongodb-schema-design

To design optimal MongoDB schemas, you must detect anti-patterns like unbounded arrays and apply embedding versus referencing based on access patterns, which eliminates slow queries and reduces storage costs.

Choose embedding versus referencing in MongoDB based on data cardinality and access patterns; embedding is suited for data accessed together, while referencing prevents oversized documents when dealing with large datasets.

Common MongoDB schema anti-patterns causing performance issues include unbounded arrays, unnecessary collections, and excess indexes, which lead to slow queries and hitting the 16MB document limit.

You can review an existing MongoDB collection schema to troubleshoot slow queries by providing collection metadata to identify schema pitfalls, validate schemas, and receive indexing advice.

MongoDB design patterns like approximation, archiving, versioning, and time-series collections help manage specific workloads and prevent oversized documents by optimizing data model structures.