Data Modeling and Schema Design for MongoDB Search Part 2: Use Cases and Patterns

Summary

The post presents best practices for data modeling and schema design in MongoDB Search, covering use cases and patterns such as the Entity Pattern to guide developers in structuring collections for effective search. It emphasizes that searchable items, like movies, cast members, or genres, should be modeled as first-class documents in their own collections rather than as nested structures or ambiguous attributes. The Entity Pattern addresses ‘find anything anywhere’ and as-you-type suggestions by representing all domain entities as documents in a specialized collection with stable identifiers and a type field for filtering. The Attribute Pattern is explicitly discouraged for search because its key-value structure prevents type-sensitive field mapping, making it better to model attributes as straightforward fields. The post details a multi-indexing strategy on the name field that applies left-edge, phonetic, shingled, and language-agnostic analysis to enable flexible matching and relevancy scoring, with optional type-specific metadata nested in sub-documents.

Takeaways

1. 1

   For search, model each domain entity as a first-class document in its own collection with a type discriminator to enable filtering, faceting, and boosting by entity type.

2. 2

   Avoid the Attribute Pattern for search; instead, model each searchable attribute as a separate field with consistent data types to leverage type-sensitive indexing and mapping.

3. 3

   The Entity Pattern uses multi-indexing on the name field (e.g., left-edge, phonetic, shingling) to match query text in many ways and weigh each analysis for relevancy.

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