---
title: "Lucene"
description: "1 posts about Lucene, summarised, each linking to the original."
---

# Lucene
> 1 posts about Lucene, summarised, each linking to the original.

## Articles

### [Data Modeling and Schema Design for MongoDB Search Part 1: Practical Considerations](https://yomu.fyi/post/data-modeling-and-schema-design-for-mongodb-search-part-1-practical-co.md)
- Company: [MongoDB](https://yomu.fyi/company/mongodb.md)
- Published: Oct 6, 2026
- Type: Explainer

Data modeling for MongoDB Search requires balancing conceptual, logical, and physical schema decisions against workload demands and system constraints. The $search stage must be the first in an aggregation pipeline, queries a single Search index, and runs against a single collection, with results returned in relevancy order. The underlying Lucene engine supports two mappings for arrays of sub-documents: default document mapping flattens fields into one index object, while embeddedDocuments mapping creates separate Lucene index objects per sub-document. Embedded mapping enables cross-field matching within sub-documents but explodes the index object count and causes heavy reindexing on any update, as the entire block is deleted and re-added. This post establishes the foundational concepts and physical constraints that will inform schema patterns in Part 2.