MongoDB Modeling and Indexes

Module:Core concept

Model documents around access patterns; embed bounded data read with a parent and reference independently growing or shared entities.

How to apply it

Indexes speed reads but consume storage and slow writes. Design compound indexes around equality filters, sort, and range predicates. Use explain execution statistics on representative data and inspect documents examined, keys examined, and sort stages.

Worked example

A project list may benefit from an index on organizationId, createdAt descending, and _id descending; benchmark if status should also be indexed.

Common mistakes to avoid

Indexing every field or assuming one index optimizes every query.

Practice task

Implement this concept in the capstone. Add one positive test, one negative or failure-path test, and a short note explaining the trade-off you chose.

Tenant-scoped MongoDB query

projectSchema.index({ organizationId: 1, createdAt: -1, _id: -1 });

const projects = await Project.find({
  organizationId: req.auth.organizationId,
  status: 'active'
}).sort({ createdAt: -1, _id: -1 }).limit(25).lean();

💡 This index is illustrative. Validate query plans using representative data; never omit the tenant predicate from tenant-owned queries.

Key Takeaways

Model documents around access patterns; embed bounded data read with a parent and reference independently growing or shared entities.
Indexes speed reads but consume storage and slow writes.

Knowledge Check

What is the strongest production practice for MongoDB Modeling and Indexes?