MongoDB Index Guide

MongoDB Indexes: Types, Algorithms, and Examples

Use compound indexes that match real filter and sort shapes. Add multikey, text, geospatial, wildcard, hashed, or TTL behavior only when the matching query or lifecycle requirement needs it.

What Is a MongoDB Index?

An index is a data structure that helps the database find candidate records without scanning every row. It can accelerate matching queries but requires storage and maintenance during writes.

How MongoDB Index Algorithms Work

MongoDB ordinary indexes are generally B-tree structures; specialized types index arrays, text, geospatial values, hashed keys, or flexible document fields.

MongoDB index lookup algorithm diagram
Conceptual algorithm illustration for MongoDB; implementation details depend on index type and database version.

MongoDB Index Types and Individual Guides

Each link opens its own guide with an algorithm image, create/query example, use cases, limitations, and tuning steps.

  1. 2d Geospatial Indexes
  2. 2dsphere Geospatial Indexes
  3. Clustered Collections and Indexes
  4. Collation-Aware Indexes
  5. Compound Indexes
  6. Hashed Indexes
  7. Hidden Indexes
  8. Multikey Indexes
  9. Partial Indexes
  10. Single-Field Indexes
  11. Sparse Indexes
  12. Text Indexes
  13. TTL Expiration Indexes
  14. Unique Indexes
  15. Vector Search Indexes
  16. Wildcard Indexes

Which MongoDB Index Should I Use?

Use compound indexes that match real filter and sort shapes. Add multikey, text, geospatial, wildcard, hashed, or TTL behavior only when the matching query or lifecycle requirement needs it.

Start with the query workload, required consistency, ordering, data distribution, and write rate. Do not select a structure from its name alone.

Match Indexes to Filters, Sorts, and Joins

Write down the actual predicates, sort keys, joins, and returned columns. The order of keys and supported operators determine whether the engine can use an index.

How to Read MongoDB Query Plans

Use the engine's explain or execution-plan tooling to compare estimated and actual rows, keys examined, pages read, sorting, filtering, and lookup operations.

Index Storage and Write Performance

Indexes use disk and cache and add work to inserts, updates, and deletes. Specialized indexes may emit multiple entries per row or maintain separate structures.

Index Statistics and Maintenance

Monitor size, usage, build state, selectivity, fragmentation or compaction where relevant, and write latency. Make maintenance changes using the engine's version-specific procedures.

Common MongoDB Indexing Mistakes

  • Adding indexes without checking real query plans.
  • Ignoring key order, operator compatibility, or data distribution.
  • Assuming an index removes all filtering or row-fetch work.
  • Forgetting write amplification, backups, and deployment impact.

MongoDB Index Version and Feature Notes

Available index types and behavior vary by engine release, storage engine, extension, edition, data type, and operator. Verify each feature against the database actually deployed.

Official reference: MongoDB documentation: Index Types.

MongoDB Indexing Questions

Does adding an index guarantee a faster query?

No. The optimizer may choose a table or partition scan when many rows match or index lookups would cost more.

Should every searchable column have an index?

No. Index only measured workloads and account for maintenance, storage, and data distribution.