MongoDB Index Design & Performance Optimization

I will analyse your MongoDB query workload and implement an optimised index strategy — covering single-field, compound, multikey (array), text, geospatial, and partial indexes — with explain() plan analysis for your critical queries confirming index utilisation, and removal of unused indexes consuming unnecessary write overhead. MongoDB index design requires understanding the ESR (Equality, Sort, Range) rule for compound index field ordering, the multikey index expansion for array fields, and the circumstances under which MongoDB will use an index intersection versus requiring a compound index — all of which determine whether a query uses an index or falls back to a collection scan.

The engagement covers MongoDB explain() analysis for your top-10 most frequent and most resource-intensive queries, index coverage assessment, compound index design following ESR ordering, partial index design for sparse query patterns, background index build to avoid write locking, and an index usage report using $indexStats to identify unused indexes for removal.

Designed for development teams with MongoDB collections growing past the point where collection scan queries are acceptable — typically 100K+ documents in frequently queried collections.