MongoDB Performance Optimization & Query Tuning

I will conduct a MongoDB performance optimisation engagement — using the MongoDB profiler, currentOp(), and explain() to identify slow queries, collection scan operations, suboptimal aggregation pipelines, and schema design decisions causing performance problems — and implementing fixes covering index strategy, query rewriting, and targeted schema refactoring. MongoDB performance problems frequently trace back to three root causes: missing indexes on filter fields, embedding decisions that made sense at low document counts but produce excessive document scans at scale, and aggregation pipelines with $lookup on unindexed foreign fields that degrade to collection scan joins.

The engagement covers profiler configuration and slow query log analysis, explain() for the top 10 resource-consuming operations, index strategy implementation (following the ESR rule for compound indexes), aggregation pipeline rewriting with stage reordering for early filtering, targeted schema refactoring for collections where the embedding/referencing decision is causing performance problems, and a before/after performance comparison.

Designed for development teams whose MongoDB application is experiencing performance degradation as data volumes grow and whose existing ad hoc optimisation has not resolved the underlying problems.