mongodb-query-optimizer

MongoDB query optimization analyzes query shapes and indexes to reduce latency by detecting COLLSCANs and in-memory sorts, then proposing compound indexes following equality→sort→range rules to lower resource consumption.

Slow MongoDB queries are diagnosed by running explain() to examine keys and documents examined versus returned, identifying collection scans, in-memory sorts, and aggregation pipeline anti-patterns causing performance bottlenecks.

Create compound MongoDB indexes by following equality→sort→range rules, prioritizing equality fields first, then sort fields, then range fields, while identifying opportunities for covered queries to minimize document fetches.

Atlas Performance Advisor and slow query logs are supported when you provide Atlas project and cluster credentials or a MongoDB connection string, allowing direct analysis of collection indexes and query execution stats.

Provide the query shape, database name, and collection name to analyze MongoDB aggregation pipelines, optionally including a connection string to run live explain plans and load aggregation optimization references.

Yes, MongoDB query optimization falls back to query-shape advice when a connection string or Atlas credentials are unavailable, providing index recommendations and pipeline guidance based on the query structure alone.