How to Optimize MongoDB Aggregation Pipelines for Large-Scale Performance

MongoDB's aggregation framework becomes a performance bottleneck when collections scale to hundreds of millions or billions of documents, especially with poorly structured pipelines. Key issues include placing $match stages too late, unindexed $group and $sort operations, and costly $lookup joins that default to nested loop scans. Critical optimizations involve reordering pipeline stages to filter early, leveraging indexes for streaming operations, and pruning unused fields with $project to reduce memory overhead. MongoDB enforces a 100 MB per-stage memory limit, making index-backed operations and the allowDiskUse option essential for large aggregations. Sharding strategies and continuous profiling further help production teams maintain predictable query performance and resource usage at scale.