Real-time analytics dashboards require computing complex metrics across large datasets. In MongoDB, unoptimized aggregation pipelines can quickly cause CPU spikes and slow load times.
This guide details how to structure and optimize MongoDB aggregation pipelines for sub-second execution.
1. The Importance of Stage Ordering
The order of stages in your aggregation pipeline determines the dataset size passed to subsequent stages.
- Always put `$match` first: This filters the dataset as early as possible.
- Put `$project` last: Projecting fields early disables index utilization in subsequent stages.
2. Using Covered Indexes for Aggregations
To prevent MongoDB from reading raw documents from disk (cold storage), design compound indexes that cover the entire aggregation query.
- Field Ordering: Match fields first, group fields second, and project fields third in the compound index.
- Explain Plan: Verify that your pipeline returns
IXSCAN(Index Scan) and avoidsCOLLSCAN(Collection Scan) in the execution plan.