MongoDB's aggregation pipeline processes documents through stages. Here's how to use it effectively.
Basic Stages
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Grouping and Accumulation
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Lookups (Joins)
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Array Operations
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Date Operations
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Conditional Logic
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Text Search
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Faceted Search
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Output Stages
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Performance
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Best Practices
Pipeline Order:
✓ $match early to filter
✓ $project early to reduce size
✓ $sort after $match for index use
✓ $limit after $sort
Performance:
✓ Create indexes for $match fields
✓ Use $project to limit fields
✓ Avoid $unwind when possible
✓ Use allowDiskUse for large data
Design:
✓ Break complex pipelines into steps
✓ Test each stage independently
✓ Use explain() to analyze
✓ Consider $merge for materialized views
Conclusion
MongoDB's aggregation pipeline is powerful for data transformation and analysis. Structure pipelines with filtering first, use indexes effectively, and break complex operations into readable stages. For production, always analyze with explain() and consider creating materialized views for frequently-run aggregations.