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aggregation-performance

Aggregation performance

Last reviewed May 28, 2026 Content v20260528
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This lesson

This lesson teaches Aggregation performance: document modeling, query operators, and aggregation patterns for MongoDB.

Aggregation pipelines power analytics in-place—$match early and index-backed filters appear in every data interview.

You will apply Aggregation performance in contexts like: In-app dashboards, funnel metrics, and ETL-lite without exporting every row to a warehouse.

Copy JavaScript shell queries from each lesson into mongosh or MongoDB Atlas Data Explorer—inspect matched documents and explain plans. The in-browser lab (execution_profile: none) ships later; mongosh is the practice path now. Also put $match as early as possible in the pipeline.

When find, operators, and basic aggregation pipelines feel familiar in mongosh.

Index keys used in $match and $sort, allowDiskUse for large sorts, and avoid massive $lookup without filters.

Explain aggregation

db.orders.aggregate([
  { $match: { status: 'open' } },
  { $sort: { createdAt: -1 } },
  { $limit: 20 }
]).explain('executionStats')

Practice: Run aggregation pipelines in mongosh.

Tips

  • Match early; project only needed fields before heavy stages
  • Index { status: 1, createdAt: -1 } if that query is hot
  • Pre-filter $lookup sub-pipeline
  • Consider $merge during off-peak for large materializations

Important interview questions and answers

  1. Q: allowDiskUse?
    A: Lets large sorts spill to disk—slower but prevents memory cap errors.
  2. Q: $lookup cardinality?
    A: Joining huge collections without $match explodes RAM.

Self-check

  1. Two ways to speed $match stage.
  2. Risk of late $project?

Tip: explain('executionStats') on pipelines reveals slow stages.

Interview prep

allowDiskUse?
Spills large sorts to disk when memory cap hit.
$lookup filter?
Pre-filter sub-pipeline to limit join size.

Interview tip Lesson completion confidence

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  • allowDiskUse?
  • Lookup filter?

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