Aggregation Challenges

Introduction

Aggregation pipelines are the highest-leverage skill in MongoDB. These challenges progress from a one-stage $group through to a multi-stage $facet with $lookup and window functions — the exact shapes you'll write in dashboards, exports, and ETL.

Understanding the topic

10 progressive aggregation challenges:

• 1. Group orders by month → revenue & count.
• 2. Top 10 customers by lifetime spend.
• 3. $lookup orders → customer profile.
• 4. $unwind line items, then top-selling SKU.
• 5. Rolling 7-day active users with $setWindowFields.
• 6. $facet KPI dashboard in one query.
• 7. Funnel analysis — events → conversion %.
• 8. Cohort retention by signup month.
• 9. Materialise a daily rollup with $merge.
• 10. Geo aggregation — top zip codes by revenue.

Informative example

Challenge 5 — rolling 7-day active users:

db.events.aggregate([
  { $match: { type:"login", ts:{ $gte: ISODate("2025-01-01") } } },
  { $group: { _id: { day: { $dateTrunc: { date:"$ts", unit:"day" } }, userId:"$userId" } } },
  { $group: { _id: "$_id.day", dau: { $sum: 1 } } },
  { $setWindowFields: {
      sortBy: { _id: 1 },
      output: { wau: { $sum: "$dau", window:{ range:[-6,0], unit:"day" } } }
  } },
  { $sort: { _id: 1 } }
]);

Best practices

• Build pipelines stage-by-stage; preview with { $limit: 5 } after each addition.
• Move $match + $sort as early as possible — they use indexes.
• Persist heavy pipelines with $merge if dashboards re-run them often.