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MongoDB Aggregation Pattern Pack — Vol. 1: Reporting & Dashboards

Copy-paste aggregation patterns for reporting and dashboards, using Atlas sample data.

MongoDB Aggregation Pattern Pack — Vol. 1: Reporting & Dashboards
John Lynn

Ten copy-paste MongoDB reporting patterns for mongosh, run against Atlas sample data.

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About

About the Book

If you already know $match and $group, the hard part is often building the report: totals by day, top-N per store, period-over-period comparisons, or several dashboard cards in one response.

This book gives you ten of those patterns. Each chapter includes a pipeline you can run against Atlas sample_supplies.sales (and sample_mflix where a join is required), notes on when the pattern fits, and common mistakes to watch for. Examples use mongosh, with leading commas so stages are easy to reorder and comment out.

This is not a catalog of every operator. It is a set of reporting pipelines you can adapt and ship.

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About the Author

John Lynn

John Lynn works in data and AI. He wrote an Amazon ebook on the MongoDB Aggregation Framework and a Field Guide to MongoDB Aggregation for Studio 3T. This pattern pack is for people who already know the basics and need practical reporting pipelines they can run and adapt.

Contents

Table of Contents

About

How to read these patterns

  1. Leading commas
  2. Conventions in this pack

Sample data

  1. On Atlas
  2. On local MongoDB
  3. What a sale looks like

How to run the examples

  1. mongosh
  2. Compass
  3. Node scripts

Pattern 1 — Time-bucket totals

  1. When this pattern fits
  2. When it does not fit
  3. Sample document (trimmed from Atlas)
  4. Pipeline
  5. Expected output shape
  6. Watch out
  7. Variations

Pattern 2 — Top-N per category

  1. When this pattern fits
  2. When it does not fit
  3. Sample data
  4. Pipeline
  5. Expected output shape
  6. Watch out
  7. Variations

Pattern 3 — Running totals / cumulative series

  1. When this pattern fits
  2. When it does not fit
  3. Sample data
  4. Pipeline
  5. Expected output shape
  6. Watch out
  7. Variations

Pattern 4 — Period-over-period

  1. When this pattern fits
  2. When it does not fit
  3. Sample data
  4. Pipeline
  5. Expected output shape
  6. Watch out
  7. Variations

Pattern 5 — Null-safe metrics

  1. When this pattern fits
  2. When it does not fit
  3. Sample data
  4. Pipeline
  5. Expected output shape
  6. Watch out
  7. Variations

Pattern 6 — Multi-collection report via $lookup

  1. When this pattern fits
  2. When it does not fit
  3. Sample data
  4. Pipeline
  5. Expected output shape
  6. Watch out
  7. Variations

Pattern 7 — Faceted dashboard payload

  1. When this pattern fits
  2. When it does not fit
  3. Sample data
  4. Pipeline
  5. Expected output shape
  6. Watch out
  7. Variations

Pattern 8 — Histogram / distribution

  1. When this pattern fits
  2. When it does not fit
  3. Sample data
  4. Pipeline
  5. Expected output shape
  6. Watch out
  7. Variations

Pattern 9 — Active vs churned in a window

  1. When this pattern fits
  2. When it does not fit
  3. Sample data
  4. Pipeline
  5. Expected output shape
  6. Watch out
  7. Variations

Pattern 10 — Pagination-friendly report pages

  1. When this pattern fits
  2. When it does not fit
  3. Sample data
  4. Pipeline — page and count
  5. Keyset variation (preferred for deep pages)
  6. Expected output shape (facet form)
  7. Watch out
  8. Variations

What’s next

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