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MongoDB Aggregation Pattern Pack, Vol. 1: Reporting & Dashboards (Python)

Python / PyMongo · Copy-paste reporting patterns against Atlas sample data

MongoDB Aggregation Pattern Pack, Vol. 1: Reporting & Dashboards (Python)
John Lynn

Ten copy-paste MongoDB reporting patterns for Python and PyMongo, 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 itself: totals by day, top-N per store, period-over-period comparisons, or several dashboard cards in one response.

This book walks through ten patterns against Atlas sample_supplies.sales, plus sample_mflix for joins. For each one it covers when the pattern fits and the mistakes to watch for. Examples use Python and PyMongo, with leading commas in pipeline lists so you can comment stages in and out while you work. The pipelines match the Node / mongosh edition stage for stage.

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

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Author

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 in Python
  2. pymongo notes

Sample data

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

How to run the examples

  1. PyMongo setup
  2. A smoke test
  3. Running a pattern
  4. Compass and mongosh

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

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