[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8},"actionData","errors","root",{"_29":-5,"_39":-5,"_40":41,"_42":43,"_44":45,"_46":47,"_48":49,"_50":51,"_52":53,"_54":55,"_56":57,"_58":59},"routes/_store+/_readers+/email_author.$slug/route",{"_9":10,"_11":-5,"_12":-5},"book",{"_13":14,"_15":16,"_17":18,"_19":20,"_21":22,"_23":24},"currentUser","formToken","id","Z2lkOi8vbGVhbnB1Yi9TdG9yZTo6Qm9va1Byb2R1Y3QvODQ4Nzk","title","MongoDB Aggregation Pattern Pack — Vol. 1: Reporting & Dashboards","state","published","contactAuthorMethod","email","aboutText","

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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Kick off your book project, get started with GhostAI, get better at marketing, or spend the day doing all three! Free live workshops on Zoom. Saturday, June 27, 2026.

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