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Practical MongoDB Aggregations

You're reading from   Practical MongoDB Aggregations The official guide to developing optimal aggregation pipelines with MongoDB 7.0

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Product type Paperback
Published in Sep 2023
Publisher Packt
ISBN-13 9781835080641
Length 312 pages
Edition 1st Edition
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Author (1):
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Paul Done Paul Done
Author Profile Icon Paul Done
Paul Done
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Toc

Table of Contents (20) Chapters Close

Preface 1. Chapter 1: MongoDB Aggregations Explained 2. Part 1: Guiding Tips and Principles FREE CHAPTER
3. Chapter 2: Optimizing Pipelines for Productivity 4. Chapter 3: Optimizing Pipelines for Performance 5. Chapter 4: Harnessing the Power of Expressions 6. Chapter 5: Optimizing Pipelines for Sharded Clusters 7. Part 2: Aggregations by Example
8. Chapter 6: Foundational Examples: Filtering, Grouping, and Unwinding 9. Chapter 7: Joining Data Examples 10. Chapter 8: Fixing and Generating Data Examples 11. Chapter 9: Trend Analysis Examples 12. Chapter 10: Securing Data Examples 13. Chapter 11: Time-Series Examples 14. Chapter 12: Array Manipulation Examples 15. Chapter 13: Full-Text Search Examples 16. Afterword
17. Index 18. Other books you may enjoy Appendix

Largest graph network

Sometimes your data may include graph relationships between records within a single collection. Take, for instance, a document management system database that houses whitepapers citing other whitepapers within the same collection. Visualizing the chain of dependencies becomes crucial in such situations. This example shows how you can traverse these sorts of relationships within a collection.

Scenario

Your organization wants to know the best targets for a new marketing campaign based on a social network database such as Twitter.

You want to search the collection of social network users, each holding a user's name and the names of others who follow them. You want to traverse each user record's followed_by array to determine which user has the most extensive network reach.

Note

This example uses a simple data model for brevity. However, this is unlikely to be an optimum data model for using $graphLookup at scale for social network users with...

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