Book Image

Practical MongoDB Aggregations

By : Paul Done
Book Image

Practical MongoDB Aggregations

By: Paul Done

Overview of this book

Officially endorsed by MongoDB, Inc., Practical MongoDB Aggregations helps you unlock the full potential of the MongoDB aggregation framework, including the latest features of MongoDB 7.0. This book provides practical, easy-to-digest principles and approaches for increasing your effectiveness in developing aggregation pipelines, supported by examples for building pipelines to solve complex data manipulation and analytical tasks. This book is customized for developers, architects, data analysts, data engineers, and data scientists with some familiarity with the aggregation framework. It begins by explaining the framework's architecture and then shows you how to build pipelines optimized for productivity and scale. Given the critical role arrays play in MongoDB's document model, the book delves into best practices for optimally manipulating arrays. The latter part of the book equips you with examples to solve common data processing challenges so you can apply the lessons you've learned to practical situations. By the end of this MongoDB book, you’ll have learned how to utilize the MongoDB aggregation framework to streamline your data analysis and manipulation processes effectively.
Table of Contents (20 chapters)
2
Part 1: Guiding Tips and Principles
7
Part 2: Aggregations by Example
16
Afterword

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...