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

State change boundaries

Continuing on the industrial IT theme, an organization managing a large estate of devices needs to identify operating trends and rhythms across its devices to control costs and enable proactive measures, such as predictive maintenance. In this example, you will discover how to build an aggregation pipeline to identify patterns for when devices are in use or idle.

Note

For this example, you require MongoDB version 5.0 or above. This is because you will use time-series collections, the $setWindowFields stage, and the $shift operator introduced in version 5.0.

Scenario

You are monitoring various industrial devices (e.g., heaters and fans) contained in the business locations of your clients. You want to understand the typical patterns of when these devices are on and off to help you optimize for sustainability by reducing energy costs and their carbon footprint. The source database contains periodic readings for every device, capturing whether each is...