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

Can all stages use expressions?

There are many types of stages in the aggregation framework that don't allow expressions to be embedded. Here are some examples of some of the most popular of these stages:

  • $match
  • $limit
  • $skip
  • $sort
  • $count
  • $lookup
  • $out

Some of these stages may be a surprise to you if you've never really thought about it before. You might consider $match to be the most surprising item in this list. The content of a $match stage is just a set of query conditions with the same syntax as MongoDB Query Language rather than an aggregation expression. There is a good reason for this. The aggregation engine reuses the MongoDB Query Language query engine to perform a regular query against the collection, enabling the query engine to use all its usual optimizations. The query conditions are taken as-is from the $match stage at the top of the pipeline. Therefore, the $match filter must use the same syntax as MongoDB Query Language...