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

Using explain plans to identify performance bottlenecks

When you're using the MongoDB Query Language to develop queries, it is essential to view the explain plan for a query to determine whether you've used the appropriate index and determine whether you need to optimize other aspects of the query or the data model. An explain plan allows you to fully understand the performance implications of the query you have created.

The same applies to aggregation pipelines. However, an explain plan tends to be even more critical with aggregations because considerably more complex logic can be assembled and run inside the database. There are far more opportunities for performance bottlenecks to occur, thus requiring optimization.

The MongoDB database engine will do its best to apply its own aggregation pipeline optimizations at runtime. Nevertheless, there could be some optimizations that only you can make. A database engine should never optimize a pipeline in such a way as to...