Preface

This book is about the MongoDB aggregation framework. It provides a set of practical, easy-to-digest principles and approaches for increasing your effectiveness in developing aggregation pipelines, supported by examples for building pipelines to solve various data manipulation and analysis tasks.

The aggregation framework is a runtime component of MongoDB with an API for client applications. By using the aggregation framework, developers can invoke queries to perform sophisticated transformations, analytics, and computations on the data held directly within the database. MongoDB aggregations help with tasks such as filtering, grouping, sorting, and calculating aggregated values from large datasets without needing to extract and process the data externally.

How will this book help you?

Mastering the MongoDB aggregation framework can seem overwhelming. The focus of this book is to streamline your learning process for aggregations and make difficult concepts and steps simpler to understand. You'll be able to craft aggregation pipelines that exhibit increased performance and scalability by using the guidance and practical examples shared in this book. Instead of inefficiently extracting vast amounts of data for external processing, you'll learn how to shift more of your data processing tasks directly to the database. Furthermore, your developed pipelines will be more robust and adaptable, ready to evolve in response to changing business requirements.

Who this book is for

This book is for developers, architects, data analysts, data engineers, and data scientists who have a foundational grasp of MongoDB and preliminary experience with its aggregation framework; that is, a working knowledge of MongoDB is assumed. This book is not for beginners who want to learn about aggregation pipelines from the ground up.

Given the programming-centric approach of MongoDB aggregations, this book is for readers with some coding experience. While knowledge of JavaScript is advantageous, proficiency in any modern programming language is sufficient.

This book will empower readers to elevate their capabilities regarding the richness, agility, performance, and scalability of the pipelines they develop.

What this book covers

Chapter 1, MongoDB Aggregations Explained, provides a level-set of what aggregations are and how to use them.

Chapter 2, Optimizing Pipelines for Productivity, helps you to develop composable and adaptable pipelines.

Chapter 3, Optimizing Pipelines for Performance, informs you how to reduce the latency of your aggregations.

Chapter 4, Harnessing the Power of Expressions, helps you leverage the power of expressions for transforming data, especially arrays.

Chapter 5, Optimizing Pipelines for Sharded Clusters, provides considerations for executing your pipelines against large volumes of data.

Chapter 6, Foundational Examples: Filtering, Grouping, and Unwinding, provides examples of common data manipulation patterns used in many aggregation pipelines, which are relatively straightforward to understand and adapt.

Chapter 7, Joining Data Examples, offers guidance on joining together data from different collections.

Chapter 8, Fixing and Generating Data Examples, provides tools and techniques to clean data within a dataset.

Chapter 9, Trend Analysis Examples, showcases the capabilities of the MongoDB aggregation framework in performing advanced data analytics.

Chapter 10, Securing Data Examples, helps you discover ways to use aggregation pipelines to secure the data in a MongoDB database and reduce the risk of a data breach.

Chapter 11, Time-Series Examples, shows examples of how you can use aggregation pipelines to extract insight from time-series data.

Chapter 12, Array Manipulation Examples, shows how to break down array manipulation problems into manageable pieces, streamlining your assembly of solutions.

Chapter 13, Full-Text Search Examples, demonstrates how to build aggregation pipelines that leverage full-text search capabilities in MongoDB Atlas.

To get the most out of this book

You will require the following software:

Software covered in the book	Operating system requirements
MongoDB version 4.4 or newer	Windows, macOS, or Linux
MongoDB Atlas Search	Windows, macOS, or Linux
MongoDB Shell	Windows, macOS, or Linux

After reading this book, we encourage you to check out some of the other resources available at https://www.mongodb.com/developer or https://learn.mongodb.com/

Download the example code files

You can download the example code files for this book from GitHub at https://github.com/PacktPublishing/Practical-MongoDB-Aggregations. If there's an update to the code, it will be updated in the GitHub repository.

We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!

Conventions used

There are a number of text conventions used throughout this book.

Code in text: Indicates code words in text, database collection names, folder names, filenames, file extensions, pathnames, dummy URLs, and user input. Here is an example: "When considering the $sort and $group stages, it becomes evident why they have to block."

A block of code is set as follows:

db.persons.find(
    {"vocation": "ENGINEER"},
    {"_id": 0, "vocation": 0, "address": 0},
  ).sort(
    {"dateofbirth": -1}
  ).limit(3);

Bold: Indicates a new term, an important word, or words that you see onscreen. For instance, words in menus or dialog boxes appear in bold. Here is an example: "A MongoDB database, version 4.2 or greater, that is network accessible from your workstation."

Tips or important notes

Appear like this.

Get in touch

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