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Practical MongoDB Aggregations

Practical MongoDB Aggregations

By : Paul Done
5 (17)
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Practical MongoDB Aggregations

Practical MongoDB Aggregations

5 (17)
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)
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2
Part 1: Guiding Tips and Principles
7
Part 2: Aggregations by Example
16
Afterword

Faceted classification

A typical scenario, often seen as a navigation bar on the left-hand side of an e-commerce product search website, is the need to characterize the same data across multiple dimensions or facets. This example will show you how to perform these faceting queries from a single aggregation pipeline.

Scenario

You want to provide faceted search capability on your retail website to enable customers to refine their product search by selecting specific characteristics against the product results listed on the web page. It is beneficial to classify the products by different dimensions, where each dimension, or facet, corresponds to a particular field in a product record (e.g., product rating or product price).

Each facet should be broken down into a separate range so that a customer can select a specific sub-range (e.g., 4-5 stars) for a particular facet (e.g., rating). The aggregation pipeline will analyze the products collection by each facet's field (rating...

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