Book Image

Mastering MongoDB 7.0 - Fourth Edition

By : Marko Aleksendrić, Arek Borucki, Leandro Domingues, Malak Abu Hammad, Elie Hannouch, Rajesh Nair, Rachelle Palmer
Book Image

Mastering MongoDB 7.0 - Fourth Edition

By: Marko Aleksendrić, Arek Borucki, Leandro Domingues, Malak Abu Hammad, Elie Hannouch, Rajesh Nair, Rachelle Palmer

Overview of this book

Mastering MongoDB 7.0 explores the latest version of MongoDB, an exceptional NoSQL database solution that aligns with the needs of modern web applications. This book starts with an informative overview of MongoDB’s architecture and developer tools, guiding you through the process of connecting to databases seamlessly. This MongoDB book explores advanced queries in detail, including aggregation pipelines and multi-document ACID transactions. It delves into the capabilities of the MongoDB Atlas developer data platform and the latest features, such as Atlas Vector Search, and their role in AI applications, enabling developers to build applications with the scalability and performance that today’s organizations need. It also covers the creation of resilient search functionality using MongoDB Atlas Search. Mastering MongoDB 7.0’s deep coverage of advanced techniques encompasses everything from role-based access control (RBAC) to user management, auditing practices, and encryption across data, network, and storage layers. By the end of this book, you’ll have developed the skills necessary to create efficient, secure, and high-performing applications using MongoDB. You’ll have the confidence to undertake complex queries, integrate robust applications, and ensure data security to overcome modern data challenges.
Table of Contents (20 chapters)
4
Chapter 4: Connecting to MongoDB

MongoDB Atlas Search

Before MongoDB Atlas Search, developers had to rely on various other solutions to implement search functionality in their applications. Some of these solutions included:

  • Algolia: This is a powerful and flexible search and discovery solution. Even though it's used in many applications, it can be quite costly. This cost is driven by the quantity of records stored and the volume of API actions performed. If you have a large dataset, Algolia might not be the best fit for you.
  • Elasticsearch: This is a distributed, RESTful search and analytics engine suitable for a growing number of use cases. Just like Atlas Search, it is based on Apache Lucene.
  • Solr: This is another powerful search platform built on Apache Lucene. It's highly reliable, scalable, and fault-tolerant, providing distributed indexing, replication, and load-balanced querying.
  • The built-in $text index of MongoDB: This is the easiest solution to implement but was not suitable...