What this book covers
Chapter 1, MongoDB – A Database for the Modern Web, will act as a quick refresher of the structure and its key components for businesses. You will learn how the database has evolved over time and how different designs get to be driven by data modeling.
Chapter 2, Schema Design and Data Modeling, will explain the pros and cons of each approach and help you identify the best route to take for each case (key-value, document-based, graph, and CAP theorem). You will learn how to model your data for different use cases along with the tradeoffs of the different designs. Furthermore, you will learn how to configure the drivers for each language to make sure that you are making the most of MongoDB.
Chapter 3, MongoDB CRUD Operations, will showcase the MongoDB shell and its capabilities. This chapter will show you how to perform all the CRUD operations and administration tasks using the shell. You will learn to use the aggregation framework for prototyping, getting quick insights from data and where it shines compared to the older MapReduce framework. You will also learn how to use the new shell, mongosh, and how to migrate from the old one. Finally, you will know how to use the versioned API and the rapid, regular MongoDB release cycle to ensure code sustainability.
Chapter 4, Auditing, explores what auditing is and how is it different from regular application logging. You will learn how to set up auditing on-premises and in the cloud and how to identify irregular activity and single it out. Finally, you will have a case study bringing it all together that can serve as a reference for an end-to-end auditing implementation.
Chapter 5, Advanced Querying, will teach you how to query MongoDB, both using ODM and the driver from Ruby, along with instructions to use from the PHP and Python perspectives. It will show you how to avoid expensive operations and design queries so that they require the least possible maintenance down the line. You will be able to update and delete documents without impacting the underlying storage, and perform complex queries using regular expressions and arrays. You will also learn how and when you need to change streams.
Chapter 6, Multi-Document ACID Transactions, explores the theory behind transactions. How do the different transaction levels compare? When you should use transactions and what are the drawbacks? You will also configure different concern levels for transactions, and find out what the limitations of multi-document transactions in MongoDB are as of version 6.x.
Chapter 7, Aggregation, acts as a deep dive into the aggregation framework and how it can be a solution instead of complex queries or building data pipelines for ETL in code. You will learn how to use aggregation for a generic use case, as well as for more specific cases, such as window operators and time series. You will also be able to create and update materialized views from an RDBMS perspective.
Chapter 8, Indexing, showcases the different types of indexes and how to use them to improve querying efficiency. You will also learn to troubleshoot slow queries and optimize them. You will also understand what the drawbacks are of using too many indexes.
Chapter 9, Monitoring, Backup, and Security, explores monitoring, backups, and deployment for developers. The chapter shows how a developer or DBA can administer a MongoDB server or servers in their own data center or the cloud using MongoDB Atlas. It also shows how you can monitor ongoing operations and keep an eye on the cluster’s health, and how can you comply with GDPR using security updates, new in 6.x. Finally, you will learn how to find the optimal path for deployment and upgrading existing clusters.
Chapter 10, Managing Storage Engines, introduces the concept of storage engines. This chapter explains why they matter and how WiredTiger can help users administer MongoDB better.
Chapter 11, MongoDB Tooling, provides an answer to the following questions: how does tooling in the MongoDB ecosystem work? How can you use Realm, Search, Serverless, and Charts to build applications more quickly and robustly? How can you deploy and administer Atlas Kubernetes Operator to manage resources in Atlas without leaving Kubernetes? How can you use data from multiple devices, including IoT and mobile? How is MongoDB innovative in these use cases?
Chapter 12, Harnessing Big Data with MongoDB, showcases the integration of MongoDB with other data sources in the big data ecosystem, HDFS, message queues, and Kafka. It tells you how you can design MongoDB and where your source of truth and aggregator operations should lie when you store and process datasets. You will also learn to use MongoDB Atlas Data Lake as a warehouse and when should you use it as opposed to AWS Data Lake solutions.
Chapter 13, Mastering Replication, explores replication for MongoDB, along with setting up and administering replica sets. You will learn why we need replication as a concept and which workloads require replication as a first-class concern. You will learn to connect and administer replica sets from different drivers. Finally, you will see the latest updates to replica set setup and administration in MongoDB 6.x.
Chapter 14, Mastering Sharding, shows how to horizontally scale a MongoDB installation and how to set up and administer a sharded cluster. You will also learn how to decide on a sharding strategy and reshard data if the requirements change.
Chapter 15, Fault Tolerance and High Availability, explores various tips and tricks that you can use with all the concepts that you covered in the previous chapters.