High Performance with MongoDB: Best practices for performance tuning, scaling, and architecture

Design, optimize, and operate high-performance MongoDB deployments, now with a bonus chapter exploring evolving practices and capabilities

Key Features

• Identify and fix performance bottlenecks with practical diagnostic and optimization strategies
• Optimize schema design, indexing, storage, and system resources for real-world workloads
• Scale confidently with in-depth coverage of replication, sharding, and MongoDB performance enhancements
• Purchase of the print or Kindle book includes a free PDF eBook

Book Description

With data as the new competitive edge, performance has become the need of the hour. As applications handle exponentially growing data and user demand for speed and reliability rises, three industry experts distill their decades of experience to offer you guidance on designing, building, and operating databases that deliver fast, scalable, and resilient experiences.

MongoDB’s document model and distributed architecture provide powerful tools for modern applications, but unlocking their full potential requires a deep understanding of architecture, operational patterns, and tuning best practices. This MongoDB book takes a hands-on approach to diagnosing common performance issues and applying proven optimization strategies from schema design and indexing to storage engine tuning and resource management.

Whether you’re optimizing a single replica set or scaling a sharded cluster, this book provides the tools to maximize deployment performance. Its modular chapters let you explore query optimization, connection management, and monitoring while real-world case studies and code examples reinforce proven practices. A new bonus chapter, Evolving High-Performance Practices, highlights MongoDB's advances in query execution, Intelligent Workload Management, OpenTelemetry, diagnostics, and driver behavior.

What you will learn

• Diagnose and resolve common performance bottlenecks in deployments
• Design schemas and indexes that maximize throughput and efficiency
• Tune the WiredTiger storage engine and manage system resources for peak performance
• Leverage sharding and replication to scale and ensure uptime
• Monitor, debug, and maintain deployments proactively to prevent issues
• Improve application responsiveness through client driver configuration

Who this book is for

This book is for developers, database administrators, system architects, and DevOps engineers focused on performance optimization of MongoDB. Whether you’re building high-throughput applications, managing deployments in production, or scaling distributed systems, you’ll gain actionable insights. Basic knowledge of MongoDB is assumed, with chapters designed progressively to support learners at all levels.

Table of Contents

1. Systems and MongoDB Architecture
2. Schema Design for Performance
3. Indexes
4. Aggregations
5. Replication
6. Sharding
7. Storage Engines
8. Change Streams
9. Transactions
10. Client Libraries
11. Managing Connections and Network Performance
12. Advanced Query and Indexing Concepts
13. Operating Systems and System Resources
14. Monitoring and Observability
15. Debugging Performance Issues
16. Evolving High-Performance Practices

Asya Kamsky is a Principal KnowItAll at MongoDB, where she has worked since 2012. Before discovering MongoDB, she spent nearly two decades coaxing relational databases into doing what they were never designed to do at a series of startups, most of which no one remembers. Asya has an extensive background in software development, databases, and telling people what they're doing wrong.

Ger Hartnett is a Lead Engineer on MongoDB's product performance team, where he loves working on fascinating optimization and scaling challenges. Before MongoDB, he founded a startup that built a project communication platform (that had scaling issues). Prior to that, he architected and tuned embedded software at Intel, where he co-authored a book. Even before that, he developed systems at companies including Tellabs, Digital, and Motorola.

Alex Bevilacqua is the Lead Product Manager at MongoDB for Developer Experience. Prior to joining MongoDB in 2018, he worked as a software engineer and systems architect, implementing solutions in a number of languages, technologies, and frameworks. Aside from his passion for programming that consumes more than just his working hours, Alex can typically be found at an arena with one of his kids, both of whom play rep hockey.