Database Performance at Scale: A Practical Guide

Discover critical considerations and best practices for improving database performance based on what has worked, and failed, across thousands of teams and use cases in the field. This open access book provides practical guidance for understanding the database-related opportunities, trade-offs, and traps you might encounter while trying to optimize data-intensive applications for high throughput and low latency. Whether you are building a new system from the ground up or trying to optimize an existing use case for increased demand, this book covers the essentials.

The book begins with a look at the many factors impacting database performance at the extreme scale that today’s game changing applications face—or at least hope to achieve. You’ll gain insight into the performance impact of both technical and business requirements, and how those should influence your decisions around database infrastructure and topology. The authors share an inside perspective on often-overlooked engineering details that could be constraining—or helping—your team’s database performance. The book also covers benchmarking and monitoring practices by which to measure and validate the outcomes from the decisions that you make.

The ultimate goal of the book is to help you discover new ways to optimize database performance for your team’s specific use cases, requirements, and expectations.

What You Will Learn

• Understand often overlooked factors that impact database performance at scale
• Recognize data-related performance and scalability challenges associated with your project
• Select a database architecture that’s suited to your workloads, use cases, and requirements
• Avoid common mistakes that could impede your long-term agility and growth
• Jumpstart teamwide adoption of best practices for optimizing database performance at scale

Who This Book Is For

Individuals and teams looking to optimize distributed database performance for an existing project or to begin a new performance-sensitive project with a solid and scalable foundation. This will likely include software architects, database architects, and senior software engineers who are either experiencing or anticipating pain related to database latency and/or throughput.

About the Author

Felipe Cardeneti Mendes is an IT specialist with years of experience on distributed systems and open source technologies. He has co-authored three Linux books and is a frequent speaker at public events and conferences to promote open source technologies. At ScyllaDB, he works as a Solution Architect.

Piotr Sarna is a software engineer who is keen on open-source projects and the Rust and C++ languages. He previously developed an open-source distributed file system and had a brief adventure with the Linux kernel during an apprenticeship at Samsung Electronics. He's also a long-time contributor and maintainer of ScyllaDB, as well as libSQL. Piotr graduated from University of Warsaw with an MSc in Computer Science.

Pavel "Xemul" Emelyanov is an ex-Linux kernel hacker now speeding up row cache, tweaking the IO scheduler, and helping to pay back technical debt for component interdependencies. He is a Principal Engineer at ScyllaDB.

Cynthia Dunlop is a technology writer who specializes in application development. She has co-authored four books and hundreds of articles on everything from C/C++ memory error detection to continuous testing and DevOps. Cynthia holds a bachelor’s degree from UCLA and a master’s degree from Washington State University.