Database Performance at Scale A Practical Guide /

• Main Authors: Mendes, Felipe Cardeneti (Author), Sarna, Piotr (Author), Emelyanov, Pavel (Author), Dunlop, Cynthia (Author)
• Corporate Author: SpringerLink (Online service)
• Format: Electronic eBook
• Language: English
• Published: Berkeley, CA : Apress : Imprint: Apress, 2023.
• Edition: 1st ed. 2023.
• Subjects: Database management.; Open source software.; Database Management.; Open Source.
• Online Access: Link to Metadata

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.