Take a deep dive into one of the most significant SQL Server features–support for In-Memory Online Transaction Processing. The latest developments are covered, including support of off-row storage, columnstore indexes and operational analytics, changes in programmability and native compilation, and more. This book describes the architecture and internals of the In-Memory OLTP Engine and explains how to develop, deploy, and maintain systems using it. With it you can dramatically increase transaction throughput to handle thousands of transactions per second supporting millions of customers.
Learn the architecture and the internals of In-Memory OLTP in order to recognize when technology can make a difference. Recognize opportunities for In-Memory OLTP in new development and understand how to benefit from it in existing systems. Don’t be without Dmitri Korotkevitch and the deep expertise he imparts in Expert SQL Server In-Memory OLTP, 2nd Edition as you move forward in using SQL Server’s In-Memory OLTP technology.
Dmitri Korotkevitch is the five-star author of Pro SQL Server Internals, and brings the same combination of clear thinking and deep expertise to help you in this second edition. The book:
- Explains In-Memory OLTP internals, architecture and programmability, including data storage, indexing, multi-version concurrency control, transaction logging and recovery, and native compilation
- Covers SQL Server 2016 technology enhancements, including columnstore indexes and off-row storage
- Guides in using In-Memory OLTP in new development and existing systems
What You’ll Learn:
- Grasp how SQL Server stores and works with data in memory-optimized tables
- Properly design and index memory-optimized tables
- Plan successful deployments, including the required memory size and I/O configuration
- Accelerate T-SQL processing through the creation of natively compiled modules
- Monitor and report on the benefits and performance of your In-Memory OLTP solutions
- Benefit from the technology in existing systems and in the systems with the mixed workload