SQL Server 2025 Query Performance Tuning, Fritchey G., 2026

SQL Server 2025 Query Performance Tuning, Fritchey G., 2026.

   It’s never easy to "introduce" you to six to nine months of work. The concept of an introduction is simple enough: what’s the purpose of the book, who is the book written for, tips on how best to use the book. However, the introduction also has to set the stage for the rest of the book. As you’ll see when you read it, I open and close the book with a simple concept: query tuning is hard. When I present live sessions on this topic, I'll tell people up front. The easy way to tune queries is to simply throw money at the problem. Buy bigger hardware. Move to a higher service tier on your cloud provider. You can always make things faster by spending more money. It’s when the money runs out that you might just need to spend something else, time. Time learning how to identify poorly performing queries, understand how to investigate them to see where the problem may lie, and then apply a fix and validate that it worked. That’s where this book comes in.




Hash lndex.
The hash index is different from the other index types within SQL Server. A hash index uses a calculation to create a hash value of the key. The hash values are stored in buckets, or a table of values. The hash calculation is a constant, so for any given value, the same hash value will always be calculated.

Hash tables are very efficient. A hash value is a good way to retrieve a single row. However, when you start to have a lot of rows with the same hash value, that efficiency begins to drop.

The key to making the hash index efficient is getting the correct distribution across your hash buckets. When you create the index, you supply a bucket count. In the example in Listing 19-3,1 supplied a value of 50,000 for the bucket count. When you consider there are currently about 19,000 rows in the table, I’ve made the bucket count more than big enough to hold the existing data, and I left room for growth over time.

Contents.
About the Author.
About the Technical Reviewer.
Acknowledgments.
Introduction.
Chapter 1: Query Performance Tuning.
Chapter 2: Execution Plan Generation and the Query Optimizer.
Chapter 3: Methods for Capturing Query Performance Metrics.
Chapter 4: Analyzing Query Behavior Using Execution Plans.
Chapter 5: Statistics, Data Distribution, and Cardinality.
Chapter 6: Using the Query Store for Query Performance and Execution Plans.
Chapter 7: Execution Plan Cache Behavior.
Chapter 8: Query Recompilation.
Chapter 9: Index Architecture.
Chapter 10: Index Behaviors.
Chapter 11: Key Lookups and Solutions.
Chapter 12: Dealing with Index Fragmentation.
Chapter 13: Parameter-Sensitive Queries: Causes and Solutions.
Chapter 14: Query Design Analysis.
Chapter 15: Reduce Query Resource Use.
Chapter 16: Blocking and Blocked Processes.
Chapter 17: Causes and Solutions for Deadlocks.
Chapter 18: Row-by-Row Processing from Cursors and Other Causes.
Chapter 19: Memory-Optimized OLTP Tables and Procedures.
Chapter 20: Graph Databases.
Chapter 21: Intelligent Query Processing.
Chapter 22: Automated Tuning in Azure and SQL Server.
Chapter 23: A Query Tuning Methodology.
Index.



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