SQL Data Storage and Tuning: A Comprehensive Guide ( Part 5 of 16 )

SQL Data Storage and Tuning: A Comprehensive Guide

Summary

SQL Server supports three primary data storage and processing technologies that are crucial for optimizing performance:

1. Row-Based Storage : This is the most common method where data from all columns is stored together. It can be either in unsorted heaps or sorted B-Tree indexes. B-Tree indexes sort data based on index keys.

Clustered Indexes : Store data from all table columns. They ensure that the data is physically sorted in the order of the index.

Nonclustered Indexes : Store another copy of the data in separate physical indexes and reference clustered indexes through the row-id (clustered index key values). When data is not present in the nonclustered index, SQL Server performs a key lookup operation through the clustered index B-Tree, which can be expensive at scale.

SQL Server accesses data in two primary ways:

Index Scan : Reads all rows from the index, typically less efficient.

Index Seek : Isolates and processes a subset of the index rows, usually more efficient. Writing queries to utilize index seeks and analyzing their efficiency to ensure they don’t process large amounts of data is essential.

SQL Server maintains information about indexes and data distribution in statistics. These statistics are used to estimate the number of rows each operator will need to process in the execution plan. Accurate cardinality estimation is crucial for generating efficient execution plans. Up-to-date statistics are key to correct cardinality estimation.

When analyzing execution plans, pay attention to:

- The efficiency of Index Seek operators.

- The choices of join type.

- The number of key and RID lookups.

- Cardinality estimations (improper estimations are a common cause of poor execution plans).

Make sure statistics are up to date, add the required indexes, and simplify and refactor queries to address any issues.

Troubleshooting Checklist

1. Analyze Your Statistics Maintenance Strategy :

- Regularly review and update your statistics to ensure accuracy.

2. Add a T2371 Trace Flag :

- Reduce the automatic statistics-update threshold if your system has databases with compatibility levels below SQL Server 2016 (level 130).

3. Improve Index Maintenance Strategies :

- Ensure that statistics on filtered indexes are frequently updated. Rebuild them if necessary.

4. Consider Upgrading Compatibility Level :

- Especially if you are using SQL Server 2017 and later versions. Be mindful of the cardinality estimation model, which may require separate handling.

5. Review Temporary Tables and Table Variable Usage :

- Optimize their usage as they can significantly impact performance.

6. Refactor Non-SARGable Predicates :

- Adjust non-SARGable predicates in critical queries to enhance performance.

7. Check the Correctness of Cardinality Estimations :

- Ensure that cardinality estimations are accurate to avoid poor execution plans.

8. Reduce Usage of Multistatement Functions :

- Multistatement functions and constructs can negatively impact cardinality estimations and should be minimized.

9. Analyze the Efficiency of Index Seek Operators :

- Ensure that Index Seek operators are efficient and not processing large amounts of data unnecessarily.

10. Validate Join Columns and Types :

- Ensure that join columns have the same data types and review the choice of join types for optimal performance.

11. Consider Creating Covering Indexes :

- When you notice a high number of key lookups in critical queries, creating covering indexes can improve performance.

By following these best practices and troubleshooting steps, you can ensure that your SQL Server environment is well-tuned and optimized for peak performance. Efficient data storage and query execution are pivotal to maintaining a robust and responsive database system.

Stay tuned for more insights as we continue to explore the depths of SQL Server tuning and optimization. Happy tuning!