T-SQL Performance Best Practices

Optimizing your T-SQL code is crucial for ensuring that your SQL Server database performs efficiently. This document outlines key best practices to help you write faster, more scalable T-SQL queries and scripts.

1. Indexing Strategies

Proper indexing is fundamental to query performance. Without appropriate indexes, SQL Server may resort to full table scans, which can be extremely slow for large tables.

• Understand Your Queries: Analyze your most frequent and resource-intensive queries. Identify columns used in WHERE clauses, JOIN conditions, and ORDER BY clauses.
• Choose the Right Index Type:
  • Clustered Indexes: Define the physical storage order of data in a table. Every table should ideally have one, typically on the primary key.
  • Nonclustered Indexes: Provide a logical ordering of rows without affecting the physical storage. They are useful for columns frequently used in searches.
  • Covering Indexes: Include all columns required by a query, allowing it to be satisfied from the index alone without touching the base table.
• Avoid Over-Indexing: Too many indexes can slow down INSERT, UPDATE, and DELETE operations, as each index needs to be maintained.
• Maintain Indexes: Regularly rebuild or reorganize fragmented indexes.

2. Query Optimization Techniques

Writing efficient T-SQL statements can significantly reduce execution time and resource consumption.

• Use SELECT Specific Columns: Avoid SELECT *. Only retrieve the columns you actually need. This reduces I/O and network traffic.
• Filter Data Early: Apply WHERE clauses as early as possible in your queries to reduce the number of rows processed.
• Understand Joins:
  • Use the Appropriate Join Type: Ensure you are using the correct join (INNER JOIN, LEFT JOIN, etc.) for your needs.
  • Join on Indexed Columns: Joins are much faster when performed on columns that are indexed.
  • Avoid Cross Joins (CROSS JOIN) unless necessary: They generate a Cartesian product and can be very resource-intensive.
• Minimize Cursors and Loops: Set-based operations are generally much more efficient than row-by-row processing. Look for opportunities to rewrite cursor-based logic using JOINs, subqueries, or Common Table Expressions (CTEs).
• Use CTEs (Common Table Expressions): CTEs can improve the readability and structure of complex queries, and sometimes performance.
• Avoid Functions in WHERE Clauses on Indexed Columns: Applying a function to a column in a WHERE clause (e.g., WHERE YEAR(OrderDate) = 2023) often prevents the use of indexes on that column. Consider rewriting like this: WHERE OrderDate >= '2023-01-01' AND OrderDate < '2024-01-01'.

3. Data Manipulation Best Practices

Efficient data modification statements are as important as efficient queries.

• Use Bulk Operations: For large data loads, use tools like BULK INSERT or the Table-Valued Parameter (TVP) feature instead of row-by-row INSERTs.
• Batch Transactions: Break down large UPDATE or DELETE operations into smaller batches to avoid excessively long transactions that can lock resources and impact performance.
• Consider Using TRUNCATE TABLE: For deleting all rows from a table, TRUNCATE TABLE is much faster and uses fewer resources than DELETE FROM without a WHERE clause. It's a minimally logged operation.

4. Stored Procedures and Functions

Stored procedures and functions can offer performance benefits.

• Stored Procedures:
  • Execution Plan Caching: Once compiled, stored procedures have their execution plans cached, which can significantly speed up repeated executions.
  • Reduced Network Traffic: A single call to a stored procedure can replace multiple individual SQL statements, reducing network round trips.
  • Parameterization: Use parameters effectively to allow for plan reuse.
• Functions:
  • Scalar Functions: Use scalar functions cautiously, especially within queries, as they can sometimes lead to performance issues if not written efficiently or if they are called excessively.
  • Table-Valued Functions: These can be a good alternative to inline views or complex subqueries and can sometimes be optimized better.

5. Database Design Considerations

A well-designed database schema is the foundation of good performance.

• Normalization: While aiming for normalization is good, sometimes denormalization can improve read performance for specific reporting scenarios, but this should be done judiciously.
• Data Types: Use the most appropriate and smallest data types possible for your columns (e.g., use INT instead of BIGINT if the range of values allows, use VARCHAR(50) instead of VARCHAR(MAX) if data is consistently short).
• Primary Keys and Foreign Keys: Implement these constraints. They not only ensure data integrity but also provide valuable information for the query optimizer.

6. Advanced Techniques

• Partitioning: For very large tables, consider partitioning to improve manageability and query performance by allowing operations to target specific partitions.
• Query Hints: Use query hints (e.g., OPTION (RECOMPILE), WITH (NOLOCK)) with extreme caution. They can override the optimizer's decisions and sometimes degrade performance if used incorrectly.
• Statistics: Ensure database statistics are up-to-date. The query optimizer relies on accurate statistics to create efficient execution plans.

By applying these best practices, you can significantly enhance the performance and scalability of your T-SQL applications.