1. Never Use SELECT * Statement
Avoid using SELECT * because it retrieves all columns, which can result in unnecessary overhead and inefficiency, especially when only a subset of columns is needed. Instead, specify the required columns explicitly to improve performance and clarity
2. Use Common Table Expressions (CTEs) Instead of Temp Tables
CTEs are temporary result sets used within a query. Unlike temp tables, which are stored physically in TempDB, CTEs are stored in memory and are faster, more efficient, and easier to use for simple temporary data manipulations.
3. Use UNION ALL Instead of UNION
UNION eliminates duplicates, while UNION ALL does not. If duplicates are not a concern, using UNION ALL is faster since it avoids the additional step of removing duplicates.
4. Use COUNT(1) Instead of COUNT(*) and COUNT(Column_Name)
COUNT(1) counts rows without evaluating any specific column value, making it more efficient than COUNT(*), which checks every column, or COUNT(Column_Name), which only counts non-null values.
5. Use Stored Procedures
Stored procedures are precompiled, reusable SQL code that can improve performance, reduce network traffic, and provide better security by encapsulating logic.
6. Use BETWEEN Instead of IN
BETWEEN is more efficient when working with a range of values (e.g., dates or numbers). IN is typically used for multiple discrete values and can be less optimized for large sets of values.
7. Never Use sp_ for User Defined Stored Procedures
The sp_ prefix is reserved for system stored procedures. Using it for user-defined procedures can cause confusion and conflict, as well as degrade performance due to how SQL Server looks for system procedures first.
8. Practice Using Schema Names
Always use the schema name (e.g., dbo.TableName) when referring to tables, views, or procedures. It helps clarify the object’s location and avoids potential issues with ambiguity, especially in complex environments with multiple schemas.
9. Try to Avoid Cursors — Use WHILE Loop
Cursors are slow because they process one row at a time. While loops can often achieve the same results without the performance penalty, processing multiple rows in a set-based manner is generally preferred over row-by-row operations.
10. Set NOCOUNT ON
NOCOUNT ON prevents the "rows affected" message from being returned after each SQL operation. This reduces unnecessary network traffic and can improve performance, especially for stored procedures that handle many queries.
11. Use TRY-CATCH for Error Handling
TRY-CATCH blocks allow for error handling in SQL Server, ensuring that any issues during query execution are caught, logged, and managed gracefully, improving code robustness and reliability.
12. Remove Unused Indexes
Unused indexes take up space and can slow down data modification operations. Regularly review and drop indexes that are not benefiting query performance.
13. Always Create an Index on the Table
Indexes speed up query performance by providing quick access to data. It’s essential to create indexes on frequently queried columns, especially those used in JOIN, WHERE, and ORDER BY clauses.
14. Use Alias Names
Use aliases for tables and columns to make queries more readable and easier to maintain. Aliases are especially useful when dealing with long table names or complex queries involving multiple tables.
15. Use Transaction Management
Transactions ensure data consistency and integrity by grouping multiple operations into a single unit of work. If any part of the transaction fails, the changes are rolled back to maintain the database’s state.
16. Use the Correct Index Name Format While Creating an Index
Use a consistent and meaningful naming convention for indexes, such as including the table name and the columns involved. This helps with index identification and future maintenance.
17. Drop Index Before Bulk Insertion of Data
Inserting large volumes of data into a table with indexes can be slow. Dropping indexes before bulk insertion and recreating them afterward improves performance by reducing overhead.
18. Avoid Loops in Coding
Loops can significantly slow down performance because they process data row by row. SQL is optimized for set-based operations, so whenever possible, rewrite logic to work with sets rather than using loops.
19. Avoid Correlated Subqueries or Subqueries — Use JOIN Instead
Correlated subqueries and other types of subqueries can be slow because they execute for each row processed. JOIN operations are typically faster since they work with sets of data and allow SQL to optimize the query execution.
20. Minimize the Number of Joins
Excessive joins can degrade query performance, especially when dealing with large datasets. Minimize the number of joins or consider restructuring queries to avoid unnecessary or redundant joins.