SQL Server is a powerful relational database management system that is widely used to store and retrieve data. Writing efficient and effective queries is crucial for optimizing the performance of your SQL Server database. In this article, we will explore some valuable tips and tricks that can help you enhance the speed and efficiency of your SQL Server queries.

Use appropriate indexes

Indexes play a vital role in improving query performance. Analyze your query execution plans and identify frequently used columns in WHERE, JOIN, or ORDER BY clauses. Creating indexes on these columns allows the database engine to locate and retrieve data more quickly, resulting in faster query execution.

Be mindful of SELECT *

Rather than using the SELECT * syntax, explicitly specify the columns you need in your query. Retrieving only the necessary columns reduces the amount of data transferred and can significantly improve query performance, especially when dealing with larger tables.

Utilize JOINs effectively

Selecting the appropriate JOIN types (INNER JOIN, LEFT JOIN, etc.) based on the relationship between tables is essential. Ensure that the join conditions are accurately defined and that the necessary indexes are in place. Incorrectly defined or missing join conditions can lead to incorrect results or poor query performance.

Minimize cursor usage

Cursors are generally less efficient compared to set-based operations. Whenever possible, leverage set-based operations to manipulate and retrieve data. By doing so, you can improve the performance and efficiency of your queries.

Parameterize your queries

Using parameterized queries not only helps prevent SQL injection attacks but also improves query performance. Parameterization allows the database engine to cache and reuse query execution plans, resulting in faster execution times.

Use EXISTS instead of COUNT

When checking for the existence of records, consider using the EXISTS keyword instead of COUNT. EXISTS stops evaluating the condition as soon as a match is found, while COUNT evaluates the entire result set. This can significantly impact the performance of your queries, especially when dealing with large datasets.

Optimize data types and lengths

Carefully choose the appropriate data types and lengths for your columns. Avoid using larger data types than necessary, as it can negatively impact query performance and increase storage requirements. Properly aligning data types with the data being stored can significantly enhance query execution.

Handle NULL values effectively

Be mindful of NULL values in your data and handle them appropriately. Use functions like ISNULL or COALESCE to replace NULL values with defaults when needed. By properly managing NULL values, you can ensure accurate query results and improve performance.

Limit the result set

If you only need a subset of records, utilize the TOP or LIMIT clause to limit the number of rows returned by your queries. Restricting the result set can greatly improve query performance, particularly when dealing with large tables.

Regularly update statistics

Statistics help the query optimizer make informed decisions about query execution plans. It’s essential to keep your statistics up to date by running the UPDATE STATISTICS command on your tables. This ensures that the query optimizer has accurate information to generate optimal execution plans.

Understand query execution plans

Analyze the query execution plans to gain insights into how your queries are being executed. Identify areas for optimization, such as missing indexes, expensive operations, or suboptimal JOINs. Query execution plans are invaluable tools for optimizing query performance.

Avoid unnecessary nesting

Minimize excessive nesting of subqueries or unnecessary use of temporary tables. Simplify your queries by using appropriate JOINs and WHERE clauses. This reduces the complexity of the query and improves readability, making it easier to optimize and maintain in the long run.

Utilize proper data normalization

Normalize your database schema to reduce redundancy and improve data consistency. 

Try to avoid nested transactions.

  • Use the @@TRANCOUNT variable to determine whether a transaction needs to be started (to avoid nested transactions).
  • Start a transaction as late as possible and commit/rollback the transaction as fast as possible to reduce the time period of resource locking.

Try to avoid the use of temporary tables

Unless really required, try to avoid the use of temporary tables. Rather use table variables. In 99% of cases, table variables reside in memory, hence it is a lot faster. Temporary tables reside in the TempDb database. So operating on temporary tables require inter database communication and hence will be slower.

Try to avoid joining between two types of columns

When joining between two columns of different data types, one of the columns must be converted to the type of the other. The column whose type is lower is the one that is converted.

If you are joining tables with incompatible types, one of them can use an index, but the query optimizer cannot choose an index on the column that it converts. For example

In this case, SQL Server converts the integer column to float, because int is lower in the hierarchy than float. It cannot use an index on large_table.int_column, although it can use an index on smalltable.float_column.

Try to avoid deadlocks

  • Always access tables in the same order in all your Stored Procedures and triggers consistently.
  • Keep your transactions as short as possible. Touch as few data as possible during a transaction.
  • Never, ever wait for user input in the middle of a transaction.
  • Write TSQL using “Set based approach” rather than “Procedural approach”. The database engine is optimized for Set based SQL. Hence, Procedural approach (use of Cursor or UDF to process rows in a result set) should be avoided when large result sets (more than 1000) have to be processed.


Good Practices You Must Follow

Good practices in User Defined Functions

Do not call functions repeatedly within your Stored Procedures, triggers, functions, and batches. For example, you might need the length of a string variable in many places of your procedure, but don’t call the LEN function whenever it’s needed; instead, call the LEN function once, and store the result in a variable for later use.

Good practices in Stored Procedures
  • Do not use “SP_XXX” as a naming convention. Using “SP_XXX” as the naming convention also increases the possibility of conflicting with an existing system Stored Procedure.
  • Use “Set Nocount On” to eliminate extra network trip.
  • Use the WITH RECOMPILE clause in the EXECUTE statement (first time) when the index structure changes (so that the compiled version of the Stored Procedure can take advantage of the newly created indexes).
  • Use default parameter values for easy testing.
Good practices in Triggers
  • Try to avoid the use of triggers. Firing a trigger and executing the triggering event is an expensive process.
  • Never use triggers that can be implemented using constraints.
  • Do not use the same trigger for different triggering events (Insert, Update, Delete).
  • Do not use transactional code inside a trigger. The trigger always runs within the transactional scope of the code that fires the trigger.
Good practices in Views
  • Use views for re-using complex TSQL blocks, and to enable it for indexed views (Will be discussed later).
  • Use views with the SCHEMABINDING option if you do not want to let users modify the table schema accidentally.
  • Do not use views that retrieve data from a single table only (that will be an unnecessary overhead). Use views for writing queries that access columns from multiple tables.
Good practices in Transactions

Prior to SQL Server 2005, after BEGIN TRANSACTION and each subsequent modification statement, the value of @@ERROR had to be checked. If its value was non-zero, then the last statement caused an error, and if an error occurred, the transaction had to be rolled back and an error had to be raised (for the application). In SQL Server 2005 and onwards, the Try…Catch block can be used to handle transactions in TSQL. So try to use Try…Catch based transactional code.

The article was published on June 18, 2014 @ 11:11 AM

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