Tips for Database Query Optimization

When it comes to writing SQL stored procedures, there are several best practices you can follow to improve readability, performance, and maintainability. Here are some key guidelines to consider:

Use a consistent naming convention: Choose meaningful names for stored procedures that accurately describe their functionality. Follow a naming convention that is consistent with your database schema and organization's standards.

Begin with error handling and transaction management: Place error handling code at the beginning of your stored procedure to catch any potential exceptions. Additionally, consider using transactions to ensure data integrity and provide the ability to roll back changes if an error occurs.

Validate input parameters: Validate and sanitize all input parameters to prevent SQL injection attacks and ensure the stored procedure behaves as expected. Use parameterized queries or prepared statements to avoid concatenating user input directly into your SQL statements.

Use appropriate comments and documentation: Document your stored procedures thoroughly, including their purpose, input/output parameters, and any assumptions or dependencies. Use comments to explain complex logic or non-obvious decision-making processes.

Keep stored procedures concise and modular: Aim to write stored procedures that focus on a specific task and perform it efficiently. Avoid creating monolithic procedures that try to handle multiple functionalities. Break down complex tasks into smaller, reusable modules that can be called from different procedures.

Optimize performance: Strive for efficient query execution by considering proper indexing, avoiding unnecessary joins or subqueries, and minimizing data manipulation. Use appropriate database-specific techniques, such as query optimization tools or execution plans, to identify and address performance bottlenecks.

Handle and log exceptions: Implement appropriate exception handling mechanisms in your stored procedures. Capture and log errors to aid troubleshooting and debugging efforts. Provide meaningful error messages that can help diagnose issues and guide developers or users to resolve them.

Maintain security and access controls: Ensure that your stored procedures adhere to your organization's security policies and access controls. Grant the necessary privileges to execute the procedure, and limit access to sensitive data or operations.

Test thoroughly: Before deploying a stored procedure to a production environment, test it thoroughly using various scenarios, including both valid and invalid input data. Check for correctness, performance, and compatibility across different database versions or platforms.

Regularly review and refactor: Continuously review your stored procedures for potential improvements, such as simplifying complex logic, removing redundant code, or optimizing performance. Refactoring can help maintain the quality and efficiency of your codebase over time.

A sargable SQL query, short for "Search ARGument-able," refers to a query that is designed to optimize performance by utilizing indexes effectively. It is a query that allows the database engine to use indexes efficiently to retrieve the desired results, resulting in improved query performance.

The term "sargable" comes from the concept of being able to use search arguments in a way that benefits from index usage. Sargable queries can take advantage of indexes on columns, allowing the database engine to quickly narrow down the dataset and avoid performing full table scans.

Here are some characteristics of a sargable query:

Avoid applying functions or expressions on columns in the WHERE clause: When a function or expression is applied to a column in the WHERE clause, it can prevent the use of an index. For example, instead of using WHERE YEAR(DateColumn) = 2023, it's better to use WHERE DateColumn >= '2023-01-01' AND DateColumn < '2024-01-01'.

Use simple comparison operators: Simple comparison operators such as equals (=), greater than (>), less than (<), and between are more likely to be sargable. Complex operators like LIKE, NOT LIKE, or negations can hinder index usage.

Avoid negations or the NOT operator: Negations or the use of the NOT operator can make a query non-sargable. Instead of using NOT column = value, it is better to use column <> value or column IS NULL if appropriate.

Be cautious with wildcard characters in LIKE statements: If wildcard characters are used at the beginning of a string in a LIKE statement (e.g., LIKE '%value'), it may not be sargable. Using wildcard characters only at the end of the string (LIKE 'value%') allows the query to use an index efficiently.

Consider using OR clauses with caution: The use of OR clauses can sometimes lead to non-sargable queries. If possible, rephrase the query to use UNION or combine multiple queries with UNION ALL instead of using OR.

By writing sargable queries, you can improve the performance of your SQL statements by allowing the database engine to leverage indexes effectively and efficiently retrieve the desired data. It's important to note that the actual impact of sargability depends on the specific database system and its query optimizer, so it's a good practice to analyze query execution plans to verify the effectiveness of your indexing strategy.

Here are a few key points to understand about implicit conversion:

Data Type Precedence: Every data type has a predefined precedence level that determines which data type takes precedence in case of a mismatch. When two different data types are involved in an operation, the database engine will implicitly convert the data type with lower precedence to the data type with higher precedence.

Avoiding Implicit Conversion: While implicit conversion can be convenient, it can also have performance implications. It's generally best to avoid implicit conversion whenever possible and instead ensure that the data types of the operands match explicitly. This can be achieved by explicitly converting the data types using appropriate conversion functions or by ensuring that the columns and variables have compatible data types from the start.

Impact on Query Performance: Implicit conversion can sometimes impact query performance negatively. When the database engine performs an implicit conversion, it may prevent the use of indexes or require additional processing overhead. Therefore, it's important to review the execution plans and performance of queries involving implicit conversions and consider optimizing them if necessary.

Potential Data Loss or Unexpected Results: Implicit conversion may result in data loss or unexpected behavior if the conversion is not handled carefully. For example, converting a string to a numeric data type may cause truncation or rounding, leading to loss of precision or incorrect results.

To handle implicit conversion explicitly, you can use explicit conversion functions provided by the specific database system. For example:

CAST: The CAST function allows you to explicitly convert one data type to another. For instance, CAST(expression AS datatype).

CONVERT: The CONVERT function is another way to explicitly convert data types. It has slightly different syntax and supports additional formatting options. For example, CONVERT(datatype, expression, style).

By explicitly converting data types using these functions, you can ensure clarity, maintain control over the conversions, and avoid any unexpected behaviors that might arise from implicit conversions.

It's generally recommended to be aware of implicit conversions in your SQL queries, understand their implications, and handle them explicitly when necessary to ensure the desired behavior, performance, and data integrity.

In SQL Server, index scan and index seek are two different methods used by the query optimizer to retrieve data from an index. They represent different access methods to fetch data and have different performance characteristics. Here's an overview of each:

Index Scan:

An index scan involves scanning the entire index from start to end.

It reads all the pages of the index, whether they contain the desired data or not.

It is typically used when a large portion of the data in the table is required or when there is no selective condition to filter the data efficiently.

Index scans can be beneficial when the index is covering (includes all the columns required by the query), reducing the need for additional lookups to retrieve the data.

Index Seek:

An index seek is a more efficient access method that directly navigates to the specific location in the index where the desired data is located.

It uses the index structure to quickly locate and retrieve the required rows.

It is used when the query is highly selective and can efficiently narrow down the search space using the index key.

Index seeks are generally preferred over index scans because they minimize disk I/O and fetch only the necessary data.

The choice between an index scan and an index seek depends on various factors, such as the selectivity of the query, the size of the table, the data distribution, and the available indexes. The query optimizer determines the most efficient access method based on statistics and cost estimates.

It's important to note that a covering index (an index that includes all the columns required by a query) can often improve performance by allowing the optimizer to perform index seeks instead of scans. Covering indexes can eliminate the need for additional disk I/O and reduce the number of lookups to the base table.

In summary, index scans read all the index pages, while index seeks directly navigate to the desired location in the index. Index seeks are generally more efficient and desirable for selective queries, whereas index scans may be used when a large portion of the data is required or when the query is not selective enough. Proper indexing, including covering indexes, can help optimize query performance and improve the choice between index scan and index seek.

By following these best practices, you can enhance the quality, performance, and maintainability of your SQL stored procedures.

1. Read queries are frequently very inefficient. Consider writing a stored procedure for complex Read queries.
2. Ensure that the SQL Server instance has ‘Optimize for Ad Hoc’ enabled. This will store a plan stub in memory the first time a query is passed, rather than storing a full plan. This can help with memory management.
3. SELECT * is not always a good idea and you should only move the data you really need to move and only when you really need it, in order to avoid network, disk and memory contention on your server.
4. Keep transactions as short as possible and never use them unnecessarily. The longer a lock is held the more likely it is that another user will be blocked. Never hold a transaction

open after control is passed back to the application – use optimistic locking instead.

5. For small sets of data that are infrequently updated such as lookup values, build a method of caching them in memory on your application server rather than constantly querying

them in the database.

6. When processing within a transaction, do not  updates until last if possible, to minimize the need for exclusive locks.
7. Cursors within SQL Server can cause severe performance bottlenecks.
8. The WHILE loop within SQL Server is just as bad as a cursor.
9. Ensure your variables and parameters are the same data types as the columns. An implicit or explicit conversion can lead to table scans and slow performance.
10. A function on columns in the WHERE clause or JOIN criteria means that SQL Server can’t use indexes appropriately and will lead to table scans and slow performance.
11. Use the  DISTINCT, ORDER BY,  UNION carefully.
12. Table variables do not have any statistics within SQL Server. This makes them useful for working in situations where a statement level recompile can slow performance. But, that lack of statistics makes them very inefficient where you need to do searches or joins. Use table variables only

where appropriate.

13. Multi-statement user-defined functions work through table variables- which don’t work well in situations where statistics are required. Avoid using them If a join or filtering is required.
14. One of the most abused query hints is NO_LOCK. This can lead to extra or missing rows in data sets. Instead of using NO_LOCK consider using a snapshot isolation level such as READ_COMMITTED_SNAPSHOT.
15. Avoid creating stored procedures that have a wide range of data supplied to them as parameters. These are compiled to use just one query plan.
16. Try not to interleave data definition language with your data manipulation language queries within SQL Server. This can lead to recompiles which hurts performance.
17. Temporary tables have statistics which get updated as data is inserted into them. As these updates occur, you can get recompiles. Where possible, substitute table

variables to avoid this issue.

18. If possible, avoid NULL values in your database. If not, use the appropriate IS NULL and IS NOT NULL code.
19. A view is meant to mask or modify how tables are presented to the end user. These are fine constructs. But when you start joining one view to another or nesting views within views, performance will suffer. Refer only to tables within a view.
20. Use extended events to monitor the queries in your system in order to identify slow running queries.
21. You get exactly one clustered index on a table. Ensure you have it in the right place. First choice is the most frequently accessed column, which may or may not be the primary key. Second choice is a column that structures the storage in a way that helps performance.

This is a must for partitioning data.

22. Clustered indexes work well on columns that are used a lot for ‘range’ WHERE clauses such as BETWEEN and LIKE, where it is frequently used in ORDER BY clauses or in GROUP BY clauses.
23. If clustered indexes are narrow (involve few columns) then this will mean that less storage is needed for non-clustered indexes for that table.
24. Avoid using a column in a clustered index that has values that are frequently updated.
25. Keep your indexes as narrow as possible. This means reducing the number and size of the columns used in the index key. This helps make the index more efficient.
26. Always index your foreign key columns if you are likely to delete rows from the referenced table. This avoids a table scan.
27. A clustered index on a GUID can lead to serious fragmentation of the index due to the random

nature of the GUID. You can use the function NEWSEQUENTIALID() to generate a GUID that will

not lead to as much fragmentation.

28. Performance is enhanced when indexes are placed on columns used in WHERE, JOIN, ORDER BY, GROUP, and TOP.
29. A unique index absolutely performs faster than a non-unique index, even with the same values.
30. Data normalization is a performance tuning technique as well as a storage mechanism.
31. Referential integrity constraints such as foreign keys actually help performance, because the optimizer can recognize these enforced constraints and make better choices for joins and other data access.
32. Make sure your database doesn’t hold ‘historic’ data that is no longer used. Archive it out, either into a special ‘archive’ database, a reporting OLAP database, or on file. Large tables mean longer table scans and deeper indexes. This in turn can mean that locks are held for longer. Admin tasks such as Statistics  updates, DBCC checks, and index builds take longer, as do backups.
33. Separate out the reporting functions from the OLTP production functions. OLTP databases usually have short transactions with a lot of updates whereas reporting databases, such as OLAP and data warehouse systems, have longer data-heavy queries. If possible, put them on different servers.