What are the best practices for optimizing SQL queries?

- closely check the query execution plan to decide on indexing and also deleting the unused indexes. - Try to use temp tables wherever possible while transforming data. - use proper indexes, note down the columns frequently used in joining condition and where clause. - Try to replace IN operator with an inner join to a lookup table - Use numeric columns in where and joining conditions. - use proper indexes, note down the columns frequently used in joining condition and where clause. - Plan regular index rebuild checks - Avoid subqueries and cursors, try to implement CTE - Possibility of incremental loads - Partition tables to help with parallel processing. - Use database hints- eg. dictate your code touse specific indexes if applicable

Indexing: Utilize indexes on columns frequently used in WHERE, JOIN, and ORDER BY clauses to speed up data retrieval. Limit Data Returned: Retrieve only necessary columns using SELECT and use LIMIT to restrict the number of rows returned for large result sets. JOIN Optimization: Ensure proper JOIN conditions to avoid Cartesian products and use INNER JOIN, LEFT JOIN, etc., as needed. Avoid Function in WHERE: Minimize the use of functions on columns in the WHERE clause to allow for index usage. Use EXISTS and NOT EXISTS: Prefer EXISTS and NOT EXISTS over IN and NOT IN for subqueries, especially with large datasets. Analyze Query Execution Plan: Regularly analyze query execution plans to identify bottlenecks and optimize query performance

Index usage in SQL queries can significantly optimize performance by allowing the database engine to quickly locate rows based on the indexed columns, rather than scanning the entire table. Indexes help in quickly locating the rows that match the conditions specified in the query's WHERE clause.This speeds up data retrieval,for large tables.By using indexes, the database engine can minimize the number of disk reads required to satisfy a query, reducing disk I/O and improving overall query performance.Indexes can also assist in sorting and grouping operations, allowing the database engine to efficiently process ORDER BY and GROUP BY clauses. Over-indexing can lead to increased storage requirements and slower data modification operations

A selective query filters out a small portion of rows from a table, while a non-selective query retrieves a large portion of rows. Aim for selective queries whenever possible, as they allow the database engine to efficiently locate and retrieve the necessary data. Construct your queries to leverage indexes effectively. Ensure that the columns involved in filtering, sorting, and joining are indexed appropriately. This helps the database engine quickly locate and access the required rows, leading to improved performance. Choose the appropriate join type (e.g., INNER JOIN, LEFT JOIN) based on the relationship between the tables and the desired result set. Also, consider the order of tables in the join and use join conditions.

Write Clear and Concise SQL Statements: Simplify queries to improve readability and performance. Prefer JOINs Over Subqueries: Replace subqueries with JOINs, as they are generally more efficient and make the query more straightforward to understand. Use Explicit JOINs: Avoid implicit syntax to gain better control over the join process and enhance clarity, reducing the likelihood of errors. Specify Columns in SELECT Statements: Instead of using SELECT *, specify only the necessary columns. This practice minimizes the amount of data processed and transferred, speeding up query execution.

Joins allow you to combine data from multiple tables into a single result set, reducing the need for multiple queries or subsequent data processing steps. By joining tables based on indexed columns, you can efficiently filter data without needing to retrieve and process unnecessary rows. This reduces the amount of data transferred between the database and the application, leading to improved performance. Subqueries allow you to filter data based on the results of another query. By using subqueries strategically, you can apply selective filtering criteria that reduce the number of rows processed by subsequent operations, thereby improving query performance.

Use CASE statements instead of multiple nested IF statements for conditional logic within a query. Minimize the use of functions within WHERE clauses, as they can prevent index usage. Consider rewriting complex logic outside of the query if it significantly impacts performance.

Conditional logic in SQL queries can optimize performance by selectively executing different parts of the query based on specific conditions. Conditional logic, such as CASE statements or IF conditions, allows you to retrieve only the necessary data based on certain conditions. By selectively fetching data, you can minimize the amount of data transferred between the database and the application, leading to improved query performance. Conditional logic can influence the control flow of query execution, allowing you to optimize the sequence of operations based on the underlying data characteristics or performance considerations. For example, you can use conditional branching to prioritize certain query paths or to handle exceptional cases.

Function calls in SQL queries can aid in optimization by encapsulating complex or repetitive logic into reusable functions. Functions allow you to encapsulate commonly used logic into reusable units, reducing code duplication and promoting maintainability. By calling functions instead of repeating the same logic in multiple places within a query, you can streamline query development and reduce the risk of errors. Well-designed functions can promote query optimization by encapsulating performance-critical operations or computations. By isolating these operations within functions, you can focus on optimizing them independently of the surrounding query context, leading to more efficient execution plans.

Minimize the use of scalar and table-valued functions in SELECT, WHERE, and JOIN clauses, as they can degrade performance. Consider using inline table-valued functions or stored procedures instead of scalar functions for better performance.

Batch operations in SQL help optimize query performance by reducing the number of round trips between the application and the database server. Batch operations allow multiple SQL statements to be sent to the database server in a single network request. This reduces the overhead associated with establishing and maintaining multiple connections, resulting in improved network performance and reduced latency. By grouping multiple SQL statements into a single batch, you can reduce the overhead associated with transaction management. This is particularly beneficial when performing multiple data manipulation operations within a single transaction, as it minimizes the number of transaction boundaries and associated locking overhead.

Use bulk INSERT, UPDATE, and DELETE operations instead of individual statements whenever possible. Wrap multiple DML operations within a single transaction to minimize overhead. Consider using temporary tables or table variables for complex data manipulation tasks.

Vou falar algo n?o técnico, mas uma das melhores práticas no meu ver, é entender de forma clara o intuito daquela query, com base nisso, poderá otimizar muito mais do que na tentativa e erro em algo que n?o entende o fim (o objetivo da query)!

Databases built for analytical tasks can automate many optimization tasks such as index creation. Therefore, choosing an analytical (mostly) tuning-free database can significantly reduce the optimization effort.

The grammar of Business Intelligence comprises hierarchies, dimensions, relationships, and metrics. Relational databases excel in OLTP, but struggle with hierarchies and dimensions, leading to denormalization. Star-schema and snowflakes aren't ideal solutions. Instead, databases native to both dimensions and hierarchies are preferable.