SQL Best Practices
- Use explicit column names: Instead of using the asterisk (*) to select all columns, explicitly specify the columns you need. This improves query performance and makes the query’s intent clearer.
- Avoid using SELECT DISTINCT unnecessarily: SELECT DISTINCT can be resource-intensive, especially on large tables. Only use it when necessary to eliminate duplicates.
- Use proper indexing: Indexes can significantly improve query performance. Identify the columns frequently used in WHERE, JOIN, and ORDER BY clauses, and create indexes on those columns.
- Avoid using SQL functions in WHERE clauses: Applying functions to columns in the WHERE clause can prevent the database engine from using indexes efficiently. Instead, try to restructure the query to use the column directly.
- Use JOINs instead of subqueries: JOIN operations are generally more efficient than subqueries. Use appropriate JOINs to combine data from multiple tables.
- Optimize query ordering: Arrange the conditions in the WHERE clause in the order that will eliminate the greatest number of rows first. This can help the database engine optimize the query execution plan.
- Limit the use of NULL values: NULL values can cause unexpected behavior in queries. Avoid using NULLs for primary keys and important columns unless necessary.
- Use parameterized queries or prepared statements: To prevent SQL injection attacks, use parameterized queries or prepared statements, which allow you to separate SQL code from user input.
- Properly handle transactions: Wrap a series of SQL statements in a transaction when necessary to ensure data consistency and integrity. Use the appropriate transaction isolation level based on your requirements.
- Regularly analysed and optimize queries: Monitor query performance and identify slow-running queries. Use tools like EXPLAIN or query profiling to understand the query execution plan and optimize as needed.
- Document your queries: Add comments to your queries to explain their purpose, assumptions, and any potential side effects. This helps with maintainability and allows others to understand the query’s intent.
- Test queries with representative data: Ensure that your queries perform well with realistic data volumes. Test them on different datasets to identify potential bottlenecks or performance issues.
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