๐๐จ๐ฆ๐ ๐๐๐ฌ๐ญ ๐ฉ๐ซ๐๐๐ญ๐ข๐๐๐ฌ ๐ญ๐จ ๐ก๐๐ฅ๐ฉ ๐ฒ๐จ๐ฎ ๐จ๐ฉ๐ญ๐ข๐ฆ๐ข๐ณ๐ ๐ฒ๐จ๐ฎ๐ซ ๐๐๐ ๐ช๐ฎ๐๐ซ๐ข๐๐ฌ:
1. Simplify Joins
โข Decompose complex joins into simpler, more manageable queries when possible.
โข Index columns that are used as foreign keys in joins to enhance join performance.
2. Query Structure Optimization
โข Apply WHERE clauses as early as possible to filter out rows before they are processed further.
โข Utilize LIMIT or TOP clauses to restrict the number of rows returned, which can significantly reduce processing time.
3. Partition Large Tables
โข Divide large tables into smaller, more manageable partitions.
โข Ensure that each partition is properly indexed to maintain query performance.
4. Optimize SELECT Statements
โข Limit the columns in your SELECT clause to only those you need. Avoid using SELECT * to prevent unnecessary data retrieval.
โข Prefer using EXISTS over IN for subqueries to improve query performance.
5. Use Temporary Tables Wisely
โข Temporary Tables: Use temporary tables to save intermediate results when you have a complex query. This helps break down a complicated query into simpler steps, making it easier to manage and faster to run.
6. Optimize Table Design
โข Normalize your database schema to eliminate redundant data and improve consistency.
โข Consider denormalization for read-heavy systems to reduce the number of joins needed.
7. Avoid Correlated Subqueries
โข Replace correlated subqueries with joins or use derived tables to improve performance.
โข Correlated subqueries can be very inefficient as they are executed multiple times.
8. Use Stored Procedures:
โข Put complicated database tasks into stored procedures. These are pre-written sets of instructions saved in the database. They make your queries run faster because the database doesnโt have to figure out how to execute them each time
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Post #2086
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