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๐’๐จ๐ฆ๐ž ๐๐ž๐ฌ๐ญ ๐ฉ๐ซ๐š๐œ๐ญ๐ข๐œ๐ž๐ฌ ๐ญ๐จ ๐ก๐ž๐ฅ๐ฉ ๐ฒ๐จ๐ฎ ๐จ๐ฉ๐ญ๐ข๐ฆ๐ข๐ณ๐ž ๐ฒ๐จ๐ฎ๐ซ ๐’๐๐‹ ๐ช๐ฎ๐ž๐ซ๐ข๐ž๐ฌ:

1. Simplify Joins

โ€ข Decompose complex joins into simpler, more manageable queries when possible.
โ€ข Index columns that are used as foreign keys in joins to enhance join performance.

2. Query Structure Optimization

โ€ข Apply WHERE clauses as early as possible to filter out rows before they are processed further.
โ€ข Utilize LIMIT or TOP clauses to restrict the number of rows returned, which can significantly reduce processing time.

3. Partition Large Tables

โ€ข Divide large tables into smaller, more manageable partitions.
โ€ข Ensure that each partition is properly indexed to maintain query performance.

4. Optimize SELECT Statements

โ€ข Limit the columns in your SELECT clause to only those you need. Avoid using SELECT * to prevent unnecessary data retrieval.
โ€ข Prefer using EXISTS over IN for subqueries to improve query performance.

5. Use Temporary Tables Wisely

โ€ข 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

โ€ข Normalize your database schema to eliminate redundant data and improve consistency.
โ€ข Consider denormalization for read-heavy systems to reduce the number of joins needed.

7. Avoid Correlated Subqueries

โ€ข Replace correlated subqueries with joins or use derived tables to improve performance.
โ€ข Correlated subqueries can be very inefficient as they are executed multiple times.

8. Use Stored Procedures:

โ€ข 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 doesnโ€™t have to figure out how to execute them each time

Like this post if you need more ๐Ÿ‘โค๏ธ

Hope it helps :)
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