Increase the Performance of SQL Procedure

Kiran Kumar

If a stored procedure is taking much time to load the data or any DML operations that lead to the performance of the application, how can we optimize it in a database can any one provide suggestions, you can suggest not to specific SP can include any database entities and in front end source code and business logic which is implemented in dotnet

3 Replies

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Amit Mohanty1y ago
Accepted answer
Result caching can be implemented either at the application level (in .NET code) or at the database level (within SQL Server), depending on your specific needs.

Application-Level Caching: Use MemoryCache, Redis, or other caching libraries and store the results of a query in memory. Flexibility and control over cache expiration are possible, but coding is required.

SQL Server Caching: SQL Server automatically caches the execution plan of queries but does not have result caching. You may simulate result caching by using temporary tables or Materialized Views (SQL Server 2022+) for result caching at the database level.

In-Memory OLTP: For high-performance scenarios, employ SQL Server's In-Memory OLTP that stores data in RAM to provide faster access to them.

You can use application-level caching for flexibility, and SQL Server caching options for specific, high-performance needs.
Kiran Kumar1y ago
Hi Amit

It was nice explain however I have a query about

Result Caching: Cache frequently accessed data

To get this feature do we need to initiate the database from .net code else write the code in SQL server similar to procedures within the database else it is built in feature that is something will get from conjuration in SQL Server IDE
Amit Mohanty1y ago
To make stored procedures and DML operations work better, and to improve how the whole application performs, you should think about a complete method. This means making database items, queries, business logic, and front-end code better.

To optimize slow stored procedures and improve performance:
Database-Side:
1. Indexing: Create appropriate indexes on frequently queried columns.
2. Query Optimization: Rewrite complex queries, avoid subqueries, and use set-based operations.
3. Avoid Locks: Use appropriate isolation levels and locking hints.
4. Batch Processing: Break large DML operations into smaller batches.
5. Result Caching: Cache frequently accessed data.
6. Avoid Scalar Functions in SELECTs: Use joins or calculated columns instead.
Application-Side:
1. Connection Pooling: Use connection pooling to reduce connection overhead.
2. Asynchronous Operations: Use async and await for database calls.
3. Efficient Data Retrieval: Only fetch necessary columns and use pagination.
4. Caching: Cache frequently accessed data at the application level.
5. Optimize Business Logic: Avoid multiple requests for the same data, and use bulk operations.
6. Pagination and Client-Side Sorting: Implement pagination and filter data client-side.