How do you speed up selecting and computing from a large data set in sql server

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To improve the performance of querying and computations on a large dataset in SQL Server, you can consider several best practices:

1. Indexes: Properly designing indexes for your tables is crucial as it significantly speeds up data retrieval by allowing the database engine to quickly locate the required data. Be sure that you have appropriate non-clustered indexes on columns used in your query's WHERE, JOIN conditions and ORDER BY clauses. You can use the Database Engine Tuning Advisor or DMVs (Dynamic Management Views) to help determine if indexes are needed and how they should be designed.

2. Partitioning: Large tables can benefit from partitioning, dividing them into smaller more manageable pieces based on a logical or physical boundary such as time or geographic location. This helps in faster data access and improves query performance by reducing the amount of data that needs to be scanned for a particular query.

3. Query optimization: Write efficient queries, making use of SQL Server features like JOINs instead of subqueries and using the EXISTS keyword over IN when checking for existence. You can also make use of query hints to optimize your queries or leverage SQL Server's built-in optimization features like Query Store and Automatic Plan Correction.

4. Memory management: Allocating enough memory to the server or specific query can help improve performance, as it allows SQL Server to cache more data in its memory and reduce disk I/O which is a bottleneck for large datasets. You can use tools like Memory Profiler or SQL Server Management Studio to monitor the server's memory usage and identify any potential issues.

5. Database design: A well-designed database, with appropriate normalization levels and denormalized data where necessary can help reduce the number of joins required in queries. This leads to faster query execution as fewer tables need to be accessed during a query, which ultimately reduces I/O and processing time.

6. Use of appropriate data types: Choosing the correct data type for each column is essential as it allows SQL Server to store, read and process your data more efficiently while reducing storage requirements. Avoid using large or unnecessarily complex data types like VARCHAR(MAX) for columns that don't require it, and make use of the built-in SQL Server date/time or numeric data types.

7. Hardware considerations: Upgrading your server's hardware, like adding more RAM, using faster solid-state drives (SSD), or utilizing parallel processing with multiple CPUs can help significantly improve the performance of querying and computations on large datasets.