# Sorting vs Hashing in SQL Query Optimization > [!Note] > `Effective query optimization in SQL hinges on choosing between **Sorting**, which enables **sequential data access** through ordered scans, and **Hashing**, which builds **in-memory hash tables** for grouping and joining, all while carefully managing **memory consumption** to avoid performance degradation.` Sorting reorders every row according to a defined key before producing results, making it indispensable during **index scans** and a prerequisite for the **Stream Aggregate** operator to emit outputs as grouping keys change. By guaranteeing that rows arrive in sorted order, it unlocks efficient, sequential processing of data streams. Hashing, on the other hand, applies a **hash function** to distribute rows into buckets without any prior ordering. This approach constructs **in-memory hash tables**, proving highly efficient for handling large volumes of unsorted data during **Hash Join** and **Hash Aggregate** operations. However, because hashing relies on memory, spills to tempdb can occur when bucket sizes exceed available space, leading to potential performance hits. Both **Sorting** and **Hashing** play foundational roles in SQL query execution plans, directly influencing the speed and resource utilization of **aggregation** and **join** operations. --- ## ## References - Korotkevitch, D. (2022). _SQL Server advanced troubleshooting and performance tuning: Best practices and techniques_. O’Reilly Media. - Nevarez, B. (2022). _SQL Server query tuning and optimization: Optimize Microsoft SQL Server 2022 queries and applications_. Packt Publishing.