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In the world of SQL Server, understanding the difference between Query Hash and Plan Hash can be incredibly beneficial when it comes to performance tuning. Both of these important metrics can be found in the SQL Server Management Studio (SSMS) and other performance monitoring tools. But what are they? In this blog post, we will delve into what Query Hash and Plan Hash are, their differences, and how they can be utilized in SQL Server 2022, Azure SQL, Microsoft Fabric, Delta Lake, OpenAI + SQL, and Databricks.

Let's start by understanding what a Query Hash is. A Query Hash is a numerical representative of a SQL query. SQL Server generates this hash based on the query's text, enabling it to identify identical or similar queries. Query Hash can be incredibly helpful when seeking to identify resource-intensive queries that are run frequently. Using the Query Hash, you can identify these queries and optimize them for better performance.

On the other hand, a Plan Hash represents the query execution plan. Similar to a Query Hash, a Plan Hash is a numerical representation, but it is based on the execution plan rather than the query text. This helps in identifying and grouping similar execution plans. It is essential for identifying queries that use the same execution plan, which can help in troubleshooting performance issues linked to inefficient execution plans.

Let's see how you can retrieve these hash values in SQL Server 2022. You can use the following T-SQL script to get the Query Hash and Plan Hash for all currently executing requests.

-- T-SQL code to get Query Hash and Plan Hash
SELECT 
    r.session_id,
    t.text as Query_Text,
    r.query_hash,
    r.plan_handle,
    p.query_plan
FROM sys.dm_exec_requests r
CROSS APPLY sys.dm_exec_sql_text(r.sql_handle) t
CROSS APPLY sys.dm_exec_query_plan(r.plan_handle) p

In Azure SQL, the method for retrieving the Query and Plan Hash is similar to SQL Server. However, it becomes more interesting when we bring Microsoft Fabric into the picture. Microsoft Fabric is an open-source framework that allows developers to create distributed, scalable, secure, and reliable applications. It provides a SQL-like interface for querying data, and it supports extracting Query and Plan Hashes, which can be used for optimizing and troubleshooting distributed applications.

Delta Lake, an open-source storage layer that brings ACID transactions to Apache Spark and big data workloads, also supports querying with SQL. Since it's compatible with Apache Spark's Data Source API, Delta Lake can be used with Databricks to extract Query Hash and Plan Hash. This combination provides an excellent platform for managing and optimizing big data workloads.

Lastly, the combination of OpenAI and SQL can be used to predict, analyze, and optimize SQL queries. By using machine learning models to predict the Query Hash and Plan Hash, it's possible to predict the performance of a SQL query before it's executed. This can lead to significant improvements in query performance and resource utilization.

In conclusion, understanding and using Query Hash and Plan Hash can be crucial to optimizing SQL queries and improving application performance. Whether you're working with SQL Server, Azure SQL, Microsoft Fabric, Delta Lake, OpenAI + SQL, or Databricks, these metrics provide invaluable insights into query performance and optimization.