Synapse stored procedure vs SQL Server stored procedure

pmscorca 792 Reputation points


I've more experience with SQL Server and less one with Synapse Analytics.
I need to implement some stored procedures in a dedicated SQL pool.

The procedures have to manage some tens of thousands of rows.

The code of a Synapse stored procedure is interpreted at run-time while the code of a SQL Server stored procedure is pre-compiled.
So, which are the right practices to follow in order to obtain the maximum benefit from a Synapse stored procedure respect to SQL Server?


Azure Synapse Analytics
Azure Synapse Analytics
An Azure analytics service that brings together data integration, enterprise data warehousing, and big data analytics. Previously known as Azure SQL Data Warehouse.
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  1. phemanth 5,570 Reputation points Microsoft Vendor

    Thanks for reaching out to Microsoft Q&A

    Here are some key practices to get the most out of Synapse stored procedures compared to SQL Server, considering you have more experience with SQL Server and your procedures will handle tens of thousands of rows:

    Focus on Optimization, not Pre-compilation:

    • Understand the difference: Unlike SQL Server, Synapse procedures are interpreted at runtime, not pre-compiled. This means compilation overhead is minimal compared to execution time for large datasets.
    • Optimize for large queries: Since pre-compilation isn't a major factor, focus on writing well-structured, optimized T-SQL code for handling large datasets efficiently.

    Leverage Synapse Features:

    • Temporary tables: Utilize temporary tables to stage and process data within the stored procedure. This can improve performance for complex operations on large datasets.
    • Bulk insert/delete: For bulk data manipulation, leverage BULK INSERT and BULK DELETE statements for faster data loading and deletion.

    Keep it Simple:

    • Minimize nesting: While Synapse allows some nesting of stored procedures (up to 8 levels), it's best to keep them relatively simple for better readability and maintainability.
    • Avoid complex logic: Break down complex logic into smaller, reusable procedures for better organization and potentially improved performance.

    Additional Considerations:

    • Parameterization: Ensure your procedures accept parameters for dynamic behavior and reusability.
    • Error handling: Implement proper error handling mechanisms to capture and report issues during execution.


    Hope this helps. Do let us know if you any further queries.

    If this answers your query, do click Accept Answer and Yes for was this answer helpful. And, if you have any further query do let us know.

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