SQL Tran Docs
  • Overview
    • About
    • Getting started
    • Object lifecycle
    • Why is there no AI inside?
    • Performance considerations
    • Security considerations
    • Assessment
    • Unlocking projects
    • Interface walk-through
    • Translation scenarios
    • Prerequisites for Azure Marketplace deployment
  • Emulation Scenarios
    • Emulation in SQL Tran
    • SQL Server to Fabric Warehouse
      • Data types
      • Case sensitivity
      • Cursors
        • Basic cursor loop
        • Cursor types
        • Fetch direction modes
        • Cursors in control flow
        • Nested cursors
        • Data modification cursors
        • Multiple cursors
        • Subqueries and filtering
      • Named procedure parameters
      • Result set limiting
      • MERGE statements
      • Computed columns
      • External tables
      • Materialized views
      • Identity columns
      • Unsupported system objects
    • Synapse Analytics to Fabric Warehouse
      • Emulations
      • Limitations
    • SQL Server to Synapse Analytics
    • Oracle to PostgreSQL
  • Project wizard
    • Source database
    • Target database
    • Wrapping up
  • Projects
    • Project list
    • Overview
    • Workspace
    • Reports
    • Tests
    • Scratch pad
    • Settings
      • Project name
      • Mapping
      • Database connections
    • Navigation
    • Object complexity
    • Static analysis
    • Translation errors
    • Exporting and importing projects
  • Workspace
    • Object tree
    • Data lineage
    • Code
    • Actions
      • Overriding source
      • Overriding target
      • Ignoring objects
  • Tests
    • Workflow
    • Configure SQL Tran
    • Connecting to databases
      • Fabric Warehouse
      • Synapse Dedicated SQL Pool
      • Azure SQL Database, Azure SQL Managed Instance, Microsoft SQL Server
    • Tables
    • Views
    • Procedures
    • Functions
    • Triggers
    • Performance tests
  • Scripter
    • About
    • Supported databases
    • SQL Server
    • Azure SQL
    • Synapse Dedicated Pool
    • Oracle
    • PostgreSQL
    • MySQL
Powered by GitBook
On this page
  • Performance of SQL Tran
  • Performance of translated code
  1. Overview

Performance considerations

Performance of SQL Tran

When selecting a region where your want SQL Tran deployed, select the region closest to your users, to minimize network latency.

If you have users that are geographically dispersed, you may want to deploy multiple instances of SQL Tran to improve the user experience.

Parsing, analysis and transpilation speed linearly increases with CPU cores as our engine is parallelizable and multi-threaded. Basic plan comes with 4 CPU cores, while Pro and BYOL plans come with 8 CPU cores. This means that most operations in Pro and BYOL plans are twice as fast compared to the Basic plan.

Performance of translated code

SQL Tran will follow best practices when translating the code and generate as idiomatic code as possible.

Still, it is important to keep track of the performance to know which areas, due to different architectures of target database compared to the source, might require special attention and possible manual rewrite. For this reason, all tests in SQL Tran automatically provide performance comparison between source and target. Time required to execute a specific procedure or function, as well as time it took for a view to return its data will be reported.

PreviousWhy is there no AI inside?NextSecurity considerations

Last updated 7 days ago