"Invalid Managed Identity" error in vulnerability assessments for SQL Managed Instance
On a recent case, a customer received the error "Invalid Managed Identity" when trying to scan a database. The error suggests that there is an issue with the managed identity or the RBAC permissions required, but the same error occurs when there is a connectivity issue between SQL Managed Instance and the storage account selected. Checking RBAC permissions The RBAC permissions can be manually checked in Azure Portal, or you can use the below script in Azure CLI, providing the resource details. #SQL Managed Instance Details $serverResourceGroup = '' $serverName = '' #Storage Account Details $storageAccountResourceGroup = '' $storageAccount = '' ############################################################################################################################## $sqlIdentity = ((az sql mi show -g $serverResourceGroup -n $serverName | ConvertFrom-Json).Identity).principalId $storageId = (az storage account show -g $storageAccountResourceGroup -n $storageAccount | ConvertFrom-Json).id $permissions = $NULL $permissions = az role assignment list --all --assignee $sqlIdentity | ConvertFrom-Json | Where-Object {$_.scope -eq $storageId -and $_.roleDefinitionName -eq 'Storage Blob Data Contributor'} if ($permissions -eq $NULL) {Write-Host "RBAC permissions do not exist"} else {Write-Host "RBAC Permissions exist"} It will return a simple message to confirm if the permissions exist. Connectivity issues If the permissions do exist, then it may be due to connectivity issues between SQL Managed Instance and the storage account. Listed below are ways to check this. Storage account networking configuration The storage account can be configured to allow the following access: Public - All Public - Selected networks Private If the access is set to Selected Networks, make sure the SQL Managed Instance subnet is in the list. If the access is private only, then the SQL Managed Instance would need to be able to resolve the Private IP in DNS. NSG/Firewall rules and routing Check that there are no rules blocking connections between each resource and that the routing is configured correctly. DNS settings If the DNS settings are custom or a private endpoint is being used, the DNS configuration for the SQL Managed Instance virtual network may need to be configured, for example, adding a private DNS zone. Network Peering If the connectivity is through a private endpoint in a different virtual network, check that there is communication between them, such as network peering. Disclaimer Please note that products and options presented in this article are subject to change. This article reflects the documentation in January 2026. I hope this article was helpful for you, please feel free to share your feedback in the comments section.Deep dive into the SSMA Code Conversion Copilot Architecture
The Problem We Set Out to Solve Migrating from Oracle PL/SQL to SQL Server T‑SQL is notoriously complex. While SSMA’s rule engine covers hundreds of conversion rules, edge cases, custom logic, and nuanced syntax but it often slips through. Developers end up spending hours manually fixing scripts, validating correctness, and worrying about regressions. The Copilot was built to tackle this pain point: augment SSMA’s rule engine with large language models (LLMs) that can reason about tricky conversions, explain their logic, and accelerate the migration process. But building trust in AI‑generated code meant we had to design an architecture that was controllable, reliable, and secure. SSMA Code Conversion Copilot was released back in the month of May and some of the use cases are elaborated here. This blog talks about the inner working of Copilot. ⚙️ Semantic Kernel for Skill / Plugin Management At the heart of SSMA Copilot lies Semantic Kernel, Microsoft’s open‑source framework for integrating LLMs. It offers two big capabilities: Prompt management — defining prompts as reusable “skills” with parameters like model, temperature, and token count. Agentic orchestration — automating workflows by chaining tools and prompts together. For Copilot, we deliberately chose only prompt management at this point. We have also added native skills such as checking the correctness of syntax and semantics but have not used agentic orchestration for the current implementation. ❌ Why Not Agentic Features? Agentic orchestration can be powerful, but in practice it wasn’t reliable enough for production migrations. Tool selection logic sometimes failed, leading to incorrect validations or spurious edits. Moreover, we saw an issue with latency. Instead, we implemented a deterministic workflow that gave us full control. ✅ Manual Orchestration Workflow Our workflow looks like this (please refer to the diagram): Partial Migration: SSMA generates a baseline conversion. Copilot Authentication: The Copilot is authenticated using the inputs provided by the user. This is where the model is also decided. Alternately, the user can use the managed endpoint that is controlled by Microsoft. LLM Completion: Copilot fills in gaps. Moreover, it explains the solution, points out the error that it is trying to resolve in simple language. Parsing & Compilation: A target‑dialect parser checks syntax. This catches unsupported constructs or binding issues far more reliably than prompt tuning. Spurious Edit Detection: LLMs are instructed to only enhance flagged portions of code. Any edits to “correct” blocks incur penalties, with a strict threshold of zero spurious edits allowed. Query Execution & Data Generation: Where possible, we generate minimal synthetic data (two rows per table) to validate equivalence between source and target queries. Semantic Equivalence Checks: For cases where execution isn’t feasible, we use LLM‑based scoring to judge logical fidelity. This loop repeats until syntactic and semantic correctness is achieved. By using this workflow, we avoided regression spirals and ensured predictable outcomes across dialects. This workflow was tested using our built-in evaluation framework which has leveraged the rich test cases of SSMA. 🔑 Feature Comparison Managed Endpoint Authentication was released with SSMA 10.4 in November 2025. Managed Endpoint BYOK Provisioning OpenAI Endpoint No Yes LLM Model Selection Automatic Manual Authentication Mandatory Entra ID OpenAI Endpoint and Key Private Endpoint Support No Yes Cross Tenant Dependency* Yes No Pricing Free Consumption in actuals *Cross Tenant Dependency: The endpoint is hosted in Microsoft tenant while the authentication happens in the user tenant. 🔒 Privacy and Data Handling A critical point: we don’t store your data. The scripts you provide are used only for generating the migration output. Once the process completes, the data is flushed. No proprietary code or schema information is retained. This design ensures: Security: We run OpenAI in Microsoft tenant following all security protocols. Trust: Copilot is a tool, not a repository. Compliance: Aligns with enterprise privacy expectations. 🌟 Why This Matters By combining Semantic Kernel framework, SSMA Copilot delivers reliable migrations without sacrificing flexibility. And with the managed endpoint, it’s now easier and safer than ever to adopt — no keys, no storage, no friction. This isn’t just about faster migrations. It’s about building trust in AI‑assisted workflows, ensuring correctness, and giving enterprises confidence that their data is secure. Get started with your Copilot based migration journey using SSMA for OracleAssess and upgrade to SQL Server 2025 with SSMS Migration Component
Upgrade to SQL Server 2025 with confidence. Starting with SSMS 22, the Hybrid & Migration component now includes upgrade assessment for SQL Server 2025. This feature enables you to quickly evaluate readiness for upgrade. SSMS also provides a streamlined migration path to the instance of higher version if in-place upgrade is not preferred.Setting up SQL Managed Instance Link to an Availability Group (Updated September 2025)
On a recent case, a customer was trying to set up SQL Managed Instance Link to partner with an on premise Always On Availability Group (AG). Using the public documentation will work, but there are some differences required to ensure a failover does not break the connection. Please note that Managed Instance Link can only be created with an availability group with a single database. This post will be using steps from the following documents. Prepare your environment for a link - Azure SQL Managed Instance Configure link with scripts - Azure SQL Managed Instance The steps to set this up are below Create a database master key on the AG nodes Enabling trace flags on the AG nodes (Optional) Testing network connectivity between SQL Managed Instance and the AG Create certificates on the AG nodes Import AG certificate public keys to SQL Managed Instance Import the certificate public key of the SQL Managed Instance to AG nodes Import Azure-trusted root certificate authority keys to AG nodes Alter the mirroring endpoint on AG nodes Create a distributed availability group on AG Set up the managed Instance Link Create a database master key Firstly, a master encryption key is required on all nodes of the AG if it does not already exist. The script below will not return any rows if it does not exist. -- Run on SQL Server USE master; GO SELECT * FROM sys.symmetric_keys WHERE name LIKE '%DatabaseMasterKey%'; If no rows are returned, the following query can be used. Keep a note of the passwords in a confidential and secure place. -- Run on SQL Server -- Create a master key USE master; GO CREATE MASTER KEY ENCRYPTION BY PASSWORD = '<strong_password>'; Enabling trace flags on AG nodes (Optional) To optimise the performance of the link, we recommend enabling the trace flags T1800 and T9567 on all nodes. Instructions on this are here and will require a restart of the service. Testing network connectivity between SQL Managed Instance and the AG Instructions on how to run the connectivity tests can be found here, and can be done by SSMS GUI or T-SQL. For these tests, the SQL Managed Instance will need to be able to connect to the Mirror endpoint using the IP of the availability group. The SQL Managed Instance IPs can can change (for example during maintenance). For that reason NSG and firewall rules should allow access to ports 5022 and 11000-11999 for the whole subnet range of the SQL Managed Instance. Create certificates on AG nodes The below query will need to be run on all AG nodes. Pay particular attention to the @cert_expiry_date variable to ensure it is a date in the future. It will need to be replaced after expiry. -- Create the SQL Server certificate for the instance link USE MASTER -- Customize SQL Server certificate expiration date by adjusting the date below DECLARE @cert_expiry_date AS varchar(max)='03/30/2025' -- Build the query to generate the certificate DECLARE @sqlserver_certificate_name NVARCHAR(MAX) = N'Cert_' + @@servername + N'_endpoint' DECLARE @sqlserver_certificate_subject NVARCHAR(MAX) = N'Certificate for ' + @sqlserver_certificate_name DECLARE @create_sqlserver_certificate_command NVARCHAR(MAX) = N'CREATE CERTIFICATE [' + @sqlserver_certificate_name + '] ' + char (13) + ' WITH SUBJECT = ''' + @sqlserver_certificate_subject + ''',' + char (13) + ' EXPIRY_DATE = '''+ @cert_expiry_date + ''''+ char (13) IF NOT EXISTS (SELECT name from sys.certificates WHERE name = @sqlserver_certificate_name) BEGIN PRINT (@create_sqlserver_certificate_command) -- Execute the query to create SQL Server certificate for the instance link EXEC sp_executesql @stmt = @create_sqlserver_certificate_command END ELSE PRINT 'Certificate ' + @sqlserver_certificate_name + ' already exists.' GO Import AG certificate public keys to SQL Managed Instance The public keys of the AG certificates need to be imported into the SQL Managed Instance. The below query will return the data required for the next step. -- Run on SQL Server -- Show the name and the public key of generated SQL Server certificate USE MASTER GO DECLARE @sqlserver_certificate_name NVARCHAR(MAX) = N'Cert_' + @@servername + N'_endpoint' DECLARE @PUBLICKEYENC VARBINARY(MAX) = CERTENCODED(CERT_ID(@sqlserver_certificate_name)); SELECT @sqlserver_certificate_name as 'SQLServerCertName' SELECT @PUBLICKEYENC AS SQLServerPublicKey; Example result The next step will need to be run in PowerShell and can be run through Azure Cloud Shell. Below is an example script to log in to Azure. # Run in Azure Cloud Shell (select PowerShell console) # Enter your Azure subscription ID $SubscriptionID = "<subscriptionid>" # Login to Azure and select subscription ID if ((Get-AzContext ) -eq $null) { echo "Logging to Azure subscription" Login-AzAccount } Select-AzSubscription -SubscriptionName $SubscriptionID Replace the values of $CertificateName, $PublicKeyEncoded and $ManagedInstanceName and then run this. # Run in Azure Cloud Shell (select PowerShell console) # =============================================================================== # POWERSHELL SCRIPT TO IMPORT SQL SERVER PUBLIC CERTIFICATE TO SQL MANAGED INSTANCE # ===== Enter user variables here ==== # Enter the name for the server SQLServerCertName certificate – for example, "Cert_sqlserver1_endpoint" $CertificateName = "<sqlservercertname>" # Insert the certificate public key blob that you got from SQL Server – for example, "0x1234567..." $PublicKeyEncoded = "<sqlserverpublickey>" # Enter your managed instance short name – for example, "sqlmi" $ManagedInstanceName = "<managedinstancename>" # ==== Do not customize the below cmdlets==== # Find out the resource group name $ResourceGroup = (Get-AzSqlInstance -InstanceName $ManagedInstanceName).ResourceGroupName # Upload the public key of the authentication certificate from SQL Server to Azure. New-AzSqlInstanceServerTrustCertificate -ResourceGroupName $ResourceGroup -InstanceName $ManagedInstanceName -Name $CertificateName -PublicKey $PublicKeyEncoded Repeat this step for all AG node values. Import the certificate public key of SQL Managed Instance to AG nodes Using the same PowerShell session, run this script to get the public key of the SQL Managed Instance certificate, replacing the value of the variable $ManagedInstanceName. # Run in Azure Cloud Shell (select PowerShell console) # =============================================================================== # POWERSHELL SCRIPT TO EXPORT MANAGED INSTANCE PUBLIC CERTIFICATE # ===== Enter user variables here ==== # Enter your managed instance short name – for example, "sqlmi" $ManagedInstanceName = "<managedinstancename>" # ==== Do not customize the following cmdlet ==== # Find out the resource group name $ResourceGroup = (Get-AzSqlInstance -InstanceName $ManagedInstanceName).ResourceGroupName # Fetch the public key of the authentication certificate from Managed Instance. Outputs a binary key in the property PublicKey. Get-AzSqlInstanceEndpointCertificate -ResourceGroupName $ResourceGroup -InstanceName $ManagedInstanceName -EndpointType "DATABASE_MIRRORING" | out-string</managedinstancename> On all nodes of the AG run the following query, replacing the certificate name and the value of the PublicKey with the output from the previous step. -- Run on SQL Server USE MASTER CREATE CERTIFICATE [<managedinstancefqdn>] FROM BINARY = <publickey></publickey></managedinstancefqdn> Import Azure-trusted root certificate authority keys to AG nodes Use the steps here to import the Microsoft PKI root-authority certificate and DigiCert PKI root-authority certificate onto all nodes of the AG. Alter the mirroring endpoint on AG nodes The mirroring endpoint on the AG nodes will also need to be updated to allow the Managed Instance to authenticate with the AG using the newly created certificate. This can be done by navigating to Server Objects > Endpoints > Database Mirroring. Right click on the endpoint and select Script Endpoint as > CREATE To > New Query Editor Window. Change CREATE ENDPOINT to ALTER ENDPOINT and add this command after AUTHENTICATION = WINDOWS NEGOTIATE CERTIFICATE, changing the cert_name value. CERTIFICATE [cert_name] Once updated, execute the query. For multiple Availability groups on the same instance, the next section would need to be repeated for each one. Create a distributed availability group on the AG (running against the primary server) Use this script to set up the distributed availability group, updating the sections in angle brackets (<>). The SQLServerIP will need to be the IP of the listener of the availability group. -- Run on SQL Server -- Create a distributed availability group for the availability group and database -- ManagedInstanceName example: 'sqlmi1' -- ManagedInstanceFQDN example: 'sqlmi1.73d19f36a420a.database.windows.net' USE MASTER CREATE AVAILABILITY GROUP [<dagname>] WITH (DISTRIBUTED) AVAILABILITY GROUP ON N'<agnameonsqlserver>' WITH ( LISTENER_URL = 'TCP://<sqlserverip>:<endpointport>', AVAILABILITY_MODE = ASYNCHRONOUS_COMMIT, FAILOVER_MODE = MANUAL, SEEDING_MODE = AUTOMATIC, SESSION_TIMEOUT = 20 ), N'<agnameonsqlmi>' WITH ( LISTENER_URL = 'tcp://<managedinstancefqdn>:5022;Server=[<managedinstancename>]', AVAILABILITY_MODE = ASYNCHRONOUS_COMMIT, FAILOVER_MODE = MANUAL, SEEDING_MODE = AUTOMATIC ); GO</managedinstancename></managedinstancefqdn></agnameonsqlmi></endpointport></sqlserverip></agnameonsqlserver></dagname> Set up the managed Instance Link The final step is to set up the link, the script for this is here. Please ensure that the $SQLServerIP is the IP of the AG listener and not the IP of the primary node. Once this is executed, the MI Link should be up and running and failing over to another node on the AG will not stop MI Link from syncing. Disclaimer Please note that products and options presented in this article are subject to change. This article reflects MI Link and the documentation in September 2025. I hope this article was helpful for you, please feel free to share your feedback in the comments section.QTip: Getting timeouts and exceptions using Azure SQL DB Query Store
Requirements: Azure SQL Database SQL Server Management Studio (SSMS) Tables testcodes and testinvoice used for this demo 1-Configure Query Store to collect data and save as soon as possible to be able to review it * Data flush interval is the time to send information to disk ** Statistics collection interval is the time range to collect data , query store is not collecting one record by execution is collecting statistics of each execution and you can get max, min, avg but not excecution by excecution *** Query store capture mode ALL will collect information of all executions query store will not make any kind of filter 2-Create table testcodes CREATE TABLE [dbo].[testcodes]( [Code] [char](5) NULL, [Description] [nchar](10) NULL ) ON [PRIMARY] GO 3-Create table testinvoice CREATE TABLE [dbo].[testinvoice]( [code] [char](5) NULL, [pieces] [int] NULL ) ON [PRIMARY] GO 4-Add some codes to be used 5-Add some records to invoice Exception ... 6-Run query to get information from invoices and get description from codes select *,description=(select testcodes.Code from testcodes where testcodes.code=invoice.code) from testinvoice invoice Is possible to see execution without error 7-Now add in codes a second code BBB to force exception 8-Run query from point 6 again Result is an exception 9-Run query to see queries with exceptions or timeouts starting 2 days ago (declare @datestart as datetime = dateadd(D,-2,getdate());) declare @datestart as datetime = dateadd(D,-2,getdate()); declare @datefinish as datetime = getdate(); /* if you want to set to specific time */ --set @datestart = '2025-04-09 00:00:00'; --set @datefinish = '2025-04-09 23:59:59'; select rs.last_execution_time, rs.execution_type_desc, qt.query_sql_text, q.query_id, CONVERT(VARCHAR(1000), q.query_hash, 1) as strqueryhash, p.plan_id, rs.last_cpu_time, rs.last_duration, rs.count_executions, rs.last_rowcount, rs.last_logical_io_reads, rs.last_physical_io_reads, rs.last_query_max_used_memory, rs.last_tempdb_space_used, rs.last_dop, p.is_forced_plan, p.last_force_failure_reason, p.last_force_failure_reason_desc FROM sys.query_store_query_text AS qt JOIN sys.query_store_query AS q ON qt.query_text_id = q.query_text_id JOIN sys.query_store_plan AS p ON q.query_id = p.query_id JOIN sys.query_store_runtime_stats AS rs ON p.plan_id = rs.plan_id where rs.last_execution_time>= @datestart and rs.last_execution_time<=@datefinish and (rs.execution_type=3 or rs.execution_type=4) -- 3 timeout, 4 error --and qt.query_sql_text like '%actual%' --and q.query_hash=0x009C458D20394C37 --and p.plan_id=12 ORDER BY rs.last_execution_time DESC 10-In query there are some commented lines that you can use to add more filters or modify them To see all records comment line 20 declare @datestart as datetime = dateadd(D,-2,getdate()); declare @datefinish as datetime = getdate(); /* if you want to set to specific time */ --set @datestart = '2025-04-09 00:00:00'; --set @datefinish = '2025-04-09 23:59:59'; select rs.last_execution_time, rs.execution_type_desc, qt.query_sql_text, q.query_id, CONVERT(VARCHAR(1000), q.query_hash, 1) as strqueryhash, p.plan_id, rs.last_cpu_time, rs.last_duration, rs.count_executions, rs.last_rowcount, rs.last_logical_io_reads, rs.last_physical_io_reads, rs.last_query_max_used_memory, rs.last_tempdb_space_used, rs.last_dop, p.is_forced_plan, p.last_force_failure_reason, p.last_force_failure_reason_desc FROM sys.query_store_query_text AS qt JOIN sys.query_store_query AS q ON qt.query_text_id = q.query_text_id JOIN sys.query_store_plan AS p ON q.query_id = p.query_id JOIN sys.query_store_runtime_stats AS rs ON p.plan_id = rs.plan_id where rs.last_execution_time>= @datestart and rs.last_execution_time<=@datefinish --and (rs.execution_type=3 or rs.execution_type=4) -- 3 timeout, 4 error --and qt.query_sql_text like '%actual%' --and q.query_hash=0x009C458D20394C37 --and p.plan_id=12 ORDER BY rs.last_execution_time DESC Now you can see all records 11-Reproduce error several times within 1 minute to see value in execution count (number of executions inside of statistics collection interval) Timeout ... 12-Configure command timeout different to 0 for this demo use 10 (seconds) in parameters previous to connect Second option 12-Use query below that will run 1000 times and at some point will fail INSERT INTO [dbo].[testinvoice] SELECT * FROM [dbo].[testinvoice] GO 1000 13-Run query from point 9 to see data in query store Now you can reproduce and get data about all excecutions , exceptions and timeouts good luck!
Leveraging GitHub Copilot for T-SQL Code Conversion: A Deep Dive into Code Explainability
Converting database code between different relational database management systems (RDBMS) is an essential part of database migration , especially when moving from Oracle’s PL/SQL to SQL Server’s T-SQL. Both Oracle and SQL Server have unique syntax, functions, and programming conventions that require significant adjustments when converting complex SQL queries, stored procedures, or functions. While manual conversion can be a painstaking process, GitHub Copilot, an AI-powered code assistant, can ease this burden by offering real-time suggestions and automating many aspects of the conversion. In the first part of this two-part blog, we explore how GitHub Copilot can be a powerful tool for code conversion, helping developers quickly adapt code from one language or framework to another. By providing context-aware suggestions and completions, Copilot simplifies the process of rewriting and refactoring code. In the second part, we dive deeper into Copilot’s explainability feature, showcasing how it enhances the code conversion process from Oracle to Azure SQL. Code explainability in GitHub Copilot refers to the tool’s ability to offer suggestions and comments that help developers understand what a given piece of code is doing. When dealing with database migrations, such as converting Oracle SQL code to Azure SQL (SQL Server), GitHub Copilot’s ability to explain, suggest, and refactor code can ease the transition. It can assist in making the conversion process smoother, more efficient, and less error-prone by explaining the logic behind Oracle-specific queries and suggesting corresponding changes in Azure SQL’s syntax We'll go through multiple examples, analyze the differences between the two languages, and show how GitHub Copilot handles these challenges. Understanding the Key Differences: PL/SQL vs T-SQL Before jumping into examples, it's important to understand few of the fundamental differences between PL/SQL (Oracle) and T-SQL (SQL Server): Syntax: While both are procedural SQL dialects, there are key differences in the way they handle variables, control structures, and flow control. Functions: Each platform has its own set of built-in functions (e.g., string manipulation, date handling, etc.), and these need to be mapped correctly during conversion. Error Handling: Error handling and exception management differ between the two, with PL/SQL using EXCEPTION blocks and T-SQL using TRY...CATCH. Cursor Handling: While both support cursors for iterating through results, their syntax differs. Leveraging GitHub Copilot for Oracle PL/SQL to SQL Server T-SQL Code Conversion: A Deep Dive into Complex Examples with Explainability Converting database code between different relational database management systems (RDBMS) is an essential part of database migration , especially when moving from Oracle’s PL/SQL to SQL Server’s T-SQL. Both Oracle and SQL Server have unique syntax, functions, and programming conventions that require significant adjustments when converting complex SQL queries, stored procedures, or functions. While manual conversion can be a painstaking process, GitHub Copilot, an AI-powered code assistant, can ease this burden by offering real-time suggestions and automating many aspects of the conversion. In the first part of this two-part blog, we explore how GitHub Copilot can be a powerful tool for code conversion, helping developers quickly adapt code from one language or framework to another. By providing context-aware suggestions and completions, Copilot simplifies the process of rewriting and refactoring code. In the second part, we dive deeper into Copilot’s explainability feature, showcasing how it enhances the code conversion process from Oracle to Azure SQL. Code explainability in GitHub Copilot refers to the tool’s ability to offer suggestions and comments that help developers understand what a given piece of code is doing. When dealing with database migrations, such as converting Oracle SQL code to Azure SQL (SQL Server), GitHub Copilot’s ability to explain, suggest, and refactor code can ease the transition. It can assist in making the conversion process smoother, more efficient, and less error-prone by explaining the logic behind Oracle-specific queries and suggesting corresponding changes in Azure SQL’s syntax We'll go through multiple examples, analyse the differences between the two languages, and show how GitHub Copilot handles these challenges. Understanding the Key Differences: PL/SQL vs T-SQL Before jumping into examples, it's important to understand few of the fundamental differences between PL/SQL (Oracle) and T-SQL (SQL Server): Syntax: While both are procedural SQL dialects, there are key differences in the way they handle variables, control structures, and flow control. Functions: Each platform has its own set of built-in functions (e.g., string manipulation, date handling, etc.), and these need to be mapped correctly during conversion. Error Handling: Error handling and exception management differ between the two, with PL/SQL using EXCEPTION blocks and T-SQL using TRY...CATCH. Cursor Handling: While both support cursors for iterating through results, their syntax differs. Example 1: Complex Stored Procedure Conversion Let’s start with a complex example that involves handling parameters, cursors, and error handling. We'll look at a PL/SQL stored procedure in Oracle that processes employee records and outputs their details. CREATE OR REPLACE PROCEDURE GetEmployeeDetails (emp_id IN NUMBER) IS CURSOR emp_cursor IS SELECT employee_id, first_name, last_name, hire_date FROM employees WHERE employee_id = emp_id; emp_record emp_cursor%ROWTYPE; emp_full_name VARCHAR2(100); BEGIN OPEN emp_cursor; LOOP FETCH emp_cursor INTO emp_record; EXIT WHEN emp_cursor%NOTFOUND; -- Concatenate first and last name emp_full_name := emp_record.first_name || ' ' || emp_record.last_name; -- Output employee details DBMS_OUTPUT.PUT_LINE('Employee ID: ' || emp_record.employee_id); DBMS_OUTPUT.PUT_LINE('Full Name: ' || emp_full_name); DBMS_OUTPUT.PUT_LINE('Hire Date: ' || TO_CHAR(emp_record.hire_date, 'YYYY-MM-DD')); END LOOP; CLOSE emp_cursor; EXCEPTION WHEN NO_DATA_FOUND THEN DBMS_OUTPUT.PUT_LINE('No employee found with the given ID.'); WHEN OTHERS THEN DBMS_OUTPUT.PUT_LINE('An error occurred.'); END; Oracle PL/SQL Example In Oracle, we might have a procedure that uses a cursor to fetch employee records and print them. The procedure also includes error handling for potential issues during execution. Key Points in Oracle PL/SQL: Cursor: PL/SQL uses CURSOR for iterating through rows. Concatenation: Oracle uses || for string concatenation. Error Handling: PL/SQL uses EXCEPTION blocks for handling errors such as NO_DATA_FOUND. Converting to SQL Server T-SQL with GitHub Copilot When you type the PL/SQL code into an IDE with GitHub Copilot enabled (e.g., Visual Studio Code), Copilot will start suggesting the equivalent SQL Server T-SQL code. Here’s how Copilot converts the procedure. Copilot’s Suggested SQL Server T-SQL Code CREATE PROCEDURE GetEmployeeDetails (@emp_id INT) AS BEGIN DECLARE @emp_id INT, @first_name NVARCHAR(50), @last_name NVARCHAR(50); DECLARE @emp_full_name NVARCHAR(100); DECLARE emp_cursor CURSOR FOR SELECT employee_id, first_name, last_name, hire_date FROM employees WHERE employee_id = @emp_id; OPEN emp_cursor; FETCH NEXT FROM emp_cursor INTO @emp_id, @first_name, @last_name, @hire_date; WHILE @@FETCH_STATUS = 0 BEGIN -- Concatenate first and last name SET @emp_full_name = @first_name + ' ' + @last_name; -- Output employee details PRINT 'Employee ID: ' + CAST(@emp_id AS NVARCHAR); PRINT 'Full Name: ' + @emp_full_name; PRINT 'Hire Date: ' + CONVERT(NVARCHAR, @hire_date, 23); FETCH NEXT FROM emp_cursor INTO @emp_id, @first_name, @last_name, @hire_date; END CLOSE emp_cursor; DEALLOCATE emp_cursor; BEGIN TRY -- Error handling code (example) END TRY BEGIN CATCH PRINT 'An error occurred.'; END CATCH END; Key Points in T-SQL: Cursor: SQL Server uses DECLARE CURSOR to declare a cursor, similar to PL/SQL but with different syntax for opening and fetching. Concatenation: SQL Server uses + for string concatenation. Error Handling: SQL Server uses TRY...CATCH for error handling, unlike PL/SQL’s EXCEPTION block. GitHub Copilot provides a T-SQL equivalent for an Oracle PL/SQL stored procedure. Explanation of the Conversion Process Cursor Declaration and Fetching: PL/SQL uses CURSOR with OPEN, FETCH, and CLOSE to process rows. T-SQL uses DECLARE CURSOR to define the cursor and fetches rows with FETCH NEXT. Both approaches are similar, but T-SQL requires explicit DEALLOCATE to release the cursor. Error Handling: PL/SQL uses EXCEPTION blocks to handle errors like NO_DATA_FOUND. T-SQL uses TRY...CATCH blocks to capture and handle errors, a major difference in error handling between the two systems. String Concatenation: PL/SQL uses || for concatenating strings, while T-SQL uses +. This is a simple yet crucial change when converting between the two dialects. Example 2: Complex Query with Date Handling Let’s now look at a query that involves date handling and conditional logic. These are often tricky when converting between PL/SQL and T-SQL due to differences in how each platform handles dates. Oracle PL/SQL Query Example SELECT employee_id, first_name, hire_date, CASE WHEN hire_date < TO_DATE('2000-01-01', 'YYYY-MM-DD') THEN 'Veteran' ELSE 'New Hire' END AS employee_status FROM employees WHERE hire_date BETWEEN TO_DATE('1990-01-01', 'YYYY-MM-DD') AND SYSDATE; Key Points in PL/SQL: TO_DATE: Used to convert strings into dates. SYSDATE: Returns the current system date. CASE: Used for conditional logic to categorize employees as either "Veteran" or "New Hire". Copilot’s Suggested SQL Server T-SQL Code SELECT employee_id, first_name, hire_date, CASE WHEN hire_date < CONVERT(DATE, '2000-01-01', 23) THEN 'Veteran' ELSE 'New Hire' END AS employee_status FROM employees WHERE hire_date BETWEEN CONVERT(DATE, '1990-01-01', 23) AND GETDATE(); Key Points in T-SQL: CONVERT: SQL Server uses CONVERT for date formatting, similar to Oracle’s TO_DATE, but with different syntax and style codes. GETDATE(): Equivalent to Oracle’s TO_DATE In conclusion, GitHub Copilot streamlines code conversion by offering both quick suggestions and detailed explanations. The first part highlighted its ability to assist with code transitions, while the second part focused on how its explainability feature enhances the Oracle to Azure SQL migration, providing guided, efficient, and error-free conversion. This combination accelerates development and reduces potential issues during migration.
A quick way to generate Azure SQL migration assessments
Overview Azure Migrate doesn’t allow users to import the SQL Server Inventory and discovery information from sources other than Azure Migrate appliance as of today. As a result, users who cannot deploy Azure Migrate appliance to discover their SQL Server estate for technical, business or compliance reasons or have already discovered their SQL Server estate using tools like rvtools and want a quick and approximate way to check readiness and total cost savings by moving to Azure, cannot leverage the rich capabilities SQL assessment and business case capabilities that Azure Migrate offers. To help customers quickly get migration readiness and cost savings by migrating to Azure, we are introducing “Import SQL Server Discovery data as CSV” feature in Azure Migrate that allows users to import the SQL Server inventory as a CSV file and use the discovery data to generate assessments and business cases. This feature is launched in private preview. In this blog post, we will do a deep dive on how to use this feature to generate quick assessments for your off-Azure SQL Server estate. Here are the high-level steps for importing your off-Azure SQL Server inventory as a CSV: 1) Step-1: Identify the SQL Server Instances and extract the discovery data into a CSV file. 2) Step-2: Validate and upload the inventory data to Azure Migrate 3) Step-3: Generate and view Assessments Pre-requisites: Import SQL Discovery data as a CSV feature is built on top of the new Azure Migrate experience which is also in Private Preview phase. To use Import SQL Discovery data as CSV feature, you must onboard to the private preview of new Azure Migrate experience and here is the form to sign up for Private preview: Azure Migrate Private Preview Sign-Up Survey. We will share the instructions to onboard via the contact details you provided. Make sure you have the credentials to connect to the SQL Server Instances of interest Make sure you have the requisite roles and privileges on each SQL Server Instance to access and extract the required metadata from Dynamic Management Views. To generate a login with required privileges, please run the scripts listed here: Configure custom login for SQL Server Discovery An Azure Subscription. Please note you don't need to deploy an Azure Migrate appliance to use the SQL import CSV feature. Step-1: Identify and extract the discovery data of SQL Server Instances you intend to migrate to Azure SQL into a CSV file Identify the list of all off-Azure SQL Server Instances you intend to migrate to Azure SQL from either on-premises infrastructure or other cloud platforms like Amazon Web Services (AWS), Google Cloud Platform (GCP). To extract the discovery data, please run the SQL Script below against the SQL Server Instance which you would like to import into Azure Migrate via CSV and store the output into a CSV file. /* PLEASE ENTER THE FULLY QUALIFIED DOMAIN NAME OF THE HOST IN THE LINE BELOW BEFORE RUNNING THE QUERY */ Declare @FullyQualifiedDomainName varchar(200) = ''; With ServerCores AS( SELECT cpu_count AS [Logical CPU Count], hyperthread_ratio AS [Hyperthread Ratio], cpu_count/hyperthread_ratio AS [Physical CPU Count], sqlserver_start_time FROM sys.dm_os_sys_info ), Config AS( SELECT value_in_use AS [Max Server Memory(MB) In Use] FROM sys.configurations WHERE name = 'max server memory (MB)'), UserCount AS( SELECT count(database_id) AS [Number User Databases] FROM sys.databases WHERE database_id > 4 ), DbSize AS( SELECT SUM(CAST(Size AS bigint))*8/1024 AS [Max server memory (in MB)] FROM sys.master_files WHERE database_id > 4 ), TempDbSize AS ( SELECT SUM(CAST(Size AS bigint))*8/1024 AS [TempDb Size (In MB)] FROM sys.master_files WHERE db_name(database_id) = 'tempdb' ), Logins AS (Select count(name) AS [NumOfLogins] from sys.syslogins), Properties AS ( select db.name as database_name, db.database_id as database_id, cast(db.compatibility_level as int) as database_compatibility_level, db.collation_name as database_collation, db.is_broker_enabled as is_service_broker_enabled, '0' as [Number of Nics], SERVERPROPERTY ('servername') as instance_name, SERVERPROPERTY ('productversion') as instance_version, SERVERPROPERTY ('edition') as instance_edition, SERVERPROPERTY ('productlevel') as instance_level, SERVERPROPERTY ('IsHadrEnabled') as is_hadr_enabled, SERVERPROPERTY ('collation') as server_collation, CASE WHEN SERVERPROPERTY ('IsClustered') = 1 THEN 'TRUE' ELSE 'FALSE' END as is_failover_clustered, COALESCE(SERVERPROPERTY ('IsIntegratedSecurityOnly'), 0) as is_integrated_security_only, COALESCE(SERVERPROPERTY ('IsPolyBaseInstalled'), 0) as is_polybase_installed, CASE WHEN 'A' = 'a' THEN 0 ELSE 1 END as is_server_case_sensitive, /* '' + @host_platform + '' as host_platform,*/ db.state as database_state, /* db.is_auto_close_on as is_auto_close_on,'+ CASE when @check_cdc = 1 THEN 'db.is_cdc_enabled' else '0' END + ' as is_cdc_enabled,*/ CASE when db.user_access = 0 THEN 1 ELSE 0 END as is_multi_user, CASE when db.recovery_model = 0 THEN 0 ELSE 1 END as is_full_recovery_model, db.is_read_only as is_read_only, case when source_database_id IS NULL THEN 0 ELSE 1 end as is_snapshot, case when is_published = 1 OR is_subscribed = 1 OR is_merge_published = 1 or is_distributor = 1 THEN 1 ELSE 0 end as is_replication_enabled, db.is_encrypted from sys.databases db where db.name not in ('master', 'tempdb', 'model', 'msdb') and is_distributor <> 1), EachDbSize AS( SELECT database_name = DB_NAME(database_id), CAST(SUM(CAST(size AS bigint)) * 8. / 1024 AS DECIMAL(12,2)) AS [Database size (in MB)], convert(varchar(12),CAST((sum(case when physical_name like '%ldf'then 0 else size end)*8.0)/1024.0 AS numeric(8,2))) AS [Data file size (in MB)], convert(varchar(12),CAST((sum(case when physical_name like '%ldf'then size else 0 end)*8.0)/1024.0 AS numeric(8,2))) AS [Log file size (in MB)] FROM sys.master_files WHERE database_id > 4 GROUP BY database_id) select @FullyQualifiedDomainName as [Fully qualified domain name], instance_name as [Instance Name], p.database_name as [Database Name], database_state as [Database Status], instance_edition [SQL Edition], instance_version as [SQL Version], [Logical CPU Count], [Hyperthread Ratio], [Physical CPU Count], [TempDb size (in MB)], is_failover_clustered as [Is FCI Enabled], '' as [Failover cluster name], '' as [Failover cluster instance role], '' as [Failover cluster shared disk count], '' as [Is failover cluster multi subnet], [Max server memory (in MB)], [Number of Nics], instance_level as [Service pack], '' as [ESU status], 'FALSE' as [IS AG enabled], instance_level as [Build version], 'Online' as [Service status], 'FALSE' as [Is database highly available], 'FALSE' as [Is part of distributed availability group], [Database size (in MB)], [Data file size (in MB)], [Log file size (in MB)], database_compatibility_level as [Compatibility level], '' as [Availability group ID], '' as [Availability group name], '' as [Availability group type], '' as [Availability replica ID], '' as [Availability replica name], '' as [Commit mode], '' as [Replica type], '' as [Replica state], 'FALSE' as [Is AG multi subnet], '' as [AG replica sync status], '' as [AG replica seed mode], '' as [AG replica read mode] from ServerCores, Config, UserCount, DbSize, TempDbSize,Logins, Properties p LEFT JOIN EachDbSize s on p.database_name = s.database_name order by p.database_name Collate the output of all SQL Server Instances of interest into a single file. Step-2: Validate and upload the CSV file To validate and upload the generated CSV file with SQL Server discovery data: 1) Go to the Azure Migrate experience link you would have received from Microsoft after you signed up for the private preview of this feature. Click on “Create Project” to create a new Project (if not done already): 2) Fill in the required fields in create project wizard 3) Click on the created project and select “Using custom Import” option under “Start Discovery” button. 4) In the Discover page, select “File Type” as “SQL Server inventory (CSV)” 5) (Optional) Click on “Download” button under Step 1: Download template to view and understand the template expected by Azure Migrate. Optionally, you can skip this step as the CSV file generated by SQL script shared above confirms to the template. 6) Browse and select the inventory file in Step-2 and click on Validate to validate the uploaded SQL Inventory data. 7) If the uploaded doesn’t contain any warnings or error messages, it will show validation completed successfully with 0 errors and warnings. If there are any errors or warnings Azure Migrate will show a count of warning and error messages. To view and resolve the warnings or errors (if any) with the imported file, click on Download ErroDetails.csv, which displays errors/warning messages corresponding to each database along with remedial action. Please note warnings are non-blocking messages, i.e., user can still go ahead and complete the Import operation. Whereas errors are blocking in nature i.e. users will not be allowed to import unless the error messages are resolved. 8) Once the validation phase is completed, click on Import button to complete the import operation. Step-3: Create Assessments using imported SQL Discovery Data SQL Server Instances and the databases uploaded via CSV import route are treated at par with the SQL Server instances that are discovered by Azure Migrate appliance. You can view the SQL Server Instance properties like Version details, Number of databases, Support status and all other properties uploaded via CSV file. The quality of property details and the assessments generated is directly dependent on the quality of data uploaded via CSV. To create SQL assessments for the uploaded SQL Server Instances, go to the Azure Migrate project's resource page and click on Create Assessments button: Give a name for the assessment and click on Add workloads to add the imported and appliance discovered workloads: In the following page, select the SQL Server instances that have value “Import” for Discovery Source column. You can also create assessments on a combination of workloads that are imported and appliance discovered. Click Add button to add the workloads and click on Review + Create Assessments to create assessment. For imported SQL Server instances, the quality of assessments is only as good as the data uploaded via CSV, please ensure that the data uploaded via CSV accurately depicts the metadata of the SQL Server instance. Like regular SQL assessments, you will have all the assessment configuration options like choosing Sizing criteria (performance-based vs as on-premises), migration strategy (optimizing cost vs migration to PaaS), comfort factor etc. By default, Azure Migrate will try to create a performance-based incase the performance-metrics are not available, it defaults to As-on-premises assessment. Assessment created on the imported SQL Servers will help you know which SQL Server Instances are ready for migration to Azure SQL, the recommended Azure SQL Target type. Linking and Rediscovery Linking If the host where the imported SQL Instance currently hosted is discovered via import of VM Servers or via appliance, the SQL Server Instance will be automatically associated with that host. SQL Server Instance will be rolled up under that VM Server in the discovery view. The SQL Server instance will be uniquely identified based on the FQDN and the SQL Server Instance name. The combination of attributes is used for conflict resolution as well. Rediscovery and precedence rules 1) If the SQL Server instance is originally imported via CSV and is reuploaded again via CSV, upload operation will rewrite all the properties. 2) If the SQL server instance is first imported via CSV and then discovered via Azure Migrate appliance and the user has provided credentials to do deep discovery via Azure Migrate appliance, then the appliance discovered data will override the data imported via CSV. 3) If the SQL server instance is first imported via CSV and then discovered via Azure Migrate appliance and the user has not provided SQL Server credentials to do deep discovery, then the appliance discovered data will only update the additional data that appliance discovered. 4) If the SQL Server Instance is first discovered via the Azure Migrate appliance and is deep discovered (correct SQL Server credentials are provided), and if the SQL Server is imported via CSV, data imported via CSV will be discarded. 5) If the SQL Server Instance is first discovered via the Azure Migrate appliance and is only inventoried (correct SQL Server credentials are not provided), and if the SQL Server is later imported via CSV, upload operation will completely overwrite the appliance discovered details. How to onboard to Private Preview To onboard Preview of SQL Inventory import as CSV, please fill the Signup form for Private preview What’s Next 1) In the next release will ship out capabilities to create Business case for SQL Servers imported via CSV. 2) We will also build support for Availability Groups and Failover cluster Instances for the SQL Server instances imported via CSV.SQL MI Restore with valid SAS key failing with Operating System Error 86
We will provide details about a workaround for resolving the "Operating System Error 86" encountered during database restoration from Azure Storage on SQL Managed Instance. In this scenario, the Storage Account was not behind firewall and the credential already had a valid SAS token.
