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// Package: Microsoft.SqlServer.TransactSql.ScriptDom 170.0.64

using Microsoft.SqlServer.TransactSql.ScriptDom;

// Parse SQL Server 2025 syntax
var parser = new TSql170Parser(true);
IList<ParseError> errors;
var fragment = parser.Parse(new StringReader(sql), out errors);

// Supports SQL Server 2025 new T-SQL features

<!-- Database.sqlproj (SDK-style with SQL Server 2025 compatibility) -->
<Project Sdk="Microsoft.Build.Sql/2.0.0">
  <PropertyGroup>
    <Name>MyDatabase</Name>
    <!-- Use SQL Server 2022 (160) provider until 2025 provider available -->
    <DSP>Microsoft.Data.Tools.Schema.Sql.Sql160DatabaseSchemaProvider</DSP>
    <TargetFramework>net8.0</TargetFramework>
    <SqlServerVersion>Sql160</SqlServerVersion>

    <!-- SQL Server 2025 features will still work in runtime database -->
    <!-- Only build-time validation uses Sql160 provider -->
  </PropertyGroup>

  <ItemGroup>
    <Folder Include="Tables\" />
    <Folder Include="Views\" />
    <Folder Include="StoredProcedures\" />
  </ItemGroup>
</Project>

-- Create table with vector column
CREATE TABLE Documents (
    Id INT PRIMARY KEY IDENTITY,
    Title NVARCHAR(200),
    Content NVARCHAR(MAX),
    -- Half-precision vectors support up to 3,996 dimensions
    ContentVector VECTOR(1536)  -- OpenAI ada-002: 1,536 dims
    -- ContentVector VECTOR(3072)  -- OpenAI text-embedding-3-large: 3,072 dims
    -- ContentVector VECTOR(3996)  -- Maximum: 3,996 dims
);

-- Insert vectors (T-SQL built-in embedding generation)
INSERT INTO Documents (Title, Content, ContentVector)
VALUES (
    'AI Documentation',
    'Azure AI services...',
    CAST('[0.1, 0.2, 0.3, ...]' AS VECTOR(1536))
);

-- Semantic similarity search with DiskANN
DECLARE @QueryVector VECTOR(1536) = CAST('[0.15, 0.25, ...]' AS VECTOR(1536));

SELECT TOP 10
    Id,
    Title,
    Content,
    VECTOR_DISTANCE('cosine', ContentVector, @QueryVector) AS Similarity
FROM Documents
ORDER BY Similarity;

-- Create DiskANN vector index for performance
CREATE INDEX IX_Documents_Vector
ON Documents(ContentVector)
USING VECTOR_INDEX
WITH (
    DISTANCE_METRIC = 'cosine',  -- or 'euclidean', 'dot_product'
    VECTOR_SIZE = 1536
);

-- Hybrid search: Combine vector similarity with traditional filtering
SELECT TOP 10
    Id,
    Title,
    VECTOR_DISTANCE('cosine', ContentVector, @QueryVector) AS Similarity
FROM Documents
WHERE Title LIKE '%Azure%'  -- Traditional keyword filter
ORDER BY Similarity;

-- Create external model from ONNX file
CREATE EXTERNAL MODEL AIModel
FROM 'https://storage.account.blob.core.windows.net/models/model.onnx'
WITH (
    TYPE = 'ONNX',
    INPUT_DATA_FORMAT = 'JSON',
    OUTPUT_DATA_FORMAT = 'JSON'
);

-- Use model for predictions
DECLARE @Input NVARCHAR(MAX) = '{"text": "Hello world"}';
SELECT PREDICT(MODEL = AIModel, DATA = @Input) AS Prediction;

-- Grant model permissions (new SQL Server 2025 permission)
GRANT CREATE ANY EXTERNAL MODEL TO [ModelAdmin];
GRANT ALTER ANY EXTERNAL MODEL TO [ModelAdmin];
GRANT EXECUTE ON EXTERNAL MODEL::AIModel TO [AppUser];

-- Example: Azure OpenAI integration
-- Model definitions built directly into T-SQL
-- Access through REST APIs with built-in authentication

-- Enable RCSI (required for LAQ)
ALTER DATABASE MyDatabase
SET READ_COMMITTED_SNAPSHOT ON;

-- Optimized locking is automatically enabled at database level
-- No additional configuration needed for SQL Server 2025

-- Verify optimized locking status
SELECT name, is_read_committed_snapshot_on
FROM sys.databases
WHERE name = 'MyDatabase';

-- Monitor optimized locking performance
SELECT *
FROM sys.dm_tran_locks
WHERE request_session_id = @@SPID;

-- SQL Server 2025 uses change feed (automatic)
-- Azure Arc agent handles replication to Fabric OneLake

-- Traditional SQL Server 2016-2022 approach (CDC):
-- EXEC sys.sp_cdc_enable_db;
-- EXEC sys.sp_cdc_enable_table ...

-- SQL Server 2025: Change feed is built-in, no CDC setup needed

-- Monitor replication lag
SELECT
    database_name,
    table_name,
    last_sync_time,
    rows_replicated,
    replication_lag_seconds
FROM sys.dm_fabric_replication_status;

-- New JSON data type
CREATE TABLE Products (
    Id INT PRIMARY KEY,
    Name NVARCHAR(100),
    Metadata JSON  -- Native JSON type
);

-- JSON functions enhanced
INSERT INTO Products (Id, Name, Metadata)
VALUES (1, 'Laptop', JSON('{"brand": "Dell", "ram": 16, "ssd": 512}'));

-- Query JSON with improved performance
SELECT
    Id,
    Name,
    JSON_VALUE(Metadata, '$.brand') AS Brand,
    JSON_VALUE(Metadata, '$.ram') AS RAM
FROM Products;

-- RegEx matching
SELECT REGEXP_LIKE('test@example.com', '^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$') AS IsValidEmail;

-- RegEx replace
SELECT REGEXP_REPLACE('Phone: 555-1234', '\d+', 'XXX') AS MaskedPhone;

-- RegEx extract
SELECT REGEXP_SUBSTR('Order #12345', '\d+') AS OrderNumber;

-- Call REST API from T-SQL
DECLARE @Response NVARCHAR(MAX);

EXEC sp_invoke_external_rest_endpoint
    @url = 'https://api.example.com/data',
    @method = 'GET',
    @headers = '{"Authorization": "Bearer token123"}',
    @response = @Response OUTPUT;

SELECT @Response AS APIResponse;

-- Enrich database data with external APIs
UPDATE Customers
SET EnrichedData = (
    SELECT JSON_VALUE(response, '$.data')
    FROM OPENROWSET(REST, 'https://api.example.com/customer/' + CustomerId)
)
WHERE CustomerId = 12345;

-- Enable at database level
ALTER DATABASE MyDatabase
SET PARAMETER_SENSITIVE_PLAN_OPTIMIZATION = ON;

-- Check status
SELECT name, is_parameter_sensitive_plan_optimization_on
FROM sys.databases
WHERE name = 'MyDatabase';

-- Monitor OPPO usage
SELECT
    query_plan_hash,
    parameter_values,
    execution_count,
    avg_duration_ms
FROM sys.dm_exec_query_stats
WHERE is_parameter_sensitive = 1;

-- Create login with managed identity
CREATE LOGIN [managed-identity-name] FROM EXTERNAL PROVIDER;

-- Grant permissions
CREATE USER [managed-identity-name] FOR LOGIN [managed-identity-name];
GRANT CONTROL ON DATABASE::MyDatabase TO [managed-identity-name];

-- Use in connection strings
-- Connection string: Server=myserver;Database=mydb;Authentication=Active Directory Managed Identity;

-- Classify sensitive columns
ADD SENSITIVITY CLASSIFICATION TO
    Customers.Email,
    Customers.CreditCard
WITH (
    LABEL = 'Confidential',
    INFORMATION_TYPE = 'Financial'
);

-- Query classification
SELECT
    schema_name(o.schema_id) AS SchemaName,
    o.name AS TableName,
    c.name AS ColumnName,
    s.label AS SensitivityLabel,
    s.information_type AS InformationType
FROM sys.sensitivity_classifications s
INNER JOIN sys.objects o ON s.major_id = o.object_id
INNER JOIN sys.columns c ON s.major_id = c.object_id AND s.minor_id = c.column_id;

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<!-- Database.publish.xml -->
<Project>
  <PropertyGroup>
    <TargetConnectionString>Server=tcp:server2025.database.windows.net;Database=MyDb;Authentication=ActiveDirectoryManagedIdentity;</TargetConnectionString>
    <BlockOnPossibleDataLoss>True</BlockOnPossibleDataLoss>
    <TargetDatabaseName>MyDatabase</TargetDatabaseName>
    <ProfileVersionNumber>1</ProfileVersionNumber>
  </PropertyGroup>

  <ItemGroup>
    <SqlCmdVariable Include="Environment">
      <Value>Production</Value>
    </SqlCmdVariable>
  </ItemGroup>
</Project>

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name: Deploy to SQL Server 2025

on:
  push:
    branches: [main]

jobs:
  build-and-test:
    runs-on: ubuntu-latest
    steps:
    - uses: actions/checkout@v4

    - name: Setup .NET 8
      uses: actions/setup-dotnet@v4
      with:
        dotnet-version: '8.0.x'

    - name: Install SqlPackage 170.2.70
      run: dotnet tool install -g Microsoft.SqlPackage --version 170.2.70

    - name: Build DACPAC
      run: dotnet build Database.sqlproj -c Release

    - name: Run tSQLt Unit Tests
      run: |
        # Run unit tests and capture results
        # Abort if tests fail
        echo "Running tSQLt unit tests..."
        # Add your tSQLt test execution here

    - name: Generate Deployment Report
      run: |
        sqlpackage /Action:DeployReport \
          /SourceFile:bin/Release/Database.dacpac \
          /TargetConnectionString:"${{ secrets.SQL_CONNECTION_STRING }}" \
          /OutputPath:deploy-report.xml \
          /p:BlockOnPossibleDataLoss=True

    - name: Publish to SQL Server 2025
      run: |
        sqlpackage /Action:Publish \
          /SourceFile:bin/Release/Database.dacpac \
          /TargetConnectionString:"${{ secrets.SQL_CONNECTION_STRING }}" \
          /p:TargetPlatform=SqlServer2025 \
          /p:BlockOnPossibleDataLoss=True \
          /DiagnosticsFile:publish.log \
          /DiagnosticsLevel:Verbose

    - name: Upload Artifacts
      if: always()
      uses: actions/upload-artifact@v4
      with:
        name: deployment-logs
        path: |
          publish.log
          deploy-report.xml

trigger:
- main

pool:
  vmImage: 'windows-2022'

steps:
- task: MSBuild@1
  displayName: 'Build Database Project'
  inputs:
    solution: 'Database.sqlproj'
    configuration: 'Release'

- task: SqlAzureDacpacDeployment@1
  displayName: 'Deploy to SQL Server 2025'
  inputs:
    azureSubscription: 'Azure Subscription'
    authenticationType: 'servicePrincipal'
    serverName: 'server2025.database.windows.net'
    databaseName: 'MyDatabase'
    deployType: 'DacpacTask'
    deploymentAction: 'Publish'
    dacpacFile: '$(Build.SourcesDirectory)/bin/Release/Database.dacpac'
    additionalArguments: '/p:TargetPlatform=SqlServer2025'

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<PropertyGroup>
  <TargetPlatform>SqlServer2025</TargetPlatform>
</PropertyGroup>

-- Verify vector support
SELECT SERVERPROPERTY('IsVectorSupported') AS VectorSupport;

-- Track model execution
SELECT
    model_name,
    AVG(execution_time_ms) AS AvgExecutionTime,
    COUNT(*) AS ExecutionCount
FROM sys.dm_exec_external_model_stats
GROUP BY model_name;

-- Classify all PII columns
ADD SENSITIVITY CLASSIFICATION TO dbo.Customers.Email
WITH (LABEL = 'Confidential - GDPR', INFORMATION_TYPE = 'Email');