---
name: azure-sql-optimization
description: |
  Azure SQL Database optimization and platform-specific features.
  PROACTIVELY activate for: (1) Azure SQL Database optimization, (2) DTU vs vCore selection and right-sizing, (3) automatic tuning (force last good plan, create/drop index), (4) Hyperscale tier and read replicas, (5) Serverless tier and auto-pause, (6) Azure SQL performance monitoring (Query Performance Insight, Intelligent Insights), (7) elastic pools, (8) Always Encrypted with secure enclaves, (9) Managed Instance vs Azure SQL DB tradeoffs, (10) failover groups and geo-replication.
  Provides: tier-selection matrix, automatic-tuning enablement steps, Hyperscale architecture overview, monitoring queries, and elastic pool sizing guidance.
---

# Azure SQL Database Optimization

Platform-specific optimization for Azure SQL Database.

## Quick Reference

### Service Tier Comparison

| Tier | Best For | Max Size | Key Features |
|------|----------|----------|--------------|
| Basic | Dev/test, light workloads | 2 GB | Low cost |
| Standard | General workloads | 1 TB | S0-S12 DTUs |
| Premium | High I/O, low latency | 4 TB | P1-P15 DTUs |
| General Purpose (vCore) | Most workloads | 16 TB | Serverless option |
| Business Critical | High availability | 4 TB | In-memory, read replicas |
| Hyperscale | Large databases | 100 TB | Auto-scaling storage |

### DTU vs vCore

| Aspect | DTU | vCore |
|--------|-----|-------|
| Pricing | Bundled resources | Separate compute/storage |
| Control | Limited | Fine-grained |
| Reserved capacity | No | Yes (up to 72% savings) |
| Serverless | No | Yes (General Purpose) |
| Best for | Simple workloads | Predictable, migrated workloads |

## Performance Monitoring

### Resource Consumption
```sql
-- Last 15 minutes (avg 15-second intervals)
SELECT
    end_time,
    avg_cpu_percent,
    avg_data_io_percent,
    avg_log_write_percent,
    avg_memory_usage_percent,
    max_worker_percent,
    max_session_percent
FROM sys.dm_db_resource_stats
ORDER BY end_time DESC;

-- Historical (last 14 days, hourly)
SELECT
    start_time,
    end_time,
    avg_cpu_percent,
    avg_data_io_percent,
    avg_log_write_percent
FROM sys.resource_stats
WHERE database_name = DB_NAME()
ORDER BY start_time DESC;
```

### Query Performance Insight
```sql
-- Top CPU consumers last hour
SELECT TOP 20
    qt.query_sql_text,
    rs.avg_cpu_time / 1000 AS avg_cpu_ms,
    rs.count_executions,
    rs.avg_cpu_time * rs.count_executions / 1000 AS total_cpu_ms
FROM sys.query_store_query q
JOIN sys.query_store_query_text qt ON q.query_text_id = qt.query_text_id
JOIN sys.query_store_plan p ON q.query_id = p.query_id
JOIN sys.query_store_runtime_stats rs ON p.plan_id = rs.plan_id
JOIN sys.query_store_runtime_stats_interval rsi ON rs.runtime_stats_interval_id = rsi.runtime_stats_interval_id
WHERE rsi.start_time >= DATEADD(hour, -1, GETUTCDATE())
ORDER BY rs.avg_cpu_time * rs.count_executions DESC;
```

## Automatic Tuning

### Enable Automatic Tuning
```sql
-- Enable all auto-tuning options
ALTER DATABASE current
SET AUTOMATIC_TUNING (
    FORCE_LAST_GOOD_PLAN = ON,
    CREATE_INDEX = ON,
    DROP_INDEX = ON
);

-- Check current settings
SELECT * FROM sys.database_automatic_tuning_options;
```

### View Tuning Recommendations
```sql
-- Current recommendations
SELECT
    name,
    reason,
    score,
    state_desc,
    is_revertable_action,
    is_executable_action,
    details
FROM sys.dm_db_tuning_recommendations;
```

### Apply Recommendations
```sql
-- Force a specific query plan
EXEC sp_query_store_force_plan @query_id = 12345, @plan_id = 67890;

-- Unforce plan
EXEC sp_query_store_unforce_plan @query_id = 12345, @plan_id = 67890;
```

## Hyperscale Features

### Storage Auto-Scaling
- Automatically grows up to 128 TB
- No need to pre-provision storage
- Pay only for storage used

### Read Scale-Out
```sql
-- Connection string option
ApplicationIntent=ReadOnly

-- In application code
"Server=myserver.database.windows.net;Database=mydb;ApplicationIntent=ReadOnly;..."
```

### Named Replicas
```sql
-- Create named replica
ALTER DATABASE MyDatabase
ADD SECONDARY ON SERVER MySecondaryServer
WITH (SERVICE_OBJECTIVE = 'HS_Gen5_2', SECONDARY_TYPE = Named, NAME = N'MyReadReplica');
```

## Serverless Configuration

### Configure Auto-Pause
```sql
-- Via Azure Portal, CLI, or PowerShell
-- Set auto-pause delay (minutes), min/max vCores

-- Check current usage
SELECT
    cpu_percent,
    auto_pause_delay_in_minutes_configured
FROM sys.dm_db_resource_stats_serverless;
```

### Serverless Best Practices
1. **Use for intermittent workloads** - Saves cost during idle periods
2. **Set appropriate min vCores** - Prevents cold starts for time-sensitive apps
3. **Monitor auto-pause** - Auto-resume adds latency
4. **Consider always-on for consistent workloads** - Provisioned may be cheaper

## Connection Optimization

### Connection Pooling
```csharp
// .NET connection string
"Server=tcp:myserver.database.windows.net,1433;Database=mydb;
 Min Pool Size=10;Max Pool Size=100;Connection Timeout=30;"
```

### Retry Logic
```csharp
// Azure SQL requires retry logic for transient faults
var options = new SqlRetryLogicOption()
{
    NumberOfTries = 5,
    DeltaTime = TimeSpan.FromSeconds(1),
    MaxTimeInterval = TimeSpan.FromSeconds(30)
};
```

### Connection Best Practices
1. **Use connection pooling** - Reduce connection overhead
2. **Implement retry logic** - Handle transient faults (error 40613, 40197)
3. **Use redirect connection mode** - Better performance after initial connection
4. **Close connections promptly** - Don't hold connections unnecessarily

## Azure-Specific Limitations

### Not Supported
- SQL Agent (use Azure Functions, Logic Apps)
- BULK INSERT from files (use Blob Storage)
- Linked servers (use Elastic Query)
- FILESTREAM
- Cross-database queries in same server (use Elastic Query)

### Workarounds

#### Bulk Insert from Blob Storage
```sql
-- Create credential
CREATE DATABASE SCOPED CREDENTIAL BlobCredential
WITH IDENTITY = 'SHARED ACCESS SIGNATURE',
SECRET = 'your_sas_token';

-- Create external data source
CREATE EXTERNAL DATA SOURCE BlobStorage
WITH (
    TYPE = BLOB_STORAGE,
    LOCATION = 'https://youraccount.blob.core.windows.net/container',
    CREDENTIAL = BlobCredential
);

-- Bulk insert
BULK INSERT MyTable
FROM 'data.csv'
WITH (DATA_SOURCE = 'BlobStorage', FORMAT = 'CSV', FIRSTROW = 2);
```

#### Elastic Query for Cross-Database
```sql
-- On target database
CREATE MASTER KEY ENCRYPTION BY PASSWORD = 'password';

CREATE DATABASE SCOPED CREDENTIAL ElasticCredential
WITH IDENTITY = 'username', SECRET = 'password';

CREATE EXTERNAL DATA SOURCE RemoteDB
WITH (
    TYPE = RDBMS,
    LOCATION = 'remote-server.database.windows.net',
    DATABASE_NAME = 'RemoteDatabase',
    CREDENTIAL = ElasticCredential
);

CREATE EXTERNAL TABLE dbo.RemoteTable (...)
WITH (DATA_SOURCE = RemoteDB);
```

## Cost Optimization

### Reserved Capacity
- Up to 72% savings vs pay-as-you-go
- 1-year or 3-year terms
- Exchange/refund flexibility

### Right-Sizing
```sql
-- Check if over-provisioned
SELECT
    AVG(avg_cpu_percent) AS avg_cpu,
    MAX(avg_cpu_percent) AS max_cpu,
    AVG(avg_data_io_percent) AS avg_io,
    MAX(avg_data_io_percent) AS max_io
FROM sys.dm_db_resource_stats
WHERE end_time >= DATEADD(day, -7, GETUTCDATE());

-- If avg < 40% consistently, consider downsizing
```

### Hyperscale Cost Considerations
- Compute: Per-second billing
- Storage: Per-hour billing for used space
- Read replicas: Additional compute cost
- Memory not automatically released (monitor and scale appropriately)


---
Source attribution: JosiahSiegel
Original project: https://github.com/JosiahSiegel/claude-plugin-marketplace/blob/5a1b1123b9e50aa9a66a61005ca6fe012cc7442d/plugins/tsql-master/skills/azure-sql-optimization/SKILL.md
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Dave AI.

## Upstream license notice

MIT License

Copyright (c) 2025 Josiah Siegel

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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SOFTWARE.




# Dave AI resource metadata

Creator / publisher: JosiahSiegel
Original project: https://github.com/JosiahSiegel/claude-plugin-marketplace/blob/5a1b1123b9e50aa9a66a61005ca6fe012cc7442d/plugins/tsql-master/skills/azure-sql-optimization/SKILL.md
Attribution basis: Recovered original package and original project license at a pinned revision

Fields of work: Development & Engineering, Product Management
Roles: Backend Developer, Data Engineer, Database Administrator, ML / AI Engineer, Product Manager
Specialties: Database
Tools: Azure
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: fb60959c108f42e68dbca2ce6bacba267227f4fae03a73f1ebfce15b1b8e70ac
Metadata reviewed: 2026-10-27
This metadata supersedes earlier Dave AI catalog attribution. Original instruction and supporting files are preserved.