Proven Strategies to Optimize Azure SQL Database Performance

Optimizing Azure SQL Database performance is crucial for ensuring your applications run efficiently and cost-effectively. In this article, we are sharing the proven strategies to optimize Azure SQL database performance.

We can divide these strategies into 3 sections which are given below

• Resource and Application Layer Optimization
• Monitor Performance
• Database and Index Tuning

Resource and Application Layer Optimization

1. Right-size the Database

Choose the correct service tier and compute size (e.g., vCore or DTU) for your workload. Use Azure Monitor to track resource utilization (CPU, data IO, log IO) and adjust your tier accordingly. The serverless tier is a cost-effective option for unpredictable or intermittent workloads, as it automatically scales and even pauses during inactivity.

• Service Tier: Choose the right pricing tier (DTU-based or vCore-based) for your workload.
• Scaling:
  • Scale up during peak loads.
  • Scale down during low usage.
• Use Azure Advisor to get size recommendations.
• Use Serverless Tier for unpredictable workloads (auto-pauses when idle).
• Enable Auto-scaling in Hyperscale.
• Regularly purge/archive old data.

2. Caching

For read-heavy workloads, use an in-memory cache like Azure Cache for Redis to store frequently accessed data. This reduces the load on the database and can significantly lower latency and increase throughput.

3.Connection Pooling

Configure your application’s connection pooling settings to manage database connections efficiently, which can prevent bottlenecks and improve concurrency.

4.Retry Logic

Implement retry logic in your applications to gracefully handle transient network failures or database throttling, which are common in a cloud environment.

Monitor Performance

Azure SQL has some very good built-in tools that can help you find and fix performance issues automatically.

1. Performance Recommendations: The Azure Portal’s “Performance recommendation” page analyzes your database’s usage patterns and provides actionable advice. It can suggest creating or dropping indexes, or parameterizing queries to improve performance. You can apply these recommendations manually or enable Automatic Tuning to have Azure SQL automatically implement and validate them for you. If a change has a negative impact, the service will revert it.
2. Query Performance Insight: This tool is available in the Azure Portal and helps you identify the most resource-intensive queries, so you know where to focus your tuning efforts. It shows you the top queries by CPU, data IO, and log IO consumption over a specified time period.
3. Use Azure Monitor and SQL Insights for CPU, DTU, IO, and memory tracking

Database and Index Tuning

Efficient database design and optimized queries are fundamental to good performance.

Indexes

Indexes are one of the most effective ways to improve query performance. They allow the database engine to quickly locate data without scanning the entire table.

• Analyze Query Patterns: Identify the columns frequently used in WHERE, JOIN, and ORDER BY clauses, as these are excellent candidates for indexing.
• Choose the Right Type: Use clustered indexes on columns that sort the table data logically, such as a primary key. Use non-clustered indexes for columns used in lookups or for frequent search queries.
• Avoid Over-Indexing: Too many indexes can slow down INSERT, UPDATE, and DELETE operations.
• Filtered indexes for queries on specific data subsets.
• Use Index Recommendations in Azure SQL’s Automatic Tuning feature.
• Regularly review and remove unused or duplicate indexes to reduce storage and maintenance overhead.

You can read more about effective Indexing Strategy for database performance tuning from the following article An effective Indexing Strategy for database performance tuning

Query Optimization

Poorly written queries can consume excessive resources, even with proper indexing.

• Rewrite Queries: Simplify complex queries, reorder JOINs, and avoid unnecessary operations.
• Use SELECT with specific columns: Avoid SELECT * to retrieve only the data you need, which reduces network traffic and I/O.
• Optimize Joins: Indexing foreign keys and choosing the correct join type can dramatically improve performance.
• Batch Queries: Instead of executing many individual queries, use batching or stored procedures to reduce network round trips and improve efficiency.
• FORCE LAST GOOD PLAN to avoid plan regression.
• Avoid scalar functions in SELECT statements (can cause row-by-row execution).
• Monitor Query Store for long-running queries and high resource consumers.

You can read more about Query Optimization from the following article Optimizing Query Performance: A Complete Guide for SQL Developers and DBAs

Partition Large Tables

• Horizontal partitioning helps manage and query large datasets efficiently.
• Use Clustered Columnstore indexes for analytical workloads.

Use In-Memory Features

• Memory-optimized tables for high-frequency OLTP.
• Natively compiled stored procedures to reduce CPU usage.

Manage Statistics

• Keep statistics updated (AUTO_UPDATE_STATISTICS ON).
• Use UPDATE STATISTICS manually for large data changes.

Optimize TempDB

• Spread TempDB files across multiple files to improve concurrency.
• For Managed Instance or IaaS, configure optimal TempDB sizing.

Secure & Control Access

• Use Transparent Data Encryption (TDE) — no performance hit.
• Restrict network access with VNet rules and Private Endpoints.
• Minimize expensive security functions in queries.

You are read more articles about database performance optimization and query tuning from the following link Database Performance Optimization and Query Tuning