Maximize Performance: 5 Azure SQL Database Composite Index Strategies

Azure SQL Database is a fantastic platform-as-a-service (PaaS) offering that takes away much of the burden of database administration. However, managing performance remains a key responsibility. Slow queries can drive up your resource consumption (DTUs or vCores), costing you more and potentially forcing you into higher service tiers.

While many factors influence database speed, indexing is often the most impactful. For queries that filter data based on multiple columns, a well-designed Azure SQL Database composite index is frequently the answer to unlocking faster performance.

A composite index combines multiple columns into a single ordered structure. This allows Azure SQL Database to locate relevant data rows much more efficiently than scanning the entire table or using individual indexes. But creating composite indexes effectively requires a smart strategy.

Ready to make your Azure SQL DB queries fly? Let’s explore 5 key strategies for designing and optimizing your Azure SQL Database composite indexes.

Performance Challenges in Azure SQL Database

Even with the power of the Azure cloud, poorly performing queries can strain your database resources. Common performance culprits include:

• Missing indexes.
• Inefficient query design.
• Outdated statistics.
• Ineffective indexing strategies for common query patterns.

When your queries involve multiple conditions in the WHERE clause (e.g., filtering sales by region AND date, or finding users by status AND last login time), composite indexes become particularly relevant.

Understanding Composite Indexes in Azure SQL DB

Azure SQL Database is built on the SQL Server engine. This means the principles of SQL Server indexing, including composite (or concatenated) indexes, apply directly.

A composite index is a B-tree structure ordered by the columns you specify, from left to right. For example, an index on (Region, SaleDate) on a Sales table is sorted first by Region, then by SaleDate within each region.

CREATE INDEX IX_Sales_Region_Date ON Sales (Region, SaleDate);

This allows Azure SQL DB to efficiently find data when you query WHERE Region = 'West' AND SaleDate >= '2024-01-01'. It can quickly navigate to the ‘West’ section of the index and then scan forward from ‘2024-01-01’.

Maximize Performance: 5 Azure SQL Database Composite Index Strategies

Here’s how to approach composite indexing specifically for your Azure SQL Database environment:

Strategy 1: Analyze Your Azure SQL DB Workload

Effective indexing starts with understanding how your database is being used. What are the most frequent or most resource-intensive queries? Which tables are involved, and what columns are used in their WHERE and ORDER BY clauses?

• Use Azure’s Tools: Leverage Azure Portal features like Query Performance Insight and Intelligent Insights to identify top consuming queries and get performance recommendations.
• Query DMVs: Dive into SQL Server Dynamic Management Views (DMVs) available in Azure SQL DB via tools like SQL Server Management Studio (SSMS) or Azure Data Studio. Key DMVs include:
  • sys.dm_db_index_usage_stats: Shows which indexes are being used.
  • sys.dm_exec_query_stats: Provides statistics about cached query plans.
  • sys.dm_db_missing_index_details and sys.dm_db_missing_index_group_stats: Can suggest indexes the optimizer wanted but didn’t find.
• Capture Extended Events: Set up Extended Events sessions to capture specific database activity details for deeper analysis.

Identify queries with multi-column filters that are causing high CPU or Data I/O wait times (visible in Azure metrics). These are prime candidates for composite index optimization.

• Monitor Azure SQL Database performance using Query Performance Insight.
• Learn about monitoring performance using Dynamic Management Views.

Strategy 2: Design Composite Indexes with Query Patterns in Mind

Once you know your workload, design composite indexes that match your query patterns.

• Apply the Leftmost Prefix Rule: Place the columns most frequently used in the leading WHERE clause filters first in the index definition. For queries with both equality (=) and range (>, <) conditions, place equality columns before range columns.
  • Example: Query: WHERE CustomerID = 101 AND OrderDate >= '2024-01-01' -> Index: ON Orders (CustomerID, OrderDate)
• Consider ORDER BY: If queries frequently order results by certain columns after filtering, include those columns in the composite index in the correct order and direction (ASC/DESC) to avoid costly sort operations.

Strategy 3: Leverage Included Columns for Covering Indexes

Azure SQL Database supports the INCLUDE clause in CREATE INDEX statements (as it’s a SQL Server feature). This is incredibly powerful for reducing I/O.

• How it Works: Columns listed in the INCLUDE clause are stored in the leaf level of the index but are not part of the index key used for sorting or searching.

• The Benefit: If a query’s SELECT list and WHERE clause columns are all present in the index key or the INCLUDE list, Azure SQL DB can execute the query using only the index, without needing to go back to the base table. This is called a “covering index” and significantly reduces logical and physical I/O.

• Example: Query: SELECT OrderID, TotalAmount FROM Orders WHERE CustomerID = 101 AND OrderDate >= '2024-01-01'

  • Index: CREATE INDEX IX_Orders_Cust_Date_Cover ON Orders (CustomerID, OrderDate) INCLUDE (OrderID, TotalAmount);
  • This index covers the query, allowing Azure SQL DB to retrieve all necessary data directly from the index structure.
• Read more about Indexes with Included Columns in SQL Server (applies to Azure SQL DB).

Strategy 4: Monitor Index Usage and Performance Metrics (Azure Focus)

Creating indexes is just the start. You need to see if they are being used and if they are improving performance in Azure.

• Check Usage: Use sys.dm_db_index_usage_stats to see which indexes are being hit by queries. An index that isn’t used is just adding write overhead and consuming storage.
• Relate to Azure Metrics: Observe how your indexing changes impact Azure SQL DB metrics like DTU/vCore usage (especially CPU, Data I/O, Log I/O), latency, and throughput. Reduced resource consumption is a tangible benefit in a PaaS environment.
• Review Missing Index DMVs: Periodically check sys.dm_db_missing_index_details and sys.dm_db_missing_index_group_stats. While these are suggestions, they indicate columns the optimizer frequently wants to combine.

Strategy 5: Test Iteratively Using Execution Plans (Azure SQL DB Specifics)

The most reliable way to verify your indexing strategy is by looking at the execution plan.

• Generate Plans: Use “Display Estimated Execution Plan” (Ctrl+L) or “Display Actual Execution Plan” (Ctrl+M) in SSMS or Azure Data Studio. Use SET SHOWPLAN_ALL ON or SET STATISTICS PROFILE ON within your query batch for text-based plans.

• Interpret the Plan: Look for Index Seek or Index Scan operations using your composite index. See if the Seek Predicates and Predicates (or Index Condition and Filter in other databases) show the composite index being used effectively for your WHERE conditions. Check for Table Scan or Clustered Index Scan if you expected an index to be used. Identify expensive operations like Key Lookups or Sorts that might be eliminated by a covering index.

• Learn how to Display and Save Execution Plans in SQL Server (applies to Azure SQL DB).

Azure SQL Database Specific Considerations

• Resource Governance: Poorly performing queries due to bad indexing directly impact your DTU/vCore consumption. Efficient indexing can potentially allow you to use a lower service tier, saving costs, or handle more workload within your current tier.
• Automatic Tuning: Azure SQL Database offers automatic tuning options, including “Force Last Good Plan” and “Create Missing Indexes” (currently in preview for some tiers). While these are helpful, they shouldn’t replace a proactive indexing strategy. Auto-indexing suggestions from Intelligent Insights or DMVs should be validated using the strategies above before implementation.
• Index Maintenance: Azure handles the physical storage and maintenance of indexes. However, the logical overhead of index maintenance (CPU, I/O for inserts/updates/deletes) still impacts your resource usage. Keep indexes relevant and minimal.

Practical Azure SQL DB Examples

• Sales Query: Find sales in ‘California’ for ‘Electronics’ category in the last month.
  • Index: CREATE INDEX IX_Sales_State_Category_Date ON Sales (SaleState, Category, SaleDate); (Assumes SaleState and Category are equality filters, SaleDate is range).
  • Query: SELECT OrderID, Amount FROM Sales WHERE SaleState = 'CA' AND Category = 'Electronics' AND SaleDate >= DATEADD(month, -1, GETDATE());
  • EXPLAIN PLAN should show an Index Seek on IX_Sales_State_Category_Date. Adding INCLUDE (OrderID, Amount) would create a covering index.
• Logging Query: Find errors for a specific ApplicationID within a date range.
  • Index: CREATE INDEX IX_Logs_AppID_Timestamp ON Logs (ApplicationID, LogTimestamp);
  • Query: SELECT LogMessage FROM Logs WHERE ApplicationID = 5 AND LogTimestamp BETWEEN '2025-04-01' AND '2025-04-19';
  • EXPLAIN PLAN should show an Index Seek. Including LogMessage could make it covering if LogMessage isn’t too large (watch index size!).

Conclusion

Optimizing your Azure SQL Database composite indexes is a fundamental step in managing performance and controlling costs in a PaaS environment. By employing these 5 key strategies – analyzing your workload with Azure tools and DMVs, designing indexes to match query patterns, leveraging included columns for covering, monitoring usage and relating it to Azure metrics, and iteratively testing with execution plans – you can ensure your composite indexes are effective.

Don’t let inefficient indexing hold back your Azure SQL Database. Implement these strategies to maximize performance and get the most value from your Azure investment!

What are your go-to techniques for composite indexing in Azure SQL Database? Share your tips in the comments below!