Identify and implement index changes for queries

Before implementing index changes, it’s important to identify the queries that are causing performance issues. Azure SQL provides several tools and techniques to monitor query performance, including Dynamic Management Views (DMVs), Query Store, and Azure SQL Analytics.

1. Dynamic Management Views (DMVs):

DMVs allow you to retrieve information about the current state of the database and query execution. They can be used to identify queries that have a high average execution time, high CPU usage, or those that perform a large number of scans or seeks on tables.

For example, the sys.dm_exec_query_stats DMV provides information about the execution statistics of each query, including the number of times it has been executed, average CPU time, and average duration. By analyzing this information, you can identify queries that might benefit from index changes.

2. Query Store:

Query Store is a built-in feature of Azure SQL that captures query execution plans, runtime statistics, and other performance-related data. It allows you to compare the performance of queries over time and identify those that have regressed.

By analyzing the information provided by Query Store, you can identify queries with high execution times or inefficient execution plans. These queries may require index changes to improve their performance.

Implementing Index Changes:

Once you have identified the queries that need optimization, you can implement index changes to improve their performance. Azure SQL provides several methods to create and modify indexes, such as clustered indexes, non-clustered indexes, and columnstore indexes.

1. Clustered Indexes:

A clustered index determines the physical order of data in a table. It helps to improve the performance of queries that involve range scans or sorting. If a table does not have a clustered index, consider creating one on a column that is frequently used for filtering or sorting.

To create a clustered index, you can use the following syntax:

sql
CREATE CLUSTERED INDEX [index_name] ON [table_name]([column_name])

2. Non-clustered Indexes:

Non-clustered indexes are separate structures that store a subset of the columns in a table. They provide faster data retrieval for queries that include the indexed columns. You can create non-clustered indexes on columns that are frequently used in WHERE or JOIN clauses.

To create a non-clustered index, you can use the following syntax:

sql
CREATE NONCLUSTERED INDEX [index_name] ON [table_name]([column_name])

3. Columnstore Indexes:

Columnstore indexes are designed for tables with large amounts of data. They store the data in a columnar format, which allows for better compression and the ability to scan large amounts of data quickly. Columnstore indexes are suitable for data warehousing or analytics scenarios.

To create a columnstore index, you can use the following syntax:

sql
CREATE CLUSTERED COLUMNSTORE INDEX [index_name] ON [table_name]

4. Index Maintenance:

After creating or modifying indexes, it’s important to regularly monitor and maintain them to ensure optimal performance. Azure SQL provides options to automatically manage index maintenance, such as the Automatic Index Management feature.

Automatic Index Management uses machine learning to monitor query performance and recommends index changes based on query patterns. It can create, drop, or modify indexes as needed to improve performance. You can enable it by using the Azure portal or PowerShell commands.

Conclusion:

Identifying and implementing index changes for queries is an important aspect of administering Microsoft Azure SQL solutions. By utilizing tools like DMVs, Query Store, and Azure SQL Analytics, you can identify poorly performing queries. Leveraging the power of clustered indexes, non-clustered indexes, and columnstore indexes, you can improve query performance and optimize your Azure SQL solution. Regular index maintenance, including using features like Automatic Index Management, will ensure ongoing performance improvements.