SQL Server 2022: IS [NOT] DISTINCT FROM Predicate

SQL Server 2022 introduces a new predicate, IS [NOT] DISTINCT FROM, which simplifies the comparison of nullable columns. This feature is a boon for developers who often struggle with the nuanced behavior of NULL values in SQL comparisons. In this blog, we’ll explore how this new predicate works, its benefits, and provide a detailed business use case to illustrate its practical application.

Business Use Case: Analyzing Customer Orders

Imagine a retail company, JB Retail, that maintains a database (JBDB) to track customer orders. The company wants to analyze orders to identify customers who have updated their email addresses. However, due to some data migration issues, there are instances where old and new email addresses might be stored as NULL values.

To accurately identify customers who have changed their email addresses (or those whose email addresses are currently NULL but were previously not NULL), the IS [NOT] DISTINCT FROM predicate becomes very useful. This new feature allows us to simplify the logic and handle NULL comparisons more gracefully.

Setting Up the JBDB Database

First, let’s create the JBDB database and set up a sample table CustomerOrders to illustrate our use case.

-- Create JBDB database
CREATE DATABASE JBDB;
GO

-- Use the JBDB database
USE JBDB;
GO

-- Create CustomerOrders table
CREATE TABLE CustomerOrders (
    OrderID INT PRIMARY KEY,
    CustomerID INT,
    OldEmail NVARCHAR(255),
    NewEmail NVARCHAR(255),
    OrderDate DATE
);
GO

-- Insert sample data into CustomerOrders
INSERT INTO CustomerOrders (OrderID, CustomerID, OldEmail, NewEmail, OrderDate)
VALUES
    (1, 101, 'old_email1@example.com', 'new_email1@example.com', '2024-01-15'),
    (2, 102, 'old_email2@example.com', NULL, '2024-02-20'),
    (3, 103, NULL, 'new_email3@example.com', '2024-03-05'),
    (4, 104, 'old_email4@example.com', 'old_email4@example.com', '2024-04-10'),
    (5, 105, NULL, NULL, '2024-05-12');
GO

Understanding IS [NOT] DISTINCT FROM Predicate 🧩

The IS DISTINCT FROM predicate compares two expressions and returns TRUE if they are distinct (i.e., not equal or one is NULL and the other is not). The IS NOT DISTINCT FROM predicate, on the other hand, returns TRUE if they are not distinct (i.e., equal or both are NULL).

This is particularly useful when dealing with nullable columns, as NULL values are traditionally not equal to anything, including themselves, in SQL. The new predicate addresses this challenge.

Example Queries

Finding Customers Who Have Updated Their Email Address

    SELECT CustomerID, OldEmail, NewEmail
    FROM CustomerOrders
    WHERE OldEmail IS DISTINCT FROM NewEmail;

    This query identifies customers whose email addresses have changed. The IS DISTINCT FROM predicate ensures that it catches cases where either the old or new email could be NULL.

    Finding Customers Whose Email Address Remains Unchanged

    SELECT CustomerID, OldEmail, NewEmail
    FROM CustomerOrders
    WHERE OldEmail IS NOT DISTINCT FROM NewEmail;

    This query retrieves customers whose email addresses have not changed, including cases where both old and new emails are NULL.

      Detailed Business Use Case 🎯

      Let’s dive deeper into how JB Retail can use these queries to improve their customer relationship management. The company plans to send personalized emails to customers whose email addresses have been updated, acknowledging the change and ensuring it was intentional.

      Business Workflow

      Identify Updated Emails: The company will first use the IS DISTINCT FROM query to extract a list of customers with updated emails.

      SELECT CustomerID, OldEmail, NewEmail
      FROM CustomerOrders
      WHERE OldEmail IS DISTINCT FROM NewEmail;
      1. This query helps them identify cases where:
        • The old email was NULL and the new email is not, indicating a new addition.
        • The new email was NULL and the old email is not, indicating a removal.
        • Both emails are different but not NULL, indicating an actual change.
      2. Personalized Communication: Once the list is prepared, JB Retail can use it to send personalized communication to these customers. This step ensures that customers are aware of the changes and can report if the change was not authorized.
      3. Customer Service Follow-up: For cases where both old and new emails are NULL, the company can follow up with these customers to update their contact information, ensuring they do not miss out on important communications.

      Find Customers with NULL Values in Either Old or New Email

      This query helps identify customers where either the old or new email address is NULL, but not both.

      SELECT CustomerID, OldEmail, NewEmail
      FROM CustomerOrders
      WHERE OldEmail IS DISTINCT FROM NewEmail
      AND (OldEmail IS NULL OR NewEmail IS NULL);

      List Orders with Same Email Address Before and After

      This query lists orders where the email address remained the same before and after, but takes NULL into account.

      SELECT OrderID, CustomerID, OldEmail, NewEmail
      FROM CustomerOrders
      WHERE OldEmail IS NOT DISTINCT FROM NewEmail
      AND (OldEmail IS NOT NULL AND NewEmail IS NOT NULL);

      Find Orders with NULL Emails in Both Old and New

      This query identifies orders where both the old and new email addresses are NULL.

      SELECT OrderID, CustomerID, OldEmail, NewEmail
      FROM CustomerOrders
      WHERE OldEmail IS NOT DISTINCT FROM NewEmail
      AND OldEmail IS NULL;

      Identify Changes Where Old Email is NULL and New Email is Not

      This query finds orders where the old email address was NULL and the new email address is not NULL.

      SELECT OrderID, CustomerID, OldEmail, NewEmail
      FROM CustomerOrders
      WHERE OldEmail IS DISTINCT FROM NewEmail
      AND OldEmail IS NULL
      AND NewEmail IS NOT NULL;

      Find Orders Where Both Emails are Different or Both are NULL

      This query lists orders where the old and new emails are either both different or both NULL.

      SELECT OrderID, CustomerID, OldEmail, NewEmail
      FROM CustomerOrders
      WHERE (OldEmail IS DISTINCT FROM NewEmail
      AND OldEmail IS NOT NULL AND NewEmail IS NOT NULL)
      OR (OldEmail IS NULL AND NewEmail IS NULL);

      These queries leverage the IS [NOT] DISTINCT FROM predicate to handle various scenarios involving NULL values, providing flexibility and clarity in managing data comparisons. Feel free to adapt these queries based on your specific needs!

      Conclusion 🏁

      The introduction of the IS [NOT] DISTINCT FROM predicate in SQL Server 2022 is a significant enhancement for database developers and administrators. It simplifies the handling of NULL values in comparisons, making queries more readable and efficient.

      In the case of JB Retail, this feature enables a more accurate and efficient way to handle email updates, ensuring that the company maintains accurate customer contact information and strengthens its customer relationship management processes.

      With these new tools at your disposal, handling NULL values in SQL Server has never been easier! 🎉

      For more tutorials and tips on SQL Server, including performance tuning and database management, be sure to check out our JBSWiki YouTube channel.

      Thank You,
      Vivek Janakiraman

      Disclaimer:
      The views expressed on this blog are mine alone and do not reflect the views of my company or anyone else. All postings on this blog are provided “AS IS” with no warranties, and confers no rights.

        SQL Server Unused Indexes: Identification, Monitoring, and Management

        Indexes are crucial for optimizing query performance in SQL Server. However, not all indexes are used effectively; some might remain unused, consuming space and resources unnecessarily. In this comprehensive blog, we’ll delve into the concept of unused indexes, how to identify them, the potential risks of deleting them, and best practices for managing them. We’ll also explore real-world scenarios and provide the necessary T-SQL scripts for monitoring and handling unused indexes.


        🔍 What is an Unused Index?

        An unused index is an index that exists in the database but is not used by the SQL Server query optimizer. This could be due to several reasons:

        1. Outdated Query Patterns: The index may have been useful for queries that are no longer executed.
        2. Changes in Data Distribution: Alterations in data patterns may render the index less effective or redundant.
        3. Incorrect Index Design: The index might not align with the current workload or data structure.

        Unused indexes can lead to unnecessary resource consumption, such as additional storage space and increased overhead during data modification operations (INSERT, UPDATE, DELETE).

        Risks of Removing Unused Indexes ⚠️

        While removing unused indexes can free up resources, it can also lead to unexpected performance issues if not done carefully. Here are some potential risks:

        1. Impact on Rarely Used Queries: An index might appear unused but could be critical for infrequent queries, such as quarterly reports.
        2. Incorrect Monitoring Period: A short monitoring period might not capture all usage patterns, leading to incorrect conclusions.

        Best Practices for Monitoring Unused Indexes 📊

        1. Extended Monitoring Period: Monitor index usage over an extended period (e.g., several months) to capture all usage patterns.
        2. Analyze Workload Patterns: Understand your workload and identify critical periods (e.g., end-of-month processing).
        3. Test Before Removing: Always test the impact of removing an index in a non-production environment.

        Advantages of Managing Unused Indexes 🌟

        1. Improved Performance: Reducing the number of unused indexes can improve performance for data modification operations.
        2. Reduced Storage Costs: Freeing up storage space by removing unused indexes.
        3. Simplified Maintenance: Fewer indexes to maintain and monitor.

        🔧 How to Identify Unused Indexes

        Identifying unused indexes involves monitoring the usage statistics provided by SQL Server. The sys.dm_db_index_usage_stats dynamic management view (DMV) is a valuable resource for this purpose.

        📋 T-SQL Script to Identify Unused Indexes

        The following script retrieves information about indexes that haven’t been used since the last server restart:

        SELECT 
            i.name AS IndexName,
            i.object_id,
            o.name AS TableName,
            s.name AS SchemaName,
            i.index_id,
            u.user_seeks,
            u.user_scans,
            u.user_lookups,
            u.user_updates
        FROM 
            sys.indexes AS i
        JOIN 
            sys.objects AS o ON i.object_id = o.object_id
        JOIN 
            sys.schemas AS s ON o.schema_id = s.schema_id
        LEFT JOIN 
            sys.dm_db_index_usage_stats AS u 
            ON i.object_id = u.object_id AND i.index_id = u.index_id
        WHERE 
            i.is_primary_key = 0
            AND i.is_unique_constraint = 0
            AND o.type = 'U'
            AND u.index_id IS NULL
            AND u.object_id IS NULL
        ORDER BY 
            s.name, o.name, i.name;

        This script filters out primary key and unique constraint indexes, focusing on user-created indexes that have not been used since the last server restart.


        ⚠️ Potential Issues with Deleting Unused Indexes

        While removing unused indexes can free up resources, it also carries potential risks:

        1. Hidden Usage: Some indexes may not show usage in the DMV statistics if they are used infrequently or during specific maintenance operations.
        2. Future Requirements: An index deemed unused might be needed for future queries or batch jobs, especially if they run infrequently (e.g., quarterly reports).
        3. Inaccurate Assessment: Short monitoring periods can lead to incorrect conclusions about an index’s utility.

        ⏲️ Best Time Frame for Monitoring

        It’s advisable to monitor index usage over a prolonged period, ideally encompassing a full business cycle (e.g., monthly, quarterly). This ensures that all potential usage patterns, including infrequent but critical operations, are accounted for.


        🛠️ Handling Unused Indexes

        Best Practices for Managing Unused Indexes

        1. Prolonged Monitoring: As mentioned, extend the monitoring period to capture all usage patterns.
        2. Review Before Deletion: Before removing an index, consult with application developers and database administrators to understand its purpose.
        3. Testing and Staging: Always test the impact of removing an index in a staging environment before applying changes to production.
        4. Documentation: Maintain documentation of all indexes and their intended purpose to avoid unintentional removal.

        📜 Example Scenarios

        1. Beneficial Removal of an Unused Index

        Scenario: A retail company finds an unused index on a transactional table that has not been utilized for over a year. The index occupies significant disk space and slows down data modification operations.

        Action: After thorough analysis and consultation, the company decides to remove the index, resulting in improved performance and reduced storage costs.

        T-SQL for Removing the Index:

        DROP INDEX IndexName ON SchemaName.TableName;

        2. Problematic Removal of a Used Index

        Scenario: A financial services company removes an index that appears unused based on a short monitoring period. The index was actually used for a quarterly reconciliation job, leading to significantly slower performance and extended processing times during the next quarter.

        Lesson Learned: The company learned the importance of comprehensive monitoring and consultation before making changes.


        🏢 Business Use Cases

        Cost Optimization

        Removing unused indexes can free up valuable disk space and reduce maintenance overhead, leading to cost savings. This is particularly beneficial for organizations with large databases where storage costs are a significant concern.

        Performance Enhancement

        By eliminating unnecessary indexes, the performance of data modification operations can be improved, leading to faster transaction processing and more efficient database operations.


        🏁 Conclusion

        Managing unused indexes in SQL Server requires careful analysis and a comprehensive approach. While removing unused indexes can provide benefits like reduced storage costs and improved performance, it is crucial to ensure that the indexes are genuinely unused and not required for infrequent operations. By following best practices and leveraging the right tools, you can optimize your SQL Server environment effectively.

        For any questions or further guidance, feel free to reach out or leave a comment! Happy optimizing! 🚀

        For more tutorials and tips on SQL Server, including performance tuning and database management, be sure to check out our JBSWiki YouTube channel.

        Thank You,
        Vivek Janakiraman

        Disclaimer:
        The views expressed on this blog are mine alone and do not reflect the views of my company or anyone else. All postings on this blog are provided “AS IS” with no warranties, and confers no rights.