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! πŸŽ‰

      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 β€œAS IS” with no warranties, and confers no rights.

        SQL Server 2022 Performance Tuning Tips: Optimizing for Peak Efficiency

        SQL Server 2022 introduces numerous enhancements aimed at improving performance and efficiency. Whether you’re dealing with query optimization, index management, or memory allocation, these new features and best practices can help you achieve significant performance gains. In this blog, we’ll explore specific tuning tips and tricks for SQL Server 2022, highlighting changes that enhance query performance without requiring any code changes. We’ll also address how these improvements solve longstanding issues from previous versions. Practical T-SQL examples will be provided to help you implement these tips. Let’s dive in! πŸŽ‰

        Key SQL Server 2022 Enhancements for Performance Tuning βš™οΈ

        1. Intelligent Query Processing (IQP) Enhancements: SQL Server 2022 continues to enhance IQP features, including Adaptive Joins, Batch Mode on Rowstore, and more.
        2. Automatic Plan Correction: This feature helps to identify and fix suboptimal execution plans automatically.
        3. Increased Parallelism: SQL Server 2022 offers more granular control over parallelism, improving the performance of complex queries.
        4. Optimized TempDB Usage: Improvements in TempDB management reduce contention and improve query performance.

        Specific Tuning Tips and Tricks πŸ”§

        1. Leverage Intelligent Query Processing (IQP) 🧠

        SQL Server 2022 builds on the IQP feature set, which adapts to your workload to optimize performance. Here are some specific IQP features to take advantage of:

        • Batch Mode on Rowstore: This feature allows batch mode processing on traditional rowstore tables, providing significant performance improvements for analytical workloads.

        Example Query:

        -- Without Batch Mode on Rowstore
        SELECT SUM(SalesAmount) 
        FROM Sales.SalesOrderDetail
        WHERE ProductID = 707;
        
        -- With Batch Mode on Rowstore (SQL Server 2022)
        SELECT SUM(SalesAmount) 
        FROM Sales.SalesOrderDetail WITH (USE HINT ('ENABLE_BATCH_MODE'))
        WHERE ProductID = 707;
        • Adaptive Joins: SQL Server dynamically chooses the best join strategy (nested loop, hash join, etc.) during query execution, optimizing performance based on actual data distribution.

        Example Query:

        -- Without Adaptive Joins
        SELECT p.ProductID, p.Name, SUM(s.Quantity) AS TotalSold
        FROM Production.Product p
        JOIN Sales.SalesOrderDetail s ON p.ProductID = s.ProductID
        GROUP BY p.ProductID, p.Name;
        
        -- With Adaptive Joins (SQL Server 2022)
        SELECT p.ProductID, p.Name, SUM(s.Quantity) AS TotalSold
        FROM Production.Product p
        JOIN Sales.SalesOrderDetail s ON p.ProductID = s.ProductID
        GROUP BY p.ProductID, p.Name;

        2. Utilize Automatic Plan Correction πŸ› οΈ

        Automatic Plan Correction helps to identify and fix inefficient execution plans. This feature automatically captures query performance baselines and identifies regressions, correcting them as needed.

        Enabling Automatic Plan Correction:

        ALTER DATABASE SCOPED CONFIGURATION 
        SET AUTOMATIC_TUNING = AUTO_PLAN_CORRECTION = ON;

        3. Optimize TempDB Usage πŸ—„οΈ

        TempDB can often become a bottleneck in SQL Server. SQL Server 2022 introduces several enhancements to manage TempDB more efficiently:

        • Memory-Optimized TempDB Metadata: Reduces contention on system tables in TempDB, particularly beneficial for workloads with heavy use of temporary tables.

        Enabling Memory-Optimized TempDB Metadata:

        ALTER SERVER CONFIGURATION SET MEMORY_OPTIMIZED_TEMPDB_METADATA = ON;

        4. Fine-Tune Parallelism Settings πŸƒβ€β™‚οΈ

        SQL Server 2022 offers more granular control over parallelism, which can improve the performance of complex queries by better utilizing CPU resources.

        Setting MAXDOP (Maximum Degree of Parallelism):

        -- Setting MAXDOP for the server
        EXEC sys.sp_configure 'max degree of parallelism', 8;
        RECONFIGURE;
        
        -- Setting MAXDOP for a specific query
        SELECT * 
        FROM LargeTable 
        OPTION (MAXDOP 4);

        Solving Previous Issues with SQL Server 2022 πŸ”„

        1. Resolving Parameter Sniffing Issues 🎯

        Parameter sniffing can lead to suboptimal plans being reused, causing performance issues. SQL Server 2022’s Parameter Sensitive Plan Optimization addresses this by creating multiple plans for different parameter values.

        Example T-SQL Query:

        -- Enabling Parameter Sensitive Plan Optimization
        ALTER DATABASE SCOPED CONFIGURATION 
        SET PARAMETER_SENSITIVE_PLAN_OPTIMIZATION = ON;

        2. Handling Query Store Performance Overhead πŸ“ˆ

        The Query Store feature in SQL Server 2022 has been enhanced to minimize performance overhead while still capturing valuable query performance data.

        Best Practices:

        • Limit Data Capture: Configure Query Store to capture only significant queries to reduce overhead.
        • Use Read-Only Secondary Replicas: Leverage Always On Availability Groups to offload Query Store data collection to read-only replicas.

        Business Use Case: E-Commerce Platform πŸ›’

        Consider an e-commerce platform experiencing slow query performance during peak shopping seasons. By implementing SQL Server 2022’s performance tuning features, the platform can:

        • Improve Checkout Process Speed: Use IQP features like Batch Mode on Rowstore to optimize complex analytical queries that calculate discounts and shipping costs.
        • Enhance Product Search Efficiency: Utilize Adaptive Joins to dynamically optimize search queries based on the data distribution of products.
        • Reduce Database Contention: Apply TempDB optimization techniques to handle the high volume of temporary data generated during transactions.

        Conclusion πŸŽ‰

        SQL Server 2022 offers a wealth of new features and enhancements designed to optimize performance and solve long-standing issues. By leveraging Intelligent Query Processing, Automatic Plan Correction, and other tuning tips, you can achieve significant performance gains without extensive code changes. Whether you’re running a high-traffic e-commerce platform or a complex analytical workload, these tuning tips can help you get the most out of your SQL Server 2022 environment.

        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 β€œAS IS” with no warranties, and confers no rights.

        SQL Server 2022: Seamless Integration with Azure Synapse Link for Real-Time Analytics

        SQL Server 2022 introduces a powerful new featureβ€”Azure Synapse Link integration, which enables seamless, real-time analytics and data warehousing capabilities. This integration bridges the gap between operational databases and analytical platforms, allowing businesses to perform analytics on fresh data without the complexities of ETL processes. In this blog, we’ll explore the features, benefits, and practical applications of SQL Server 2022’s integration with Azure Synapse Analytics. Let’s dive into the future of data analytics! 🌟

        1. What is Azure Synapse Link? 🌐

        Azure Synapse Link is a feature that provides a direct, near real-time connection between SQL Server and Azure Synapse Analytics. It allows you to continuously replicate data from SQL Server to Azure Synapse Analytics, enabling immediate analysis of transactional data.

        Key Benefits:

        • Real-Time Insights: Get up-to-the-minute analytics on operational data.
        • Simplified ETL: Eliminates the need for complex ETL processes by directly linking operational and analytical stores.
        • Scalability: Leverages the scalability of Azure Synapse Analytics to handle large datasets and complex queries.

        2. How SQL Server 2022 Integrates with Azure Synapse Link πŸ”„

        SQL Server 2022 integrates with Azure Synapse Link by enabling Change Data Capture (CDC) on selected tables. This setup captures data changes in SQL Server and automatically replicates them to a dedicated SQL pool in Azure Synapse Analytics.

        Step-by-Step Setup:

        Enable Change Data Capture (CDC) on SQL Server:
        CDC needs to be enabled on the tables you want to replicate. Here’s an example of how to enable CDC:

          USE YourDatabaseName;
          EXEC sys.sp_cdc_enable_db;
          GO
          
          EXEC sys.sp_cdc_enable_table
              @source_schema = N'dbo',
              @source_name   = N'YourTableName',
              @role_name     = NULL;
          GO

          Configure Azure Synapse Link:
          In Azure Synapse Analytics, set up a dedicated SQL pool and link it with your SQL Server. The data from the CDC-enabled tables will be continuously replicated to this dedicated pool.

          Perform Analytics in Azure Synapse Analytics:
          Once the data is in Azure Synapse Analytics, you can leverage its powerful analytics capabilities, including SQL, Apache Spark, and Data Explorer, to perform complex queries and derive insights.

            3. Advantages of Using Azure Synapse Link with SQL Server 2022 ⚑

            The integration offers several key advantages:

            • Real-Time Analytics: With Azure Synapse Link, you can perform analytics on the latest data as soon as it changes, providing real-time insights into your business operations.
            • Reduced Data Movement Overhead: Traditional ETL processes can be resource-intensive and time-consuming. Azure Synapse Link eliminates the need for these processes, reducing the overhead and complexity associated with data movement.
            • Seamless Integration: The setup is straightforward, with minimal changes required to your existing SQL Server setup. This seamless integration ensures that you can quickly start leveraging the benefits of Azure Synapse Analytics.
            • Scalable Analytics: Azure Synapse Analytics offers massive scalability, allowing you to run complex queries on large datasets efficiently. This is particularly beneficial for businesses with growing data volumes.

            4. Use Cases for SQL Server 2022 and Azure Synapse Link πŸ“ˆ

            Real-Time Customer Insights: Retailers can use this integration to analyze customer behavior in real-time, optimizing inventory management, and personalizing marketing efforts based on the latest data.

            Operational Analytics: Businesses can perform real-time monitoring and analytics on operational data, such as sales transactions or IoT sensor data, to make informed decisions and respond quickly to changing conditions.

            Fraud Detection: Financial institutions can leverage the real-time data replication capabilities to detect and respond to fraudulent activities as they occur, enhancing security and reducing losses.

            Data Warehousing: By continuously feeding data into Azure Synapse Analytics, businesses can maintain up-to-date data warehouses, enabling more accurate and timely reporting and analytics.

            5. Example Scenario: Real-Time Sales Analytics for E-commerce πŸ›’

            Imagine an e-commerce platform using SQL Server to manage its transaction data. By enabling Azure Synapse Link, the platform can replicate sales data to Azure Synapse Analytics in real-time. This setup allows the analytics team to perform real-time analysis on sales trends, customer preferences, and inventory levels. The results can inform dynamic pricing strategies, optimize stock levels, and improve overall customer satisfaction.

            -- Enabling CDC on the Sales table
            USE ECommerceDB;
            EXEC sys.sp_cdc_enable_db;
            GO
            
            EXEC sys.sp_cdc_enable_table
                @source_schema = N'dbo',
                @source_name   = N'Sales',
                @role_name     = NULL;
            GO

            Once the data is in Azure Synapse Analytics, analysts can run complex queries to derive insights:

            -- Sample query to analyze sales trends
            SELECT ProductID, SUM(Quantity) AS TotalSold, SUM(TotalAmount) AS TotalRevenue
            FROM SynapsePool.dbo.Sales
            GROUP BY ProductID
            ORDER BY TotalRevenue DESC;

            This real-time data analytics capability can significantly enhance decision-making, leading to more agile and data-driven business operations.

            Conclusion πŸŽ‰

            SQL Server 2022’s integration with Azure Synapse Link marks a significant advancement in real-time data analytics and data warehousing. By bridging the gap between operational databases and analytical platforms, businesses can gain immediate insights into their data, making informed decisions faster and more accurately. This integration not only simplifies the data architecture but also leverages the powerful analytics capabilities of Azure Synapse Analytics, offering unparalleled scalability and performance.

            Whether you’re looking to optimize customer experiences, enhance operational efficiencies, or maintain up-to-date data warehouses, SQL Server 2022 and Azure Synapse Link provide the tools you need to succeed in a data-driven world. Embrace the future of analytics with SQL Server 2022 and Azure Synapse Link! πŸš€βœ¨

            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 β€œAS IS” with no warranties, and confers no rights.