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.

        Mastering LAG and LEAD Functions in SQL Server 2022 with the IGNORE NULLS Option

        SQL Server 2022 introduced a powerful enhancement to the LAG and LEAD functions with the IGNORE NULLS option. This feature allows for more precise analysis and reporting by skipping over NULL values in data sets. In this blog, we’ll explore how to use these functions effectively using the JBDB database, and we’ll demonstrate their application with a detailed business use case.

        Business Use Case: Sales Data Analysis

        Imagine a retail company, JBStore, that wants to analyze its sales data to understand sales trends better. They aim to compare each month’s sales with the previous and next months, ignoring any missing data (represented by NULL values). This analysis will help identify trends and outliers, aiding in better decision-making.

        Setting Up the JBDB Database

        First, let’s set up the JBDB database and create a SalesData table with some sample data, including NULL values to represent months with no sales data.

        -- Create JBDB database
        CREATE DATABASE JBDB;
        GO
        
        -- Use the JBDB database
        USE JBDB;
        GO
        
        -- Create SalesData table
        CREATE TABLE SalesData (
            SalesMonth INT,
            SalesAmount INT
        );
        
        -- Insert sample data, including NULLs
        INSERT INTO SalesData (SalesMonth, SalesAmount)
        VALUES
            (1, 1000),
            (2, 1500),
            (3, NULL),
            (4, 1800),
            (5, NULL),
            (6, 2000);
        GO

        LAG and LEAD Functions: A Quick Recap

        The LAG function allows you to access data from a previous row in the same result set without the use of a self-join. Similarly, the LEAD function accesses data from a subsequent row. Both functions are part of the SQL window functions family and are particularly useful in time series analysis.

        Using LAG and LEAD with IGNORE NULLS

        The IGNORE NULLS option is a game-changer, as it allows you to skip over NULL values, providing more meaningful results. Here’s how you can use it with the LAG and LEAD functions:

        Example 1: LAG Function with IGNORE NULLS
        SELECT 
            SalesMonth,
            SalesAmount,
            LAG(SalesAmount, 1) IGNORE NULLS OVER (ORDER BY SalesMonth) AS PreviousMonthSales
        FROM 
            SalesData;

        In this example, LAG(SalesAmount, 1) IGNORE NULLS retrieves the sales amount from the previous month, skipping over any NULL values.

        Example 2: LEAD Function with IGNORE NULLS
        SELECT 
            SalesMonth,
            SalesAmount,
            LEAD(SalesAmount, 1) IGNORE NULLS OVER (ORDER BY SalesMonth) AS NextMonthSales
        FROM 
            SalesData;

        Here, LEAD(SalesAmount, 1) IGNORE NULLS retrieves the sales amount from the next month, again skipping over NULL values.

        Practical Example: Analyzing Sales Trends

        Let’s combine these functions to analyze sales trends more effectively.

        SELECT 
            SalesMonth,
            SalesAmount,
            LAG(SalesAmount, 1) IGNORE NULLS OVER (ORDER BY SalesMonth) AS PreviousMonthSales,
            LEAD(SalesAmount, 1) IGNORE NULLS OVER (ORDER BY SalesMonth) AS NextMonthSales
        FROM 
            SalesData;

        This query provides a complete view of each month’s sales, the previous month’s sales, and the next month’s sales, excluding any NULL values. This is incredibly useful for identifying patterns, such as periods of growth or decline.

        Detailed Business Use Case: Data-Driven Decision Making

        By utilizing the IGNORE NULLS option with LAG and LEAD functions, JBStore can:

        1. Identify Growth Periods: Detect months where sales increased significantly compared to the previous or next month.
        2. Spot Anomalies: Easily identify months with unusually high or low sales, excluding months with missing data.
        3. Trend Analysis: Understand longer-term trends by comparing sales over multiple months.

        These insights can inform marketing strategies, inventory planning, and more.

        Calculate Difference Between Current and Previous Month’s Sales:

        SELECT SalesMonth, SalesAmount, SalesAmount - LAG(SalesAmount, 1) IGNORE NULLS OVER (ORDER BY SalesMonth) AS SalesDifference FROM SalesData;

        Identify Months with Sales Decrease Compared to Previous Month:

        WITH CTE AS (
            SELECT 
                SalesMonth,
                SalesAmount,
                LAG(SalesAmount, 1) IGNORE NULLS OVER (ORDER BY SalesMonth) AS PreviousMonthSales
            FROM 
                SalesData
        )
        SELECT 
            SalesMonth,
            SalesAmount,
            PreviousMonthSales
        FROM 
            CTE
        WHERE 
            SalesAmount < PreviousMonthSales;
        
        

        Find the Second Previous Month’s Sales:

        SELECT SalesMonth, SalesAmount, LAG(SalesAmount, 2) IGNORE NULLS OVER (ORDER BY SalesMonth) AS SecondPreviousMonthSales FROM SalesData;

        Calculate the Rolling Average of the Last Two Months (Ignoring NULLs):

        SELECT SalesMonth, SalesAmount, (SalesAmount + LAG(SalesAmount, 1) IGNORE NULLS OVER (ORDER BY SalesMonth)) / 2 AS RollingAverage FROM SalesData;

        Compare Sales Between Current Month and Two Months Ahead:

        SELECT SalesMonth, SalesAmount, LEAD(SalesAmount, 2) IGNORE NULLS OVER (ORDER BY SalesMonth) AS SalesTwoMonthsAhead FROM SalesData;

        Identify Consecutive Months with Sales Increase:

        WITH CTE AS ( SELECT SalesMonth, SalesAmount, LAG(SalesAmount, 1) IGNORE NULLS OVER (ORDER BY SalesMonth) AS PreviousMonthSales FROM SalesData ) SELECT SalesMonth, SalesAmount FROM CTE WHERE SalesAmount > PreviousMonthSales;

        Find Months with No Sales and Their Preceding Sales Month:

        SELECT SalesMonth, SalesAmount, LAG(SalesAmount, 1) IGNORE NULLS OVER (ORDER BY SalesMonth) AS PrecedingMonthSales FROM SalesData WHERE SalesAmount IS NULL;

        Calculate Cumulative Sales Sum Ignoring NULLs:

        SELECT 
            SalesMonth,
            SalesAmount,
            SUM(ISNULL(SalesAmount, 0)) OVER (ORDER BY SalesMonth ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS CumulativeSales
        FROM 
            SalesData;
        
        

        Identify the First Month with Sales After a Month with NULL Sales:

        SELECT SalesMonth, SalesAmount, LEAD(SalesAmount, 1) IGNORE NULLS OVER (ORDER BY SalesMonth) AS FirstNonNullSalesAfterNull FROM SalesData WHERE SalesAmount IS NULL;

          Conclusion 🎉

          The LAG and LEAD functions with the IGNORE NULLS option in SQL Server 2022 offer a more refined way to analyze data, providing more accurate and meaningful results. Whether you’re analyzing sales data, customer behavior, or any other time series data, these functions can significantly enhance your analytical capabilities.

          Happy querying! 🚀

          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.

          Exploring the APPROX_COUNT_DISTINCT Function in SQL Server 2022

          With the release of SQL Server 2022, a range of powerful new functions has been introduced, including the APPROX_COUNT_DISTINCT function. This function provides a fast and memory-efficient way to estimate the number of unique values in a dataset, making it an invaluable tool for big data scenarios where traditional counting methods may be too slow or resource-intensive. In this blog, we will explore the APPROX_COUNT_DISTINCT function, using the JBDB database for practical demonstrations and providing a detailed business use case to illustrate its benefits. Let’s dive into the world of approximate distinct counts! 🎉


          Business Use Case: E-commerce Customer Segmentation 📦

          In an e-commerce business, understanding the diversity of customer behavior is crucial for personalized marketing and inventory management. The JBDB database contains customer transaction data, including CustomerID, ProductID, and PurchaseDate. The business aims to estimate the number of unique customers making purchases each month and the variety of products they are buying. Using the APPROX_COUNT_DISTINCT function, the company can quickly analyze this data to identify trends, optimize stock levels, and tailor marketing campaigns.


          Understanding the APPROX_COUNT_DISTINCT Function 🧠

          The APPROX_COUNT_DISTINCT function estimates the number of distinct values in a column, offering a performance-efficient alternative to the traditional COUNT(DISTINCT column) approach. It is particularly useful in large datasets where an exact count is less critical than performance and resource usage.

          Syntax:

          APPROX_COUNT_DISTINCT ( column_name )
          
          • column_name: The column from which distinct values are counted.

          Example 1: Estimating Unique Customers per Month 📅

          Let’s calculate the estimated number of unique customers making purchases each month in the JBDB database.

          Setup:

          USE JBDB;
          GO
          
          CREATE TABLE CustomerTransactions (
              TransactionID INT PRIMARY KEY,
              CustomerID INT,
              ProductID INT,
              PurchaseDate DATE
          );
          
          INSERT INTO CustomerTransactions (TransactionID, CustomerID, ProductID, PurchaseDate)
          VALUES
          (1, 101, 2001, '2023-01-05'),
          (2, 102, 2002, '2023-01-10'),
          (3, 101, 2003, '2023-01-15'),
          (4, 103, 2001, '2023-02-05'),
          (5, 104, 2002, '2023-02-10'),
          (6, 102, 2004, '2023-02-15'),
          (7, 105, 2005, '2023-03-05'),
          (8, 106, 2001, '2023-03-10');
          GO

          Query to Estimate Unique Customers:

          SELECT 
              FORMAT(PurchaseDate, 'yyyy-MM') AS Month,
              APPROX_COUNT_DISTINCT(CustomerID) AS EstimatedUniqueCustomers
          FROM CustomerTransactions
          GROUP BY FORMAT(PurchaseDate, 'yyyy-MM');
          

          Output:

          MonthEstimatedUniqueCustomers
          2023-012
          2023-023
          2023-032

          This output gives an approximate count of unique customers making purchases in each month, providing quick insights into customer engagement over time.


          Example 2: Estimating Product Variety by Month 📊

          Now, let’s estimate the variety of products purchased each month to understand product diversity and demand trends.

          Query to Estimate Product Variety:

          SELECT 
              FORMAT(PurchaseDate, 'yyyy-MM') AS Month,
              APPROX_COUNT_DISTINCT(ProductID) AS EstimatedUniqueProducts
          FROM CustomerTransactions
          GROUP BY FORMAT(PurchaseDate, 'yyyy-MM');
          
          

          Output:

          MonthEstimatedUniqueProducts
          2023-013
          2023-023
          2023-032

          This data helps the business understand which months had the highest product variety, aiding in inventory and supply chain management.


          Example 3: Comparing Traditional and Approximate Counts 🔄

          To illustrate the efficiency of APPROX_COUNT_DISTINCT, let’s compare it with the traditional COUNT(DISTINCT column) method.

          Traditional COUNT(DISTINCT) Method:

          SELECT 
              FORMAT(PurchaseDate, 'yyyy-MM') AS Month,
              COUNT(DISTINCT CustomerID) AS ExactUniqueCustomers
          FROM CustomerTransactions
          GROUP BY FORMAT(PurchaseDate, 'yyyy-MM');
          
          

          Approximate COUNT(DISTINCT) Method:

          SELECT 
              FORMAT(PurchaseDate, 'yyyy-MM') AS Month,
              APPROX_COUNT_DISTINCT(CustomerID) AS EstimatedUniqueCustomers
          FROM CustomerTransactions
          GROUP BY FORMAT(PurchaseDate, 'yyyy-MM');
          

          Comparison:

          MonthExactUniqueCustomersEstimatedUniqueCustomers
          2023-0122
          2023-0233
          2023-0322

          The approximate method provides similar results with potentially significant performance improvements, especially in large datasets.


          Estimating Unique Products by Customer:

          • Calculate the estimated number of unique products purchased by each customer:
          SELECT 
              CustomerID,
              APPROX_COUNT_DISTINCT(ProductID) AS EstimatedUniqueProducts
          FROM CustomerTransactions
          GROUP BY CustomerID;

          Estimating Unique Purchase Dates:

          • Estimate the number of unique purchase dates in the dataset:
          SELECT 
              APPROX_COUNT_DISTINCT(PurchaseDate) AS EstimatedUniquePurchaseDates
          FROM CustomerTransactions;
          

          Regional Sales Analysis:

          • If the dataset includes a region column, estimate unique customers per region:
          SELECT 
              Region,
              APPROX_COUNT_DISTINCT(CustomerID) AS EstimatedUniqueCustomers
          FROM CustomerTransactions
          GROUP BY Region;

          Conclusion 🏁

          The APPROX_COUNT_DISTINCT function in SQL Server 2022 is a powerful tool for quickly estimating the number of distinct values in large datasets. This function is particularly useful in big data scenarios where performance and resource efficiency are crucial. By leveraging APPROX_COUNT_DISTINCT, businesses can gain rapid insights into customer behavior, product diversity, and other key metrics, enabling more informed decision-making. Whether you’re analyzing e-commerce data, customer segmentation, or product sales, this function offers a robust solution for your data analysis needs. Happy querying! 🎉

          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.