Deleting Duplicated Rows using Common Table Expressions (CTE)

In this article, we will explore the use of Common Table Expressions (CTEs) in SQL Server to delete duplicated rows from a table. We will also discuss how to resolve the error “target DML table is not hash partitioned” that prevents us from executing this query.

Introduction

When working with large datasets, it’s common to encounter duplicate records. In many cases, these duplicates can be removed to improve data quality and reduce storage requirements. One popular method for removing duplicates is by using Common Table Expressions (CTEs) in SQL Server.

A CTE is a temporary result set that is defined within the execution of a single SELECT, INSERT, UPDATE, or DELETE statement. It allows us to perform complex queries without having to declare a separate temporary table.

In this article, we will discuss how to use CTEs to delete duplicated rows from a table and provide guidance on resolving the error “target DML table is not hash partitioned.”

Understanding Duplicate Records

Before we dive into deleting duplicates using CTEs, let’s understand what constitutes a duplicate record. A duplicate record is a row in a table that has the same values as another row in the same table.

For example, consider the following table:

In this table, there are two duplicate records because both rows have the same values for Name and City.

Using CTEs to Delete Duplicates

To delete duplicates using CTEs, we can use the following query:

WITH CTE AS (
  SELECT [col1], [col2], [col3], [col4], [col5], [col6], [col7],
         RN = ROW_NUMBER() OVER (PARTITION BY [col1], [col2], [col3], [col4], [col5], [col6], [col7] ORDER BY col1)
  FROM dbo.Table1
)
DELETE FROM CTE WHERE RN > 1

This query works by:

1. Defining a CTE named CTE that selects all columns from the Table1 table.
2. Using the ROW_NUMBER() function to assign a unique number (RN) to each row within each partition of the data (i.e., rows with the same values for [col1], [col2], etc.).
3. Partitioning the data by all columns using the PARTITION BY clause.
4. Ordering the data by the first column in each partition using the ORDER BY clause.
5. Deleting all rows with an RN value greater than 1, effectively removing duplicates.

Resolving the “target DML table is not hash partitioned” Error

The error “target DML table is not hash partitioned” occurs when the query processor cannot produce a query plan for a DML (Data Manipulation Language) statement because the target table is not hash partitioned.

Hash partitioning is a technique used to divide large tables into smaller, more manageable pieces called partitions. Each partition can be processed independently, which improves performance and reduces memory usage.

To resolve this error, you need to ensure that the target DML table is hash partitioned before executing the query.

One way to achieve this is by creating a new partition scheme for the table and specifying it in the CREATE TABLE statement:

CREATE TABLE dbo.Table1 (
  [col1] INT,
  [col2] VARCHAR(50),
  [col3] DATE,
  [col4] DECIMAL(10, 2),
  [col5] BIT,
  [col6] INT,
  [col7] VARCHAR(100)
) ON [partition_scheme_name]

In the example above, we create a new table named Table1 with the same columns as before. We also specify a partition scheme name [partition_scheme_name].

To create a hash partition scheme, you can use the following syntax:

CREATE PARTITION SCHEME [partition_scheme_name] FOR TABLE [table_name]
    (
      RANGE RIGHT REPEATABLE (MOD ([col1], 10))
    );

In this example, we create a partition scheme named [partition_scheme_name] for the Table1 table. The range is set to divide the data into partitions of size 10, using the first column ([col1]) as the partition key.

Once you’ve created the new partition scheme and specified it in the CREATE TABLE statement, you can execute the original query without encountering the “target DML table is not hash partitioned” error.

Additional Considerations

When working with large datasets, it’s essential to consider additional factors that may impact performance:

• Indexing: Create indexes on columns used in the WHERE, JOIN, and ORDER BY clauses to improve query performance.
• Data Types: Use data types that match the data being stored. For example, use INT for integer values and VARCHAR(50) for string values.
• Partitioning: Consider partitioning large tables based on columns used in queries or data that varies by season or time zone.

By following these best practices and using CTEs to delete duplicates, you can improve the performance and efficiency of your SQL Server queries.

Last modified on 2024-09-02