Effective Techniques for Managing Duplicate Data in Non-Unique Tables

Introduction

In data management, ensuring the integrity and cleanliness of your datasets is crucial. Duplicate data can lead to inaccurate analysis, misleading insights, and inefficiencies. While managing duplicates is relatively straightforward when you have a primary key, the challenge magnifies when dealing with tables that lack a unique identifier. This blog explores how to delete duplicate data from a table without a primary key, ensuring your data remains reliable and accurate.

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Understanding the Problem

Before diving into solutions, it’s important to understand why duplicate data occurs, and why its removal is essential. Duplicates often arise from:

Data entry errors: Manual data entry can introduce duplicates if the same data is entered multiple times.
Data integration: Merging datasets from different sources without proper checks can result in duplicate entries.
System errors: Bugs in the system that lead to the creation of redundant data. Duplicates, particularly in large datasets, can distort analytical results, lead to incorrect decision-making, and inflate storage costs.

Challenges in Deleting Duplicates Without a Primary Key

A primary key uniquely identifies each row in a table, making it easy to detect and remove duplicates. The absence of a primary key means that conventional methods of filtering based on unique identifiers are unavailable, necessitating more creative and careful approaches.

Approaches to Identify and Delete Duplicates

Using ROW_NUMBER() with Partitioning

One of the most effective ways to identify and delete duplicates is using the ROW_NUMBER() window function in SQL. This function assigns a unique number to each row within a partition of a dataset, effectively allowing us to mark duplicates.

Example:

We use ROW_NUMBER() to assign a sequential number to rows considered duplicates based on the values of column1 and column2.
We then delete all rows where row_num is greater than 1, removing duplicates while keeping the first occurrence.

WITH RankedRows AS (
 SELECT  *, 
 ROW_NUMBER() OVER (PARTITION BY column1, column2 ORDER BY (SELECT NULL)) AS row_num
 FROM your_table
)
DELETE FROM your_table
WHERE EXISTS (
    SELECT 1 
    FROM RankedRows r 
    WHERE r.row_num > 1 
    AND your_table.column1 = r.column1 
    AND your_table.column2 = r.column2
)

WITH RankedRows AS (

SELECT *,

ROW_NUMBER() OVER (PARTITION BY column1, column2 ORDER BY (SELECT NULL)) AS row_num

FROM your_table

)

DELETE FROM your_table

WHERE EXISTS (

SELECT 1

FROM RankedRows r

WHERE r.row_num > 1

AND your_table.column1 = r.column1

AND your_table.column2 = r.column2

)

Grouping and Deleting

Another approach is to group the data based on all columns and then delete entries that do not match the minimum or maximum ROWID within each group. This method leverages ROWID, which can be used as a pseudo-primary key to identify and remove duplicates.

DELETE FROM your_table
WHERE ROWID NOT IN (
    SELECT MIN(ROWID)
    FROM your_table
    GROUP BY column1, column2, column3
)

DELETE FROM your_table

WHERE ROWID NOT IN (

SELECT MIN(ROWID)

FROM your_table

GROUP BY column1, column2, column3

)

Example:

Here:

We group the rows based on all columns that define a record’s uniqueness.
We retain only the row with the smallest ROWID in each group, ensuring that only one instance of each duplicate remains.

Using Common Table Expressions (CTEs) with Deletion

Common Table Expressions (CTEs) can be used to identify and selectively delete duplicates. CTEs make SQL queries more readable and easier to manage, especially when dealing with complex data structures.

WITH CTE AS (
    SELECT 
        column1, column2, column3, 
        COUNT() AS count
    FROM 
        your_table
    GROUP BY 
        column1, column2, column3
    HAVING 
        COUNT() > 1
)
DELETE FROM your_table
WHERE (column1, column2, column3) IN (
    SELECT column1, column2, column3 FROM CTE
)
AND ROWID NOT IN (
    SELECT MIN(ROWID)
    FROM your_table
    GROUP BY column1, column2, column3
)

WITH CTE AS (

SELECT

column1, column2, column3,

COUNT() AS count

FROM

your_table

GROUP BY

column1, column2, column3

HAVING

COUNT() > 1

)

DELETE FROM your_table

WHERE (column1, column2, column3) IN (

SELECT column1, column2, column3 FROM CTE

)

AND ROWID NOT IN (

SELECT MIN(ROWID)

FROM your_table

GROUP BY column1, column2, column3

)

Examples:

In this query:

The CTE identifies groups of duplicate rows.
The subsequent DELETE statement removes all but one of these duplicates, ensuring that each group retains only a single row.

Using Temporary Tables

In some cases, using a temporary table to store the unique data and then replacing the original table with this temporary table can be an efficient solution.

Example:

CREATE TABLE temp_table AS
SELECT DISTINCT  FROM your_table;
DROP TABLE your_table;
ALTER TABLE temp_table RENAME TO your_table;

CREATE TABLE temp_table AS

SELECT DISTINCT FROM your_table;

DROP TABLE your_table;

ALTER TABLE temp_table RENAME TO your_table;

Here:

We create a temporary table that contains unique records by selecting distinct rows.
The original table is then dropped, and the temporary table is renamed to the original table’s name.

Best Practices for Managing Duplicates

Enforce Data Integrity at Insertion: Use unique constraints or indexes to prevent duplicate data from being inserted.
Regular Data Audits: Schedule regular checks for duplicate data, especially in tables, without enforcing unique constraints.
Use Transactional Integrity: Ensure that all data manipulations (inserts, updates) are conducted within transactions to prevent partial data from leading to duplicates.
Data Normalization: Normalize data where applicable to reduce redundancy, which is often a duplication source.

Conclusion

Deleting duplicate data without a primary key requires careful consideration and the right approach to avoid unintended data loss. The methods outlined in this blog—using ROW_NUMBER(), grouping and deleting CTEs, and temporary tables—provide robust solutions for managing duplicates.

By understanding the nature of your data and applying these techniques, you can maintain the integrity of your datasets, leading to more accurate analyses and better decision-making.

Drop a query if you have any questions regarding Data Integrity and we will get back to you quickly.

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FAQs

1. Will deleting duplicates without a primary key affect the remaining data integrity?

ANS: – If done correctly, these methods should only remove the redundant entries, leaving the unique data intact. However, backing up your data before performing any deletion operations is crucial to prevent accidental data loss.

2. Can I automate deleting duplicates without a primary key?

ANS: – Yes, you can automate the process using scheduled SQL scripts or stored procedures that run at regular intervals. Ensure these scripts are well-tested and include error handling to avoid unintended data loss.

WRITTEN BY Aehteshaam Shaikh

Aehteshaam Shaikh is working as a Research Associate - Data & AI/ML at CloudThat. He is passionate about Analytics, Machine Learning, Deep Learning, and Cloud Computing and is eager to learn new technologies.