SQL remove duplicates

Removing duplicates from a SQL database is a common task that can help improve data quality and query performance. Duplicates occur when there are multiple rows in a table with identical values in one or more columns. You can eliminate these duplicates using various SQL techniques and clauses. In this explanation, I’ll provide an overview of how to remove duplicates in SQL.

Methods to Remove Duplicates in SQL

Using DISTINCT Keyword

One of the simplest ways to remove duplicates from a result set is by using the DISTINCT keyword in your SQL query. This keyword ensures that only unique rows are returned in the result set. Here’s an example:

SELECT DISTINCT column1, column2
FROM your_table;

In this query, replace column1 and column2 with the columns you want to consider when identifying duplicates. The result will contain distinct combinations of values from these columns.

Using GROUP BY Clause

Another method to remove duplicates is by using the GROUP BY clause. This allows you to group rows by one or more columns and apply aggregate functions like COUNT, SUM, or AVG. To eliminate duplicates, you can use GROUP BY on all columns or a subset of columns that you want to consider unique. Here’s an example:

SELECT column1, column2
FROM your_table
GROUP BY column1, column2;

This query groups the rows based on the values in column1 and column2, returning only one row for each unique combination.

Using ROW_NUMBER() Function and CTE

In some cases, you may want to remove duplicates and keep only one instance of each unique row. The ROW_NUMBER() function can be useful for this purpose. This function assigns a unique number to each row within a partition based on specified criteria. You can then use it to filter out duplicates. Here’s an example:

  SELECT column1, column2,
         ROW_NUMBER() OVER (PARTITION BY column1, column2 ORDER BY column1, column2) AS rn
  FROM your_table
SELECT column1, column2
WHERE rn = 1;

In this query, the ROW_NUMBER() function is used to assign a unique number (rn) to each row based on the values in column1 and column2. Rows with rn = 1 are the first occurrence of each unique combination, effectively removing duplicates.

Using Temporary Tables or Subqueries

Another approach to remove duplicates is to use temporary tables or subqueries. You can create a new table or subquery that contains only the distinct rows and then use it in subsequent queries. Here’s an example using a subquery:

SELECT column1, column2
  SELECT column1, column2
  FROM your_table
  GROUP BY column1, column2
) AS distinct_rows;

In this example, the subquery retrieves distinct rows based on column1 and column2, and the outer query selects the desired columns from the subquery.

Considerations for Removing Duplicate Rows

When removing duplicate rows, it’s essential to consider the following factors:

Data Integrity: Ensure that the process of removing duplicates doesn’t compromise the integrity of the data. For instance, if a customer’s order history is spread across multiple rows due to duplicate records, removing all but one instance might lead to inconsistencies.

Data Quality: Evaluate the impact of removing duplicates on data quality metrics, such as average row count per table, data consistency, and overall data accuracy.

Performance: Consider the performance implications of removing duplicates, especially for large tables. Identify efficient methods that minimize the impact on query performance.

Data Backup: Always create a backup of the original table before attempting to remove duplicates. This ensures that you can revert to the original data if necessary.

Data Usage: Analyze how the data is being used and determine if removing duplicates is indeed necessary for the intended purposes. For instance, if duplicate rows are used in specific calculations or analyses, removing them might affect the results.

Testing and Monitoring: Thoroughly test the data after removing duplicates to ensure that the data integrity and quality have been maintained. Monitor the impact on query performance and data usage patterns.


Removing duplicates from SQL tables is essential to maintain data accuracy and improve the quality of your queries and reports. SQL offers various techniques, such as using DISTINCT, GROUP BY and HAVING, Common Table Expressions (CTEs), and window functions like ROW_NUMBER(), to achieve this. The method you choose depends on your specific requirements and the structure of your data, so choose the one that best suits your needs.