SQL Delete Duplicate Rows

How do you remove duplicate rows from a table in SQL?

Removing duplicate rows from a table in SQL involves identifying and deleting rows that have identical values across specified columns. This process ensures data integrity and optimizes query performance. Different methods exist, each with its own advantages and considerations.

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Description

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Removing duplicate rows is a common task in database management. Duplicate data can lead to inaccurate analysis, inefficient queries, and wasted storage space. SQL provides several ways to identify and eliminate these duplicates. A crucial step is defining which columns constitute a duplicate. For instance, if a table stores customer information, duplicates might be based on a combination of customer ID and name. A simple approach is to use the `DELETE` statement in conjunction with a `WHERE` clause and a subquery to identify the duplicates. This method can be efficient for smaller datasets but might become slow for large tables. More sophisticated techniques, such as using window functions, offer better performance for larger datasets. These methods leverage the database's ability to efficiently identify and filter rows based on specific criteria, leading to more optimized queries and improved data quality.

Why SQL Delete Duplicate Rows is important

Removing duplicate rows is crucial for maintaining data integrity and consistency. It prevents inaccurate analysis, improves query performance, and optimizes storage space. Clean data is essential for reliable reporting and decision-making.

SQL Delete Duplicate Rows Example Usage


-- Find all customers who live in 'New York'.
SELECT *
FROM Customers
WHERE City = 'New York';

-- Find all products priced above $10.
SELECT *
FROM Products
WHERE Price > 10;

-- Find all orders placed in 2023.
SELECT *
FROM Orders
WHERE OrderDate >= '2023-01-01' AND OrderDate <= '2023-12-31';

-- Find customers who are not from 'California'.
SELECT *
FROM Customers
WHERE City != 'California';

SQL Delete Duplicate Rows Syntax

Common Mistakes

Using `DISTINCT` in a `DELETE` statement. `DISTINCT` only affects the result set of a `SELECT` statement, not the underlying table.
Forgetting to specify the columns to identify duplicates, leading to unintended deletions.
Not considering potential performance issues when dealing with large datasets, leading to slow query execution.
Using incorrect `GROUP BY` clauses, resulting in incorrect duplicate identification.

Frequently Asked Questions (FAQs)

What SQL technique works best for removing duplicates in very large tables?

For multi-million-row tables, the blog post recommends using window functions such as ROW_NUMBER() inside a Common Table Expression (CTE). By partitioning on the columns that define a duplicate and deleting any row whose generated row number is greater than 1, you avoid expensive correlated subqueries and gain the benefit of the database’s sequential scanning and indexing strategies. This approach is generally faster and more memory-efficient than a plain DELETE ... WHERE IN (subquery) pattern.

Why are window functions faster than a simple DELETE with a subquery?

A DELETE statement that relies on a subquery must repeatedly compare each candidate row against the duplicate criteria, which can become sluggish as the table grows. Window functions, on the other hand, calculate row numbers in one pass and let you filter on that derived value. This minimizes random I/O, leverages available indexes, and reduces lock contention—leading to noticeably faster runtimes on sizable datasets.

How can Galaxy’s AI Copilot speed up writing de-duplication queries?

Galaxy’s context-aware AI Copilot can auto-generate the full CTE + ROW_NUMBER() query template based on the columns you mark as “should be unique.” It also suggests index hints, names the query for future reuse, and lets you share the vetted script with teammates via Collections. This means you spend less time hand-coding boilerplate and more time enforcing data quality at scale.