SQL Query to Find Duplicate Names in a Table
Last Updated : 30 Dec, 2024
Duplicate records in a database can create confusion, generate incorrect results, and waste storage space. It’s essential to identify and remove duplicates to maintain data accuracy and database performance.
In this article, we’ll discuss the reasons for duplicates, how to find duplicate records in SQL, and best practices to prevent them. We’ll also provide a step-by-step guide with examples and outputs to find duplicate names in a table using SQL.
Finding Duplicate Names in a Table
Suppose we are working with a database of an e-commerce website. Now, some usernames are saved more than once and so are their email ids. This is going to cause erroneous analytical results for the e-commerce website as saving this data more than once is unnecessary.
Before writing a query, decide which columns we want to check for duplicates. For example:
- To find duplicate names, focus on the
Names column. - To identify duplicate email IDs, include the
EmailId column.
Let’s create a sample table Users1 to illustrate how to find and manage duplicates.
Query:
CREATE Table Users1 (ID VARCHAR(20) Primary Key,
Names VARCHAR(30), EmailId VARCHAR(30), Age INT);
INSERT INTO Users1 VALUES('O1201', 'Radhika Malhotra', '[email protected]', 21);
INSERT INTO Users1 VALUES('O1202', 'Aryan Ray', '[email protected]', 25);
INSERT INTO Users1 VALUES('O1203', 'Sam Das', '[email protected]', 54);
INSERT INTO Users1 VALUES('O1204', 'Radhika Malhotra', '[email protected]', 21);
INSERT INTO Users1 VALUES('O1205', 'Aryan Ray', '[email protected]', 25);
INSERT INTO Users1 VALUES('O1206', 'Radhika Malhotra', '[email protected]', 21);
SELECT * FROM Users1;
Output:
Users1 TableFind Duplicate Names in the Table
The query identifies duplicate entries in the Names column of the Users1 table. It groups the rows by Names, counts the occurrences of each name, and filters the results to show only those names with a count greater than 1.
Query:
SELECT Names,COUNT(*) AS Occurrence FROM
Users1 GROUP BY Names HAVING COUNT(*)>1;
Output:
Explanation:
This query is simple. Here, we are using the GROUP BY clause to group the identical rows in the Names column. Then we are finding the number of duplicates in that column using the COUNT() function and show that data in a new column named Occurrence. Having clause only keeps the groups that have more than one occurrence. Regular database monitoring and proper design practices help prevent such issues in the future.
Why Are Duplicate Records Problematic?
Duplicate records may arise from application bugs, user input errors, or poor database design. By managing duplicates, we improve data integrity, performance, and analytics accuracy. They can cause:
- Incorrect analysis: Redundant data skews insights and decision-making.
- Storage inefficiency: Duplicates occupy unnecessary space.
- Database inconsistencies: Erroneous data creates confusion and affects reliability.
Conclusion
Duplicate records can disrupt database operations, analytics, and storage efficiency. By using GROUP BY, COUNT(), and advanced queries like DELETE with subqueries, we can effectively identify and remove duplicates. Regularly monitoring our database and enforcing unique constraints ensures clean and reliable data, improving overall performance and consistency. Data quality is key to business success manage it wisely.
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