Use GROUP BY with HAVING COUNT(*) > 1 to find duplicates in a single column, such as email, returning the duplicate values and their counts. 2. For duplicates based on multiple columns, group by all relevant columns (e.g., first_name and last_name) to identify repeated combinations. 3. To detect full row duplicates, group by all columns in the table to find rows where every field is identical. 4. Use window functions like ROW_NUMBER() OVER (PARTITION BY column ORDER BY id) in a CTE to assign numbers to rows within groups, then filter where row_num > 1 to identify all but the first occurrence of each duplicate. Always test queries on a small dataset, consider retention strategy, and use indexes on grouping or partitioning columns for better performance.
Finding duplicate values in a SQL table is a common task when cleaning or analyzing data. The key is to identify rows that have identical values in one or more columns. Here’s how to do it effectively.
1. Find duplicates based on a single column
If you want to find duplicate entries in a specific column (e.g., email), use GROUP BY with HAVING to filter groups that appear more than once:
SELECT email, COUNT(*) AS count FROM users GROUP BY email HAVING COUNT(*) > 1;
This query returns all email addresses that appear multiple times, along with how many times they appear.
2. Find duplicates based on multiple columns
When duplicates are defined by a combination of columns (e.g., first_name and last_name), group by both:
SELECT first_name, last_name, COUNT(*) AS count FROM users GROUP BY first_name, last_name HAVING COUNT(*) > 1;
This helps catch cases where the same name combination appears more than once, even if individual names aren't unique.
3. Find full row duplicates (all columns identical)
If you suspect entire rows are duplicated, you can group by all columns:
SELECT col1, col2, col3, COUNT(*) FROM your_table GROUP BY col1, col2, col3 HAVING COUNT(*) > 1;
Replace col1, col2, col3 with all relevant columns. This shows rows where every field matches another row.
4. Include row identifiers to see which rows are duplicated
To inspect specific duplicate rows (e.g., to delete or investigate them), you can use window functions:
WITH Duplicates AS (
SELECT *,
ROW_NUMBER() OVER (PARTITION BY email ORDER BY id) AS row_num
FROM users
)
SELECT * FROM Duplicates WHERE row_num > 1;This assigns a number to each row within the same email group, ordered by id. Rows with row_num > 1 are duplicates (keeps the first one based on id).
- Use
ROW_NUMBER()to identify all but the first occurrence. - Use
RANK()orDENSE_RANK()if you want different tie-breaking behavior.
Tips:
- Always test on a small dataset or use
LIMITfirst. - Consider whether you want to keep one instance of a duplicate or remove all.
- Indexes on the columns used in
PARTITION BYorGROUP BYcan improve performance.
Basically, use GROUP BY and HAVING COUNT(*) > 1 for summaries, and window functions like ROW_NUMBER() when you need to see or remove specific duplicate rows.
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