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Find Duplicates Examples in SQL

Find duplicate emails, IDs or records. This page gives you the syntax, five practical examples, common mistakes, and copy-ready SQL you can adapt.

Updated 2026-06-125 practical examplesCopy-ready SQL

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What Find Duplicates does

Find duplicate emails, IDs or records. SQL syntax can vary by database, but the pattern below is a useful starting point for reports and analysis.

Syntax or pattern

SELECT email, COUNT(*) FROM customers GROUP BY email HAVING COUNT(*) > 1;
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5 practical examples

1

Use Find Duplicates in a sales report

Apply the Find Duplicates pattern to a sales table.

-- Find Duplicates example for sales SELECT customer_id, order_date, total_amount FROM orders WHERE total_amount > 100;

This shows how the Find Duplicates pattern can support a simple sales analysis.

2

Use Find Duplicates for customers

Apply the Find Duplicates pattern to customer records.

-- Find Duplicates example for customers SELECT customer_id, email, status FROM customers WHERE status = 'Active';

This is useful when customer records need filtering, labeling or summarizing.

3

Use Find Duplicates for products

Apply the Find Duplicates pattern to product or inventory data.

-- Find Duplicates example for products SELECT product_id, product_name, category FROM products;

Product tables are good practice data for this SQL pattern.

4

Use Find Duplicates for monthly reporting

Apply the Find Duplicates pattern to a monthly reporting query.

-- Find Duplicates example for monthly reporting SELECT DATE_TRUNC('month', order_date) AS month, SUM(total_amount) AS sales FROM orders GROUP BY DATE_TRUNC('month', order_date);

This turns row-level transactions into a report-friendly result.

5

Use Find Duplicates during data checks

Apply the Find Duplicates pattern to find data quality issues.

-- Find Duplicates example for data checks SELECT customer_id, COUNT(*) AS records FROM orders GROUP BY customer_id HAVING COUNT(*) > 1;

This is a useful pattern for auditing data before building a report.

Common mistakes to avoid

  • Forgetting that SQL dialects vary across PostgreSQL, SQL Server, MySQL, BigQuery and SQLite.
  • Using SELECT * in production reports when only a few columns are needed.
  • Not checking join keys, duplicate rows or NULL values before trusting results.

FAQ

Will this SQL work in every database?

The idea is portable, but function names and date syntax may vary. Check your database dialect if a function is not recognized.

Should I use this in a report query?

Yes, if the pattern matches the business question and you have checked filters, joins and row counts.

Why does my result have too many rows?

The most common reasons are duplicate join keys, missing filters or grouping at the wrong level of detail.