🗄️ SQL examples

Anti Join Examples in SQL

Find rows in one table that do not have a match in another table. 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 Anti Join does

Find rows in one table that do not have a match in another table. SQL syntax can vary by database, but the pattern below is a useful starting point for reports and analysis.

Syntax or pattern

SELECT c.* FROM customers c LEFT JOIN orders o ON c.customer_id=o.customer_id WHERE o.order_id IS NULL;
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5 practical examples

1

Use Anti Join in a sales report

Apply the Anti Join pattern to a sales table.

-- Anti Join example for sales SELECT customer_id, order_date, total_amount FROM orders WHERE total_amount > 100;

This shows how the Anti Join pattern can support a simple sales analysis.

2

Use Anti Join for customers

Apply the Anti Join pattern to customer records.

-- Anti Join 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 Anti Join for products

Apply the Anti Join pattern to product or inventory data.

-- Anti Join example for products SELECT product_id, product_name, category FROM products;

Product tables are good practice data for this SQL pattern.

4

Use Anti Join for monthly reporting

Apply the Anti Join pattern to a monthly reporting query.

-- Anti Join 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 Anti Join during data checks

Apply the Anti Join pattern to find data quality issues.

-- Anti Join 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.