group by in duckdb Examples & 2025 Guide

If you're working with DuckDB—the high-speed analytics database built for ease and performance—mastering GROUP BY is essential for data aggregation, reporting, and analysis. But even seasoned developers and analysts can struggle with complex syntax, nested aggregates, or multi-level grouping, especially as queries grow. That's where AI2sql platform steps in, letting you convert natural language requests into production-ready SQL for DuckDB—no coding or database expertise required.

This guide gives you proven group by in duckdb examples, explains how and why it works, and shares practical techniques. Whether you're new to SQL, automating BI workflows, or tired of stackoverflow copy-paste, AI2sql removes the complexity from GROUP BY in DuckDB, saving hours (and headaches).

1. What is group by in duckdb?

The GROUP BY statement in DuckDB enables you to aggregate data based on shared column values—for example, getting sales by category, user activity by month, or average scores per test. It is similar to GROUP BY in other SQL dialects, with optimizations for DuckDB’s columnar engine and syntax support.

• Purpose: Summarize large data sets by categories, users, or time periods

• Common aggregates: COUNT, SUM, AVG, MIN, MAX

• Syntax: Compatible with PostgreSQL, SQLite, and modern SQL

2. How group by in duckdb Works

You use GROUP BY to tell DuckDB: “combine all rows that share a value in these columns, then compute aggregate results for each group.”

Basic syntax:

SELECT column1, AGG_FUNCTION(column2)
FROM table_name
GROUP BY column1;

How DuckDB differs: Supports arbitrary grouping, window functions, and is highly performant for large datasets—without extra configuration.

3. Key Features & Benefits

• Analytical Speed: DuckDB processes group by queries rapidly, even on millions of rows.

• Syntax Parity: Uses familiar SQL—works like GROUP BY in PostgreSQL or SQLite.

• Multi-Key Grouping: Group by multiple columns for granular insights.

• Advanced Functions: Use HAVING, window functions, subqueries, and more.

4. Real-World Examples

Here are several practical group by in duckdb examples you’ll use all the time:

Example 1: Counting Orders by Customer

SELECT customer_id, COUNT(*) AS total_orders
FROM orders
GROUP BY customer_id;

Example 2: Sales by Product Category With Filter

SELECT category, SUM(amount) AS total_sales
FROM sales
WHERE sale_date > '2025-01-01'
GROUP BY category;

Example 3: Average Score per Student, Filtering on Score

SELECT student_id, AVG(score) AS avg_score
FROM tests
GROUP BY student_id
HAVING AVG(score) > 80;

For more, visit our group by in duckdb Examples or dive deeper with our group by in duckdb Tutorial.

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Conclusion

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