SQL GROUP BY vs DISTINCT

Understanding GROUP BY vs DISTINCT

GROUP BY and DISTINCT are often mentioned in the same breath because both can affect the uniqueness of rows in a result set. Yet they serve fundamentally different purposes. Knowing when to choose one over the other is critical for writing clear, performant SQL and for avoiding subtle data-quality bugs.

What Is GROUP BY?

The GROUP BY clause partitions your result set into subsets—one for each unique combination of the specified columns—so that you can apply aggregate functions such as SUM, COUNT, AVG, MIN, or MAX to each subset. The output contains one row per group.

SELECT customer_id,
COUNT(*) AS order_count,
SUM(total) AS total_spend
FROM orders
GROUP BY customer_id;

Here, every distinct customer_id becomes a group. Aggregates are then calculated per customer.

What Is DISTINCT?

The DISTINCT keyword removes duplicate rows from the result set. It performs a set operation, returning only unique combinations of the selected columns without any aggregation.

SELECT DISTINCT customer_id
FROM orders;

This query simply lists each customer once, with no additional calculations.

Key Differences

1. Purpose

• GROUP BY: Enables aggregation over groups.
• DISTINCT: Deduplicates rows.

2. Output Shape

• GROUP BY: One row per group; usually includes aggregate columns.
• DISTINCT: One row per unique combination of selected columns; no implicit aggregates.

3. Performance

• Both operations require sorting or hashing, but GROUP BY with aggregates may be faster because aggregation can occur during grouping. Conversely, DISTINCT often has to materialize the full row set before deduplication.
• Add proper indexes on grouping or distinct columns to minimize in-memory sorts.

When to Use Which

• Use GROUP BY when you need aggregates (SUM, AVG, etc.).
• Use DISTINCT when you simply need a list of unique values or unique combinations without any calculations.
• If you need both uniqueness and aggregation, GROUP BY usually wins because you can include non-aggregated columns (in most RDBMS) by adding them to the GROUP BY list.

Practical Example

Suppose you want to know how many different products each customer purchased. An incorrect instinct might be to write:

-- ❌ Incorrect: DISTINCT inside COUNT counts all distinct rows globally
SELECT customer_id,
COUNT(DISTINCT product_id)
FROM orders;

Most databases will error, or worse, implicitly act like a window function. The right approach:

SELECT customer_id,
COUNT(DISTINCT product_id) AS unique_products
FROM orders
GROUP BY customer_id;

The combination of GROUP BY and COUNT(DISTINCT ...) delivers a per-customer tally.

Performance Considerations

Indexing

Create composite indexes that match the column order in your GROUP BY or DISTINCT clause. This allows the database to perform an index-only scan, reducing sort cost.

Aggregation Pushdown

Modern SQL engines push aggregation closer to the storage layer. A well-written GROUP BY can sometimes outperform DISTINCT because aggregation reduces row counts early.

Memory Spills

Both clauses can force disk spills if the number of unique combinations is large. Monitor execution plans and tune work_mem (Postgres) or equivalent settings to keep operations in memory.

Best Practices

• Favor explicitness. If you only need deduplication, use DISTINCT. If you need aggregates, use GROUP BY.
• Don’t select non-grouped, non-aggregated columns when using GROUP BY; most databases disallow it, and those that do allow it (e.g., MySQL in loose mode) can return indeterminate values.
• Use window functions (ROW_NUMBER, DENSE_RANK) for advanced deduplication scenarios rather than misusing DISTINCT.
• Benchmark both approaches with EXPLAIN to understand real-world performance in your dataset.

How Galaxy Fits In

Galaxy’s modern SQL editor helps you spot whether DISTINCT or GROUP BY is appropriate through its AI copilot and smart autocomplete. As you type, Galaxy surfaces context-aware recommendations like Did you mean to aggregate on these columns? This mitigates common mistakes and enforces team-wide best practices when collaborating in shared Galaxy Collections.

Conclusion

While GROUP BY and DISTINCT can both influence row uniqueness, they attack different problems: aggregation vs. deduplication. Mastering their differences ensures cleaner queries, faster runtimes, and more reliable analytics pipelines.