SQL GROUP BY vs DISTINCT

SQL GROUP BY vs DISTINCT

GROUP BY organizes rows into named groups so you can aggregate them, whereas DISTINCT filters duplicate rows out of the result set. Picking the right clause affects correctness, performance, and maintainability of your queries.

Overview

At first glance, GROUP BY and DISTINCT appear to do something similar: they collapse duplicate data so you see a shorter result set. The similarity is superficial. DISTINCT is a deduplication operator; it filters the final projection of your query so every row is unique for the columns listed in the SELECT list. GROUP BY partitions the underlying rows into logical buckets called groups. Once groups exist you can perform aggregate calculations—SUM(), COUNT(), AVG(), MIN(), MAX(), or user-defined aggregates—over each bucket.

Why does the distinction matter?

Knowing when to use each clause directly affects:

• Correctness. Choosing DISTINCT when you need an aggregate returns misleading results; choosing GROUP BY when you merely need unique rows adds unnecessary computation.
• Performance. Query optimizers implement different physical plans for each clause. A wrong choice can multiply execution time or memory usage on large data sets.
• Maintainability. A query using the wrong construct is harder for future readers (or AI copilots like Galaxy’s) to understand and modify safely.

The mechanics of DISTINCT

The DISTINCT keyword applies to the result set after projection. Conceptually the engine performs these steps:

1. Evaluate FROM and WHERE to build the row set.
2. Evaluate the SELECT list.
3. Apply DISTINCT as a duplicate filter across the selected columns.

The output contains no aggregates unless you explicitly add one in the SELECT list, and the aggregate is applied to the entire deduplicated set, not per row.

Example: Get a list of unique customer countries

SELECT DISTINCT country
FROM customers;


Because no aggregate is involved, DISTINCT is ideal. The database scans or indexes on country, sorts/hash-deduplicates, and stops.

The mechanics of GROUP BY

GROUP BY adds an extra step:

1. Engine evaluates FROM and WHERE.
2. Rows are partitioned into groups based on the grouping columns.
3. Aggregate functions (if any) are calculated per group.
4. Engine returns one row per group containing the grouping columns plus aggregate results.

If no aggregate functions appear, most databases still honor the grouping: you get one row per group just like with DISTINCT. But the intent—and thus the optimizer’s choices—differ.

Example: Get each customer country and number of customers

SELECT country, COUNT(*) AS customer_count
FROM customers
GROUP BY country;


Without GROUP BY you cannot compute COUNT per country in portable SQL.

When DISTINCT and GROUP BY yield the same result

You can sometimes replace one with the other:

-- Using DISTINCT
SELECT DISTINCT country, city
FROM customers;

-- Using GROUP BY (no aggregates)
SELECT country, city
FROM customers
GROUP BY country, city;


Both return unique (country, city) pairs. Internally the optimizer may even rewrite one form into the other. But there are subtle differences:

• Many RDBMSs prohibit mixing non-grouped columns with aggregates unless they appear in the GROUP BY. With DISTINCT you can project additional columns only if they are functionally dependent on the distinct list (standard SQL forbids this but some engines allow it).
• Window functions behave differently; they execute after GROUP BY but before DISTINCT.

Performance considerations

How engines implement DISTINCT

Databases usually perform a hash aggregate or sort-deduplicate on the selected columns. Memory consumption scales with the cardinality (number of unique rows).

How engines implement GROUP BY

Similar physical operators—hash or sort aggregate—are used, but the engine must also compute aggregate functions. Some databases support streaming aggregation (no intermediate hash table) if input is sorted on grouping keys.

Rule of thumb

For pure deduplication, DISTINCT may be slightly faster because the planner can skip computing aggregates and sometimes use index-only scans. But if you need aggregates, GROUP BY is generally faster and clearer.

Best practices

• Write for intent. Use DISTINCT for deduplication, GROUP BY for aggregation.
• Explicitly list grouping columns. Avoid relying on engine-specific extensions that let you omit columns.
• Leverage indexes. Index grouping/deduplication columns to enable index-only scans.
• Watch functional dependencies. In PostgreSQL and others, you can GROUP BY a key column and select columns functionally dependent on it; verify constraints so results remain deterministic.
• Profile large queries. Examine actual execution plans; sometimes rewriting DISTINCT to GROUP BY (or vice versa) yields big wins.

Common misconceptions cleared

Misconception 1: DISTINCT is just shorthand for GROUP BY

While results can match, DISTINCT operates on the projection, not the raw rows, and lacks built-in aggregation semantics.

Misconception 2: GROUP BY is slower

Slowness usually stems from missing indexes or mis-written queries. When aggregates are required, GROUP BY is often more efficient.

Misconception 3: You can’t mix DISTINCT with aggregates

You can, but the logic is different. COUNT(DISTINCT col) deduplicates a single column before counting, unlike GROUP BY col which counts each row per value of col.

Practical real-world example

Suppose you manage a SaaS application with a page_views table:

CREATE TABLE page_views (
user_id bigint,
session_id uuid,
page_url text,
viewed_at timestamp
);


Business question 1: How many sessions happened yesterday?

SELECT COUNT(DISTINCT session_id)
FROM page_views
WHERE viewed_at >= CURRENT_DATE - INTERVAL '1 day'
AND viewed_at < CURRENT_DATE;


DISTINCT is perfect: we only need the number of unique session_id values.

Business question 2: For each user, how many page views did they have yesterday?

SELECT user_id, COUNT(*) AS views
FROM page_views
WHERE viewed_at >= CURRENT_DATE - INTERVAL '1 day'
AND viewed_at < CURRENT_DATE
GROUP BY user_id;


Now each group is a user.

Using Galaxy to work with GROUP BY and DISTINCT

Because Galaxy is a developer-oriented SQL editor with AI assistance, it can:

• Suggest converting DISTINCT queries into GROUP BY when you start adding aggregates.
• Highlight non-grouped columns in the SELECT list to prevent runtime errors.
• Generate descriptive query names like “unique_customer_countries” or “page_views_per_user”.
• Offer instant execution plans so you can compare DISTINCT vs GROUP BY performance side by side.

Conclusion

DISTINCT filters duplicates; GROUP BY forms groups for aggregation. Understanding the difference lets you write correct, performant SQL and leverage modern tools like Galaxy’s copilot effectively.