TrickySQL#477 min readJul 11, 2026
NATURAL JOIN
practice
Practice modeTest yourself instead of reading straight through
Interviewers love NATURAL JOIN because it looks friendly, but it can quietly change behavior when a schema changes.
Question: What is NATURAL JOIN in SQL?
Answer: NATURAL JOIN is a shortcut join that automatically matches columns with the same name in both tables. It joins rows where all those same-named columns are equal, and it shows only one copy of each matched column in the result. It is convenient, but it is risky because adding a new column with the same name can silently change the query.
Interview-Ready Answer: I would say that NATURAL JOIN is shorthand for joining tables on every column name they share. It removes the duplicate join columns from the output, so the result looks clean. The big warning is that it is schema-sensitive: if someone later adds another same-named column, the join condition changes without the SQL text changing, which is why I usually prefer JOIN ... ON or JOIN ... USING in production.
%22%20stroke%3D%22%234f46e5%22%20stroke-width%3D%221.5%22%2F%3E%3Ctext%20x%3D%22500%22%20y%3D%22308%22%20text-anchor%3D%22middle%22%20font-size%3D%2215%22%20font-weight%3D%22bold%22%20fill%3D%22%23ffffff%22%20font-family%3D%22Segoe%20UI%2C%20Arial%2C%20sans-serif%22%3ENatural%20Join%3C%2Ftext%3E%3C%2Fsvg%3E)
What it is
NATURAL JOIN is a form of equi-join, which means a join that matches rows using equality. The database looks at both tables, finds every column name they share, and uses all of those columns as join keys. It is basically a shortcut for writing the matching conditions yourself.
How it works under the hood
- The database inspects the two table schemas and collects the column names that appear in both tables.
- It builds an equality condition for each shared column, such as
t1.id = t2.idandt1.status = t2.status. - It evaluates those conditions just like a normal inner join, left join, right join, or full join depending on the join type you wrote.
- For the result set, it keeps one copy of each matched column instead of showing both copies side by side.
- If there are no shared column names, the join condition is effectively empty, so the result behaves like a
CROSS JOINand returns all combinations.
Memory check: same-name columns decide the match, not the business meaning of the data. That is why a harmless-looking new column can become a production bug.
When to use it
NATURAL JOIN is fine for quick ad hoc analysis, classroom examples, or throwaway queries where you fully control the schema. It is usually avoided in production code because it hides the join keys inside the syntax. In a real app, explicit joins are safer because future readers can see exactly which columns matter.
Comparison with other join styles
| Style | How keys are chosen | Risk | Best use |
|---|---|---|---|
| NATURAL JOIN | All shared names | Schema drift | Quick ad hoc queries |
| JOIN USING | Listed columns | Low | Clean, explicit equality joins |
| JOIN ON | Fully explicit | Lowest | Production SQL, complex logic |
Performance and edge cases
Performance is usually the same as the equivalent explicit join, because the optimizer rewrites NATURAL JOIN into normal join predicates. There is no magical speed boost. With indexes on the join columns, many engines can use an index nested-loop or another efficient plan; without indexes, a hash join often behaves like scanning both sides once, roughly O(n + m), while a sort-merge plan is often closer to O(n log n + m log m). The exact plan depends on the database engine and table sizes.
- NULLs: NULL values do not match with
=, so rows with NULL in a shared key usually do not join. - No shared names: the query can become a cartesian product, which can explode row counts.
- Accidental extra matches: a new same-named column can turn one join key into two or three keys overnight.
- Output shape: duplicate key columns are hidden, which can surprise you if you expected both originals.
- Dialect support: the concept is standard SQL, but support for every outer-join flavor varies by engine, so test on your database.
The practical rule is simple: if you want readability and safety, write the keys yourself. Use NATURAL JOIN only when schema control is strong and the risk of future column collisions is very low.
Real-World Story
Scenario: An e-commerce checkout service joins orders to customers to build an admin report of who bought what. A developer uses NATURAL JOIN because both tables share customer_id and the query looks neat.
Later, the customers table gets a new status column, and the orders table already has a status column for payment state. The join now matches on both customer_id and status, so many rows disappear because a payment status like PAID does not equal a customer status like ACTIVE.
What goes wrong: the report suddenly shows fewer orders, support asks why revenue looks lower, and the query itself does not fail, so the bug is silent. Logs may show normal execution time, but the business dashboard is wrong. That is the scary part: NATURAL JOIN can produce a valid-looking result that is semantically incorrect.
Memory Hook: think of NATURAL JOIN as a magnet that grabs every same-named badge in the room; add one new badge name, and the rules of who gets matched change instantly.
SQL
-- Demonstration of NATURAL JOIN and its main gotcha:
-- it joins on every column name shared by both tables.
DROP TABLE IF EXISTS bad_departments;
DROP TABLE IF EXISTS departments;
DROP TABLE IF EXISTS employees;
CREATE TABLE employees (
employee_id INTEGER PRIMARY KEY,
name VARCHAR(50) NOT NULL,
department_id INTEGER NOT NULL
);
CREATE TABLE departments (
department_id INTEGER PRIMARY KEY,
department_name VARCHAR(50) NOT NULL
);
INSERT INTO employees (employee_id, name, department_id) VALUES
(1, 'Ana', 10),
(2, 'Ben', 20),
(3, 'Cal', 30); -- no matching department, useful edge case
INSERT INTO departments (department_id, department_name) VALUES
(10, 'Sales'),
(20, 'Engineering');
-- Good case: only shared column name is department_id,
-- so NATURAL JOIN behaves like a normal join on that key.
SELECT employee_id, name, department_name
FROM employees
NATURAL JOIN departments
ORDER BY employee_id;
-- Failure path: this table shares BOTH department_id and name with employees.
-- NATURAL JOIN now requires employees.name = bad_departments.name too,
-- which is not what we want, so the result becomes empty.
CREATE TABLE bad_departments (
department_id INTEGER PRIMARY KEY,
name VARCHAR(50) NOT NULL,
budget INTEGER NOT NULL
);
INSERT INTO bad_departments (department_id, name, budget) VALUES
(10, 'Sales', 100000),
(20, 'Engineering', 250000);
SELECT *
FROM employees
NATURAL JOIN bad_departments;
-- Safe fix: spell out the intended join key explicitly.
-- This returns rows even though the tables share another same-named column.
SELECT e.employee_id, e.name, b.budget
FROM employees e
JOIN bad_departments b
ON e.department_id = b.department_id
ORDER BY e.employee_id;Follow-up & Tricky Questions:
- What columns does NATURAL JOIN use? It uses every column name that appears in both tables, not just one key column. That is why it can be convenient but also dangerous when schemas change.
- How is NATURAL JOIN different from JOIN USING?
USINGmakes you name the join columns explicitly, so it is still concise but much safer.NATURAL JOINchooses the columns for you, which is the risky part. - What happens to NULL values? NULLs do not match with normal equality, so rows with NULL in a shared join column usually do not join. An outer natural join can still preserve unmatched rows, but the join condition itself still does not treat NULL as equal.
- What if the tables share no column names? Then the join has no join predicate, so it behaves like a cartesian product. That can explode the row count very quickly.
- Why do many teams avoid NATURAL JOIN? Because it is brittle under schema drift: a new same-named column can silently change query results. Explicit joins are easier to review, safer to maintain, and clearer in code review.
- Tricky: Does NATURAL JOIN match columns with the same spelling but different meaning? Yes. The database cannot know business meaning, so it joins purely by column name and equality. That is the core reason it can break in subtle ways.
- Tricky: Does NATURAL JOIN keep both copies of the join column? No, it returns one copy of each shared join column. Many candidates miss this because they expect the result to look like a regular
JOIN ONselect * output. - Tricky: Is NATURAL JOIN faster than explicit JOIN? No special speed advantage. Most optimizers rewrite it into the equivalent explicit join, so performance depends on indexes, table size, and the chosen plan.
Common Mistakes:
- Assuming it joins on one obvious key only. Correction: it joins on all same-named columns.
- Using it in production blindly. Correction: prefer
JOIN ... ONorJOIN ... USINGso the intent is explicit. - Forgetting schema drift. Correction: a later column addition can change the result without changing the query text.
- Expecting NULLs to match. Correction: normal equality does not match NULL to NULL.
Memory Hook: NATURAL JOIN is a blind date for same-named columns: if two badges match, they get paired, and if a new badge appears, the matchmaking rules change.
Cheat Sheet:
- Joins on all shared column names.
- Removes duplicate join columns from output.
- Equivalent to an explicit equality join after rewrite.
- Dangerous when schemas change or names collide.
- Use it for quick analysis, not usually for production.
- Prefer
JOIN ... ONfor clarity and safety.
Practice Tasks:
- Write a
NATURAL JOINbetween two tables that share exactly one key column. - Add a new same-named column to one table and observe how the result changes.
- Rewrite the same query using
JOIN ... ONand compare the output columns.