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A SQL join combines rows from related tables using a matching condition. Choose INNER JOIN when you want only matching pairs; use an outer join when rows without a match must remain. The join condition and filter placement determine which records survive.
How SQL joins combine tables
A join compares rows from two inputs using a condition, usually a relationship between key columns. For example, a customer table may store each customer once, while an orders table stores that customer’s orders. Joining on the customer identifier lets a query return customer and order details together.
A join returns a row for each pair that satisfies its condition. If a key appears more than once on either side, it can produce multiple result rows; joins do not automatically deduplicate records. PostgreSQL 17 documents these join semantics and syntax in its table expressions reference.
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| Join type | Rows retained | Example question |
|---|---|---|
INNER JOIN |
Only row pairs that match the condition. | Which orders have a matching customer? |
LEFT JOIN |
Every row from the left input, plus matching right-side rows. Right-side columns are NULL when there is no match. |
Which customers have orders, and which have none? |
RIGHT JOIN |
Every row from the right input, plus matching left-side rows. Left-side columns are NULL when there is no match. |
Which entries in a right-side reference list lack a left-side match? |
FULL OUTER JOIN |
Matching pairs and unmatched rows from both inputs. | Which records appear in either source, including unmatched records? |
CROSS JOIN |
Every possible pair of rows from the two inputs. | What are all combinations of products and sizes? |
In PostgreSQL, INNER is the default join type, and OUTER is optional in LEFT OUTER JOIN, RIGHT OUTER JOIN, and FULL OUTER JOIN. A cross join of inputs with N and M rows produces N × M rows, so its result can grow quickly.
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How to join two tables with an example
This query returns every customer and any orders associated with them:
SELECT c.customer_id, c.customer_name, o.order_id
FROM customers AS c
LEFT JOIN orders AS o
ON o.customer_id = c.customer_id;The ON condition matches an order’s customer key to the customer identifier. Because the query uses LEFT JOIN, a customer remains in the result even if no order matches; the selected order field is then NULL. Replacing it with INNER JOIN would exclude customers without matching orders.
Aliases such as c and o make the query easier to scan. Qualifying selected columns with their table alias also avoids ambiguity when both tables have a column with the same name. Microsoft Learn recommends specifying join conditions in the FROM clause and using aliases and qualified names for clarity in its SQL Server joins guidance.
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Where to put conditions: ON versus WHERE
ON determines which right-side rows count as matches. WHERE filters the result after the join. With an outer join, that distinction can determine whether unmatched left-side rows survive.
Keep left-side rows while matching only qualifying right-side rows
SELECT *
FROM t1
LEFT JOIN t2
ON t1.num = t2.num
AND t2.value = 'xxx';This retains each t1 row. A t2 row is attached only when both conditions match; otherwise the t2 columns are NULL.
Filter the joined result afterward
SELECT *
FROM t1
LEFT JOIN t2
ON t1.num = t2.num
WHERE t2.value = 'xxx';The WHERE condition rejects rows where t2.value is NULL, including the NULL-extended rows created for unmatched t1 records. As a result, this query does not retain those unmatched rows. PostgreSQL explains the distinction between join conditions and later filters in its table expressions documentation.
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Find rows with no match
To find left-side records without a corresponding right-side record, use a left join and test a right-side key that cannot be NULL for a real match:
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FROM t1
LEFT JOIN t2
ON t2.num = t1.num
WHERE t2.num IS NULL;This is often called an anti-match pattern. Choose a right-side column that is guaranteed non-NULL for matching records; testing a nullable column could misclassify a matched row as unmatched.
Choose the join condition deliberately
Use ON for explicit relationships
ON is the general way to state the relationship between tables, and it is the clearest choice when teaching or reviewing a query:
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ON orders.customer_id = customers.customer_idUse USING when the shared key has the same name
USING (customer_id) is a concise alternative when both inputs have a join column with the same name. PostgreSQL documents it as shorthand for equality comparisons on the listed columns. Check your database’s syntax and output-column behavior before using it in portable queries.
Avoid relying on NATURAL JOIN in changing schemas
NATURAL JOIN infers its condition from every column name shared by the two inputs. If a schema change adds another same-named column, the join can start matching on that column too, without the query text changing. Explicit ON conditions make the intended relationship visible.
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Check keys and row counts before deduplicating
If a join returns more rows than expected, first check whether the join key is unique on each side and what relationship the data actually represents:
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- One-to-one: each key occurs at most once in both inputs, so a match produces at most one pair.
- One-to-many: a key occurs once on one side and multiple times on the other, producing one result row for each matching row on the many side.
- Many-to-many: a key occurs multiple times on both sides, producing every matching combination.
Compare row counts before and after the join and inspect repeated keys. Do not apply DISTINCT simply to hide unexpected multiplication: it may conceal a wrong condition or discard legitimate combinations.
Check syntax for your database
Join support and syntax are not identical across SQL dialects. PostgreSQL 17 documents inner, left, right, full, and cross joins, along with ON and USING. The MySQL 8.4 JOIN Clause reference lists inner/cross, left, and right forms but does not list FULL OUTER JOIN. Do not assume that syntax will work in MySQL; consult the manual for the specific engine and version you use.
Microsoft documents inner, left, right, full outer, and cross logical joins for SQL Server in its joins reference. That documentation distinguishes the logical join expressed in SQL from the physical operation used to execute it.
Understand performance through the execution plan
The join keyword describes the result you want; it does not specify a single execution algorithm. Microsoft Learn states: “SQL Server implements both logical join operations (defined by Transact-SQL syntax) and physical join operations (the actual algorithms used to execute the joins).” Its SQL Server documentation describes nested loops, merge, and hash joins, as well as adaptive joins for SQL Server 2017 and later. The optimizer selects an approach using factors such as table size, available indexes, and data distribution.
There is no reliable rule that LEFT JOIN is inherently slower than INNER JOIN, or that one physical algorithm is always best. Review the execution plan for the database and version you run, and test with representative data. Row counts, indexes, data distribution, and engine version all affect the result.
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