What is an outer join?

An outer join combines two tables and returns all rows from one or both tables, matching rows when possible but also preserving non-matching rows. The outer table's rows are always returned at least once in the result.

The main variants are left outer join, right outer join, and full outer join, depending on which table's rows are returned at least once. Outer joins are useful for including non-matching related records.

For example, a left outer join returns all rows from the left table plus any matching rows from the right table. Unmatched left rows pad nulls for right columns.

Outer joins union additional rows rather than filtering like inner joins. This makes them useful for data analysis tasks.

Database optimizers like Apache Arrow DataFusion analyze outer join queries and pick optimal execution plans via query optimization. The query execution engine performs the optimized outer join.

What does it do/how does it work?

An outer join performs a join and additionally adds rows from one or both tables that do not satisfy the join condition. It adds columns from the other table and fills them with nulls for non-matches.

Which table's rows are preserved depends on whether a left, right, or full outer join is used. The join condition still filters related rows when matches occur.

Why is it important? Where is it used?

Outer joins are important for including rows from a table even without related matches in another table. This is useful for relationally complete results, e.g. customers without any orders.

Applications include aggregating data from multiple tables, obtaining complete hierarchical results, and generating summary reports. Outer joins are an essential building block for advanced SQL queries.

FAQ

What's the difference between left and right outer joins?

• Left outer join preserves rows from left table.
• Right outer join preserves rows from right table.

When would you use a full outer join?

When you need to preserve rows from both tables, e.g. for summary reports combining data from multiple tables.

What are some challenges with outer joins?

• Performance overhead of preserving additional rows.
• Handling null values from non-matching rows.
• Avoiding duplication of rows from both tables.
• Potentially large result size.

How can I optimize outer join performance?

• Use appropriate join algorithm like hash or merge join.
• Add indices on the join key columns.
• Filter data before joining to limit result size.
• Avoid unnecessary outer joins.

References:

• [Paper] Improved Unnesting Algorithms for Join Aggregate SQL Queries
• [Blog] OUTER JOIN or INNER JOIN?
• [Documentation] Apache Arrow DataFusion Outer Joins