Full Outer Join without On Condition

As a data enthusiast, one often finds themselves working with datasets from various sources, and SQL is an excellent tool to handle such data. In this article, we will explore the concept of full outer join in SQL, focusing on how to achieve this operation without relying on the on condition.

Introduction to Full Outer Join

A full outer join is a type of join that returns all records from both tables, including those with null values in the joined columns. This is useful when you want to analyze data from two different sources and combine them into a single dataset.

Imagine having two datasets: T1 containing customer information and T2 containing order details. You can perform a full outer join on these two tables to get the combined dataset, including customers who have not placed any orders (with null values in the order columns).

Understanding the Problem

Let’s dive into the provided example. We have two SQL tables: T1 and T2. The structure of these tables is as follows:

T1
customer_key X1 X2 X3
1000         60 10 2018-02-01
1001         42 9   2018-02-01
1002         03 1   2018-02-01
1005         15 1   2018-02-01
1002         32 2   2018-02-05

T2
customer_key A1 A2 A3
1001         20 2  2018-02-17
1002         25 2  2018-02-11
1005         04 1  2018-02-17
1009         02 0  2018-02-17

We want to perform a full outer join on these two tables, filtering the results based on specific conditions. We aim to get the resulting table T3 containing customer information and order details for customers who have placed orders on the specified dates.

The provided SQL code attempts to achieve this:

create table T3
AS
select T1.customer_key, T3.customer_key, T1.X1, T1.X2
from T1
full outer join T2
on T1.Customer_key = T2.customer_key 
where T1.X3 = '2018-02-01' and T2.A3 = '2018-02-17'

However, the resulting table T3 has fewer rows than expected. This discrepancy highlights a key aspect of full outer join: when filtering on specific conditions.

The Issue with Filtering in Full Outer Join

When you use the full outer join clause without specifying any conditions, it returns all records from both tables. However, when applying filters to this result, the SQL engine treats the entire join operation as an inner join by default. This means that only rows satisfying both the filter condition and the join condition are returned.

In our example, the where clause is applied after the full outer join, which effectively turns it into an inner join on the filtered rows. As a result, we get fewer rows than expected in the resulting table T3.

Solving the Issue: Filtering Before Join

To address this issue, we can filter the data before performing the full outer join. One approach is to create subqueries for each table and apply the filters before joining them.

Here’s how you could rewrite the SQL code using this technique:

select T1.customer_key, T3.customer_key, T1.X1, T1.X2
from (select t1.*
      from T1
      where T1.X3 = '2018-02-01'
     ) t1 full outer join
     (select t2.*
      from T2
      where T2.A3 = '2018-02-17'
     ) t2
     on T1.Customer_key = T2.customer_key ;

In this revised code, we first create two subqueries: one for T1 and one for T2. We apply the filter conditions to each subquery separately. The outer join is then performed using these filtered subqueries.

By filtering before joining, we ensure that all rows satisfying both the filter conditions are included in the final result.

Additional Considerations

When dealing with full outer joins, it’s essential to understand how the filter clause affects the result.

• Moving the filter conditions to the on clause does not guarantee that all rows from both tables will be returned.
• Using or operators in the filter condition can lead to unexpected results if not applied carefully.

To avoid these issues, it’s crucial to apply filters before joining the data. This ensures that only relevant rows are included in the final result.

Conclusion

Full outer joins provide an excellent way to combine datasets from multiple sources, but filtering on specific conditions requires careful consideration. By understanding how filter clauses affect full outer joins and applying them before joining, you can ensure accurate results.

When working with complex data join operations, it’s essential to consider all factors involved. This includes not only the technical aspects of the SQL query but also how to effectively analyze and manipulate data from different sources.

Last modified on 2024-04-22