Assuming you're joining on columns with no duplicates, which is a very common case:
An inner join of A and B gives the result of A intersect B, i.e. the inner part of a Venn diagram intersection.
An outer join of A and B gives the results of A union B, i.e. the outer parts of a Venn diagram union.
Examples
Suppose you have two tables, with a single column each, and data as follows:
A B
- -
1 3
2 4
3 5
4 6
Note that (1,2) are unique to A, (3,4) are common, and (5,6) are unique to B.
Inner join
An inner join using either of the equivalent queries gives the intersection of the two tables, i.e. the two rows they have in common.
select * from a INNER JOIN b on a.a = b.b;
select a.*, b.* from a,b where a.a = b.b;
a | b
--+--
3 | 3
4 | 4
Left outer join
A left outer join will give all rows in A, plus any common rows in B.
select * from a LEFT OUTER JOIN b on a.a = b.b;
select a.*, b.* from a,b where a.a = b.b(+);
a | b
--+-----
1 | null
2 | null
3 | 3
4 | 4
Right outer join
A right outer join will give all rows in B, plus any common rows in A.
select * from a RIGHT OUTER JOIN b on a.a = b.b;
select a.*, b.* from a,b where a.a(+) = b.b;
a | b
-----+----
3 | 3
4 | 4
null | 5
null | 6
Full outer join
A full outer join will give you the union of A and B, i.e. all the rows in A and all the rows in B. If something in A doesn't have a corresponding datum in B, then the B portion is null, and vice versa.
select * from a FULL OUTER JOIN b on a.a = b.b;
a | b
-----+-----
1 | null
2 | null
3 | 3
4 | 4
null | 6
null | 5
My favorite answer is as what the first sentence in this thread suggested. Use an Adjacency List to maintain the hierarchy and use Nested Sets to query the hierarchy.
The problem up until now has been that the coversion method from an Adjacecy List to Nested Sets has been frightfully slow because most people use the extreme RBAR method known as a "Push Stack" to do the conversion and has been considered to be way to expensive to reach the Nirvana of the simplicity of maintenance by the Adjacency List and the awesome performance of Nested Sets. As a result, most people end up having to settle for one or the other especially if there are more than, say, a lousy 100,000 nodes or so. Using the push stack method can take a whole day to do the conversion on what MLM'ers would consider to be a small million node hierarchy.
I thought I'd give Celko a bit of competition by coming up with a method to convert an Adjacency List to Nested sets at speeds that just seem impossible. Here's the performance of the push stack method on my i5 laptop.
Duration for 1,000 Nodes = 00:00:00:870
Duration for 10,000 Nodes = 00:01:01:783 (70 times slower instead of just 10)
Duration for 100,000 Nodes = 00:49:59:730 (3,446 times slower instead of just 100)
Duration for 1,000,000 Nodes = 'Didn't even try this'
And here's the duration for the new method (with the push stack method in parenthesis).
Duration for 1,000 Nodes = 00:00:00:053 (compared to 00:00:00:870)
Duration for 10,000 Nodes = 00:00:00:323 (compared to 00:01:01:783)
Duration for 100,000 Nodes = 00:00:03:867 (compared to 00:49:59:730)
Duration for 1,000,000 Nodes = 00:00:54:283 (compared to something like 2 days!!!)
Yes, that's correct. 1 million nodes converted in less than a minute and 100,000 nodes in under 4 seconds.
You can read about the new method and get a copy of the code at the following URL.
http://www.sqlservercentral.com/articles/Hierarchy/94040/
I also developed a "pre-aggregated" hierarchy using similar methods. MLM'ers and people making bills of materials will be particularly interested in this article.
http://www.sqlservercentral.com/articles/T-SQL/94570/
If you do stop by to take a look at either article, jump into the "Join the discussion" link and let me know what you think.
Best Answer
Using advanced wheres:
Or, even better, using
whereIn()
: