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Don t be confused by the JOIN clause. The directional specifications of LEFT, RIGHT, and FULL only apply to OUTER JOIN statements. INNER and CROSS JOIN statements have the same set of results no matter what order you list your tables in.
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This may be more easily understood by example, so Tables 11.3 and 11.4 introduce a database of baseball players with high numbers of career home runs. To keep this simple, this database only allows one team for each player. There are two tables in this database: one for the players and one for the teams.
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TABLE 11.3
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Example Database of Top Home Run Careers Players
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playerId name homeruns hand teamId
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Barry Bonds Hank Aaron Babe Ruth Willie Mays Sammy Sosa Ken Griffey Jr.
762 755 714 660 609 593
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TABLE 11.4
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Example Database of Top Home run Careers Teams
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Braves Reds Yankees Giants
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A NATURAL JOIN matches rows based on column names they have in common. It can be risky to use these, because adding columns to tables could cause unintended results. For example, here you can t really do a NATURAL JOIN, because it would try to join on both the teamId column and the name column. That points to another problem, because the columns called name might be clear within their own tables, but they are ambiguous within the context of the database. This data would be easier to work with if there were more specific column names such as playerName and teamName. If you do this and keep teamId the same (because it refers to the same data set in both tables), then a NATURAL JOIN works as expected. Instead of using the NATURAL JOIN, you could use the ON clause to specify how to line up columns; for example:
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This would yield the same result as if you used the USING clause instead of ON:
JOIN USING teams (teamId)
If you were to set these tables up and run this operation, you would notice that the list of results was missing Sammy Sosa. This is because Chicago Cubs (CHC) does not appear in the table of teams. The way that unmatched columns are treated is determined by whether you choose to use an INNER JOIN, a CROSS JOIN, or an OUTER JOIN. If you do not specify a direction for the JOIN operation, it defaults to a CROSS JOIN. However, if you specify an ON or USING clause to restrict the results, a CROSS JOIN behaves like an INNER JOIN. As a result, an INNER JOIN is what you really see with the previous statements. There is no team listed for the teamId CHC, so any row from the players table with that teamId is ignored. The same also works in reverse any team without a matching player is ignored. In this case, you get five rows in your result, one for each player except for Sammy Sosa.
Part III
A CROSS JOIN, on the other hand, returns the cross product of the two tables. You can see the results of a CROSS JOIN by removing the ON clause or the USING clause from the previous statements. The resulting table has 24 rows in it, one for every possible combination of player and team. This makes little sense for this set of data, and most references warn you to avoid this type of JOIN because of its potentially astronomical number of resulting rows. It can be useful however, so you shouldn t dismiss it entirely. Suppose, for example, you had a table of programmers and a table of programming languages, and you wanted to construct a chart that showed which programmers knew which languages. Another JOIN type might omit a programmer that didn t know any of the languages you have listed, or omit a language that none of your programmers know, but a CROSS JOIN gives you everything. The last type of JOIN is the OUTER JOIN. An OUTER JOIN can usually be one of three types: LEFT, RIGHT, or FULL. Currently, AIR only supports LEFT OUTER JOINS, but the others will most likely be supported before long. A LEFT OUTER JOIN includes all the rows from the first table you list and gives null values for rows that don t have any matching values in the other table. If you run the same operation to join players with teams using a LEFT OUTER JOIN, you get all the players listed but a null value in the team name column for Sammy Sosa. If you choose to use a RIGHT OUTER JOIN instead, you see that Sammy Sosa is omitted again he has no representation in the right-most table (teams). You do, though, get two instances with Giants as the team, because there are two players who played for San Francisco in the table. In other words, you can interpret a LEFT OUTER JOIN as make at least one row for every row in the first table mentioned, but if there are multiple matches in the second table, then make one row for each match. The RIGHT OUTER JOIN is the same, except that it reverses the role of the tables, so you will get one or more rows for every row in the second table listed. A FULL OUTER JOIN does both, so you will get at least one row for every row in both tables.