Thus far, our queries have only accessed one table at a time. Queries can access multiple tables at once, or access the same table in such a way that multiple rows of the table are being processed at the same time. A query that accesses multiple rows of the same or different tables at one time is called a join query. As an example, say you wish to list all the weather records together with the location of the associated city. To do that, we need to compare the city column of each row of the weather table with the name column of all rows in the cities table, and select the pairs of rows where these values match.
Note: This is only a conceptual model. The join is usually performed in a more efficient manner than actually comparing each possible pair of rows, but this is invisible to the user.
This would be accomplished by the following query:
SELECT * FROM weather, cities WHERE city = name;
city | temp_lo | temp_hi | prcp | date | name | location ---------------+---------+---------+------+------------+---------------+----------- San Francisco | 46 | 50 | 0.25 | 1994-11-27 | San Francisco | (-194,53) San Francisco | 43 | 57 | 0 | 1994-11-29 | San Francisco | (-194,53) (2 rows)
Observe two things about the result set:
There is no result row for the city of Hayward. This is
because there is no matching entry in the
cities table for Hayward, so the join
ignores the unmatched rows in the weather table. We will see
shortly how this can be fixed.
There are two columns containing the city name. This is
correct because the lists of columns of the
weather and the
cities table are concatenated. In practice
this is undesirable, though, so you will probably want to
list the output columns explicitly rather than using
SELECT city, temp_lo, temp_hi, prcp, date, location FROM weather, cities WHERE city = name;
Exercise: Attempt to find out the semantics of this query when the WHERE clause is omitted.
Since the columns all had different names, the parser automatically found out which table they belong to. If there were duplicate column names in the two tables you'd need to qualify the column names to show which one you meant, as in:
SELECT weather.city, weather.temp_lo, weather.temp_hi, weather.prcp, weather.date, cities.location FROM weather, cities WHERE cities.name = weather.city;
It is widely considered good style to qualify all column names in a join query, so that the query won't fail if a duplicate column name is later added to one of the tables.
Join queries of the kind seen thus far can also be written in this alternative form:
SELECT * FROM weather INNER JOIN cities ON (weather.city = cities.name);
This syntax is not as commonly used as the one above, but we show it here to help you understand the following topics.
Now we will figure out how
we can get the Hayward records back in. What we want the query to
do is to scan the
and for each row to find the matching
cities row(s). If no matching row is found we
want some "empty values" to be
substituted for the
columns. This kind of query is called an outer join. (The joins we have seen so far are
inner joins.) The command looks like this:
SELECT * FROM weather LEFT OUTER JOIN cities ON (weather.city = cities.name); city | temp_lo | temp_hi | prcp | date | name | location ---------------+---------+---------+------+------------+---------------+----------- Hayward | 37 | 54 | | 1994-11-29 | | San Francisco | 46 | 50 | 0.25 | 1994-11-27 | San Francisco | (-194,53) San Francisco | 43 | 57 | 0 | 1994-11-29 | San Francisco | (-194,53) (3 rows)
This query is called a left outer join because the table mentioned on the left of the join operator will have each of its rows in the output at least once, whereas the table on the right will only have those rows output that match some row of the left table. When outputting a left-table row for which there is no right-table match, empty (null) values are substituted for the right-table columns.
Exercise: There are also right outer joins and full outer joins. Try to find out what those do.
We can also join a table against itself. This is
called a self join. As an example,
suppose we wish to find all the weather records that are in the
temperature range of other weather records. So we need to compare
the temp_lo and temp_hi columns of each
weather row to the temp_lo and temp_hi columns of all other
weather rows. We can do this with the
SELECT W1.city, W1.temp_lo AS low, W1.temp_hi AS high, W2.city, W2.temp_lo AS low, W2.temp_hi AS high FROM weather W1, weather W2 WHERE W1.temp_lo < W2.temp_lo AND W1.temp_hi > W2.temp_hi; city | low | high | city | low | high ---------------+-----+------+---------------+-----+------ San Francisco | 43 | 57 | San Francisco | 46 | 50 Hayward | 37 | 54 | San Francisco | 46 | 50 (2 rows)
Here we have relabeled the weather table as W1 and W2 to be able to distinguish the left and right side of the join. You can also use these kinds of aliases in other queries to save some typing, e.g.:
SELECT * FROM weather w, cities c WHERE w.city = c.name;
You will encounter this style of abbreviating quite frequently.
If you see anything in the documentation that is not correct, does not match your experience with the particular feature or requires further clarification, please use this form to report a documentation issue.