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.  Queries that access multiple tables
    (or multiple instances of the same table) at one time are called
    join queries.  They combine rows from one table
    with rows from a second table, with an expression specifying which rows
    are to be paired.  For example, to return all the weather records together
    with the location of the associated city, the database needs 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.[4]
    This would be accomplished by the following query:
SELECT * FROM weather JOIN cities ON 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 from the
       weather and
       cities tables 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 JOIN cities ON city = name;
Since the columns all had different names, the parser automatically found 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 JOIN cities ON weather.city = cities.name;
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 form:
SELECT *
    FROM weather, cities
    WHERE city = name;
    This syntax pre-dates the JOIN/ON
    syntax, which was introduced in SQL-92.  The tables are simply listed in
    the FROM clause, and the comparison expression is added
    to the WHERE clause.  The results from this older
    implicit syntax and the newer explicit
    JOIN/ON syntax are identical.  But
    for a reader of the query, the explicit syntax makes its meaning easier to
    understand: The join condition is introduced by its own key word whereas
    previously the condition was mixed into the WHERE
    clause together with other conditions.
   
    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
    weather table 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 cities table's 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
    following query:
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 JOIN weather w2
        ON 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 JOIN cities c ON w.city = c.name;
You will encounter this style of abbreviating quite frequently.
[4] 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.