Here we have relabeled the weather table as W1 and W2 to be able to distinguish the left and right side of the join. Hayward | 37 | 54 | San Francisco | 46 | 50 San Francisco | 43 | 57 | San Francisco | 46 | 50 W2.city, W2.temp_lo AS low, W2.temp_hi AS high SELECT W1.city, W1.temp_lo AS low, W1.temp_hi AS high, 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. As an example, suppose we wish to find all the weather records that are in the temperature range of other weather records. When outputting a left-table row for which there is no right-table match, empty (null) values are substituted for the right-table columns.Įxercise: There are also right outer joins and full outer joins. 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. (The joins we have seen so far are inner joins.) The command looks like this:įROM weather LEFT OUTER JOIN cities ON (weather.city = cities.name) Ĭity | temp_lo | temp_hi | prcp | date | name | location This kind of query is called an outer join. If no matching row is found we want some “ empty values” to be substituted for the cities table's columns. What we want the query to do is to scan the weather table and for each row to find the matching cities row(s). Now we will figure out how we can get the Hayward records back in. This syntax is not as commonly used as the one above, but we show it here to help you understand the following topics. Join queries of the kind seen thus far can also be written in this alternative form:įROM weather INNER 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. Weather.prcp, weather.date, cities.location SELECT weather.city, weather.temp_lo, weather.temp_hi, 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: Since the columns all had different names, the parser automatically found which table they belong to. SELECT city, temp_lo, temp_hi, prcp, date, locationĮxercise: Attempt to determine the semantics of this query when the WHERE clause is omitted. In practice this is undesirable, though, so you will probably want to list the output columns explicitly rather than using *: This is correct because the lists of columns from the weather and cities tables are concatenated. There are two columns containing the city name. We will see shortly how this can be fixed. 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. SELECTĮach INNER JOIN here may be significantly faster than the whole of the LEFT JOIN based query, due to potentially being able to use indexes on followings(user_id, followable_type) and favorites(user_id, favorable_type).There is no result row for the city of Hayward. If you, however, only return a 'small' fraction of the table, you may want to optimise this, such as using two queries with a UNION, as implied by your question title. If that table is small, or you normally return a 'large' fraction of the table any way, this may be fine. This has the downside that every record in the product table has to be checked by the WHERE clause. ON rchant_id = followings.followable_idĪND followings.followable_type = 'Merchant' One 'simple' approach (just modifying your existing query) could be to turn both of your INNER JOINs into LEFT JOINs and check that at least one of the two joined successfully in the WHERE clause.
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