3.7. Join Queries
A join query is a querying technique that matches rows from two or more tables based on a join condition in a WHERE clause and outputs only those rows that meet the condition. As part of the process of converting the winestore entity-relationship model to SQL statements, we have included the attributes required in any practical join condition.
To understand which tables can be joined in the winestore database, and how the joins are processed, it is helpful to have a copy of the ER model at hand.
3.7.1. Beware of the Cartesian Product
SELECT winery_name,region_name FROM winery, region;
This query produces—in part—the following results:
+-------------------------------+-------------+ | winery_name | region_name | +-------------------------------+-------------+ | Ryan Ridge Winery | Victoria | | Macdonald Creek Premium Wines | Victoria | | Davie's | Victoria | | Porkenberger Brook Vineyard | Victoria | | Rowley Hill Vineyard | Victoria |
The impression here is that, for example, Ryan Ridge Winery is located in the Victoria region. This might not be the case. Why? First, you can use the techniques covered so far in this chapter to check which region Ryan Ridge Winery is located in:
SELECT region_id FROM winery WHERE winery_name='Ryan Ridge Winery';
The result is region_id=2.
Now query the region table to find the name of region_id=2 using:
SELECT region_name FROM region WHERE region_id=2;
The region_name is South Australia. So, Ryan Ridge Winery isn't in Victoria at all!
What happened in the first attempt at a join query? The technical answer is that you just evaluated a cartesian product; that is, you produced as output all the possible combinations of wineries and regions. These odd results can be seen if you add an ORDER BY clause to the original query:
SELECT winery_name, region_name FROM winery, region ORDER BY winery_name, region_name;
Recall that the ORDER BY clause sorts the results after the query has been evaluated; it has no effect on which rows are returned from the query. Here is the first part of the result of the query with the ORDER BY clause:
+----------------------+-------------------+ | winery_name | region_name | +----------------------+-------------------+ | Anderson Creek Wines | New South Wales | | Anderson Creek Wines | South Australia | | Anderson Creek Wines | Victoria | | Anderson Creek Wines | Western Australia | | Anderson Group | New South Wales | | Anderson Group | South Australia | | Anderson Group | Victoria | | Anderson Group | Western Australia |
The query produces all possible combinations of the four region names and 300 wineries in the sample database! In fact, the size of the output can be accurately calculated as the total number of rows in the first table multiplied by the total rows in the second table. In this case, the output is 4 x 300 = 1,200 rows.
3.7.2. Elementary Natural Joins
A cartesian product isn't the join we want. Instead, we want to limit the results to only the sensible rows, where the winery is actually located in the region. From a database perspective, we want only rows in which the region_id in the winery table matches the corresponding region_id in the region table. This is a natural join.
Consider a revised example using a natural join:
SELECT winery_name, region_name FROM winery, region WHERE winery.region_id = region.region_id ORDER BY winery_name;
An ORDER BY clause has been added to sort the results by winery_name but this doesn't affect the join. This query produces—in part—the following sensible results:
+----------------------+-------------------+ | winery_name | region_name | +----------------------+-------------------+ | Anderson Creek Wines | Western Australia | | Anderson Group | New South Wales | | Beard | South Australia | | Beard and Sons | Western Australia | | Beard Brook | New South Wales |
Several features are shown in this first successful natural join:
The natural join can be used in many other examples in the winestore. Consider another example that finds all the wines made by all the wineries:
SELECT winery_name, wine_name, type FROM winery, wine WHERE wine.winery_id = winery.winery_id;
This query finds all wines made by wineries through a natural join of the winery and wine tables using the winery_id attribute. The result is a large table of the 1,028 wines stocked at the winestore, their types, and the relevant wineries.
You can extend this query to produce a list of wines made by a specific winery or group of wineries. To find all wines made by wineries with a name beginning with Borg, use:
SELECT winery_name, wine_name, type FROM winery, wine WHERE wine.winery_id = winery.winery_id AND winery.winery_name LIKE 'Borg%';
This example extends the previous example by producing not all natural join pairs of wines and wineries, but only those for the winery or wineries beginning with Borg. The LIKE clause is covered later, in Section 3.9.
Here are two more example join queries:
22.214.171.124. Table aliases in SQL queries
SELECT * FROM inventory i, wine w WHERE i.wine_id = 183 AND i.wine_id = w.wine_id;
In this query, the FROM clause specifies aliases for the table names. The alias inventory i means than the inventory table can be referred to as i elsewhere in the query. For example, i.wine_id is the same as inventory.wine_id. This saves typing in this query.
Aliases are powerful for complex queries that need to use the same table twice but in different ways. For example, to find any two customers with the same surname, you can write the query:
SELECT c1.cust_id, c2.cust_id FROM customer c1, customer c2 WHERE c1.surname = c2.surname AND c1.cust_id != c2.cust_id;
The final clause, c1.cust_id!=c2.cust_id, is essential; without it, all customers are reported as answers. This occurs because all customers are rows in tables c1 and c2 and, for example, a customer with cust_id=1 in table c1 has—of course—the same surname as the customer with cust_id=1 in table c2.
126.96.36.199. Using DISTINCT in joins
The next join example uses the DISTINCT operator to find red wines that cost less than $10. Wines can have more than one inventory row, and the inventory rows for the same wine can have the same per-bottle cost. The DISTINCT operator shows each wine_name and cost pair once by removing any duplicates. To find which red wines cost less than $10, use:
SELECT DISTINCT wine_name, cost FROM wine,inventory WHERE wine.wine_id=inventory.wine_id AND inventory.cost<10 AND UPPER(wine.type)='RED';
Here are two examples that use DISTINCT to show only one matching answer:
3.7.3. Joins with More than Two Tables
Queries can join more than two tables. In the next example, the query finds all details of each item from each order by a particular customer, customer #2. The example also illustrates how frequently the Boolean operators AND and OR are used:
SELECT * FROM customer, orders, items WHERE customer.cust_id = orders.cust_id AND orders.order_id = items.order_id AND orders.cust_id = items.cust_id AND customer.cust_id = 2;
In this query, the natural join is between three tables, customer, orders, and items, and the rows selected are those in which the cust_id is the same for all three tables, the cust_id is 2, and the order_id is the same in the orders and items tables.
If you remove the cust_id=2 clause, the query outputs all items in all orders by all customers. This is a large result set, but still a sensible one that is much smaller than the cartesian product!
Here are two more examples that join three tables:
Extending to four or more tables generalizes the approach further. To find the details of customers who have purchased wines from Buonopane Wines, use:
SELECT DISTINCT customer.cust_id, customer.surname, customer.firstname FROM customer, winery, wine, items WHERE customer.cust_id=items.cust_id AND items.wine_id=wine.wine_id AND wine.winery_id=winery.winery_id AND winery.winery_name='Buonopane Wines' ORDER BY customer.surname, customer.firstname;
This last query is the most complex so far and contains a four-step process. The easiest way to understand a query is usually to start with the WHERE clause and work toward the SELECT clause:
Designing a query like this is a step-by-step process. We began by testing a query to find the winery_id of wineries with the name Buonopane Wines. Then, after testing the query and checking the result, we progressively added additional tables to the FROM clause and join conditions. Finally, we added the ORDER BY clause.
The next example uses three tables but queries the complex many-to-many relationship in the winestore that exists between the wines and grape_variety tables via the wine_variety table. As outlined in the system requirements in Chapter 1, a wine can have one or more grape varieties and these are listed in a specific order (e.g., Cabernet, then Sauvignon). From the other perspective, a grape variety such as Cabernet can be in hundreds of different wines. The relationship is managed by creating an intermediate table between grape_variety and wine called wine_variety.
Here is the example query that joins all three tables. To find what grape varieties are in wine #1004, use:
SELECT variety FROM grape_variety, wine_variety, wine WHERE wine.wine_id=wine_variety.wine_id AND wine_variety.variety_id=grape_variety.variety_id AND wine.wine_id=1004 ORDER BY wine_variety.id;
The result of the query is:
+-----------+ | variety | +-----------+ | Cabernet | | Sauvignon | +-----------+ 2 rows in set (0.00 sec)
The join condition is the same as any three-table query. The only significant difference is the ORDER BY clause that presents the results in the same order they were added to the wine_variety table (assuming the first variety gets ID=1, the second ID=2, and so on).
We've now covered as much complex querying in SQL as we need to in this chapter. If you'd like to learn more, see the pointers to resources included in Appendix E. SQL examples in web database applications can be found throughout Chapter 4 to Chapter 13.
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