Suppose that you run an analysis consisting of a set of retrievals that each perform a complex SELECT of a set of records (perhaps using an expensive join), and that differ only in the way they summarize the records. That's inefficient because it unnecessarily does the work of selecting the records repeatedly. A better technique is to select the records once, and then use them to generate the summaries. In such a situation, consider the following strategy:

1. Select the set of to-be-summarized records into a temporary table. In MySQL, you can do this easily with a CREATE TEMPORARY TABLE ... SELECT statement.

2. Create any appropriate indexes on the temporary table.

3. Calculate the summaries using the temporary table.

The following example creates a summary table containing the average GNP value of countries in each continent. Then it compares the summary information to individual countries to find those countries with a GNP much less than the average and much more than the average.

First, create the summary table:

mysql> CREATE TABLE ContinentGNP
  -> SELECT Continent, AVG(GNP) AS AvgGNP
  -> FROM Country GROUP BY Continent;
mysql> SELECT * FROM ContinentGNP;
+---------------+---------------+
| Continent     | AvgGNP        |
+---------------+---------------+
| Asia          | 150105.725490 |
| Europe        | 206497.065217 |
| North America | 261854.789189 |
| Africa        | 10006.465517  |
| Oceania       | 14991.953571  |
| Antarctica    |   0.000000    |
| South America | 107991.000000 |
+---------------+---------------+

Next, compare the summary table to the original table to find countries that have a GNP less than 1% of the continental average:

mysql> SELECT
  ->   Country.Continent, Country.Name,
  ->   Country.GNP AS CountryGNP,
  ->   ContinentGNP.AvgGNP AS ContinentAvgGNP
  -> FROM Country, ContinentGNP
  -> WHERE
  ->   Country.Continent = ContinentGNP.Continent
  ->   AND Country.GNP < ContinentGNP.AvgGNP * .01
  -> ORDER BY Country.Continent, Country.Name;
+-----------+---------------------------+------------+-----------------+
| Continent | Name                      | CountryGNP | ContinentAvgGNP |
+-----------+---------------------------+------------+-----------------+
| Asia      | Bhutan                    |     372.00 |  150105.725490  |
| Asia      | East Timor                |       0.00 |  150105.725490  |
| Asia      | Laos                      |    1292.00 |  150105.725490  |
| Asia      | Maldives                  |     199.00 |  150105.725490  |
| Asia      | Mongolia                  |    1043.00 |  150105.725490  |
| Europe    | Andorra                   |    1630.00 |  206497.065217  |
| Europe    | Faroe Islands             |       0.00 |  206497.065217  |
| Europe    | Gibraltar                 |     258.00 |  206497.065217  |
| Europe    | Holy See (Vat. City State)|       9.00 |  206497.065217  |
| Europe    | Liechtenstein             |    1119.00 |  206497.065217  |
...

Use the summary table again to find countries that have a GNP more than 10 times the continental average:

mysql> SELECT
  ->   Country.Continent, Country.Name,
  ->   Country.GNP AS CountryGNP,
  ->   ContinentGNP.AvgGNP AS ContinentAvgGNP
  -> FROM Country, ContinentGNP
  -> WHERE
  ->   Country.Continent = ContinentGNP.Continent
  ->   AND Country.GNP > ContinentGNP.AvgGNP * 10
  -> ORDER BY Country.Continent, Country.Name;
+---------------+---------------+------------+-----------------+
| Continent     | Name          | CountryGNP | ContinentAvgGNP |
+---------------+---------------+------------+-----------------+
| Asia          | Japan         | 3787042.00 |  150105.725490  |
| Europe        | Germany       | 2133367.00 |  206497.065217  |
| North America | United States | 8510700.00 |  261854.789189  |
| Africa        | South Africa  |  116729.00 |   10006.465517  |
| Oceania    |   Australia      |  351182.00 |   14991.953571  |
+---------------+---------------+------------+-----------------+

The technique of using a summary table has several benefits:

• Calculating the summary information a single time reduces the overall computational burden by eliminating most of the repetition involved in performing the initial record selection.

• If the original table is a type that is subject to table-level locking, such as a MyISAM table, using a summary table leaves the original table available more of the time for updates by other clients by reducing the amount of time that the table remains locked.

• If the summary table is small enough that it's reasonable to hold in memory, you can increase performance even more by making it a HEAP table. Queries on the table will be especially fast because they require no disk I/O. When the HEAP table no longer is needed, drop it to free the memory allocated for it.

• Some queries are difficult or impossible to perform without using a summary table. For example, you cannot compute a summary from a set of rows and compare each row to the summarized value within a single query. However, you can use a summary table and join it to the original table to do this.

Use of summary tables has the disadvantage that the records they contain are up-to-date only as long as the original values remain unchanged, and thus so are any summaries calculated from them. If the original table rarely or never changes, this might be only a minor concern. For many applications, summaries that are close approximations are sufficiently accurate.

The summary table technique can be applied at multiple levels. Create a summary table that holds the results of an initial summary, and then summarize that table in different ways to produce secondary summaries. This avoids the computational expense of generating the initial summary repeatedly.

When a summary consists of a single value, you need not create a table at all. Use a SQL variable to hold the value. Then you can use the value for comparison purposes in subsequent queries without having to calculate it again.