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This chapter described how SQL handles queries that combine data from two or more tables: In a multitable query (a join), the tables containing the data are named in the FROM clause. Each row of query results is a combination of data from a single row in each of the tables, and it is the only row that draws its data from that particular combination. The most common multitable queries use the parent/child relationships created by primary keys and foreign keys. In general, joins can be built by comparing any pair(s) of columns from the two joined tables, using either a test for equality or any other comparison test. A join can be thought of as the product of two tables from which some of the rows have been removed. A table can be joined to itself; self-joins require the use of a table alias. Outer joins extend the standard (inner) join by retaining unmatched rows of one or both of the joined tables in the query results, and using NULL values for data from the other table. The SQL standard provides comprehensive support for inner and outer joins, and for combining the results of joins with other multitable operations such as unions, intersections, and differences.

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tively criticize literary works posted by other authors. Readers can give an item a star rating, indicating to other readers what might and might not be worthwhile reading. There is no technological impediment to doing something similar with streaming media content. Viewers could be encouraged to tag on ratings and reviews to streaming media content made available for public viewing. Indeed, search engines could make use of this review data as metadata, useful for indexing the media and making it easier for viewers to navigate to items of interest. Of course, malicious reviews can be posted, as well as honest opinions. It is possible to link the reviews posted to biographical and track record data about the reviewer. In this way, later readers of the reviews could form an opinion of the trustworthiness of the views expressed in the review. Adding viewer reviews to traditional radio and television programs can be done, but not in a way that permanently associates the reviews with the media, as is possible with streaming media. Indeed, such a democratic rating system makes it much harder for traditional media industries to perpetuate fictions like top ten lists, which have been so thoroughly discredited by revelations of manipulation, over the decades, as to render them virtually useless to all thinking consumers. Because navigating to media of interest imposes search costs, viewer reviews serve to minimize the time and effort spent searching for something to watch. They are potentially a very important feature of streaming media.

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(8.30)

any requests for information don t require the level of detail provided by the SQL queries described in the last two chapters. For example, each of the following requests asks for a single value or a small number of values that summarizes the contents of the database: What is the total quota for all salespeople What are the smallest and largest assigned quotas How many salespeople have exceeded their quota What is the size of the average order What is the size of the average order for each sales office How many salespeople are assigned to each sales office SQL supports these requests for summary data through column functions and the GROUP BY and HAVING clauses of the SELECT statement, which are described in this chapter.

and is located at the angle p, where p = tan 1 Px Py (8.31)

SQL lets you summarize data from the database through a set of column functions. A SQL column function takes an entire column of data as its argument and produces a single data item that summarizes the column. For example, the AVG() column function takes a column of data and computes its average. Here is a query that uses the AVG() column function to compute the average value of two columns from the SALESREPS table: What are the average quota and average sales of our salespeople

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