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1.4. Queries on multiple tables¶
In past chapters, we saw how to retrieve data from individual tables, filter data on different criteria, order the data, and format the data with various expressions. Now we turn to the question of how to retrieve data from more than one table in a single query. For example, using the tables simple_books and simple_authors, we might like to see book titles together with author’s name and birth date. The author’s name is in both tables, but book titles are in simple_books, while author birth dates are in simple_authors. How can we get these together in one result? This chapter will explain how to retrieve data from multiple tables using joins, and explore various issues that arise when working with multiple tables.
1.4.1. Tables used in this chapter¶
We will use several collections of tables in this chapter, starting with some abstract tables used to illustrate joins. These abstract tables are illustrated below, but are also available in the database for your own experimentation. Also new to this chapter is a synthetic dataset simulating the database for a used book store, as well as a more complex set of tables about books and authors. We will also continue to use the simple_books and simple_authors tables from previous chapters. Brief explanations of newly introduced tables can be found below, and a full explanation of all of the datasets can be found in Appendix A.
You may wish to spend some time doing SELECT queries on each new table as it is introduced, to get a sense of what the data looks like. Understanding your database is key!
1.4.2. Simple joins¶
To start with, we will consider an abstract example with a small amount of data. Specifically, we will work with the two tables shown below, named s and t:
There is no real meaning to this data, but you might notice that the table data suggests a relationship: table s has a column sy containing small integers and table t has a column ty similarly containing small integers. What we want to accomplish is to connect rows from table s with rows from table t when the values in the sy and ty columns are the same. The desired result looks like this:
When rows from one table are paired with rows from another table, we call the result a join of the tables. Here is a query that joins s and t to produce the result shown above:
We start our FROM clause with table s, then use the JOIN keyword to bring in table t, followed by the ON keyword, and finally a Boolean condition explaining to SQL which rows from s go with which rows from t. The Boolean condition after ON is known as a join condition. Join conditions always compare an expression on one table with an expression on another table.
To understand what happens when you run this query, consider each row in s in turn. For each row in s, look at each row in t and apply the join condition. If the join condition evaluates to
True, then make a new row by concatenating the row from s with the row from t, and add it to the result. (This can be likened to performing nested for loops in a programming language like Python or Java; the outer loop is over the rows in s, and the inner loop is over the rows in t.)
So, for example, we start by looking at the first row in s, which we can write as
('one', 1). The value of sy in this row is 1. Now, we look at each row in t to see which ones have ty also equal to 1. The first row in t is
(1, 'green'), which has a ty value of 1, so we make the row
('one', 1, 1, 'green') and add it to the output. No other rows in t match, so we move on to the next row in s,
('two', 2). Again, we consider each row in t, this time looking for a ty value equal to 2; this time we match the row
(2, 'blue'), and we add
('two', 2, 2, 'blue') to the output. This process continues until we have processed every row in s.
In the first example, each row in s matched exactly one row in t, and each row in t matched exactly one row in s. What happens if this is not the case? First, consider tables s2 and t2 below, in which one row in each table fails to match any rows in the other table:
Joining s2 and t2 using the same equality condition on columns sy and ty now gives us:
This result can again be understood by examining each row of s2 and looking for matches in t2. If there is no match, no row gets output.
We can also have the case where more than one row in one table matches some row in the other table. Here are two more tables to consider:
This time, the s3 row
('two', 2) matches two different rows in t3, so we will produce two combined rows where sy and ty both equal 2:
Tables s2, t2, s3 and t3 are also in the database accessible in the interactive tool above.
Two tables can be related via multiple columns rather than just one in each table. To join them, you would use a compound join condition using AND. In fact, join conditions do not have to be equality (although they usually are); any logical expression relating rows in one table with rows in another can be used. The conceptual model of examining each row in the first table and comparing with each row in the second table still works. See if you can figure out what this query will produce (and then try it in the interactive tool above):
SELECT * FROM s JOIN t ON sy = ty OR sy > ty ;
JOIN clauses are considered to be sub-clauses of the FROM clause. We are, of course, free to add other clauses as normal to the query, such as a WHERE clause:
SELECT * FROM s JOIN t ON sy = ty WHERE tz = 'blue';
Think of the FROM clause as being the first part of the query processed by the database. The result is some collection of rows, which we can then filter with a WHERE clause, or put in a particular order with an ORDER BY clause, and so forth.
We have a lot more to talk about with joins, but before moving on, let us see how to answer the question raised earlier, where we would like to obtain both book titles and author birth dates in one query result using simple_books and simple_authors. Here is the solution:
SELECT title, author, birth FROM simple_books JOIN simple_authors ON author = name ;
Note here that we are choosing specific columns to return as part of our result, using our SELECT clause. The column name, used in the join condition, is the column containing author names in the simple_authors table. We compare this column to the author column in simple_books for our join, but we don’t include it in the columns we retrieve; otherwise we would have the same author name showing in two different columns.
1.4.3. Names of things¶
We have (mostly) not worried about the names of things in our discussion so far. We have said that we can use a column name as an expression representing the value in the column for some row under consideration, but we now need to consider some scenarios in which a column’s name by itself is not sufficiently specific. We have also given some examples where we renamed the output columns for a SELECT query, but we deferred discussion of that technique. This section will go into more detail regarding both of these topics and more.
126.96.36.199. Name collisions and ambiguity¶
In all of our examples so far, all of the columns in the tables we queried had unique names. For example, the join of s and t contained columns named sx, sy, ty, and tz. However, we will often not be so lucky as to have distinct column names when working with multiple tables. When two columns from tables involved in a join have the same name, we say that the column names collide. When a naming collision occurs, we cannot use the column name by itself as an expression in any part of our query, because the database will not know which table’s column you mean; the database will give an error message that the column name is ambiguous.
188.8.131.52. Qualified names¶
Fortunately, there is an easy way to specify a particular column in a particular table: simply give the table name first, followed by a period, or dot (“.”) and then the column name. You can do this even if names are not ambiguous. For example, the last query above could be expressed as:
This has the added benefit of making clear where each column is coming from for anyone reading the query who is not familiar with the database.
You can also use the asterisk shortcut to mean all columns in a specific table by prefixing with the table name and a dot:
SELECT simple_books.*, simple_authors.birth FROM simple_books JOIN simple_authors ON simple_books.author = simple_authors.name ;
Such expressions using both the table name and the column name are known as qualified column names, and can be used with any database. In some database implementations, tables can be grouped together into larger containers; in those databases, it is possible to have multiple tables of the same name (in different containers), which now must be qualified using the container name. Each database implementation is different, so you will need to learn about your particular database system’s rules for qualifying names.
When doing a join, it is good practice to qualify all of your column names. This will make it easier for anyone reading or maintaining your code to understand what your query is doing.
SQL provides facilities to change the names of tables and columns within the context of a single query. This can be useful, and at times, necessary. In a previous chapter, we used column renaming to get nicer column headers in our output. For example, consider this query:
SELECT title, floor((publication_year + 99) / 100) AS century FROM simple_books;
We supplied the name “century” for the second output column (which otherwise would have a header that looked like the mathematical expression we computed). This technique is known as aliasing, and is accomplished with the AS keyword. Aliasing for columns is most often used for the purpose of giving a helpful name for the column in the output, although it can be applied for other reasons that we shall see.
Aliasing can also be used with tables. This is often used to shorten table names to keep qualified names short and readable. Here, the AS keyword is used in the FROM clause after each table that should be renamed. The alias can then be used in the SELECT, WHERE, and other clauses in place of the table name. Here is an earlier query, rewritten using table aliasing:
SELECT b.title, b.author, a.birth FROM simple_books AS b JOIN simple_authors AS a ON b.author = a.name ;
When working with large queries using many tables, aliasing can make the query significantly smaller and more readable.
One instance where table aliasing is required is when joining a table to itself. This can be done when there is some kind of relationship between rows within the same table, which happens more often than you might guess. As an example of a query we might do with our simple books and authors data, consider the question, “What books were published in the same year as The Three-Body Problem?”. Here is one way to answer that question with a query:
SELECT b2.* FROM simple_books AS b1 JOIN simple_books AS b2 ON b1.publication_year = b2.publication_year WHERE b1.title = 'The Three-Body Problem';
If this seems confusing, think about it as using two tables, b1 and b2, each containing the same data as simple_books. Work through what happens if you join b1 and b2 applying the join condition
b1.publication_year = b2.publication_year; then, filter that result with the condition
b1.title = 'The Three-Body Problem'; finally, output just the columns from b2. If you have trouble visualizing what the result should be at each step, remember that you can query the database using the interactive tool above.
When using table aliasing, you should qualify all of your column names using the aliases as a matter of good style. Some databases allow you to use original table names instead of aliases, but mixing aliases with original table names is inconsistent and confusing, and in some cases that can result in incorrect code that is difficult to debug.
Just remember, aliasing only affects the query in which the renaming occurs; a new query will know nothing about any previous aliasing applied to tables or columns.
As a final note, the AS keyword is actually optional in SQL - you can create an alias with this keyword omitted. Simply put a valid identifier string after the name of a table or after a column expression:
SELECT b.title, b.author, a.birth FROM simple_books b JOIN simple_authors a ON b.author = a.name ;
Leaving out a keyword may seem strange, but you are likely to read code at some point using this form of aliasing, so be aware. There is no consensus on which style is better; for this textbook, we will consistently use AS for additional clarity.
(Note for Oracle users: the AS keyword is optional for columns, but is not supported for table aliases - you must omit the AS in Oracle queries when aliasing a table.)
184.108.40.206. Reserved names, names with spaces, or mixed-case names¶
Usually, names of things are case-insensitive and do not contain spaces. Also, the case used when displaying the output headers for a query may be all uppercase or all lowercase, depending on the database (for this textbook, lowercase is the norm). It is possible, however, to use names which are case-sensitive and which contain spaces. To do this, put the name within double quotes. For example, the header for the output column of in the following query will be mixed-case as well as having spaces:
SELECT 42 AS "The Answer";
Reserved names (such as SQL keywords) may also need to be put inside double quotes when used as column or table names.
Very rarely, you may encounter a database where table or column names are mixed-case or contain spaces. This can occur if the database creator used double quotes in the SQL code when creating the tables. In general, this is not a good practice, as it forces the use of double quotes for any future queries using the table. Reserved words should also be avoided in general, although this can be difficult when working with multiple databases, as an allowed word in one database may be a reserved word in another database.
(Note for MySQL users: use backticks instead of double quotes. The backtick character looks like an apostrophe, but slanting in the opposite direction.)
1.4.4. Identity columns¶
If we want to make a connection between data in one table and data in another using a join, we need the tables to share some data elements in common. In our simple books dataset, the common element was the author’s name, which was present in both the simple_books and simple_authors tables; this let us join the two tables with the join condition
simple_books.author = simple_authors.name. We can be confident in our result because we know the author’s name uniquely identifies the authors in our simple database. But what if author names were not unique? Then we might join authors to books they did not actually write!
For some types of data, some element of the data is unique for every possible data item and can be used as an identifier for the data in a database. For example, international travel to many countries requires the traveler to have a passport, and the issuing country together with the passport number uniquely identifies any traveler. However, this only works for international travel; most countries do not require passports for travel within the country’s own borders, and therefore there are many people who have no passport at all. A database trying to track domestic travelers, then, cannot use passport information as a unique identifier.
Author names might seem like a good identifier for authors, but, in fact, we have to be careful here as well due to multiple authors sharing the same name. For example, there are two novelists named Richard Wright, and both a novelist and a poet named David Diop. We could further distinguish between these authors using their birth dates, or perhaps we could consider their birthplace or other attributes. That only works, of course, if we know the birth date and so forth of each author in our database. In any case it begins to be an unsatisfactory solution due to the complexity of having to store so many pieces of information about each author for any tables we want to join to our table of authors.
This type of problem comes up a lot. The solution we will adopt, which is widely used in practice, is to create an artificial unique identifier, or id, for each author in our database. Unique identifiers can take different forms. The most common scheme is to keep a counter in the database and increment it each time a row is added to a table. This counter value is then used as the id value for the new row (we will discuss how to do this in Chapter 1.6).
Another popular scheme is to use a very large integer generated at random - a universally unique identifier, or UUID. In this scheme, due to the large number of possible UUIDs, each new id value is very likely to be different from any other previously id in the table. It is also easy to detect if there is a duplicate, in which case another value can be generated.
In our database, there is a table named authors which has an author_id column holding a unique value for each row. There is also a books table, which does not have a column to store the author’s name. Instead, it also has the column author_id. Each unique author_id in books is equal to some author_id value in authors.
To get the author’s name together with their books, we will need to join books to authors using the common id value:
Note that this query requires the use of qualified column names, at least for the two author_id columns - if we simply try the query
SELECT name, title FROM books JOIN authors ON author_id = author_id ;
we will get an error message that the author_id name is ambiguous.
1.4.5. Table relationships¶
One of the strengths of relational databases compared to earlier database systems is that relationships are not explicitly stored in the database. This provides a number of advantages regarding database design and software complexity, which are mostly beyond the scope of this book. One important advantage of the relational approach is that you can easily express queries concerning relationships which were not anticipated by the designer of the database; for example, our earlier query looking for books published in the same year as another book. However, this flexibility also means that when you encounter a new relational database, you may not immediately understand the structure and relationships in the database, or how (or why) you should join two tables together.
A well structured database usually gives some indication of likely places to join tables together. One indication may be in the names of columns - e.g., book_id in a table strongly suggests a column that links to the identity column of the books table. Another indication can come in the form of foreign key constraints, a topic we will discuss in Chapter 1.7. Exploring the database to find these implicit relationships is an important first step in learning any new database.
Your database might also come with a data model diagram, discussed in Part 2 of this book. (Data models for the data sets in our database can be found in Appendix A.) The data model will typically make the relationships between tables explicit. While data can be related to each other in very complex ways, there are some basic relationship types that capture the important aspects of most relationships. These relationships are commonly called “one-to-one”, “one-to-many”, and “many-to-many”. Below, we discuss these common relationships and where they appear in our database.
One-to-one describes a relationship between two types of data. If we think of each data type as having its own table, then each row in one table has a well-defined relationship with at most one row in the other table, and vice versa. Sometimes each row in a table has exactly one corresponding row in the other table, and vice versa; other times, some rows in one or both tables may not have corresponding rows in the other table. When there is a true one-to-one correspondence between tables, it is sometimes desirable to combine the tables into one larger table (whether or not to do this is a design decision).
An example of a one-to-one relationship might appear in a database for a seller of used books. Some example data for this fictional bookstore can be found in our database in the tables bookstore_inventory and bookstore_sales. Each of the seller’s books is recorded in bookstore_inventory, listing the book’s author, title, condition, and current price. The table bookstore_sales records the sale of a book, the date it was sold, the payment type, and a receipt number. These two tables can be joined by the common column stock_number, which functions as the id column for bookstore_inventory. Every record in the bookstore_sales table corresponds to exactly one record in the bookstore_inventory table; however, any unsold books still in the seller’s possession will not have a corresponding bookstore_sales record.
A few rows from each table are illustrated below.
One-to-many refers to the case when rows in one table correspond to some number of rows in another table, but rows in the second table only correspond to at most one row in the first table. In some cases, rows in the first table always have at least one corresponding row; other times, rows can have zero or more corresponding rows.
In our database, we have a one-to-many relationship between authors and books - each author has one or more books, but each book has exactly one author. (This is not reflective of the real world - many books exist that were written by two or more authors working together! However, for simplicity our database only contains single-author books.) Note that we can also talk of many-to-one relationships, which are just the symmetric equivalent of one-to-many; we can say that the authors table is in a one-to-many relationship with books, or that the books table is in a many-to-one relationship with authors.
To connect rows from one table to rows in another table where a one-to-many relationship exists between them, the simplest approach is to include a column on the “many” side that stores id values from the “one” side. As we saw above, this strategy is used with books and authors; the authors table has the author_id column, which is unique for every row, and the books table has the corresponding column author_id.
Similarly, the books table has a one-to-many relationship with the editions table in our database. In this case, the editions table has a book_id column, which, as you might guess, contains values from the book_id column of books. (The editions table contains information about the printed editions of books: publisher information, title as printed, year printed, and so forth. 1)
Many-to-many, you can probably guess, implies that rows in one table may correspond to multiple rows in the other table, and vice versa. In our database, our examples of many-to-many relationships will involve book and author awards. For example, the Hugo Award is given out each year to a book in the science fiction genre. In our database, there are many books that have won a Hugo Award; therefore the row for the Hugo Award in the awards table relates to multiple rows in the books table. Especially good science fiction books might win both a Hugo Award and a Nebula Award; so rows in the books table can correspond to multiple awards rows.
How do you connect rows from one table to rows in another table when there is a many-to-many relationship? If you try the trick we used with one-to-many relationships, you quickly run into trouble. For example, suppose we try to store id values from books in the awards table; since many books have won the Hugo Award, we need to store many book ids, so we would have many rows for the Hugo Award, all identical except for the book id. On the other hand, if we try to store award ids in the books table, books that have won multiple awards will need multiple rows, all identical except for the award ids. 2 Having multiple nearly identical rows creates a number of problems, some of which we will explore in Chapter 3.3.
The solution is to use a third table, known as a cross-reference table, as a connector. At minimum, a cross-reference table will have one column for each of the unique id columns in the two tables being connected. For example, the books_awards table in our database has a column book_id referring to the book_id column of books and an award_id column referring to the award_id column of awards. The existence of a (book id, award id) pair in the books_awards table means that the given book has won the stated award.
We can store other information in the cross-reference table as well. In the case of books_awards, we also have a year column storing the year in which the award was given to the book. Note that the cross-reference table is really the only place we can store this information; the year doesn’t properly “belong” to the award, as an award is given out in many years; and it doesn’t properly belong to the book, as books can win awards in different years.
To use the cross-reference table, we will need to join together three tables. The basic principles for joining three tables are the same as for two; start by joining two tables, then join that result with the third table. The finished query looks like this:
Looking at the query above, think of the first join as adding award ids from the cross-reference table to the rows from the books table, and think of the second join as then bringing in the award information matching those award ids. (Again, you can break this query down into smaller pieces and try them in the interactive tool to help build your intuition about how SQL works.)
In addition to winning awards for specific books, an author can win awards for their entire body of work. Awards of this type are also stored in the awards table; however, we need another table to connect authors with these awards (since the books_awards table connects to specific books only). The cross-reference table authors_awards exists for this purpose.
1.4.6. Inner and outer joins¶
When relational database programmers use the word “join” without any qualifiers, they almost always mean the type of join we have been describing above, in which the result only contains rows that match on both sides of the join. This type of join is more formally known as an inner join. In fact, you can optionally use the keyword INNER in front of JOIN if you want to make clear what type of join you are doing; however, INNER is commonly dropped simply because the default without INNER is still an inner join.
What if you want to retrieve all rows from one table in a join, even if there are no matching rows on the other side of the join? For example, we might want a list of books, together with any awards the books have won. Since not all books have won awards, the inner join of the books, books_awards, and awards shown above only returns some of the books in our database. To get all books, and awards where present, we want an outer join.
There are three types of outer join: left, right, and full. These are implemented with the key phrases LEFT [OUTER] JOIN, RIGHT [OUTER] JOIN, and FULL [OUTER] JOIN. (The square brackets mean that the OUTER keyword is optional; that is, LEFT JOIN means the same thing as LEFT OUTER JOIN.) In an outer join, all rows from one or both tables are returned, depending on the type of outer join. In a left outer join, all of the rows from the table on the left-hand side of the LEFT JOIN key phrase are returned, but only matching rows are returned from the right-hand side table. RIGHT JOIN does the opposite, while FULL JOIN returns all rows from both tables involved in the join.
When the join specifies that all rows from a table should be returned, and a row has no match in the other table, what should the row contain for the missing data from the other table? A logical choice is to fill in those columns with
NULL values, which is exactly what happens. Here is one query to retrieve all books, as well as awards where relevant:
Note that we have to do two outer joins in the above query. The first outer join between books and books_awards is necessary because books without awards will have no matching records in the books_awards cross reference table. The result of that join, then, will have
NULL values for the award_id column coming from the books_awards table. So, when we join with awards we again need an outer join, because the
NULL award_id values will not match any rows in the awards table.
In most databases, we could instead write the query using one right outer join. (Note: at the time this book was written, SQLite did not yet support right or full outer joins, so this query may not work in the interactive tool above):
SELECT b.title, a.name AS award, ba.year FROM awards AS a JOIN books_awards AS ba ON a.id = ba.award_id RIGHT JOIN books AS b ON b.id = ba.book_id ;
Here, the awards and books_awards tables can use a regular join, as we only care about awards that are referenced in the books_awards table, and all rows in the books_awards table have a matching entry already in the awards table. However, a right outer join would have worked equally well - an outer join is equivalent to an inner join if all rows match.
The above queries do exhibit one behavior which may be unwanted, which is that we have multiple rows for books that have won multiple awards. Some databases provide a way to produce a list of awards after each book, rather than multiple rows; see the LISTAGG aggregate function in Appendix B - Aggregate functions (we discuss the use of aggregate functions in Chapter 1.9).
Here is one more example of the use of an outer join, this time using our bookstore tables - see if you can figure out what this query is doing:
SELECT inv.*, CASE WHEN sales.stock_number IS NULL THEN 'in stock' ELSE 'sold' END AS status FROM bookstore_inventory AS inv LEFT JOIN bookstore_sales AS sales ON inv.stock_number = sales.stock_number ;
1.4.7. Implicit join syntax¶
The ability to do inner joins existed in SQL long before the JOIN keyword and related key phrases. Prior to the introduction of this explicit join syntax, joins used an implicit join syntax, which is described in this section. You may prefer the explicit syntax above, and it is considered by many practitioners to be best practice to use it for the clarity it provides. However, the implicit syntax is supported by all databases and you are very likely to encounter it in practice. Additionally, most databases reduce the explicit syntax to the implicit syntax internally, which has implications for understanding how the database processes join queries. For these reasons, it is important that you understand the implicit join syntax.
Returning to our abstract examples from the start of this chapter:
In the implicit join syntax, the first step is to simply list all tables involved in the join after the FROM clause. In SQL, this implies a cross product of the tables. In a cross product of two tables, every row in one table is paired with every row from the other table. You can see this in action in the query below:
Given this result, how do we apply join conditions to get the rows we actually want? We simply put our join conditions into the WHERE clause:
SELECT * FROM s, t WHERE sy = ty;
This is equivalent in all respects to:
SELECT * FROM s JOIN t ON sy = ty ;
That is, all conditions that would normally be put after the ON keyword in a JOIN clause should be put into the WHERE clause when using the implicit join syntax. If you consider the cross product of s and t, it is easy to see how applying the join condition to filter the cross product produces the desired result. 3
One danger in using the implicit join syntax is that it separates join conditions from the part of the query that actually joins the tables, making it easy to accidentally leave out a join condition. The join conditions instead are put into the WHERE clause together with any other single-table conditions needed.
If you are joining together n tables using the implicit syntax, then always remember that you need n - 1 join conditions to ensure that all of the tables are linked in. It is important that all of the tables connect to each other either directly or through a path of other tables (if you are familiar with data structures, the tables must be the nodes of a connected graph, generally in the shape of a free tree, with the edges represented by join conditions). Remember that n - 1 join conditions may mean more than n - 1 WHERE clause conditions, if any of the join conditions are compound. If you add a join condition to your WHERE clause for each new table you add to the FROM clause as you are writing your query, you can systematically create the proper join structure.
A good clue that you have omitted a join condition is if you suddenly get many more rows than you expected. If you look more closely at the data (you may need to include more columns in your SELECT clause to see it), you can see that you have created a cross product. Consider an implicit join of books, books_awards, and awards with a missing join condition:
It looks like every book that has won an award has won every award! That is due to the cross product resulting from the missing join condition.
Implicit join syntax is standard only for inner joins. Some database implementations do provide non-standard ways of doing outer joins using the implicit form, and you may see older queries using these. Since notations vary, we will not include any examples here.
As a final note, cross products are seldom a desired result on their own. However, if you actually need a cross product and wish to be explicit about it, SQL provides the CROSS JOIN key phrase for the purpose:
SELECT * FROM s CROSS JOIN t;
1.4.8. Self-check exercises¶
This section contains some exercises using the books data set (reminder: you can get full descriptions of all tables in Appendix A). If you get stuck, click on the “Show answer” button below the exercise to see a correct answer. For each of these, try writing the answer using explicit join syntax first, and then using the implicit syntax (where possible).
Write a query listing all of the editions (publisher, year, and published title) for the book titled “The Hobbit”,
Write a query listing the distinct titles under which the book ‘The Fellowship of the Ring’ was published.
Write a query listing editions (title, corresponding book title, publisher, and publisher location) that were published since 2005 under a different name than the book.
Write a query listing author, book title, edition title, and publisher for editions published since 2010.
Write a query returning the author who won the Neustadt International Prize for Literature in 1996. Note: this is an author award, not a book award.
Write a query to list the authors who have won author awards, together with their awards and the year of the award. Give the output descriptive headers (not just “name” and “name”). Order by author name.
Write a query listing all authors, together with their (author) awards, if any.
Write a query listing authors who have not won any of the author awards listed in our database. Hint: how might you detect the absence of an award in the query above?
Write a query listing all the books by the author of “Interpreter of Maladies”.
Same as above, but show the author’s name as well.
Using the books and authors tables, find all books (author and title) published in the same year as The Three-Body Problem, excluding The Three-Body Problem itself.
Write a query to list books (author, name, and title) that have won the Nebula Award. Show the year of the award and list the most recent awards first.
Write a query giving a distinct list of book awards won by authors who have also won the Nobel Prize in Literature (an author award).
Because the database would be rather large (for use in your web browser) if we included all the known editions of all of the books in our database, the editions table only contains editions for books by author J.R.R. Tolkien. The editions data is particularly “dirty”, in the sense that there are many missing pieces of information, and the accuracy and completeness of the data are questionable. You can read more about the data and how it was collected in Appendix A.
You could argue that the books table should store an array of award ids, instead of just a single award id, thus solving the dilemma. This is actually possible in a few database implementations that support array-valued columns. However, the use of such columns is not without controversy. For this textbook, we will take the more common approach of using cross-reference tables.
Because a cross product has a number of rows equal to the number of rows in one table times the number of rows in the other table, the product is very large when the tables involved are large. Even though databases typically convert explicit joins to their implicit equivalents internally, when database systems process joins they generally do not create the cross product and then apply the WHERE clause conditions, as that would be very slow and require a lot of memory or temporary storage. However, the conceptual model is helpful in understanding the end result.