Combining and Negating Conditions with AND, OR, and NOT
Matching Patterns with LIKE
Range Filtering with BETWEEN
List Filtering with IN
Testing for Nulls with IS NULL
Chapter 5: Operators and Functions
Creating Derived Columns
Performing Arithmetic Operations
Determining the Order of Evaluation
Concatenating Strings with ||
Extracting a Substring with SUBSTRING()
Changing String Case with UPPER() and LOWER()
Trimming Characters with TRIM()
Finding the Length of a String with CHARACTER_LENGTH()
Finding Substrings with POSITION()
Performing Datetime and Interval Arithmetic
Getting the Current Date and Time
Getting User Information
Converting Data Types with CAST()
Evaluating Conditional Values with CASE
Checking for Nulls with COALESCE()
Comparing Expressions with NULLIF()
Chapter 6: Summarizing and Grouping Data
Using Aggregate Functions
Creating Aggregate Expressions
Finding a Minimum with MIN()
Finding a Maximum with MAX()
Calculating a Sum with SUM()
Calculating an Average with AVG()
Counting Rows with COUNT()
Aggregating Distinct Values with DISTINCT
Grouping Rows with GROUP BY
Filtering Groups with HAVING
Chapter 7: Joins
Qualifying Column Names
Creating Table Aliases with AS
Using Joins
Creating Joins with JOIN or WHERE
Creating a Cross Join with CROSS JOIN
Creating a Natural Join with NATURAL JOIN
Creating an Inner Join with INNER JOIN
Creating Outer Joins with OUTER JOIN
Creating a Self-Join
Chapter 8: Subqueries
Understanding Subqueries
Subquery Syntax
Subqueries vs. Joins
Simple and Correlated Subqueries
Qualifying Column Names in Subqueries
Nulls in Subqueries
Using Subqueries as Column Expressions
Comparing a Subquery Value by Using a Comparison Operator
Testing Set Membership with IN
Comparing All Subquery Values with ALL
Comparing Some Subquery Values with ANY
Testing Existence with EXISTS
Comparing Equivalent Queries
Chapter 9: Set Operations
Combining Rows with UNION
Finding Common Rows with INTERSECT
Finding Different Rows with EXCEPT
Chapter 10: Inserting, Updating, and Deleting Rows
Displaying Table Definitions
Inserting Rows with INSERT
Updating Rows with UPDATE
Deleting Rows with DELETE
Chapter 11: Creating, Altering, and Dropping Tables
Creating Tables
Understanding Constraints
Creating a New Table with CREATE TABLE
Forbidding Nulls with NOT NULL
Specifying a Default Value with DEFAULT
Specifying a Primary Key with PRIMARY KEY
Specifying a Foreign Key with FOREIGN KEY
Forcing Unique Values with UNIQUE
Adding a Check Constraint with CHECK
Creating a Temporary Table with CREATE TEMPORARY TABLE
Creating a New Table from an Existing One with CREATE TABLE AS
Altering a Table with ALTER TABLE
Dropping a Table with DROP TABLE
Chapter 12: Indexes
Creating an Index with CREATE INDEX
Dropping an Index with DROP INDEX
Chapter 13: Views
Creating a View with CREATE VIEW
Retrieving Data Through a View
Updating Data Through a View
Dropping a View with DROP VIEW
Chapter 14: Transactions
Executing a Transaction
Chapter 15: SQL Tricks
Calculating Running Statistics
Generating Sequences
Finding Sequences, Runs, and Regions
Limiting the Number of Rows Returned
Assigning Ranks
Calculating a Trimmed Mean
Picking Random Rows
Handling Duplicates
Creating a Telephone List
Retrieving Metadata
Working with Dates
Calculating a Median
Finding Extreme Values
Changing Running Statistics Midstream
Pivoting Results
Working with Hierarchies
Index
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D
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✔ Tips ■ test_column can be an expression. ■ The NOT that can precede BETWEEN is independent of the NOT that can precede test_column; see the Tips in “Matching Patterns with LIKE ” earlier in this chapter. ■ Listing 4.38 shows how to rewrite Listing 4.36 with an exclusive range, which doesn’t include the $10 and $19.95 endpoints. See Figure 4.38 for the result. ■ Specifying a character range requires some thought. Suppose you want to search for last names that begin with the letter F. The following clause won’t work because it will retrieve someone whose last name is the letter G (is the letter G, not starts with the letter G): WHERE last_name BETWEEN ‘F’ AND ‘G’ This next clause shows the correct way to specify the ending point (in most cases): WHERE last_name BETWEEN ‘F’ AND ‘Fz’ ■ In older PostgreSQL versions, convert the floating-point num- bers in Listings 4.36 and 4.38 to DECIMAL ; see “Converting Data Types with CAST() ” in Chapter 5. To run Listings 4.36 and 4.38, change the floating-point literals to: CAST(19.95 AS DECIMAL) In Microsoft Access date literals, omit the DATE keyword and surround the liter- al with # characters instead of quotes. To run Listing 4.37, change the dates in the WHERE clause to #2000-01-01# and #2000-12-31# . In Microsoft SQL Server and DB2 date literals, omit the DATE keyword. To run Listing 4.37, change the dates in the WHERE clause to ‘2000-01-01’ and ‘2000-12-31’ . In some DBMSs, low_value can exceed high_value; search your DBMS documen- tation for WHERE or BETWEEN. 120 Chapter 4 Range Filtering with BETWEEN Listing 4.38 List the titles priced between $10 and $19.95, exclusive. See Figure 4.38 for the result. SELECT title_id, price FROM titles WHERE (price > 10) AND (price < 19.95); Listing title_id price T04 12.99 T09 13.95 T12 12.99 Figure 4.38 Result of Listing 4.38. List Filtering with IN Use IN to determine whether a given value matches any value in a specified list. The IN condition’s important characteristics are: ◆ IN works with character strings, numbers, and datetimes. ◆ The IN list is a parenthesized listing of one or more comma-separated values. The list items needn’t be in any particu- lar order. ◆ IN is a convenient, shorthand clause that you can replicate by using OR . WHERE test_column IN (value1, value2, value3) is equivalent to: WHERE (test_column = value1) OR (test_column = value2) OR (test_column = value3) ◆ String comparisons are case insensitive or case sensitive, depending on your DBMS; see the DBMS Tip in “Filtering Rows with WHERE ” earlier in this chapter. ◆ You can negate an IN condition with NOT IN . ◆ You can combine IN conditions and other conditions with AND and OR . 121 Retrieving Data from a Table List Filtering with IN To filter rows by using a list: ◆ Type: SELECT columns FROM table WHERE test_column [NOT] IN (value1, value2, ); columns is one or more comma-separated column names, and table is the name of the table that contains columns. In the search condition, test_column is the name of a column in table (test_column doesn’t have to be listed in columns), and value1, value2, … are one or more comma- separated values that are compared with the value in test_column. The list values can appear in any order and must be the same as or comparable to the data type of test_column. Specify NOT IN to match val- ues that aren’t in the list (Listings 4.39 through 4.41, Figures 4.39 through 4.41). ✔ Tips ■ test_column can be an expression. ■ The NOT that can precede IN is inde- pendent of the NOT that can precede test_column; see the Tips in “Matching Patterns with LIKE ” earlier in this chapter. ■ If your list contains a large number of values, your code will be easier to read if you use one IN condition instead of many OR conditions. (Also, one IN usually runs faster than multiple OR s.) ■ For speed, list the most likely values first. If you’re testing U.S. addresses, for exam- ple, list the most populous states first: WHERE state IN (‘CA’, ‘TX’, ‘NY’, ‘FL’, ,’VT’, ‘DC’, ‘WY’) . ■ The search condition WHERE col1 BETWEEN 1 AND 5 AND col1 <> 3 usually is faster than WHERE col1 IN (1, 2, 4, 5) 122 Chapter 4 List Filtering with IN Listing 4.39 List the authors who don’t live in New York State, New Jersey, or California. See Figure 4.39 for the result. SELECT au_fname, au_lname, state FROM authors WHERE state NOT IN ('NY', 'NJ', 'CA'); Listing au_fname au_lname state Wendy Heydemark CO Paddy O'Furniture FL Figure 4.39 Result of Listing 4.39. Listing 4.40 List the titles for which advances of $0, $1,000, or $5,000 were paid. See Figure 4.40 for the result. SELECT title_id, advance FROM royalties WHERE advance IN (0.00, 1000.00, 5000.00); Listing title_id advance T02 1000.00 T08 0.00 T09 0.00 Figure 4.40 Result of Listing 4.40. ■ A compound condition’s order of evalua- tion is easier to read and manage if you use IN instead of multiple OR s; see “Combining and Negating Conditions with AND , OR , and NOT ” earlier in this chapter. ■ You also can use IN to determine whether a given value matches any value in a sub- query; see Chapter 8. ■ NOT IN is equivalent to combining tests for inequality with AND . This statement is equivalent to Listing 4.39: SELECT au_fname, au_lname, state FROM authors WHERE state <> ‘NY’ AND state <> ‘NJ’ AND state <> ‘CA’; ■ In Microsoft Access date literals, omit the DATE keyword and surround the literal with # charac- ters instead of quotes. To run Listing 4.41, change the WHERE clause to: WHERE pubdate IN (#1/1/2000#, #1/1/2001#, #1/1/2002#) In Microsoft SQL Server and DB2 date literals, omit the DATE keyword. To run Listing 4.41, change the WHERE clause to: WHERE pubdate IN (‘2000-01-01’, ‘2001-01-01’, ‘2002-01-01’) In older PostgreSQL versions, convert the floating-point number in Listing 4.40 to DECIMAL ; see “Converting Data Types with CAST() ” in Chapter 5. To run Listing 4.40, change the WHERE clause to: WHERE advance IN (CAST( 0.00 AS DECIMAL), CAST(1000.00 AS DECIMAL), CAST(5000.00 AS DECIMAL)) 123 Retrieving Data from a Table List Filtering with IN Listing 4.41 List the titles published on the first day of the year 2000, 2001, or 2002. See Figure 4.41 for the result. SELECT title_id, pubdate FROM titles WHERE pubdate IN (DATE '2000-01-01', DATE '2001-01-01', DATE '2002-01-01'); Listing title_id pubdate T05 2001-01-01 Figure 4.41 Result of Listing 4.41. Testing for Nulls with IS NULL Recall from “Nulls” in Chapter 3 that nulls represent missing or unknown values. This situation causes a problem: LIKE , BETWEEN , IN , and other WHERE -clause conditions can’t find nulls because unknown values don’t satisfy specific conditions. A null matches no value—not even other nulls. You can’t use = or <> to test whether a value is null. In the table publishers , for example, note that publisher P03 has a null in the column state because that column doesn’t apply to Germany (Listing 4.42 and Figure 4.42). I can’t use complementary comparisons to select the null, because null is neither California nor not-California; it’s undefined (Listings 4.43 and 4.44, Figures 4.43 and 4.44). To avert disaster, SQL provides IS NULL to determine whether a given value is null. The IS NULL condition’s important character- istics are: ◆ IS NULL works for columns of any data type. ◆ You can negate an IS NULL condition with IS NOT NULL . ◆ You can combine IS NULL conditions and other conditions with AND and OR . 124 Chapter 4 Testing for Nulls with IS NULL Listing 4.42 List the locations of all the publishers. See Figure 4.42 for the result. SELECT pub_id, city, state, country FROM publishers; Listing pub_id city state country P01 New York NY USA P02 San Francisco CA USA P03 Hamburg NULL Germany P04 Berkeley CA USA Figure 4.42 Result of Listing 4.42. The column state doesn’t apply to the publisher located in Germany. Listing 4.43 List the publishers located in California. See Figure 4.43 for the result. SELECT pub_id, city, state, country FROM publishers WHERE state = 'CA'; Listing pub_id city state country P02 San Francisco CA USA P04 Berkeley CA USA Figure 4.43 Result of Listing 4.43. This result doesn’t include publisher P03. To retrieve rows with nulls or non-null values: ◆ Type: SELECT columns FROM table WHERE test_column IS [NOT] NULL; columns is one or more comma-separated column names, and table is the name of the table that contains columns. In the search condition, test_column is the name of a column in table. (test_column doesn’t have to be listed in columns.) Specify NOT NULL to match non-null values (Listings 4.45 and 4.46, Figures 4.45 and 4.46). 125 Retrieving Data from a Table Testing for Nulls with IS NULL Listing 4.44 List the publishers located outside California (the wrong way—see Listing 4.45 for the correct way). See Figure 4.44 for the result. SELECT pub_id, city, state, country FROM publishers WHERE state <> 'CA'; Listing pub_id city state country P01 New York NY USA Figure 4.44 Result of Listing 4.44. This result doesn’t include publisher P03 either. The conditions state = ‘CA’ and state <> ‘CA’ aren’t complementary after all; nulls don’t match any value and so can’t be selected by using the types of conditions I’ve covered so far. Listing 4.45 List the publishers located outside California (the correct way). See Figure 4.45 for the result. SELECT pub_id, city, state, country FROM publishers WHERE state <> 'CA' OR state IS NULL; Listing pub_id city state country P01 New York NY USA P03 Hamburg NULL Germany Figure 4.45 Result of Listing 4.45. Now publisher P03 is in the result. title_id type pubdate T06 biography 2000-07-31 T07 biography 1999-10-01 T12 biography 2000-08-31 Figure 4.46 Result of Listing 4.46. Without the IS NOT NULL condition, this result would have included title T10. Listing 4.46 List the biographies whose (past or future) publication dates are known. See Figure 4.46 for the result. SELECT title_id, type, pubdate FROM titles WHERE type = 'biography' AND pubdate IS NOT NULL; Listing ✔ Tips ■ test_column can be an expression. ■ The NOT that can precede NULL is inde- pendent of the NOT that can precede test_column; see the Tips in “Matching Patterns with LIKE ” earlier in this chapter. ■ Nulls cause rows to be excluded from results only if a column containing nulls is a test column in a WHERE condition. The following query, for example, retrieves all the rows in the table publishers (refer to Figure 4.42) because the null in the col- umn state isn’t compared with anything: SELECT pub_id, city, state, country FROM publishers WHERE country <> ‘Canada’; To forbid nulls in a column, see “Forbidding Nulls with NOT NULL ” in Chapter 11. ■ It bears repeating that a null isn’t the same as an empty string ( ‘’ ). In the table authors , for example, the column au_fname contains an empty string for author A06 (last name of Kellsey). The WHERE condition to find the first name is WHERE au_fname = ‘’ and not WHERE au_fname IS NULL ■ Oracle treats an empty string ( ‘’ ) as a null; see the DBMS Tip in “Nulls” in Chapter 3. 126 Chapter 4 Testing for Nulls with IS NULL Operators and functions let you calculate results derived from column values, system- determined values, constants, and other data. You can perform: ◆ Arithmetic operations—Cut everyone’s salary by 10 percent. ◆ String operations—Concatenate personal information into a mailing address. ◆ Datetime operations—Compute the time interval between two dates. ◆ System operations—Find out what time your DBMS thinks it is. An operator is a symbol or keyword indicat- ing an operation that acts on one or more elements. The elements, called operands, are SQL expressions. Recall from the “SQL Syntax” Tips in Chapter 3 that an expression is any legal combination of symbols and tokens that evaluates to a single value (or null). In price * 2 , for example, * is the oper- ator, and price and 2 are its operands. A function is a built-in, named routine that performs a specialized task. Most functions take parenthesized arguments, which are values you pass to the function that the function then uses to perform its task. Arguments can be column names, literals, nested functions, or more-complex expressions. In UPPER(au_lname) , for example, UPPER is the function name, and au_lname is the argument. 127 Operators and Functions 5 Operators and Functions Creating Derived Columns You can use operators and functions to create derived columns. A derived column is the result of a calculation and is created with a SELECT -clause expression that is something other than a simple reference to a column. Derived columns don’t become permanent columns in a table; they’re for display and reporting purposes. The values in a derived column often are computed from values in existing columns, but you also can create a derived column by using a constant expression (such as a string, number, or date) or system value (such as the system time). Listing 5.1 shows a SELECT statement that yields a trivial arith- metic calculation; it needs no FROM clause because it doesn’t retrieve data from a table. Figure 5.1 shows the result. Recall from “Tables, Columns, and Rows” in Chapter 2 that closure guarantees that every result is a table, so even this simple result is a table: a 1 ✕ 1 table that contains the value 5. If I retrieve a column along with a constant, the constant appears in every row of the result (Listing 5.2 and Figure 5.2). Your DBMS will assign the derived column a default name, typically the expression itself as a quoted identifier. You should name derived columns explicitly with an AS clause because system-assigned names can be long, unwieldy, and inconvenient for database applications to refer to; see “Creating Column Aliases with AS ” in Chapter 4 (Listing 5.3 and Figure 5.3). 128 Chapter 5 Creating Derived Columns Listing 5.1 A constant expression in a SELECT clause. No FROM clause is needed, because I’m not retrieving data from a table. See Figure 5.1 for the result. SELECT 2 + 3; Listing 2 + 3 5 Figure 5.1 Result of Listing 5.1. This result is a table with one row and one column. Listing 5.2 Here, I’ve retrieved a column and a constant expression. See Figure 5.2 for the result. SELECT au_id, 2 + 3 FROM authors; Listing au_id 2 + 3 A01 5 A02 5 A03 5 A04 5 A05 5 A06 5 A07 5 Figure 5.2 Result of Listing 5.2. The constant is repeated in each row. ✔ Tip ■ Oracle requires a FROM clause in a SELECT statement and so creates the dummy table DUAL automati- cally to be used for SELECT ing a constant expression; search Oracle documentation for DUAL table. To run Listing 5.1, add a FROM clause that selects the constant value from DUAL : SELECT 2 + 3 FROM DUAL; DB2 requires a FROM clause in a SELECT statement and so creates the dummy table SYSIBM.SYSDUMMY1 automatically to be used for SELECT ing a constant expres- sion; search DB2 documentation for SYSIBM.SYSDUMMY1. To run Listing 5.1, add a FROM clause that selects the con- stant value from SYSIBM.SYSDUMMY1 : SELECT 2 + 3 FROM SYSIBM.SYSDUMMY1 ; In older PostgreSQL versions, convert the floating- point number in Listing 5.3 to DECIMAL ; see “Converting Data Types with CAST() ” later in this chapter. To run Listing 5.3, change the New price calcula- tion in the SELECT clause to: price * CAST((1 - 0.10) AS DECIMAL) 129 Operators and Functions Creating Derived Columns Listing 5.3 List the book prices discounted by 10 percent. The derived columns would have DBMS- specific default names if the AS clauses were removed. See Figure 5.3 for the result. SELECT title_id, price, 0.10 AS "Discount", price * (1 - 0.10) AS "New price" FROM titles; Listing title_id price Discount New price T01 21.99 0.10 19.79 T02 19.95 0.10 17.95 T03 39.95 0.10 35.96 T04 12.99 0.10 11.69 T05 6.95 0.10 6.25 T06 19.95 0.10 17.95 T07 23.95 0.10 21.56 T08 10.00 0.10 9.00 T09 13.95 0.10 12.56 T10 NULL 0.10 NULL T11 7.99 0.10 7.19 T12 12.99 0.10 11.69 T13 29.99 0.10 26.99 Figure 5.3 Result of Listing 5.3. . Microsoft SQL Server and DB2 date literals, omit the DATE keyword. To run Listing 4.41, change the WHERE clause to: WHERE pubdate IN (‘2000-01-01’, ‘2001-01-01’, ‘2002-01-01’) In older PostgreSQL. operation that acts on one or more elements. The elements, called operands, are SQL expressions. Recall from the SQL Syntax” Tips in Chapter 3 that an expression is any legal combination of symbols. to specify the ending point (in most cases): WHERE last_name BETWEEN ‘F’ AND ‘Fz’ ■ In older PostgreSQL versions, convert the floating-point num- bers in Listings 4.36 and 4.38 to DECIMAL ; see “Converting