0

Introduction

SQL (Structured Query Language) is a database sublanguage for querying and modifying relational databases. It was developed by IBM Research in the mid 70's and standardized by ANSI in 1986.
The Relational Model defines two root languages for accessing a relational database -- Relational Algebra and Relational Calculus. Relational Algebra is a low-level, operator-oriented language. Creating a query in Relational Algebra involves combining relational operators using algebraic notation. Relational Calculus is a high-level, declarative language. Creating a query in Relational Calculus involves describing what results are desired.
SQL is a version of Relational Calculus. The basic structure in SQL is the statement. Semicolons separate multiple SQL statements.
There are 3 basic categories of SQL Statements:

Language Structure

SQL is a keyword based language. Each statement begins with a unique keyword. SQL statements consist of clauses which begin with a keyword. SQL syntax is not case sensitive. The other lexical elements of SQL statements are:
  • names -- names of database elements: tables, columns, views, users, schemas; names must begin with a letter (a - z) and may contain digits (0 - 9) and underscore (_)
  • literals -- quoted strings, numeric values, datetime values
  • delimiters -- + - , ( ) = < > <= >= <> . * / || ? ;
Basic database objects (tables, views) can optionally be qualified by schema name. A dot -- ".", separates qualifiers:
    schema-name . table-name
Column names can be qualified by table name with optional schema qualification.
Note: Names can be case sensitive and contain spaces and other delimiters and can use keywords, by surrounding them with double quotation marks ("). For example,
    "1 Name w/spaces"
    "SELECT"
Quoted names must match exactly on case.

Example Tables

In the subsequent text, the following 3 example tables are used:
p Table (parts) s Table (suppliers) sp Table (suppliers & parts)
pno descr color
P1 Widget Blue
P2 Widget Red
P3 Dongle Green



sno name city
S1 Pierre Paris
S2 John London
S3 Mario Rome
sno pno qty
S1 P1 NULL
S2 P1 200
S3 P1 1000
S3 P2 200

DDL for example tables

SQL Statement Formats

The remainder of this tutorial has 3 major sections:

SELECT Statement Basics

The SQL SELECT statement queries data from tables in the database. The statement begins with the SELECT keyword. The basic SELECT statement has 3 clauses:
  • SELECT
  • FROM
  • WHERE
The SELECT clause specifies the table columns that are retrieved. The FROM clause specifies the tables accessed. The WHERE clause specifies which table rows are used. The WHERE clause is optional; if missing, all table rows are used.
For example,
    SELECT name FROM s WHERE city='Rome'
This query accesses rows from the table - s. It then filters those rows where the city column contains Rome. Finally, the query retrieves the name column from each filtered row. Using the example s table, this query produces:
    name
    Mario
A detailed description of the query actions:
  • The FROM clause accesses the s table. Contents:
      sno name city
      S1 Pierre Paris
      S2 John London
      S3 Mario Rome
  • The WHERE clause filters the rows of the FROM table to use those whose city column contains Rome. This chooses a single row from s:
      sno name city
      S3 Mario Rome
  • The SELECT clause retrieves the name column from the rows filtered by the WHERE clause:
      name
      Mario
The remainder of this subsection examines the 3 major clauses of the SELECT statement, detailing their syntax and semantics:
Extended query capabilities are covered in the next sub-section.

SELECT Clause

The SELECT clause is mandatory. It specifies a list of columns to be retrieved from the tables in the FROM clause. It has the following general format:
    SELECT [ALL|DISTINCT] select-list
select-list is a list of column names separated by commas. The ALL and DISTINCT specifiers are optional. DISTINCT specifies that duplicate rows are discarded. A duplicate row is when each corresponding select-list column has the same value. The default is ALL, which retains duplicate rows. For example,
    SELECT descr, color FROM p
The column names in the select list can be qualified by the appropriate table name:
    SELECT p.descr, p.color FROM p
A column in the select list can be renamed by following the column name with the new name. For example:
    SELECT name supplier, city location FROM s
This produces:
    supplier location
    Pierre Paris
    John London
    Mario Rome
The select list may also contain expressions. See Expressions. A special select list consisting of a single '*' requests all columns in all tables in the FROM clause. For example,
    sno pno qty
    S1 P1 NULL
    S2 P1 200
    S3 P1 1000
    S3 P2 200
The * delimiter will retrieve just the columns of a single table when qualified by the table name. For example:
    SELECT sp.* FROM sp
This produces the same result as the previous example. An unqualified * cannot be combined with other elements in the select list; it must be stand alone. However, a qualified * can be combined with other elements. For example,
    SELECT sp.*, city
    FROM sp, s
    WHERE sp.sno=s.sno
    sno pno qty city
    S1 P1 NULL Paris
    S2 P1 200 London
    S3 P1 1000 Rome
    S3 P2 200 Rome
Note: this is an example of a query joining 2 tables. See Joining Tables.

FROM Clause

The FROM clause always follows the SELECT clause. It lists the tables accessed by the query. For example,
    SELECT * FROM s
When the From List contains multiple tables, commas separate the table names. For example,
    SELECT sp.*, city
    FROM sp, s
    WHERE sp.sno=s.sno
When the From List has multiple tables, they must be joined together. See Joining Tables.

Correlation Names

Like columns in the select list, tables in the from list can be renamed by following the table name with the new name. For example,
    SELECT supplier.name FROM s supplier
The new name is known as the correlation (or range) name for the table. Self joins require correlation names.

WHERE Clause

The WHERE clause is optional. When specified, it always follows the FROM clause. The WHERE clause filters rows from the FROM clause tables. Omitting the WHERE clause specifies that all rows are used. Following the WHERE keyword is a logical expression, also known as a predicate.
The predicate evaluates to a SQL logical value -- true, false or unknown. The most basic predicate is a comparison:
    color = 'Red'
This predicate returns:
  • true -- if the color column contains the string value -- 'Red',
  • false -- if the color column contains another string value (not 'Red'), or
  • unknown -- if the color column contains null.
Generally, a comparison expression compares the contents of a table column to a literal, as above. A comparison expression may also compare two columns to each other. Table joins use this type of comparison. See Joining Tables. The = (equals) comparison operator compares two values for equality. Additional comparison operators are:
  • > -- greater than
  • < -- less than
  • >= -- greater than or equal to
  • <= -- less than or equal to
  • <> -- not equal to
For example,
    SELECT * FROM sp WHERE qty >= 200
    sno pno qty
    S2 P1 200
    S3 P1 1000
    S3 P2 200
Note: In the sp table, the qty column for one of the rows contains null. The comparison - qty >= 200, evaluates to unknown for this row. In the final result of a query, rows with a WHERE clause evaluating to unknown (or false) are eliminated (filtered out). Both operands of a comparison should be the same data type, however automatic conversions are performed between numeric, datetime and interval types. The CAST expression provides explicit type conversions; see Expressions.

Extended Comparisons

In addition to the basic comparisons described above, SQL supports extended comparison operators -- BETWEEN, IN, LIKE and IS NULL.
  • BETWEEN Operator The BETWEEN operator implements a range comparison, that is, it tests whether a value is between two other values. BETWEEN comparisons have the following format:
      value-1 [NOT] BETWEEN value-2 AND value-3
    This comparison tests if value-1 is greater than or equal to value-2 and less than or equal to value-3. It is equivalent to the following predicate:
      value-1 >= value-2 AND value-1 <= value-3
    Or, if NOT is included:
      NOT (value-1 >= value-2 AND value-1 <= value-3)
    For example,
      SELECT *
      FROM sp
      WHERE qty BETWEEN 50 and 500
      sno pno qty
      S2 P1 200
      S3 P2 200
  • IN Operator The IN operator implements comparison to a list of values, that is, it tests whether a value matches any value in a list of values. IN comparisons have the following general format:
      value-1 [NOT] IN ( value-2 [, value-3] ... )
    This comparison tests if value-1 matches value-2 or matches value-3, and so on. It is equivalent to the following logical predicate:
      value-1 = value-2 [ OR value-1 = value-3 ] ...
    or if NOT is included:
      NOT (value-1 = value-2 [ OR value-1 = value-3 ] ...)
    For example,
      SELECT name FROM s WHERE city IN ('Rome','Paris')
      name
      Pierre
      Mario
  • LIKE Operator The LIKE operator implements a pattern match comparison, that is, it matches a string value against a pattern string containing wild-card characters.
    The wild-card characters for LIKE are percent -- '%' and underscore -- '_'. Underscore matches any single character. Percent matches zero or more characters.
    Examples,
    Match Value Pattern Result
    'abc' '_b_' True
    'ab' '_b_' False
    'abc' '%b%' True
    'ab' '%b%' True
    'abc' 'a_' False
    'ab' 'a_' True
    'abc' 'a%_' True
    'ab' 'a%_' True

    LIKE comparison has the following general format:
      value-1 [NOT] LIKE value-2 [ESCAPE value-3]
    All values must be string (character). This comparison uses value-2 as a pattern to match value-1. The optional ESCAPE sub-clause specifies an escape character for the pattern, allowing the pattern to use '%' and '_' (and the escape character) for matching. The ESCAPE value must be a single character string. In the pattern, the ESCAPE character precedes any character to be escaped. For example, to match a string ending with '%', use:
      x LIKE '%/%' ESCAPE '/'
    A more contrived example that escapes the escape character:
      y LIKE '/%//%' ESCAPE '/'
    ... matches any string beginning with '%/'. The optional NOT reverses the result so that:
      z NOT LIKE 'abc%'
    is equivalent to:
      NOT z LIKE 'abc%'
  • IS NULL Operator A database null in a table column has a special meaning -- the value of the column is not currently known (missing), however its value may be known at a later time. A database null may represent any value in the future, but the value is not available at this time. Since two null columns may eventually be assigned different values, one null can't be compared to another in the conventional way. The following syntax is illegal in SQL:
      WHERE qty = NULL
    A special comparison operator -- IS NULL, tests a column for null. It has the following general format:
      value-1 IS [NOT] NULL
    This comparison returns true if value-1 contains a null and false otherwise. The optional NOT reverses the result:
      value-1 IS NOT NULL
    is equivalent to:
      NOT value-1 IS NULL
    For example,
      SELECT * FROM sp WHERE qty IS NULL
      sno pno qty
      S1 P1 NULL

Logical Operators

The logical operators are AND, OR, NOT. They take logical expressions as operands and produce a logical result (True, False, Unknown). In logical expressions, parentheses are used for grouping.
  • AND Operator The AND operator combines two logical operands. The operands are comparisons or logical expressions. It has the following general format:
      predicate-1 AND predicate-2
    AND returns:
    • True -- if both operands evaluate to true
    • False -- if either operand evaluates to false
    • Unknown -- otherwise (one operand is true and the other is unknown or both are unknown)
    Truth tables for AND:
    AND  T   F   U 
     T   T   F   U 
     F   F   F   F 
     U   U   F   U 
    Input 1 Input 2 AND Result
    True True True
    True False False
    False False False
    False True False
    Unknown Unknown Unknown
    Unknown True Unknown
    Unknown False False
    True Unknown Unknown
    False Unknown False

    For example,
      SELECT *
      FROM sp
      WHERE sno='S3' AND qty < 500
      sno pno qty
      S3 P2 200
  • OR Operator The OR operator combines two logical operands. The operands are comparisons or logical expressions. It has the following general format:
      predicate-1 OR predicate-2
    OR returns:
    • True -- if either operand evaluates to true
    • False -- if both operands evaluate to false
    • Unknown -- otherwise (one operand is false and the other is unknown or both are unknown)
    Truth tables for OR:
    OR  T   F   U 
     T   T   T   T 
     F   T   F   U 
     U   T   U   U 
    Input 1 Input 2 OR Result
    True True True
    True False True
    False False False
    False True True
    Unknown Unknown Unknown
    Unknown True True
    Unknown False Unknown
    True Unknown True
    False Unknown Unknown

    For example,
      SELECT *
      FROM s
      WHERE sno='S3' OR city = 'London'
      sno name city
      S2 John London
      S3 Mario Rome
    AND has a higher precedence than OR, so the following expression:
      a OR b AND c
    is equivalent to:
      a OR (b AND c)
  • NOT Operator The NOT operator inverts the result of a comparison expression or a logical expression. It has the following general format:
      NOT predicate-1
    Truth tables for NOT:
    NOT  
     T   F 
     F   T 
     U   U 
    Input NOT Result
    True False
    False True
    Unknown Unknown

    Example query:
      SELECT *
      FROM sp
      WHERE NOT sno = 'S3'
      sno pno qty
      S1 P1 NULL
      S2 P1 200

ORDER BY Clause

The ORDER BY clause is optional. If used, it must be the last clause in the SELECT statement. The ORDER BY clause requests sorting for the results of a query.
When the ORDER BY clause is missing, the result rows from a query have no defined order (they are unordered). The ORDER BY clause defines the ordering of rows based on columns from the SELECT clause. The ORDER BY clause has the following general format:
    ORDER BY column-1 [ASC|DESC] [ column-2 [ASC|DESC] ] ...
column-1, column-2, ... are column names specified (or implied) in the select list. If a select column is renamed (given a new name in the select entry), the new name is used in the ORDER BY list. ASC and DESC request ascending or descending sort for a column. ASC is the default. ORDER BY sorts rows using the ordering columns in left-to-right, major-to-minor order. The rows are sorted first on the first column name in the list. If there are any duplicate values for the first column, the duplicates are sorted on the second column (within the first column sort) in the Order By list, and so on. There is no defined inner ordering for rows that have duplicate values for all Order By columns.
Database nulls require special processing in ORDER BY. A null column sorts higher than all regular values; this is reversed for DESC.
In sorting, nulls are considered duplicates of each other for ORDER BY. Sorting on hidden information makes no sense in utilizing the results of a query. This is also why SQL only allows select list columns in ORDER BY.
For convenience when using expressions in the select list, select items can be specified by number (starting with 1). Names and numbers can be intermixed.
Example queries:
    SELECT * FROM sp ORDER BY 3 DESC
    sno pno qty
    S1 P1 NULL
    S3 P1 1000
    S3 P2 200
    S2 P1 200
    SELECT name, city FROM s ORDER BY name
    name city
    John London
    Mario Rome
    Pierre Paris
    SELECT * FROM sp ORDER BY qty DESC, sno
    sno pno qty
    S1 P1 NULL
    S3 P1 1000
    S2 P1 200
    S3 P2 200

Expressions

In the previous subsection on basic Select statements, column values are used in the select list and where predicate. SQL allows a scalar value expression to be used instead. A SQL value expression can be a:

Literals

A literal is a typed value that is self-defining. SQL supports 3 types of literals:
  • String -- ASCII text framed by single quotes ('). Within a literal, a single quote is represented by 2 single quotes ('').
  • Numeric -- numeric digits (at least 1) with an optional decimal point and exponent. The format is
      [ddd][[.]ddd][E[+|-]ddd]
    Numeric literals with no exponent or decimal point are typed as Integer. Those with a decimal point but no exponent are typed as Decimal. Those with an exponent are typed as Float.
  • Datetime -- datetime literals begin with a keyword identifying the type, followed by a string literal:
    • Date -- DATE 'yyyy-mm-dd'
    • Time -- TIME 'hh:mm:ss[.fff]'
    • Timestamp -- TIMESTAMP 'yyyy-mm-dd hh:mm:ss[.fff]'
    • Interval -- INTERVAL [+|-] string interval-qualifier
    The format of the string in the Interval literal depends on the interval qualifier. For year-month intervals, the format is: 'dd[-dd]'. For day-time intervals, the format is '[dd ]dd[:dd[:dd]][.fff]'.

SQL Functions

SQL has the following builtin functions:
  • SUBSTRING(exp-1 FROM exp-2 [FOR exp-3]) Extracts a substring from a string - exp-1, beginning at the integer value - exp-2, for the length of the integer value - exp-3. exp-2 is 1 relative. If FOR exp-3 is omitted, the length of the remaining string is used. Returns the substring.
  • UPPER(exp-1) Converts any lowercase characters in a string - exp-1 to uppercase. Returns the converted string.
  • LOWER(exp-1) Converts any uppercase characters in a string - exp-1 to lowercase. Returns the converted string.
  • TRIM([LEADING|TRAILING|BOTH] [FROM] exp-1) TRIM([LEADING|TRAILING|BOTH] exp-2 FROM exp-1) Trims leading, trailing or both characters from a string - exp-1. The trim character is a space, or if exp-2 is specified, it supplies the trim character. If LEADING, TRAILING, BOTH are missing, the default is BOTH. Returns the trimmed string.
  • POSITION(exp-1 IN exp-2) Searches a string - exp-2, for a match on a substring - exp-2. Returns an integer, the 1 relative position of the match or 0 for no match.
  • CHAR_LENGTH(exp-1)
    CHARACTER_LENGTH(exp-1) Returns the integer number of characters in the string - exp-1.
  • OCTET_LENGTH(exp-1) Returns the integer number of octets (8-bit bytes) needed to represent the string - exp-1.
  • EXTRACT(sub-field FROM exp-1) Returns the numeric sub-field extracted from a datetime value - exp-1. sub-field is YEAR, QUARTER, MONTH, DAY, HOUR, MINUTE, SECOND, TIMEZONE_HOUR or TIMEZONE_MINUTE. TIMEZONE_HOUR and TIMEZONE_MINUTE extract sub-fields from the Timezone portion of exp-1. QUARTER is (MONTH-1)/4+1.

System Values

SQL System Values are reserved names used to access builtin values:
  • USER -- returns a string with the current SQL authorization identifier.
  • CURRENT_USER -- same as USER.
  • SESSION_USER -- returns a string with the current SQL session authorization identifier.
  • SYSTEM_USER -- returns a string with the current operating system user.
  • CURRENT_DATE -- returns a Date value for the current system date.
  • CURRENT_TIME -- returns a Time value for the current system time.
  • CURRENT_TIMESTAMP -- returns a Timestamp value for the current system timestamp.

SQL Special Constructs

SQL supports a set of special expression constructs:
  • CAST(exp-1 AS data-type) Converts the value - exp-1, into the specified date-type. Returns the converted value.
  • COALESCE(exp-1, exp-2 [, exp-3] ...) Returns exp-1 if it is not null, otherwise returns exp-2 if it is not null, otherwise returns exp-3, and so on. Returns null if all values are null.
  • CASE exp-1 { WHEN exp-2 THEN exp-3 } ... [ELSE exp-4] END
    CASE { WHEN predicate-1 THEN exp-3 } ... [ELSE exp-4] END The first form of the CASE construct compares exp-1 to exp-2 in each WHEN clause. If a match is found, CASE returns exp-3 from the corresponding THEN clause. If no matches are found, it returns exp-4 from the ELSE clause or null if the ELSE clause is omitted.
    The second form of the CASE construct evaluates predicate-1 in each WHEN clause. If the predicate is true, CASE returns exp-3 from the corresponding THEN clause. If no predicates evaluate to true, it returns exp-4 from the ELSE clause or null if the ELSE clause is omitted. 

Expression Operators

Expression operators combine 2 subexpressions to calculate a value. There are 2 basic types -- numeric and string.
  • String Operators There is just one string operator - ||, for string concatenation. Both operands of || must be strings. The operator concatenates the second string to the end of the first. For example,
      'ab' || 'cd'  ==> 'abcd'
  • Numeric operators The numeric operators are common to most languages:
    • + -- addition
    • - -- subtraction
    • * -- multiplication
    • / -- division
    All numeric operators can be used on the standard numeric data types:
    • Integer -- TINYINT, SMALLINT, INT, BIGINT
    • Exact -- NUMERIC, DECIMAL
    • Approximate -- FLOAT, DOUBLE, REAL
    Automatic conversion is provided for numeric operators. If an integer type is combined with an exact type, the integer is converted to exact before the operation. If an exact (or integer) type is combined with an approximate type, it is converted to approximate before the operation. The + and - operators can also be used as unary operators.
    The numeric operators can be applied to datetime values, with some restrictions. The basic rules for datetime expressions are:
    • A date, time, timestamp value can be added to an interval; result is a date, time, timestamp value.
    • An interval value can be subtracted from a date, time, timestamp value; result is a date, time, timestamp value.
    • An interval value can be added to or subtracted from another interval; result is an interval value.
    • An interval can be multiplied by or divided by a standard numeric value; result is an interval value.
    A special form can be used to subtract a date, time, timestamp value from another date, time, timestamp value to yield an interval value:
    The interval-qualifier specifies the specific interval type for the result. A second special form allows a ? parameter to be typed as an interval:
In expressions, parentheses are used for grouping.

Joining Tables

The FROM clause allows more than 1 table in its list, however simply listing more than one table will very rarely produce the expected results. The rows from one table must be correlated with the rows of the others. This correlation is known as joining. An example can best illustrate the rationale behind joins. The following query:
    SELECT * FROM sp, p
Produces:
    sno pno qty pno descr color
    S1 P1 NULL P1 Widget Blue
    S1 P1 NULL P2 Widget Red
    S1 P1 NULL P3 Dongle Green
    S2 P1 200 P1 Widget Blue
    S2 P1 200 P2 Widget Red
    S2 P1 200 P3 Dongle Green
    S3 P1 1000 P1 Widget Blue
    S3 P1 1000 P2 Widget Red
    S3 P1 1000 P3 Dongle Green
    S3 P2 200 P1 Widget Blue
    S3 P2 200 P2 Widget Red
    S3 P2 200 P3 Dongle Green
Each row in sp is arbitrarily combined with each row in p, giving 12 result rows (4 rows in sp X 3 rows in p.) This is known as a cartesian product. A more usable query would correlate the rows from sp with rows from p, for instance matching on the common column -- pno:
    SELECT *
    FROM sp, p
    WHERE sp.pno = p.pno
This produces:
    sno pno qty pno descr color
    S1 P1 NULL P1 Widget Blue
    S2 P1 200 P1 Widget Blue
    S3 P1 1000 P1 Widget Blue
    S3 P2 200 P2 Widget Red
Rows for each part in p are combined with rows in sp for the same part by matching on part number (pno). In this query, the WHERE Clause provides the join predicate, matching pno from p with pno from sp. The join in this example is known as an inner equi-join. equi meaning that the join predicate uses = (equals) to match the join columns. Other types of joins use different comparison operators. For example, a query might use a greater-than join.
The term inner means only rows that match are included. Rows in the first table that have no matching rows in the second table are excluded and vice versa (in the above join, the row in p with pno P3 is not included in the result.) An outer join includes unmatched rows in the result. See Outer Join below.
More than 2 tables can participate in a join. This is basically just an extension of a 2 table join. 3 tables -- a, b, c, might be joined in various ways:
  • a joins b which joins c
  • a joins b and the join of a and b joins c
  • a joins b and a joins c
Plus several other variations. With inner joins, this structure is not explicit. It is implicit in the nature of the join predicates. With outer joins, it is explicit; see below. This query performs a 3 table join:
    SELECT name, qty, descr, color
    FROM s, sp, p
    WHERE s.sno = sp.sno
    AND sp.pno = p.pno
It joins s to sp and sp to p, producing:
    name qty descr color
    Pierre NULL Widget Blue
    John 200 Widget Blue
    Mario 1000 Widget Blue
    Mario 200 Widget Red
Note that the order of tables listed in the FROM clause should have no significance, nor does the order of join predicates in the WHERE clause.

Outer Joins

An inner join excludes rows from either table that don't have a matching row in the other table. An outer join provides the ability to include unmatched rows in the query results. The outer join combines the unmatched row in one of the tables with an artificial row for the other table. This artificial row has all columns set to null. The outer join is specified in the FROM clause and has the following general format:
    table-1 { LEFT | RIGHT | FULL } OUTER JOIN table-2 ON predicate-1
predicate-1 is a join predicate for the outer join. It can only reference columns from the joined tables. The LEFT, RIGHT or FULL specifiers give the type of join:
  • LEFT -- only unmatched rows from the left side table (table-1) are retained
  • RIGHT -- only unmatched rows from the right side table (table-2) are retained
  • FULL -- unmatched rows from both tables (table-1 and table-2) are retained
Outer join example:
    SELECT pno, descr, color, sno, qty
    FROM p LEFT OUTER JOIN sp ON p.pno = sp.pno
    pno descr color sno qty
    P1 Widget Blue S1 NULL
    P1 Widget Blue S2 200
    P1 Widget Blue S3 1000
    P2 Widget Red S3 200
    P3 Dongle Green NULL NULL

Self Joins

A query can join a table to itself. Self joins have a number of real world uses. For example, a self join can determine which parts have more than one supplier:
    SELECT DISTINCT a.pno
    FROM sp a, sp b
    WHERE a.pno = b.pno
    AND a.sno <> b.sno
    pno
    P1
As illustrated in the above example, self joins use correlation names to distinguish columns in the select list and where predicate. In this case, the references to the same table are renamed - a and b. Self joins are often used in subqueries. See Subqueries below.

Subqueries

Subqueries are an identifying feature of SQL. It is called Structured Query Language because a query can nest inside another query. There are 3 basic types of subqueries in SQL:
All subqueries must be enclosed in parentheses.

Predicate Subqueries

Predicate subqueries are used in the WHERE (and HAVING) clause. Each is a special logical construct. Except for EXISTS, predicate subqueries must retrieve one column (in their select list.)
  • IN Subquery The IN Subquery tests whether a scalar value matches the single query column value in any subquery result row. It has the following general format:
      value-1 [NOT] IN (query-1)
    Using NOT is equivalent to:
      NOT value-1 IN (query-1)
    For example, to list parts that have suppliers:
      SELECT *
      FROM p
      WHERE pno IN (SELECT pno FROM sp)
      pno descr color
      P1 Widget Blue
      P2 Widget Red
    The Self Join example in the previous subsection can be expressed with an IN Subquery:
      SELECT DISTINCT pno
      FROM sp a
      WHERE pno IN (SELECT pno FROM sp b WHERE a.sno <> b.sno)
      pno
      P1
    Note that the subquery where clause references a column in the outer query (a.sno). This is known as an outer reference. Subqueries with outer references are sometimes known as correlated subqueries.
  • Quantified Subqueries A quantified subquery allows several types of tests and can use the full set of comparison operators. It has the following general format:
      value-1 {=|>|<|>=|<=|<>} {ANY|ALL|SOME} (query-1)
    The comparison operator specifies how to compare value-1 to the single query column value from each subquery result row. The ANY, ALL, SOME specifiers give the type of match expected. ANY and SOME must match at least one row in the subquery. ALL must match all rows in the subquery, or the subquery must be empty (produce no rows). For example, to list all parts that have suppliers:
      SELECT *
      FROM p
      WHERE pno =ANY (SELECT pno FROM sp)
      pno descr color
      P1 Widget Blue
      P2 Widget Red
    A self join is used to list the supplier with the highest quantity of each part (ignoring null quantities):
      SELECT *
      FROM sp a
      WHERE qty >ALL (SELECT qty FROM sp b
                      WHERE a.pno = b.pno 
                      AND a.sno <> b.sno
                      AND qty IS NOT NULL)
      sno pno qty
      S3 P1 1000
      S3 P2 200
  • EXISTS Subqueries The EXISTS Subquery tests whether a subquery retrieves at least one row, that is, whether a qualifying row exists. It has the following general format
      EXISTS(query-1)
    Any valid EXISTS subquery must contain an outer reference. It must be a correlated subquery. Note: the select list in the EXISTS subquery is not actually used in evaluating the EXISTS, so it can contain any valid select list (though * is normally used).
    To list parts that have suppliers:
      SELECT *
      FROM p
      WHERE EXISTS(SELECT * FROM sp WHERE p.pno = sp.pno)
      pno descr color
      P1 Widget Blue
      P2 Widget Red

Scalar Subqueries

The Scalar Subquery can be used anywhere a value can be used. The subquery must reference just one column in the select list. It must also retrieve no more than one row. When the subquery returns a single row, the value of the single select list column becomes the value of the Scalar Subquery. When the subquery returns no rows, a database null is used as the result of the subquery. Should the subquery retreive more than one row, it is a run-time error and aborts query execution.
A Scalar Subquery can appear as a scalar value in the select list and where predicate of an another query. The following query on the sp table uses a Scalar Subquery in the select list to retrieve the supplier city associated with the supplier number (sno column in sp):
    SELECT pno, qty, (SELECT city FROM s WHERE s.sno = sp.sno)
    FROM sp
    pno qty city
    P1 NULL Paris
    P1 200 London
    P1 1000 Rome
    P2 200 Rome
The next query on the sp table uses a Scalar Subquery in the where clause to match parts on the color associated with the part number (pno column in sp):
    SELECT *
    FROM sp
    WHERE 'Blue' = (SELECT color FROM p WHERE p.pno = sp.pno)
    sno pno qty
    S1 P1 NULL
    S2 P1 200
    S3 P1 1000
Note that both example queries use outer references. This is normal in Scalar Subqueries. Often, Scalar Subqueries are Aggregate Queries.

Table Subqueries

Table Subqueries are queries used in the FROM clause, replacing a table name. Basically, the result set of the Table Subquery acts like a base table in the from list. Table Subqueries can have a correlation name in the from list. They can also be in outer joins. The following two queries produce the same result:
    SELECT p.*, qty
    FROM p, sp
    WHERE p.pno = sp.pno
    AND sno = 'S3'
    pno descr color qty
    P1 Widget Blue 1000
    P2 Widget Red 200
    SELECT p.*, qty
    FROM p, (SELECT pno, qty FROM sp WHERE sno = 'S3')
    WHERE p.pno = sp.pno
    pno descr color qty
    P1 Widget Blue 1000
    P2 Widget Red 200

Grouping Queries

A Grouping Query is a special type of query that groups and summarizes rows. It uses the GROUP BY Clause. A Grouping Query groups rows based on common values in a set of grouping columns. Rows with the same values for the grouping columns are placed in distinct groups. Each group is treated as a single row in the query result.
Even though a group is treated as a single row, the underlying rows can be subject to summary operations known as Set Functions whose results can be included in the query. The optional HAVING Clause supports filtering for group rows in the same manner as the WHERE clause filters FROM rows.
For example, grouping the sp table on the pno column produces 2 groups:
    sno pno qty
    S1 P1 NULL 'P1' Group
    S2 P1 200
    S3 P1 1000
    S3 P2 200 'P2' Group
  • The P1 group contains 3 sp rows with pno='P1'
  • The P2 group contains a single sp row with pno='P2'
Nulls get special treatment by GROUP BY. GROUP BY considers a null as distinct from every other null. Each row that has a null in one of its grouping columns forms a separate group. Grouping the sp table on the qty column produces 3 groups:
    sno pno qty
    S1 P1 NULL NULL Group
    S2 P1 200 200 Group
    S3 P2 200
    S3 P1 1000 1000 Group
The row where qty is null forms a separate group.

GROUP BY Clause

GROUP BY is an optional clause in a query. It follows the WHERE clause or the FROM clause if the WHERE clause is missing. A query containing a GROUP BY clause is a Grouping Query. The GROUP BY clause has the following general format:
    GROUP BY column-1 [, column-2] ...
column-1 and column-2 are the grouping columns. They must be names of columns from tables in the FROM clause; they can't be expressions. GROUP BY operates on the rows from the FROM clause as filtered by the WHERE clause. It collects the rows into groups based on common values in the grouping columns. Except nulls, rows with the same set of values for the grouping columns are placed in the same group. If any grouping column for a row contains a null, the row is given its own group.
For example,
    SELECT pno
    FROM sp
    GROUP BY pno
    pno
    P1
    P2
In Grouping Queries, the select list can only contain grouping columns, plus literals, outer references and expression involving these elements. Non-grouping columns from the underlying FROM tables cannot be referenced directly. However, non-grouping columns can be used in the select list as arguments to Set Functions. Set Functions summarize columns from the underlying rows of a group.

Set Functions

Set Functions are special summarizing functions used with Grouping Queries and Aggregate Queries. They summarize columns from the underlying rows of a group or aggregate. Using the Group By example from above, grouping the sp table on the pno column:
    sno pno qty
    S1 P1 NULL 'P1' Group
    S2 P1 200
    S3 P1 1000
    S3 P2 200 'P2' Group
A Set Function can compute the total quantities for each group:
    sno pno qty qty total
    S1 P1 NULL 'P1' Group 1200
    S2 P1 200
    S3 P1 1000
    S3 P2 200 'P2' Group 200
Null columns are ignored in computing the summary. The Set Function -- SUM, computes the arithmetic sum of a numeric column in a set of grouped/aggregate rows. For example,
    SELECT pno, SUM(qty)
    FROM sp
    GROUP BY pno
    pno  
    P1 1200
    P2 200
Set Functions have the following general format:
    set-function ( [DISTINCT|ALL] column-1 )
set-function is:
  • COUNT -- count of rows
  • SUM -- arithmetic sum of numeric column
  • AVG -- arithmetic average of numeric column; should be SUM()/COUNT().
  • MIN -- minimum value found in column
  • MAX -- maximum value found in column
The result of the COUNT function is always integer. The result of all other Set Functions is the same data type as the argument. The Set Functions skip columns with nulls, summarizing non-null values. COUNT counts rows with non-null values, AVG averages non-null values, and so on. COUNT returns 0 when no non-null column values are found; the other functions return null when there are no values to summarize.
A Set Function argument can be a column or an scalar expression.
The DISTINCT and ALL specifiers are optional. ALL specifies that all non-null values are summarized; it is the default. DISTINCT specifies that distinct column values are summarized; duplicate values are skipped. Note: DISTINCT has no effect on MIN and MAX results.
COUNT also has an alternate format:
    COUNT(*)
... which counts the underlying rows regardless of column contents. Set Function examples:
    SELECT pno, MIN(sno), MAX(qty), AVG(qty), COUNT(DISTINCT sno)
    FROM sp
    GROUP BY pno
    pno        
    P1 S1 1000 600 3
    P2 S3 200 200 1
    SELECT sno, COUNT(*) parts
    FROM sp
    GROUP BY sno
    sno parts
    S1 1
    S2 1
    S3 2

HAVING Clause

The HAVING Clause is associated with Grouping Queries and Aggregate Queries. It is optional in both cases. In Grouping Queries, it follows the GROUP BY clause. In Aggregate Queries, HAVING follows the WHERE clause or the FROM clause if the WHERE clause is missing.
The HAVING Clause has the following general format:
    HAVING predicate
Like the WHERE Clause, HAVING filters the query result rows. WHERE filters the rows from the FROM clause. HAVING filters the grouped rows (from the GROUP BY clause) or the aggregate row (for Aggregate Queries). predicate is a logical expression referencing grouped columns and set functions. It has the same restrictions as the select list for Grouping Queries and Aggregate Queries.
If the Having predicate evaluates to true for a grouped or aggregate row, the row is included in the query result, otherwise, the row is skipped (not included in the query result).
For example,
    SELECT sno, COUNT(*) parts
    FROM sp
    GROUP BY sno
    HAVING COUNT(*) > 1
    sno parts
    S3 2

Aggregate Queries

An Aggregate Query can use Set Functions and a HAVING Clause. It is similar to a Grouping Query except there are no grouping columns. The underlying rows from the FROM and WHERE clauses are grouped into a single aggregate row. An Aggregate Query always returns a single row, except when the Having clause is used. An Aggregate Query is a query containing Set Functions in the select list but no GROUP BY clause. The Set Functions operate on the columns of the underlying rows of the single aggregate row. Except for outer references, any columns used in the select list must be arguments to Set Functions. See Set Functions above.
An aggregate query may also have a Having clause. The Having clause filters the single aggregate row. If the Having predicate evaluates to true, the query result contains the aggregate row. Otherwise, the query result contains no rows. See HAVING Clause above.
For example,
    SELECT COUNT(DISTINCT pno) number_parts, SUM(qty) total_parts
    FROM sp
    number_parts total_parts

2
1400
Subqueries are often Aggregate Queries. For example, parts with suppliers:
    SELECT *
    FROM p
    WHERE (SELECT COUNT(*) FROM sp WHERE sp.pno=p.pno) > 0
    pno descr color
    P1 Widget Blue
    P2 Widget Red
Parts with multiple suppliers:
    SELECT *
    FROM p
    WHERE (SELECT COUNT(DISTINCT sno) FROM sp WHERE sp.pno=p.pno) > 1
    pno descr color
    P1 Widget Blue

Union Queries

The SQL UNION operator combines the results of two queries into a composite result. The component queries can be SELECT/FROM queries with optional WHERE/GROUP BY/HAVING clauses. The UNION operator has the following general format:
    query-1 UNION [ALL] query-2
query-1 and query-2 are full query specifications. The UNION operator creates a new query result that includes rows from each component query.
By default, UNION eliminates duplicate rows in its composite results. The optional ALL specifier requests that duplicates be retained in the UNION result.
The component queries of a Union Query can also be Union Queries themselves. Parentheses are used for grouping queries.
The select lists from the component queries must be union-compatible. They must match in degree (number of columns). For Entry Level SQL92, the column descriptor (data type and precision, scale) for each corresponding column must match. The rules for Intermediate Level SQL92 are less restrictive. See Union-Compatible Queries.

Union-Compatible Queries

For Entry Level SQL92, each corresponding column of both queries must have the same column descriptor in order for two queries to be union-compatible. The rules are less restrictive for Intermediate Level SQL92. It supports automatic conversion within type categories. In general, the resulting data type will be the broader type. The corresponding columns need only be in the same data type category:
  • Character (String) -- fixed/variable length
  • Bit String -- fixed/variable length
  • Exact Numeric (fixed point) -- integer/decimal
  • Approximate Numeric (floating point) -- float/double
  • Datetime -- sub-category must be the same,
    • Date
    • Time
    • Timestamp
  • Interval -- sub-category must be the same,
    • Year-month
    • Day-time

UNION Examples

    SELECT * FROM sp
    UNION
    SELECT CAST(' ' AS VARCHAR(5)), pno, CAST(0 AS INT)
    FROM p
    WHERE pno NOT IN (SELECT pno FROM sp)
    sno pno qty
    S1 P1 NULL
    S2 P1 200
    S3 P1 1000
    S3 P2 200
      P3 0

INSERT Statement

The INSERT Statement adds one or more rows to a table. It has two formats:
    INSERT INTO table-1 [(column-list)] VALUES (value-list)
and,
    INSERT INTO table-1 [(column-list)] (query-specification)
The first form inserts a single row into table-1 and explicitly specifies the column values for the row. The second form uses the result of query-specification to insert one or more rows into table-1. The result rows from the query are the rows added to the insert table. Note: the query cannot reference table-1.
Both forms have an optional column-list specification. Only the columns listed will be assigned values. Unlisted columns are set to null, so unlisted columns must allow nulls. The values from the VALUES Clause (first form) or the columns from the query-specification rows (second form) are assigned to the corresponding column in column-list in order.
If the optional column-list is missing, the default column list is substituted. The default column list contains all columns in table-1 in the order they were declared in CREATE TABLE, or CREATE VIEW.

VALUES Clause

The VALUES Clause in the INSERT Statement provides a set of values to place in the columns of a new row. It has the following general format:
    VALUES ( value-1 [, value-2] ... )
value-1 and value-2 are Literal Values or Scalar Expressions involving literals. They can also specify NULL. The values list in the VALUES clause must match the explicit or implicit column list for INSERT in degree (number of items). They must also match the data type of corresponding column or be convertible to that data type.

INSERT Examples

    INSERT INTO p (pno, color) VALUES ('P4', 'Brown')
    Before After
    pno descr color
    P1 Widget Blue
    P2 Widget Red
    P3 Dongle Green

=>

pno descr color
P1
Widget
Blue
P2
Widget
Red
P3
Dongle
Green
P4
NULL
Brown
    INSERT INTO sp
    SELECT s.sno, p.pno, 500
    FROM s, p
    WHERE p.color='Green' AND s.city='London'
    Before After
    sno pno qty
    S1 P1 NULL
    S2 P1 200
    S3 P1 1000
    S3 P2 200

    =>

    sno pno qty
    S1 P1 NULL
    S2 P1 200
    S3 P1 1000
    S3 P2 200
    S2 P3 500

UPDATE Statement

The UPDATE statement modifies columns in selected table rows. It has the following general format:
    UPDATE table-1 SET set-list [WHERE predicate]
The optional WHERE Clause has the same format as in the SELECT Statement. See WHERE Clause. The WHERE clause chooses which table rows to update. If it is missing, all rows are in table-1 are updated.
The set-list contains assignments of new values for selected columns. See SET Clause.
The SET Clause expressions and WHERE Clause predicate can contain subqueries, but the subqueries cannot reference table-1. This prevents situations where results are dependent on the order of processing.

SET Clause

The SET Clause in the UPDATE Statement updates (assigns new value to) columns in the selected table rows. It has the following general format:
    SET column-1 = value-1 [, column-2 = value-2] ...
column-1 and column-2 are columns in the Update table. value-1 and value-2 are expressions that can reference columns from the update table. They also can be the keyword -- NULL, to set the column to null. Since the assignment expressions can reference columns from the current row, the expressions are evaluated first. After the values of all Set expressions have been computed, they are then assigned to the referenced columns. This avoids results dependent on the order of processing.

UPDATE Examples

    UPDATE sp SET qty = qty + 20
    Before After
    sno pno qty
    S1 P1 NULL
    S2 P1 200
    S3 P1 1000
    S3 P2 200

    =>

    sno pno qty
    S1 P1 NULL
    S2 P1 220
    S3 P1 1020
    S3 P2 220
    UPDATE s
    SET name = 'Tony', city = 'Milan'
    WHERE sno = 'S3'
    Before After
    sno name city
    S1 Pierre Paris
    S2 John London
    S3 Mario Rome

    =>

    sno name city
    S1 Pierre Paris
    S2 John London
    S3 Tony Milan

DELETE Statement

The DELETE Statement removes selected rows from a table. It has the following general format:
    DELETE FROM table-1 [WHERE predicate]
The optional WHERE Clause has the same format as in the SELECT Statement. See WHERE Clause. The WHERE clause chooses which table rows to delete. If it is missing, all rows are in table-1 are removed. The WHERE Clause predicate can contain subqueries, but the subqueries cannot reference table-1. This prevents situations where results are dependent on the order of processing.

DELETE Examples

    DELETE FROM sp WHERE pno = 'P1'
    Before After
    sno pno qty
    S1 P1 NULL
    S2 P1 200
    S3 P1 1000
    S3 P2 200

    =>

    sno pno qty
    S3 P2 200
    DELETE FROM p WHERE pno NOT IN (SELECT pno FROM sp)
    Before After
    pno descr color
    P1 Widget Blue
    P2 Widget Red
    P3 Dongle Green

=>

pno descr color
P1
Widget
Blue
P2
Widget
Red

Transaction Overview

A database transaction is a larger unit that frames multiple SQL statements. A transaction ensures that the action of the framed statements is atomic with respect to recovery.
A SQL Modification Statement has limited effect. A given statement can only directly modify the contents of a single table (Referential Integrity effects may cause indirect modification of other tables.) The upshot is that operations which require modification of several tables must involve multiple modification statements. A classic example is a bank operation that transfers funds from one type of account to another, requiring updates to 2 tables. Transactions provide a way to group these multiple statements in one atomic unit.
In SQL92, there is no BEGIN TRANSACTION statement. A transaction begins with the execution of a SQL-Data statement when there is no current transaction. All subsequent SQL-Data statements until COMMIT or ROLLBACK become part of the transaction. Execution of a COMMIT Statement or ROLLBACK Statement completes the current transaction. A subsequent SQL-Data statement starts a new transaction.
In terms of direct effect on the database, it is the SQL Modification Statements that are the main consideration since they change data. The total set of changes to the database by the modification statements in a transaction are treated as an atomic unit through the actions of the transaction. The set of changes either:
  • Is made fully persistent in the database through the action of the COMMIT Statement, or
  • Has no persistent effect whatever on the database, through:
    • the action of the ROLLBACK Statement,
    • abnormal termination of the client requesting the transaction, or
    • abnormal termination of the transaction by the DBMS. This may be an action by the system (deadlock resolution) or by an administrative agent, or it may be an abnormal termination of the DBMS itself. In the latter case, the DBMS must roll back any active transactions during recovery.
The DBMS must ensure that the effect of a transaction is not partial. All changes in a transaction must be made persistent, or no changes from the transaction must be made persistent.

Transaction Isolation

In most cases, transactions are executed under a client connection to the DBMS. Multiple client connections can initiate transactions at the same time. This is known as concurrent transactions. In the relational model, each transaction is completely isolated from other active transactions. After initiation, a transaction can only see changes to the database made by transactions committed prior to starting the new transaction. Changes made by concurrent transactions are not seen by SQL DML query and modification statements. This is known as full isolation or Serializable transactions.
SQL92 defines Serializable for transactions. However, fully serialized transactions can impact performance. For this reason, SQL92 allows additional isolation modes that reduce the isolation between concurrent transactions. SQL92 defines 3 other isolation modes, but support by existing DBMSs is often incomplete and doesn't always match the SQL92 modes. Check the documentation of your DBMS for more details.
Transaction isolation controls the visibility of changes between transactions in different sessions (connections). It determines if queries in one session can see changes made by a transaction in another session. There are 4 levels of transaction isolation. The level providing the greatest isolation from other transactions is Serializable.
At transaction isolation level Serializable, a transaction is fully isolated from changes made by other sessions. Queries issued under Serializable transactions cannot see any subsequent changes, committed or not, from other transactions. The effect is the same as if transactions were serial, that is, each transaction completing before another one is begun.
At the opposite end of the spectrum is Read Uncommitted. It is the lowest level of isolation. With Read Uncommitted, a session can read (query) subsequent changes made by other sessions, either committed or uncommitted. Read uncommitted transactions have the following characteristics:
  • Dirty Read -- a session can read rows changed by transactions in other sessions that have not been committed. If the other session then rolls back its transaction, subsequent reads of the same rows will find column values returned to previous values, deleted rows reappearing and rows inserted by the other transaction missing.
  • Non-repeatable Read -- a session can read a row in a transaction. Another session then changes the row (UPDATE or DELETE) and commits its transaction. If the first session subsequently re-reads the row in the same transaction, it will see the change.
  • Phantoms -- a session can read a set of rows in a transaction that satisfies a search condition (which might be all rows). Another session then generates a row (INSERT) that satisfies the search condition and commits its transaction. If the first session subsequently repeats the search in the same transaction, it will see the new row.
The other transaction levels -- Read Committed, Repeatable Read and Serializable, will not read uncommitted changes. Dirty reads are not possible. The next level above Read Uncommitted is Read Committed, and the next above that is Repeatable Read. In Read Committed isolation level, Dirty Reads are not possible, but Non-repeatable Reads and Phantoms are possible. In Repeatable Read isolation level, Dirty Reads and Non-repeatable Reads are not possible but Phantoms are. In Serializable, Dirty Reads, Non-repeatable Reads, and Phantoms are not possible.
The isolation provided by each transaction isolation level is summarized below:
Dirty Reads Non-repeatable Reads Phantoms
Read Uncommitted Y Y Y
Read Committed N Y Y
Repeatable Read N N Y
Serializable N N N

Note: SQL92 defines the SET TRANSACTION statement to set the transaction isolation level for a session, but most DBMSs support a function/method in the Client API as an alternative.

SQL-Schema Statements in Transactions

The 3rd type of SQL Statements - SQL-Schema Statements, may participate in the transaction mechanism. SQL-Schema statements can either be:
  • included in a transaction along with SQL-Data statements,
  • required to be in separate transactions, or
  • ignored by the transaction mechanism (can't be rolled back).
SQL92 leaves the choice up to the individual DBMS. It is implementation defined behavior.

COMMIT Statement

The COMMIT Statement terminates the current transaction and makes all changes under the transaction persistent. It commits the changes to the database. The COMMIT statement has the following general format:
    COMMIT [WORK]
WORK is an optional keyword that does not change the semantics of COMMIT.

ROLLBACK Statement

The ROLLBACK Statement terminates the current transaction and rescinds all changes made under the transaction. It rolls back the changes to the database. The ROLLBACK statement has the following general format:
    ROLLBACK [WORK]
WORK is an optional keyword that does not change the semantics of ROLLBACK.

Schema Overview

A relational database contains a catalog that describes the various elements in the system. The catalog divides the database into sub-databases known as schemas. Within each schema are database objects -- tables, views and privileges. The catalog itself is a set of tables with its own schema name - definition_schema. Tables in the catalog cannot be modified directly. They are modified indirectly with SQL-Schema statements.

Tables

The database table is the root structure in the relational model and in SQL. A table (called a relation in relational) consists of rows and columns. In relational, rows are called tuples and columns are called attributes. Tables are often displayed in a flat format, with columns arrayed horizontally and rows vertically:
    C o l u m n s
    R
                           
                           
                           
                           
                           
    o
    w
    s
Database tables are a logical structure with no implied physical characteristics. Primary among the various logical tables is the base table. A base table is persistent and self contained, that is, all data is part of the table itself with no information dynamically derived from other tables. A table has a fixed set of columns. The columns in a base table are not accessed positionally but by name, which must be unique among the columns of the table. Each column has a defined data type, and the value for the column in each row must be from the defined data type or null. The columns of a table are accessed and identified by name.
A table has 0 or more rows. A row in a base table has a value or null for each column in the table. The rows in a table have no defined ordering and are not accessed positionally. A table row is accessed and identified by the values in its columns.
In SQL92, base tables can have duplicate rows (rows where each column has the same value or null). However, the relational model does not recognize tables with duplicate rows as valid base tables (relations). The relational model requires that each base table have a unique identifier, known as the Primary Key. The primary key for a table is a designated set of columns which have a unique value for each table row. For a discussion of Primary Keys, see Entity Integrity under CREATE TABLE below.
A base table is defined using the CREATE TABLE Statement. This statement places the table description in the catalog and initializes an internal entity for the actual representation of the base table.
Example base table - s:
    sno name city
    S1 Pierre Paris
    S2 John London
    S3 Mario Rome
The s table records suppliers. It has 3 defined columns:
  • sno -- supplier number, an unique identifier that is the primary key
  • name -- the name of the supplier
  • city -- the city where the supplier is located
At the current time, there are 3 rows. Other types of tables in the system are derived tables. SQL-Data statements use internally derived tables in computing results. A query is in fact a derived table. For instance, the query operator - Union, combines two derived tables to produce a third one. Much of the power of SQL comes from the fact that its higher level operations are performed on tables and produce a table as their result.
Derived tables are less constrained than base tables. Column names are not required and need not be unique. Derived tables may have duplicate rows. Views are a type of derived table that are cataloged in the database. See Views below.

Views

A view is a derived table registered in the catalog. A view is defined using a SQL query. The view is dynamically derived, that is, its contents are materialized for each use. Views are added to the catalog with the CREATE VIEW Statement. Once defined in the catalog, a view can substitute for a table in SQL-Data statements. A view name can be used instead of a base table name in the FROM clause of a SELECT statement. Views can also be the subject of a modification statement with some restrictions.
A SQL Modification Statement can operate on a view if it is an updatable view. An updatable view has the following restrictions on its defining query:
  • The query FROM clause can reference a single table (or view)
  • The single table in the FROM clause must be:
    • a base table,
    • a view that is also an updatable view, or
    • a nested query that is updatable, that is, it follows the rules for an updatable view query.
  • The query must be a basic query, not a:
  • The select list cannot contain:
    • the DISTINCT specifier,
    • an Expression, or
    • duplicate column references
Subqueries are acceptable in updatable views but cannot reference the underlying base table for the view's FROM clause.

Privileges

SQL92 defines a SQL-agent as an implementation-dependent entity that causes the execution of SQL statements. Prior to execution of SQL statements, the SQL-agent must establish an authorization identifier for database access. An authorization identifier is commonly called a user name. A DBMS user may access database objects (tables, columns, views) as allowed by the privileges assigned to that specific authorization identifier. Access privileges may be granted by the system (automatic) or by other users.
System granted privileges include:
  • All privileges on a table to the user that created the table. This includes the privilege to grant privileges on the table to other users.
  • SELECT (readonly) privilege on the catalog (the tables in the schema - definition_schema). This is granted to all users.
User granted privileges cover privileges to access and modify tables and their columns. Privileges can be granted for specific SQL-Data Statements -- SELECT, INSERT, UPDATE, DELETE.

CREATE TABLE Statement

The CREATE TABLE Statement creates a new base table. It adds the table description to the catalog. A base table is a logical entity with persistence. The logical description of a base table consists of:
  • Schema -- the logical database schema the table resides in
  • Table Name -- a name unique among tables and views in the Schema
  • Column List -- an ordered list of column declarations (name, data type)
  • Constraints -- a list of constraints on the contents of the table
The CREATE TABLE Statement has the following general format:
    CREATE TABLE table-name ({column-descr|constraint} [,{column-descr|constraint}]...)
table-name is the new name for the table. column-descr is a column declaration. constraint is a table constraint. The column declaration can include optional column constraints. The declaration has the following general format:
column-name is the name of the column and must be unique among the columns of the table. data-type declares the type of the column. Data types are described below. column-constraints is an optional list of column constraints with no separators.

Constraints

Constraint specifications add additional restrictions on the contents of the table. They are automatically enforced by the DBMS. The column constraints are:
  • NOT NULL -- specifies that the column can't be set to null. If this constraint is not specified, the column is nullable, that is, it can be set to null. Normally, primary key columns are declared as NOT NULL.
  • PRIMARY KEY -- specifies that this column is the only column in the primary key. There can be only one primary key declaration in a CREATE TABLE. For primary keys with multiple columns, use the PRIMARY KEY table constraint. See Entity Integrity below for a detailed description of primary keys.
  • UNIQUE -- specifies that this column has a unique value or null for all rows of the table.
  • REFERENCES -- specifies that this column is the only column in a foreign key. For foreign keys with multiple columns, use the FOREIGN KEY table constraint. See Referential Integrity below for a detailed description of primary keys.
  • CHECK -- specifies a user defined constraint on the table. See the table constraint - CHECK, below.


The table constraints are:
  • PRIMARY KEY -- specifies the set of columns that comprise the primary key. There can be only one primary key declaration in a CREATE TABLE Statement. See Entity Integrity below for a detailed description of primary keys.
  • UNIQUE -- specifies that a set of columns have unique values (or nulls) for all rows in the table. The UNIQUE specifier is followed by a parenthesized list of column names, separated by commas.
  • FOREIGN KEY -- specifies the set of columns in a foreign key. See Referential Integrity below for a detailed description of foreign keys.
  • CHECK -- specifies a user defined constraint, known as a check condition. The CHECK specifier is followed by a predicate enclosed in parentheses. For Intermediate Level SQL92, the CHECK predicate can only reference columns from the current table row, with no subqueries. Many DBMSs support subqueries in the check predicate. The check predicate must evaluate to not False (that is, the result must be True or Unknown) before a modification or addition of a row takes place. The check is effectively made on the contents of the table after the modification. For INSERT Statements, the predicate is evaluated as if the INSERT row were added to the table. For UPDATE Statements, the predicate is evaluated as if the row were updated. For DELETE Statements, the predicate is evaluated as if the row were deleted (Note: A check predicate is only useful for DELETE if a self-referencing subquery is used.)

Data Type

This subsection describes data type specifications. The data type categories are:
  • Character (String) -- fixed or variable length character strings. The character set is implementation defined but often defaults to ASCII.
  • Numeric -- values representing numeric quantities. Numeric values are divided into these two broad categories:
    • Exact (also known as fixed-point) -- Exact numeric values have a fixed number of digits to the left of the decimal point and a fixed number of digits to the right (the scale). The total number of digits on both sides of the decimal are the precision. A special subset of exact numeric types with a scale of 0 is called integer.
    • Approximate (also known as floating-point) -- Approximate numeric values that have a fixed precision (number of digits) but a floating decimal point.
    All numeric types are signed.
  • Datetime -- Datetime values include calendar and clock values (Date, Time, Timestamp) and intervals. The datetime types are:
    • Date -- calendar date with year, month and day
    • Time -- clock time with hour, minute, second and fraction of second, plus a timezone component (adjustment in hours, minutes)
    • Timestamp -- combination calendar date and clock time with year, month, day, hour, minute, second and fraction of second, plus a timezone component (adjustment in hours, minutes)
    • Interval -- intervals represent time and date intervals. They are signed. An interval value can contain a subset of the interval fields, for example - hour to minute, year, day to second. Interval types are subdivided into:
      • year-month intervals -- may contain years, months or combination years/months value.
      • day-time intervals -- days, hours, minutes, seconds, fractions of second.

Data type declarations have the following general format:
    Character (String)
      CHAR [(length)]
      CHARACTER [(length)]
      VARCHAR (length)
      CHARACTER VARYING (length) length specifies the number of characters for fixed size strings (CHAR, CHARACTER); spaces are supplied for shorter strings. If length is missing for fixed size strings, the default length is 1. For variable size strings (VARCHAR, CHARACTER VARYING), length is the maximum size of the string. Strings exceeding length are truncated on the right.
    Numeric
      SMALLINT
      INT
      INTEGER The integer types have default binary precision -- 15 for SMALLINT and 31 for INT, INTEGER.
      NUMERIC ( precision [, scale] )
      DECIMAL ( precision [, scale] )
      Fixed point types have a decimal precision (total number of digits) and scale (which cannot exceed the precision). The default scale is 0. NUMERIC scales must be represented exactly. DECIMAL values can be stored internally with a larger scale (implementation defined).
      FLOAT [(precision)]
      REAL
      DOUBLE
      The floating point types have a binary precision (maximum significant binary digits). Precision values are implementation dependent for REAL and DOUBLE, although the standard states that the default precision for DOUBLE must be larger than for REAL. FLOAT also uses an implementation defined default for precision (commonly this is the same as for REAL), but the binary precision for FLOAT can be explicit.

    Datetime
      DATE
      TIME [(scale)] [WITH TIME ZONE]
      TIMESTAMP [(scale)] [WITH TIME ZONE] TIME and TIMESTAMP allow an optional seconds fraction (scale). The default scale for TIME is 0, for TIMESTAMP 6. The optional WITH TIME ZONE specifier indicates that the timezone adjustment is stored with the value; if omitted, the current system timezone is assumed.
      INTERVAL interval-qualifier
      See below for a description of the interval-qualifier.


Interval Qualifier

An interval qualifier defines the specific type of an interval value. The qualifier for an interval type declares the sub-fields that comprise the interval, the precision of the highest (left-most) sub-field and the scale of the SECOND sub-field (if any). Intervals are divided into sub-types -- year-month intervals and day-time intervals. Year-month intervals can only contain the sub-fields - year and month. Day-time intervals can contain day, hour, minute, second. The interval qualifier has the following formats:
    YEAR [(precision)] [ TO MONTH ]
    
    MONTH [(precision)]
    
    {DAY|HOUR|MINUTE} [(precision)] [ TO SECOND [(scale)] ]
    
    DAY [(precision)] [ TO {HOUR|MINUTE} ]
    
    HOUR [(precision)] [ TO MINUTE ]
    
    SECOND [ (precision [, scale]) ]
The default precision is 2. The default scale is 6.

Entity Integrity

As mentioned earlier, the relational model requires that each base table have a Primary Key. SQL92, on the other hand, allows a table to created without a primary key. The advice here is to create all tables with primary keys. A primary key is a constraint on the contents of a table. In relational terms, the primary key maintains Entity Integrity for the table. It constrains the table as follows,
  • For a given row, the set of values for the primary key columns must be unique from all other rows in the table,
  • No primary key column can contain a null, and
  • A table can have only one primary key (set of primary key columns).
Note: SQL92 does not require the second restriction on nulls in the primary key. However, it is required for a relational system. Entity Integrity (Primary Keys) is enforced by the DBMS and ensures that every row has a proper unique identifier. The contents of any column in the table with Entity Integrity can be uniquely accessed with 3 pieces of information:
  • table identifier
  • primary key value
  • column name
This capability is crucial to a relational system. Having a clear, consistent identifier for table rows (and their columns) distinguishes relational systems from all others. It allows the establishment of relationships between tables, also crucial to relational systems. This is discussed below under Referential Integrity. The primary key constraint in the CREATE STATEMENT has two forms. When the primary key consists of a single column, it can be declared as a column constraint, simply - PRIMARY KEY, attached to the column descriptor. For example:
    sno VARCHAR(5) NOT NULL PRIMARY KEY
As a table constraint, it has the following format:
    PRIMARY KEY ( column-1 [, column-2] ...)
column-1 and column-2 are the names of the columns of the primary key. For example,
    PRIMARY KEY (sno, pno)
The order of columns in the primary key is not significant, except as the default order for the FOREIGN KEY table constraint, See Referential Integrity, below.

Referential Integrity

Foreign keys provide relationships between tables in the database. In relational, a foreign key in a table is a set of columns that reference the primary key of another table. For each row in the referencing table, the foreign key must match an existing primary key in the referenced table. The enforcement of this constraint is known as Referential Integrity. Referential Integrity requires that:
  • The columns of a foreign key must match in number and type the columns of the primary key in the referenced table.
  • The values of the foreign key columns in each row of the referencing table must match the values of the corresponding primary key columns for a row in the referenced table.
The one exception to the second restriction is when the foreign key columns for a row contain nulls. Since primary keys should not contain nulls, a foreign key with nulls cannot match any row in the referenced table. However, a row with a foreign key where any foreign key column contains null is allowed in the referencing table. No corresponding primary key value in the referenced table is required when any one (or more) of the foreign key columns is null. Other columns in the foreign key may be null or non-null. Such a foreign key is a null reference, because it does not reference any row in the referenced table. Like other constraints, the referential integrity constraint restricts the contents of the referencing table, but it also may in effect restrict the contents of the referenced table. When a row in a table is referenced (through its primary key) by a foreign key in a row in another table, operations that affect its primary key columns have side-effects and may restrict the operation. Changing the primary key of or deleting a row which has referencing foreign keys would violate the referential integrity constraints on the referencing table if allowed to proceed. This is handled in two ways,
  • The referenced table is restricted from making the change (and violating referential integrity in the referencing table), or
  • Rows in the referencing table are modified so the referential integrity constraint is maintained.
These actions are controlled by the referential integrity effects declarations, called referential triggers by SQL92. The referential integrity effect actions defined for SQL are:
  • NO ACTION -- the change to the referenced (primary key) table is not performed. This is the default.
  • CASCADE -- the change to the referenced table is propagated to the referencing (foreign key) table.
  • SET NULL -- the foreign key columns in the referencing table are set to null.
Update and delete have separate action declarations. For CASCADE, update and delete also operate differently:
  • For update (the primary key column values have been modified), the corresponding foreign key columns for referencing rows are set to the new values.
  • For delete (the primary key row is deleted), the referencing rows are deleted.
A referential integrity constraint in the CREATE STATEMENT has two forms. When the foreign key consists of a single column, it can be declared as a column constraint, like:
    column-descr REFERENCES references-specification
As a table constraint, it has the following format:
    FOREIGN KEY (column-list) REFERENCES references-specification
column-list is the referencing table columns that comprise the foreign key. Commas separate column names in the list. Their order must match the explicit or implicit column list in the references-specification. The references-specification has the following format:
    table-2 [ ( referenced-columns ) ]
            [ ON UPDATE { CASCADE | SET NULL | NO ACTION }]
            [ ON DELETE { CASCADE | SET NULL | NO ACTION }]
The order of the ON UPDATE and ON DELETE clauses may be reversed. These clauses declare the effect action when the referenced primary key is updated or deleted. The default for ON UPDATE and ON DELETE is NO ACTION. table-2 is the referenced table name (primary key table). The optional referenced-columns list the columns of the referenced primary key. Commas separate column names in the list. The default is the primary key list in declaration order.
Contrary to the relational model, SQL92 allows foreign keys to reference any set of columns declared with the UNIQUE constraint in the referenced table (even when the table has a primary key). In this case, the referenced-columns list is required.
Example table constraint for referential integrity (for the sp table):
    FOREIGN KEY (sno)
    REFERENCES s(sno)
    ON DELETE NO ACTION
    ON UPDATE CASCADE

CREATE TABLE Examples

Creating the example tables:
    CREATE TABLE s
    (sno VARCHAR(5) NOT NULL PRIMARY KEY,
     name VARCHAR(16),
     city VARCHAR(16)
    )
    
    CREATE TABLE p
    (pno VARCHAR(5) NOT NULL PRIMARY KEY,
     descr VARCHAR(16),
     color VARCHAR(8)
    )
    
    CREATE TABLE sp
    (sno VARCHAR(5) NOT NULL REFERENCES s,
     pno VARCHAR(5) NOT NULL REFERENCES p,
     qty INT,
     PRIMARY KEY (sno, pno)
    )
Create for sp with a constraint that the qty column can't be negative:
    CREATE TABLE sp
    (sno VARCHAR(5) NOT NULL REFERENCES s,
     pno VARCHAR(5) NOT NULL REFERENCES p,
     qty INT CHECK (qty >= 0),
     PRIMARY KEY (sno, pno)
    )

CREATE VIEW Statement

The CREATE VIEW statement creates a new database view. A view is effectively a SQL query stored in the catalog. The CREATE VIEW has the following general format:
    
    CREATE VIEW view-name [ ( column-list ) ] AS query-1
                [ WITH [CASCADED|LOCAL] CHECK OPTION ]
view-name is the name for the new view. column-list is an optional list of names for the columns of the view, comma separated. query-1 is any SELECT statement without an ORDER BY clause. The optional WITH CHECK OPTION clause is a constraint on updatable views. column-list must have the same number of columns as the select list in query-1. If column-list is omitted, all items in the select list of query-1 must be named. In either case, duplicate column names are not allowed for a view.
The optional WITH CHECK OPTION clause only applies to updatable views. It affects SQL INSERT and UPDATE statements. If WITH CHECK OPTION is specified, the WHERE predicate for query-1 must evaluate to true for the added row or the changed row.
The CASCADED and LOCAL specifiers apply when the underlying table for query-1 is another view. CASCADED requests that WITH CHECK OPTION apply to all underlying views (to any level.) LOCAL requests that the current WITH CHECK OPTION apply only to this view. LOCAL is the default.

CREATE VIEW Examples

Parts with suppliers:
    CREATE VIEW supplied_parts AS
                SELECT *
                FROM p
                WHERE pno IN (SELECT pno FROM sp)
           WITH CHECK OPTION
Access example:
    SELECT * FROM supplied_parts
    pno descr color
    P1 Widget Red
    P2 Widget Blue
Joined view:
    CREATE VIEW part_locations (part, quantity, location) AS
                SELECT pno, qty, city
                FROM sp, s
                WHERE sp.sno = s.sno
Access examples:
    SELECT * FROM part_locations
    part quantity location
    P1 NULL Paris
    P1 200 London
    P1 1000 Rome
    P2 200 Rome
    SELECT part, quantity
    FROM part_locations
    WHERE location = 'Rome'
    part quantity
    P1 1000
    P2 200

DROP TABLE Statement

The DROP TABLE Statement removes a previously created table and its description from the catalog. It has the following general format:
    DROP TABLE table-name {CASCADE|RESTRICT}
table-name is the name of an existing base table in the current schema. The CASCADE and RESTRICT specifiers define the disposition of other objects dependent on the table. A base table may have two types of dependencies:
  • A view whose query specification references the drop table.
  • Another base table that references the drop table in a constraint - a CHECK constraint or REFERENCES constraint.
RESTRICT specifies that the table not be dropped if any dependencies exist. If dependencies are found, an error is returned and the table isn't dropped. CASCADE specifies that any dependencies are removed before the drop is performed:
  • Views that reference the base table are dropped, and the sequence is repeated for their dependencies.
  • Constraints in other tables that reference this table are dropped; the constraint is dropped but the table retained.

DROP VIEW Statement

The DROP VIEW Statement removes a previously created view and its description from the catalog. It has the following general format:
    DROP VIEW view-name {CASCADE|RESTRICT}
view-name is the name of an existing view in the current schema. The CASCADE and RESTRICT specifiers define the disposition of other objects dependent on the view. A view may have two types of dependencies:
  • A view whose query specification references the drop view.
  • A base table that references the drop view in a constraint - a CHECK constraint.
RESTRICT specifies that the view not be dropped if any dependencies exist. If dependencies are found, an error is returned and the view isn't dropped. CASCADE specifies that any dependencies are removed before the drop is performed:
  • Views that reference the drop view are dropped, and the sequence is repeated for their dependencies.
  • Constraints in base tables that reference this view are dropped; the constraint is dropped but the table retained.

GRANT Statement

The GRANT Statement grants access privileges for database objects to other users. It has the following general format:
    GRANT privilege-list ON [TABLE] object-list TO user-list
privilege-list is either ALL PRIVILEGES or a comma-separated list of properties: SELECT, INSERT, UPDATE, DELETE. object-list is a comma-separated list of table and view names. user-list is either PUBLIC or a comma-separated list of user names. The GRANT statement grants each privilege in privilege-list for each object (table) in object-list to each user in user-list. In general, the access privileges apply to all columns in the table or view, but it is possible to specify a column list with the UPDATE privilege specifier:
    UPDATE [ ( column-1 [, column-2] ... ) ]
If the optional column list is specified, UPDATE privileges are granted for those columns only. The user-list may specify PUBLIC. This is a general grant, applying to all users (and future users) in the catalog.
Privileges granted are revoked with the REVOKE Statement.
The optional specificier WITH GRANT OPTION may follow user-list in the GRANT statement. WITH GRANT OPTION specifies that, in addition to access privileges, the privilege to grant those privileges to other users is granted.

GRANT Statement Examples

    GRANT SELECT ON s,sp TO PUBLIC
    
    GRANT SELECT,INSERT,UPDATE(color) ON p TO art,nan
    
    GRANT SELECT ON supplied_parts TO sam WITH GRANT OPTION

REVOKE Statement

The REVOKE Statement revokes access privileges for database objects previously granted to other users. It has the following general format:
    REVOKE privilege-list ON [TABLE] object-list FROM user-list
The REVOKE Statement revokes each privilege in privilege-list for each object (table) in object-list from each user in user-list. All privileges must have been previously granted. The user-list may specify PUBLIC. This must apply to a previous GRANT TO PUBLIC.

REVOKE Statement Examples

    REVOKE SELECT ON s,sp FROM PUBLIC
    
    REVOKE SELECT,INSERT,UPDATE(color) ON p FROM art,nan
    
    REVOKE SELECT ON supplied_parts FROM sam

Sumber
Next
Posting Lebih Baru
Previous
This is the last post.

Posting Komentar

 
Top