SQL Tutorial
SELECT Statement -- Extended Query Capabilities
This subsection details the remaining features of SELECT statements. The basics are at
SELECT Statement Basics.
The extended features are grouped as follows:
- Sorting Query Results -- using the ORDER BY clause
- Expressions -- in the SELECT clause and WHERE clause
- Literal -- self-defining values
- Function Call -- expression functions
- System Value -- builtin system values
- Special Construct -- special expression construct
- Numeric or String Operator -- expression operators
- Joining Tables -- in the FROM clause
- Outer Join -- extended join
- Self Join -- joining a table to itself
- Subqueries -- embedding a query in another
- Predicate Subqueries -- subqueries in logical expressions
- Scalar Subqueries -- subqueries in scalar expressions
- Table Subqueries -- subqueries in the FROM clause
- Grouping Queries -- using the GROUP BY clause, Set Function and HAVING clause
- GROUP BY Clause -- specifying grouping columns
- Set Functions -- summary functions
- HAVING Clause -- filtering grouped rows
- Aggregate Queries -- using Set Functions and the HAVING clause
- Union Queries -- using the query operator, UNION
- Union-Compatible Queries -- query requirements for Union
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 |
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:
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 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.
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 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 combine 2 subexpressions to calculate a value. There are 2 basic types -- numeric and string.
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:
(datetime-1 - datetime-2) interval-qualifier
The interval-qualifier specifies the specific interval type for the result.
A second special form allows a ? parameter to be typed as an interval:
? interval-qualifier
In expressions, parentheses are used for grouping.
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.
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-1predicate-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 |
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
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 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 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.)
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)
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 |
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 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 |
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 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
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 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
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
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
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 |
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.
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,
- Interval -- sub-category must be the same,
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 |
SQL Modification Statements
The remaining SQL-Data Statements (SQL DML) are the SQL Modification Statements, described in the next
sub-section:
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