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                                   SQL syntax

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   The syntax of the SQL programming language is defined and maintained by
   ISO/IEC SC 32 as part of ISO/IEC 9075. This standard is not freely
   available. Despite the existence of the standard, SQL code is not
   completely portable among different database systems without adjustments.

Contents

     * 1 Language elements
     * 2 Operators
     * 3 Comments
     * 4 Queries
          * 4.1 Subqueries
          * 4.2 Derived table
          * 4.3 Null or three-valued logic (3VL)
          * 4.4 Data manipulation
          * 4.5 Transaction controls
          * 4.6 Data definition
          * 4.7 Data types
          * 4.8 Data control
     * 5 Notes

Language elementsEdit

   {\displaystyle \left.{\begin{array}{rl}\scriptstyle {\mathtt
   {UPDATE~clause}}&\{{\mathtt {UPDATE\ country}}\\\scriptstyle {\mathtt
   {SET~clause}}&\{{\mathtt {SET\ population=~}}\overbrace {\mathtt
   {population+1}} ^{\mathtt {expression}}\\\scriptstyle {\mathtt
   {WHERE~clause}}&\{{\mathtt {WHERE\ \underbrace {{name=}\overbrace {'USA'}
   ^{expression}} _{predicate};}}\end{array}}\right\}{\scriptstyle {\texttt
   {statement}}}} 
   A chart showing several of the SQL language elements that compose a single
   statement. This adds one to the population of the USA in the country
   table.

   The SQL language is subdivided into several language elements, including:

     * Keywords are words that are defined in the SQL language. They are
       either reserved (e.g. SELECT, COUNT and YEAR), or non-reserved (e.g.
       ASC, DOMAIN and KEY). List of SQL reserved words.
     * Identifiers are names on database objects, like tables, columns and
       schemas. An identifier may not be equal to a reserved keyword, unless
       it is a delimited identifier. Delimited identifiers means identifiers
       enclosed in double quotation marks. They can contain characters
       normally not supported in SQL identifiers, and they can be identical
       to a reserved word, e.g. a column named YEAR is specifed as "YEAR".
     * Clauses, which are constituent components of statements and queries.
       (In some cases, these are optional.)^[1]
     * Expressions, which can produce either scalar values, or tables
       consisting of columns and rows of data
     * Predicates, which specify conditions that can be evaluated to SQL
       three-valued logic (3VL) (true/false/unknown) or Boolean truth values
       and are used to limit the effects of statements and queries, or to
       change program flow.
     * Queries, which retrieve the data based on specific criteria. This is
       an important element of SQL.
     * Statements, which may have a persistent effect on schemata and data,
       or may control transactions, program flow, connections, sessions, or
       diagnostics.
          * SQL statements also include the semicolon (";") statement
            terminator. Though not required on every platform, it is defined
            as a standard part of the SQL grammar.
     * Insignificant whitespace is generally ignored in SQL statements and
       queries, making it easier to format SQL code for readability.

OperatorsEdit

   Operator              Description                    Example               
   =                     Equal to                       Author = 'Alcott'     
   <>                    Not equal to (many DBMSs       Dept <> 'Sales'       
                         accept != in addition to <>)   
   >                     Greater than                   Hire_Date >           
                                                        '2012-01-31'          
   <                     Less than                      Bonus < 50000.00      
   >=                    Greater than or equal          Dependents >= 2       
   <=                    Less than or equal             Rate <= 0.05          
                         Between an inclusive range.                          
   [NOT] BETWEEN         SYMMETRIC inverts the range    Cost BETWEEN 100.00   
   [SYMMETRIC]           bounds if the first is higher  AND 500.00
                         than the second.               
                         Begins with a character        Full_Name LIKE        
   [NOT] LIKE [ESCAPE]   pattern                        'Will%'               
                         Contains a character pattern   Full_Name LIKE        
                                                        '%Will%'              
   [NOT] IN              Equal to one of multiple       DeptCode IN (101,     
                         possible values                103, 209)             
   IS [NOT] NULL         Compare to null (missing data) Address IS NOT NULL   
   IS [NOT] TRUE or IS   Boolean truth value test       PaidVacation IS TRUE  
   [NOT] FALSE           
   IS NOT DISTINCT FROM  Is equal to value or both are  Debt IS NOT DISTINCT  
                         nulls (missing data)           FROM - Receivables    
   AS                    Used to change a column name   SELECT employee AS    
                         when viewing results           department1           

   Other operators have at times been suggested or implemented, such as the
   skyline operator (for finding only those rows that are not 'worse' than
   any others).

   SQL has the case expression, which was introduced in SQL-92. In its most
   general form, which is called a "searched case" in the SQL standard:

 CASE WHEN n > 0
           THEN 'positive'
      WHEN n < 0
           THEN 'negative'
      ELSE 'zero'
 END

   SQL tests WHEN conditions in the order they appear in the source. If the
   source does not specify an ELSE expression, SQL defaults to ELSE NULL. An
   abbreviated syntax called "simple case" can also be used:

 CASE n WHEN 1
             THEN 'One'
        WHEN 2
             THEN 'Two'
        ELSE 'I cannot count that high'
 END

   This syntax uses implicit equality comparisons, with the usual caveats for
   comparing with NULL.

   There are two short forms for special CASE expressions: COALESCE and
   NULLIF.

   The COALESCE expression returns the value of the first non-NULL operand,
   found by working from left to right, or NULL if all the operands equal
   NULL.

 COALESCE(x1,x2)

   is equivalent to:

 CASE WHEN x1 IS NOT NULL THEN x1
      ELSE x2
 END

   The NULLIF expression has two operands and returns NULL if the operands
   have the same value, otherwise it has the value of the first operand.

 NULLIF(x1, x2)

   is equivalent to

 CASE WHEN x1 = x2 THEN NULL ELSE x1 END

CommentsEdit

   Standard SQL allows two formats for comments: -- comment, which is ended
   by the first newline, and /* comment */, which can span multiple lines.

QueriesEdit

   The most common operation in SQL, the query, makes use of the declarative
   SELECT statement. SELECT retrieves data from one or more tables, or
   expressions. Standard SELECT statements have no persistent effects on the
   database. Some non-standard implementations of SELECT can have persistent
   effects, such as the SELECT INTO syntax provided in some databases.^[2]

   Queries allow the user to describe desired data, leaving the database
   management system (DBMS) to carry out planning, optimizing, and performing
   the physical operations necessary to produce that result as it chooses.

   A query includes a list of columns to include in the final result,
   normally immediately following the SELECT keyword. An asterisk ("*") can
   be used to specify that the query should return all columns of the queried
   tables. SELECT is the most complex statement in SQL, with optional
   keywords and clauses that include:

     * The FROM clause, which indicates the table(s) to retrieve data from.
       The FROM clause can include optional JOIN subclauses to specify the
       rules for joining tables.
     * The WHERE clause includes a comparison predicate, which restricts the
       rows returned by the query. The WHERE clause eliminates all rows from
       the result set where the comparison predicate does not evaluate to
       True.
     * The GROUP BY clause projects rows having common values into a smaller
       set of rows.^[clarification needed] GROUP BY is often used in
       conjunction with SQL aggregation functions or to eliminate duplicate
       rows from a result set. The WHERE clause is applied before the GROUP
       BY clause.
     * The HAVING clause includes a predicate used to filter rows resulting
       from the GROUP BY clause. Because it acts on the results of the GROUP
       BY clause, aggregation functions can be used in the HAVING clause
       predicate.
     * The ORDER BY clause identifies which column[s] to use to sort the
       resulting data, and in which direction to sort them (ascending or
       descending). Without an ORDER BY clause, the order of rows returned by
       an SQL query is undefined.
     * The DISTINCT keyword^[3] eliminates duplicate data.^[4]
     * The OFFSET clause specifies the number of rows to skip before starting
       to return data.
     * The FETCH FIRST clause specifies the number of rows to return. Some
       SQL databases instead have non-standard alternatives, e.g. LIMIT, TOP
       or ROWNUM.

   The clauses of a query have a particular order of execution,^[5] which is
   denoted by the number on the right hand side. It is as follows:

   SELECT <columns>             5. 
   FROM <table>                 1. 
   WHERE <predicate on rows>    2. 
   GROUP BY <columns>           3. 
   HAVING <predicate on groups> 4. 
   ORDER BY <columns>           6. 
   OFFSET                       7. 
   FETCH FIRST                  8. 

   The following example of a SELECT query returns a list of expensive books.
   The query retrieves all rows from the Book table in which the price column
   contains a value greater than 100.00. The result is sorted in ascending
   order by title. The asterisk (*) in the select list indicates that all
   columns of the Book table should be included in the result set.

 SELECT *
  FROM  Book
  WHERE price > 100.00
  ORDER BY title;

   The example below demonstrates a query of multiple tables, grouping, and
   aggregation, by returning a list of books and the number of authors
   associated with each book.

 SELECT Book.title AS Title,
        count(*) AS Authors
  FROM  Book
  JOIN  Book_author
    ON  Book.isbn = Book_author.isbn
  GROUP BY Book.title;

   Example output might resemble the following:

 Title                  Authors
 ---------------------- -------
 SQL Examples and Guide 4
 The Joy of SQL         1
 An Introduction to SQL 2
 Pitfalls of SQL        1

   Under the precondition that isbn is the only common column name of the two
   tables and that a column named title only exists in the Book table, one
   could re-write the query above in the following form:

 SELECT title,
        count(*) AS Authors
  FROM  Book
  NATURAL JOIN Book_author
  GROUP BY title;

   However, many^[quantify] vendors either do not support this approach, or
   require certain column-naming conventions for natural joins to work
   effectively.

   SQL includes operators and functions for calculating values on stored
   values. SQL allows the use of expressions in the select list to project
   data, as in the following example, which returns a list of books that cost
   more than 100.00 with an additional sales_tax column containing a sales
   tax figure calculated at 6% of the price.

 SELECT isbn,
        title,
        price,
        price * 0.06 AS sales_tax
  FROM  Book
  WHERE price > 100.00
  ORDER BY title;

  SubqueriesEdit

   Queries can be nested so that the results of one query can be used in
   another query via a relational operator or aggregation function. A nested
   query is also known as a subquery. While joins and other table operations
   provide computationally superior (i.e. faster) alternatives in many cases,
   the use of subqueries introduces a hierarchy in execution that can be
   useful or necessary. In the following example, the aggregation function
   AVG receives as input the result of a subquery:

 SELECT isbn,
        title,
        price
  FROM  Book
  WHERE price < (SELECT AVG(price) FROM Book)
  ORDER BY title;

   A subquery can use values from the outer query, in which case it is known
   as a correlated subquery.

   Since 1999 the SQL standard allows WITH clauses for subqueries, i.e. named
   subqueries, usually called common table expressions (also called subquery
   factoring). CTEs can also be recursive by referring to themselves; the
   resulting mechanism allows tree or graph traversals (when represented as
   relations), and more generally fixpoint computations.

  Derived tableEdit

   A derived table is the use of referencing an SQL subquery in a FROM
   clause. Essentially, the derived table is a subquery that can be selected
   from or joined to. The derived table functionality allows the user to
   reference the subquery as a table. The inline view is also referred to as
   an inline view or a subselect.

   In the following example, the SQL statement involves a join from the
   initial "Book" table to the derived table "sales". This derived table
   captures associated book sales information using the ISBN to join to the
   "Book" table. As a result, the derived table provides the result set with
   additional columns (the number of items sold and the company that sold the
   books):

 SELECT b.isbn, b.title, b.price, sales.items_sold, sales.company_nm
 FROM Book b
   JOIN (SELECT SUM(Items_Sold) Items_Sold, Company_Nm, ISBN
         FROM Book_Sales
         GROUP BY Company_Nm, ISBN) sales
   ON sales.isbn = b.isbn

  Null or three-valued logic (3VL)Edit

   Main article: Null (SQL)

   The concept of Null allows SQL to deal with missing information in the
   relational model. The word NULL is a reserved keyword in SQL, used to
   identify the Null special marker. Comparisons with Null, for instance
   equality (=) in WHERE clauses, results in an Unknown truth value. In
   SELECT statements SQL returns only results for which the WHERE clause
   returns a value of True; i.e., it excludes results with values of False
   and also excludes those whose value is Unknown.

   Along with True and False, the Unknown resulting from direct comparisons
   with Null thus brings a fragment of three-valued logic to SQL. The truth
   tables SQL uses for AND, OR, and NOT correspond to a common fragment of
   the Kleene and Lukasiewicz three-valued logic (which differ in their
   definition of implication, however SQL defines no such operation).^[6]

   p AND q   p                     p OR q    p                    
             True    False Unknown           True False   Unknown 
     True    True    False Unknown   True    True True    True    
   q False   False   False False   q False   True False   Unknown 
     Unknown Unknown False Unknown   Unknown True Unknown Unknown 

   p = q     p                       q       NOT q   
             True    False   Unknown True    False   
     True    True    False   Unknown False   True    
   q False   False   True    Unknown Unknown Unknown 
     Unknown Unknown Unknown Unknown 

   There are however disputes about the semantic interpretation of Nulls in
   SQL because of its treatment outside direct comparisons. As seen in the
   table above, direct equality comparisons between two NULLs in SQL (e.g.
   {{{1}}}) return a truth value of Unknown. This is in line with the
   interpretation that Null does not have a value (and is not a member of any
   data domain) but is rather a placeholder or "mark" for missing
   information. However, the principle that two Nulls aren't equal to each
   other is effectively violated in the SQL specification for the UNION and
   INTERSECT operators, which do identify nulls with each other.^[7]
   Consequently, these set operations in SQL may produce results not
   representing sure information, unlike operations involving explicit
   comparisons with NULL (e.g. those in a WHERE clause discussed above). In
   Codd's 1979 proposal (which was basically adopted by SQL92) this semantic
   inconsistency is rationalized by arguing that removal of duplicates in set
   operations happens "at a lower level of detail than equality testing in
   the evaluation of retrieval operations".^[6] However, computer-science
   professor Ron van der Meyden concluded that "The inconsistencies in the
   SQL standard mean that it is not possible to ascribe any intuitive logical
   semantics to the treatment of nulls in SQL."^[7]

   Additionally, because SQL operators return Unknown when comparing anything
   with Null directly, SQL provides two Null-specific comparison predicates:
   IS NULL and IS NOT NULL test whether data is or is not Null.^[8] SQL does
   not explicitly support universal quantification, and must work it out as a
   negated existential quantification.^[9]^[10]^[11] There is also the "<row
   value expression> IS DISTINCT FROM <row value expression>" infixed
   comparison operator, which returns TRUE unless both operands are equal or
   both are NULL. Likewise, IS NOT DISTINCT FROM is defined as "NOT (<row
   value expression> IS DISTINCT FROM <row value expression>)". SQL:1999 also
   introduced BOOLEAN type variables, which according to the standard can
   also hold Unknown values if it is nullable. In practice, a number of
   systems (e.g. PostgreSQL) implement the BOOLEAN Unknown as a BOOLEAN NULL,
   which the standard says that the NULL BOOLEAN and UNKNOWN "may be used
   interchangeably to mean exactly the same thing".C. Date (2011). SQL and
   Relational Theory: How to Write Accurate SQL Code. O'Reilly Media, Inc.
   p. 83. ISBN 978-1-4493-1640-2.</ref>^[12]

  Data manipulationEdit

   The Data Manipulation Language (DML) is the subset of SQL used to add,
   update and delete data:

     * INSERT adds rows (formally tuples) to an existing table, e.g.:

 INSERT INTO example
  (column1, column2, column3)
  VALUES
  ('test', 'N', NULL);

     * UPDATE modifies a set of existing table rows, e.g.:

 UPDATE example
  SET column1 = 'updated value'
  WHERE column2 = 'N';

     * DELETE removes existing rows from a table, e.g.:

 DELETE FROM example
  WHERE column2 = 'N';

     * MERGE is used to combine the data of multiple tables. It combines the
       INSERT and UPDATE elements. It is defined in the SQL:2003 standard;
       prior to that, some databases provided similar functionality via
       different syntax, sometimes called "upsert".

  MERGE INTO table_name USING table_reference ON (condition)
  WHEN MATCHED THEN
  UPDATE SET column1 = value1 [, column2 = value2 ...]
  WHEN NOT MATCHED THEN
  INSERT (column1 [, column2 ...]) VALUES (value1 [, value2 ...])

  Transaction controlsEdit

   Transactions, if available, wrap DML operations:

     * START TRANSACTION (or BEGIN WORK, or BEGIN TRANSACTION, depending on
       SQL dialect) marks the start of a database transaction, which either
       completes entirely or not at all.
     * SAVE TRANSACTION (or SAVEPOINT) saves the state of the database at the
       current point in transaction

 CREATE TABLE tbl_1(id int);
  INSERT INTO tbl_1(id) VALUES(1);
  INSERT INTO tbl_1(id) VALUES(2);
 COMMIT;
  UPDATE tbl_1 SET id=200 WHERE id=1;
 SAVEPOINT id_1upd;
  UPDATE tbl_1 SET id=1000 WHERE id=2;
 ROLLBACK to id_1upd;
  SELECT id from tbl_1;

     * COMMIT makes all data changes in a transaction permanent.
     * ROLLBACK discards all data changes since the last COMMIT or ROLLBACK,
       leaving the data as it was prior to those changes. Once the COMMIT
       statement completes, the transaction's changes cannot be rolled back.

   COMMIT and ROLLBACK terminate the current transaction and release data
   locks. In the absence of a START TRANSACTION or similar statement, the
   semantics of SQL are implementation-dependent. The following example shows
   a classic transfer of funds transaction, where money is removed from one
   account and added to another. If either the removal or the addition fails,
   the entire transaction is rolled back.

 START TRANSACTION;
  UPDATE Account SET amount=amount-200 WHERE account_number=1234;
  UPDATE Account SET amount=amount+200 WHERE account_number=2345;

 IF ERRORS=0 COMMIT;
 IF ERRORS<>0 ROLLBACK;

  Data definitionEdit

   The Data Definition Language (DDL) manages table and index structure. The
   most basic items of DDL are the CREATE, ALTER, RENAME, DROP and TRUNCATE
   statements:

     * CREATE creates an object (a table, for example) in the database, e.g.:

 CREATE TABLE example(
  column1 INTEGER,
  column2 VARCHAR(50),
  column3 DATE NOT NULL,
  PRIMARY KEY (column1, column2)
 );

     * ALTER modifies the structure of an existing object in various ways,
       for example, adding a column to an existing table or a constraint,
       e.g.:

 ALTER TABLE example ADD column4 INTEGER DEFAULT 25 NOT NULL;

     * TRUNCATE deletes all data from a table in a very fast way, deleting
       the data inside the table and not the table itself. It usually implies
       a subsequent COMMIT operation, i.e., it cannot be rolled back (data is
       not written to the logs for rollback later, unlike DELETE).

 TRUNCATE TABLE example;

     * DROP deletes an object in the database, usually irretrievably, i.e.,
       it cannot be rolled back, e.g.:

 DROP TABLE example;

  Data typesEdit

   Each column in an SQL table declares the type(s) that column may contain.
   ANSI SQL includes the following data types.^[13]

   Character strings and national character strings

     * CHARACTER(n) (or CHAR(n)): fixed-width n-character string, padded with
       spaces as needed
     * CHARACTER VARYING(n) (or VARCHAR(n)): variable-width string with a
       maximum size of n characters
     * CHARACTER LARGE OBJECT(n [ K | M | G | T ]) (or CLOB(n [ K | M | G | T
       ])): character large object with a maximum size of n [ K | M | G | T ]
       characters
     * NATIONAL CHARACTER(n) (or NCHAR(n)): fixed width string supporting an
       international character set
     * NATIONAL CHARACTER VARYING(n) (or NVARCHAR(n)): variable-width NCHAR
       string
     * NATIONAL CHARACTER LARGE OBJECT(n [ K | M | G | T ]) (or NCLOB(n [ K |
       M | G | T ])): national character large object with a maximum size of
       n [ K | M | G | T ] characters

   For the CHARACTER LARGE OBJECT and NATIONAL CHARACTER LARGE OBJECT data
   types, the multipliers K (1 024), M (1 048 576), G (1 073 741 824) and T
   (1 099 511 627 776) can be optionally used when specifying the length.

   Binary

     * BINARY(n): Fixed length binary string, maximum length n.
     * BINARY VARYING(n) (or VARBINARY(n)): Variable length binary string,
       maximum length n.
     * BINARY LARGE OBJECT(n [ K | M | G | T ]) (or BLOB(n [ K | M | G | T
       ])): binary large object with a maximum length n [ K | M | G | T ].

   For the BINARY LARGE OBJECT data type, the multipliers K (1 024), M (1 048
   576), G (1 073 741 824) and T (1 099 511 627 776) can be optionally used
   when specifying the length.

   Boolean

     * BOOLEAN

   The BOOLEAN data type can store the values TRUE and FALSE.

   Numerical

     * INTEGER (or INT), SMALLINT and BIGINT
     * FLOAT, REAL and DOUBLE PRECISION
     * NUMERIC(precision, scale) or DECIMAL(precision, scale)
     * DECFLOAT(precision)

   For example, the number 123.45 has a precision of 5 and a scale of 2. The
   precision is a positive integer that determines the number of significant
   digits in a particular radix (binary or decimal). The scale is a
   non-negative integer. A scale of 0 indicates that the number is an
   integer. For a decimal number with scale S, the exact numeric value is the
   integer value of the significant digits divided by 10^S.

   SQL provides the functions CEILING and FLOOR to round numerical values.
   (Popular vendor specific functions are TRUNC (Informix, DB2, PostgreSQL,
   Oracle and MySQL) and ROUND (Informix, SQLite, Sybase, Oracle, PostgreSQL,
   Microsoft SQL Server and Mimer SQL.))

   Temporal (datetime)

     * DATE: for date values (e.g. 2011-05-03).
     * TIME: for time values (e.g. 15:51:36).
     * TIME WITH TIME ZONE: the same as TIME, but including details about the
       time zone in question.
     * TIMESTAMP: This is a DATE and a TIME put together in one variable
       (e.g. 2011-05-03 15:51:36.123456).
     * TIMESTAMP WITH TIME ZONE: the same as TIMESTAMP, but including details
       about the time zone in question.

   The SQL function EXTRACT can be used for extracting a single field
   (seconds, for instance) of a datetime or interval value. The current
   system date / time of the database server can be called by using functions
   like CURRENT_DATE, CURRENT_TIMESTAMP, LOCALTIME, or LOCALTIMESTAMP.
   (Popular vendor specific functions are TO_DATE, TO_TIME, TO_TIMESTAMP,
   YEAR, MONTH, DAY, HOUR, MINUTE, SECOND, DAYOFYEAR, DAYOFMONTH and
   DAYOFWEEK.)

   Interval (datetime)

     * YEAR(precision): a number of years
     * YEAR(precision) TO MONTH: a number of years and months
     * MONTH(precision): a number of months
     * DAY(precision): a number of days
     * DAY(precision) TO HOUR: a number of days and hours
     * DAY(precision) TO MINUTE: a number of days, hours and minutes
     * DAY(precision) TO SECOND(scale): a number of days, hours, minutes and
       seconds
     * HOUR(precision): a number of hours
     * HOUR(precision) TO MINUTE: a number of hours and minutes
     * HOUR(precision) TO SECOND(scale): a number of hours, minutes and
       seconds
     * MINUTE(precision): a number of minutes
     * MINUTE(precision) TO SECOND(scale): a number of minutes and seconds

  Data controlEdit

   The Data Control Language (DCL) authorizes users to access and manipulate
   data. Its two main statements are:

     * GRANT authorizes one or more users to perform an operation or a set of
       operations on an object.
     * REVOKE eliminates a grant, which may be the default grant.

   Example:

 GRANT SELECT, UPDATE
  ON example
  TO some_user, another_user;

 REVOKE SELECT, UPDATE
  ON example
  FROM some_user, another_user;

NotesEdit

   SQL syntaxat Wikipedia's sister projects
     *  Definitions from Wiktionary
     *  Media from Commons
     *  Textbooks from Wikibooks
     *  Resources from Wikiversity
    1. ^ ANSI/ISO/IEC International Standard (IS). Database Language SQL—Part
       2: Foundation (SQL/Foundation). 1999.
    2. ^
       Link: mw-deduplicated-inline-style
       "Transact-SQL Reference". SQL Server Language Reference. SQL Server
       2005 Books Online. Microsoft. 2007-09-15. Retrieved 2007-06-17.
    3. ^
       Link: mw-deduplicated-inline-style
       SAS 9.4 SQL Procedure User's Guide. SAS Institute. 2013. p. 248.
       ISBN 9781612905686. Retrieved 2015-10-21. Although the UNIQUE argument
       is identical to DISTINCT, it is not an ANSI standard.
    4. ^
       Link: mw-deduplicated-inline-style
       Leon, Alexis; Leon, Mathews (1999). "Eliminating duplicates - SELECT
       using DISTINCT". SQL: A Complete Reference. New Delhi: Tata
       McGraw-Hill Education (published 2008). p. 143. ISBN 9780074637081.
       Retrieved 2015-10-21. [...] the keyword DISTINCT [...] eliminates the
       duplicates from the result set.
    5. ^
       Link: mw-deduplicated-inline-style
       "What Is The Order Of Execution Of An SQL Query? - Designcise.com".
       www.designcise.com. 29 June 2015. Retrieved 2018-02-04.
    6. ^ ^a ^b
       Link: mw-deduplicated-inline-style
       Hans-Joachim, K. (2003). "Null Values in Relational Databases and Sure
       Information Answers". Semantics in Databases. Second International
       Workshop Dagstuhl Castle, Germany, January 7–12, 2001. Revised Papers.
       Lecture Notes in Computer Science. Vol. 2582. pp. 119–138.
       doi:10.1007/3-540-36596-6_7. ISBN 978-3-540-00957-3.
    7. ^ ^a ^b Ron van der Meyden, "Logical approaches to incomplete
       information: a survey" in Chomicki, Jan; Saake, Gunter (Eds.) Logics
       for Databases and Information Systems, Kluwer Academic Publishers
       Link: mw-deduplicated-inline-style
       ISBN 978-0-7923-8129-7, p. 344
    8. ^
       Link: mw-deduplicated-inline-style
       ISO/IEC. ISO/IEC 9075-2:2003, "SQL/Foundation". ISO/IEC.
    9. ^
       Link: mw-deduplicated-inline-style
       Negri, M.; Pelagatti, G.; Sbattella, L. (February 1989). "Semantics
       and problems of universal quantification in SQL". The Computer
       Journal. 32 (1): 90–91. doi:10.1093/comjnl/32.1.90. Retrieved
       2017-01-16.
   10. ^
       Link: mw-deduplicated-inline-style
       Fratarcangeli, Claudio (1991). "Technique for universal quantification
       in SQL". ACM SIGMOD Record. 20 (3): 16–24. doi:10.1145/126482.126484.
       S2CID 18326990. Retrieved 2017-01-16.
   11. ^ Kawash, Jalal (2004) Complex quantification in Structured Query
       Language (SQL): a tutorial using relational calculus; Journal of
       Computers in Mathematics and Science Teaching
       Link: mw-deduplicated-inline-style
       ISSN 0731-9258 Volume 23, Issue 2, 2004 AACE Norfolk, Virginia.
       Thefreelibrary.com
   12. ^ ISO/IEC 9075-2:2011 §4.5
   13. ^
       Link: mw-deduplicated-inline-style
       "ISO/IEC 9075-1:2016: Information technology – Database languages –
       SQL – Part 1: Framework (SQL/Framework)".
   Retrieved from
   "https://en.wikipedia.org/w/index.php?title=SQL_syntax&oldid=1075666260"
   Last edited on 7 March 2022, at 01:33
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