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.
The SQL language is subdivided into several language elements, including:
- Clauses, which are constituent components of statements and queries. (In some cases, these are optional.)
- 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.
||Not equal to (many DBMSs accept
||Greater than or equal|
||Less than or equal|
||Between an inclusive range|
||Begins with a character pattern|
|Contains a character pattern|
||Equal to one of multiple possible values|
||Compare to null (missing data)|
||Boolean truth value test|
||Is equal to value or both are nulls (missing data)|
||Used to change a column name when viewing results|
Other operators have at times been suggested or implemented, such as the skyline operator (for finding only those records that are not 'worse' than any others).
SQL has the
case/when/then/else/end 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
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" in the SQL standard—mirrors switch statements:
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
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.
is equivalent to:
CASE WHEN x1 IS NOT NULL THEN x1 ELSE x2 END
NULLIF expression has two operands and returns NULL if the operands have the same value, otherwise it has the value of the first operand.
is equivalent to
CASE WHEN x1 = x2 THEN NULL ELSE x1 END
The most common operation in SQL, the query, makes use of the declarative
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.
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:
FROMclause, which indicates the table(s) to retrieve data from. The
FROMclause can include optional
JOINsubclauses to specify the rules for joining tables.
WHEREclause includes a comparison predicate, which restricts the rows returned by the query. The
WHEREclause eliminates all rows from the result set where the comparison predicate does not evaluate to True.
GROUP BYclause projects rows having common values into a smaller set of rows.
GROUP BYis often used in conjunction with SQL aggregation functions or to eliminate duplicate rows from a result set. The
WHEREclause is applied before the
HAVINGclause includes a predicate used to filter rows resulting from the
GROUP BYclause. Because it acts on the results of the
GROUP BYclause, aggregation functions can be used in the
ORDER BYclause identifies which column[s] to use to sort the resulting data, and in which direction to sort them (ascending or descending). Without an
ORDER BYclause, the order of rows returned by an SQL query is undefined.
DISTINCTkeyword eliminates duplicate data.
LIMITclause specifies the number of records to return. Note that this clause is not supported by all SQL databases.
The clauses of a query have a particular order of execution, which is denoted by the number on the right hand side. It is as follows:
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;
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.
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)
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).
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.
NULL = NULL) 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
INTERSECT operators, which do identify nulls with each other. 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". 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."
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. SQL does not explicitly support universal quantification, and must work it out as a negated existential quantification. 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>
The Data Manipulation Language (DML) is the subset of SQL used to add, update and delete data:
INSERT INTO example (column1, column2, column3) VALUES ('test', 'N', NULL);
UPDATEmodifies a set of existing table rows, e.g.:
UPDATE example SET column1 = 'updated value' WHERE column2 = 'N';
DELETEremoves existing rows from a table, e.g.:
DELETE FROM example WHERE column2 = 'N';
MERGEis used to combine the data of multiple tables. It combines the
UPDATEelements. 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 ...])
Transactions, if available, wrap DML operations:
BEGIN WORK, or
BEGIN TRANSACTION, depending on SQL dialect) marks the start of a database transaction, which either completes entirely or not at all.
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;
COMMITmakes all data changes in a transaction permanent.
ROLLBACKdiscards all data changes since the last
ROLLBACK, leaving the data as it was prior to those changes. Once the
COMMITstatement completes, the transaction's changes cannot be rolled back.
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;
The Data Definition Language (DDL) manages table and index structure. The most basic items of DDL are the
CREATEcreates 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) );
ALTERmodifies 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 NOT NULL;
TRUNCATEdeletes 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;
DROPdeletes an object in the database, usually irretrievably, i.e., it cannot be rolled back, e.g.:
DROP TABLE example;
Each column in an SQL table declares the type(s) that column may contain. ANSI SQL includes the following data types.
- Character strings and national character strings
CHAR(n)): fixed-width n-character string, padded with spaces as needed
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
NCHAR(n)): fixed width string supporting an international character set
NATIONAL CHARACTER VARYING(n)(or
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
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(n): Fixed length binary string, maximum length n.
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 ].
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 data type can store the values
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 10S.
SQL provides the functions
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.
TIME: for time values (e.g.
TIME WITH TIME ZONE: the same as
TIME, but including details about the time zone in question.
TIMESTAMP: This is a
TIMEput together in one variable (e.g.
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
LOCALTIMESTAMP. (Popular vendor specific functions are
- 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
The Data Control Language (DCL) authorizes users to access and manipulate data. Its two main statements are:
GRANTauthorizes one or more users to perform an operation or a set of operations on an object.
REVOKEeliminates a grant, which may be the default grant.
GRANT SELECT, UPDATE ON example TO some_user, another_user; REVOKE SELECT, UPDATE ON example FROM some_user, another_user;
- ANSI/ISO/IEC International Standard (IS). Database Language SQL—Part 2: Foundation (SQL/Foundation). 1999.
- "Transact-SQL Reference". SQL Server Language Reference. SQL Server 2005 Books Online. Microsoft. 2007-09-15. Retrieved 2007-06-17.
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.
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.
- www.techonthenet.com. "SQL: SELECT LIMIT Statement". www.techonthenet.com. Retrieved 2018-02-04.
- "SQL SELECT TOP, LIMIT, ROWNUM". www.w3schools.com. Retrieved 2018-02-04.
- "What Is The Order Of Execution Of An SQL Query? - Designcise.com". www.designcise.com. Retrieved 2018-02-04.
- 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. 2582. pp. 119–138. doi:10.1007/3-540-36596-6_7. ISBN 978-3-540-00957-3.
- 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 ISBN 978-0-7923-8129-7, p. 344
- ISO/IEC. ISO/IEC 9075-2:2003, "SQL/Foundation". ISO/IEC.
- "Semantics and problems of universal quantification in SQL". Portal.acm.org. doi:10.1093/comjnl/32.1.90. Retrieved 2017-01-16.
- "Technique for universal quantification in SQL". Portal.acm.org. doi:10.1145/126482.126484. Retrieved 2017-01-16.
- Kawash, Jalal (2004) Complex quantification in Structured Query Language (SQL): a tutorial using relational calculus; Journal of Computers in Mathematics and Science Teaching ISSN 0731-9258 Volume 23, Issue 2, 2004 AACE Norfolk, Virginia. Thefreelibrary.com
- ISO/IEC 9075-2:2011 §4.5
- "ISO/IEC 9075-1:2016: Information technology – Database languages – SQL – Part 1: Framework (SQL/Framework)".