Select (SQL)

SELECT is the most common operation in SQL, called "the query". 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.[4]

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. 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[5] eliminates duplicate data.[6]

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 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;

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

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

Given a table T, the query SELECT * FROM T will result in all the elements of all the rows of the table being shown.

With the same table, the query SELECT C1 FROM T will result in the elements from the column C1 of all the rows of the table being shown. This is similar to a projection in relational algebra, except that in the general case, the result may contain duplicate rows. This is also known as a Vertical Partition in some database terms, restricting query output to view only specified fields or columns.

With the same table, the query SELECT * FROM T WHERE C1 = 1 will result in all the elements of all the rows where the value of column C1 is '1' being shown – in relational algebra terms, a selection will be performed, because of the WHERE clause. This is also known as a Horizontal Partition, restricting rows output by a query according to specified conditions.

With more than one table, the result set will be every combination of rows. So if two tables are T1 and T2, SELECT * FROM T1, T2 will result in every combination of T1 rows with every T2 rows. E.g., if T1 has 3 rows and T2 has 5 rows, then 15 rows will result.

Although not in standard, most DBMS allows using a select clause without a table by pretending that an imaginary table with one row is used. This is mainly used to perform calculations where a table is not needed.

The SELECT clause specifies a list of properties (columns) by name, or the wildcard character (“*”) to mean “all properties”.

Often it is convenient to indicate a maximum number of rows that are returned. This can be used for testing or to prevent consuming excessive resources if the query returns more information than expected. The approach to do this often varies per vendor.

In ISO SQL:2003, result sets may be limited by using

• cursors, or
• by adding a SQL window function to the SELECT-statement

ISO SQL:2008 introduced the FETCH FIRST clause.

According to PostgreSQL v.9 documentation, an SQL window function "performs a calculation across a set of table rows that are somehow related to the current row", in a way similar to aggregate functions.[7] The name recalls signal processing window functions. A window function call always contains an OVER clause.

SELECT * FROM
( SELECT
    ROW_NUMBER() OVER (ORDER BY sort_key ASC) AS row_number,
    columns
  FROM tablename
) AS foo
WHERE row_number <= 10

SELECT * FROM (
  SELECT
    RANK() OVER (ORDER BY age ASC) AS ranking,
    person_id,
    person_name,
    age
  FROM person
) AS foo
WHERE ranking <= 10

SELECT * FROM T 
FETCH FIRST 10 ROWS ONLY

SELECT * FROM T 
ORDER BY acolumn DESC OFFSET 0 ROWS FETCH FIRST 10 ROWS ONLY

SET ROWCOUNT 10
SELECT * FROM T

SELECT * FROM T 
LIMIT 10 OFFSET 20

SELECT * from T 
WHERE ROWNUM <= 10

SELECT * FROM T 
SAMPLE 10

SELECT * FROM T
ROWS 20 TO 30

SELECT * FROM T
WHERE ID_T > 10 FETCH FIRST 10 ROWS ONLY

SELECT * FROM T
WHERE ID_T > 20 FETCH FIRST 10 ROWS ONLY

Select * 
from {table} 
order by {unique_key}

select *
from {table}
order by {unique_key}
FETCH FIRST {begin_base_0 + rows} ROWS ONLY

Select *
from {table}
order by {unique_key}
LIMIT {begin_base_0 + rows}

Select TOP {begin_base_0 + rows} * 
from {table} 
order by {unique_key}

Select *
from {table}
order by {unique_key}
ROWS LIMIT {begin_base_0 + rows}

SET ROWCOUNT {begin_base_0 + rows}
Select * 
from {table} 
order by {unique_key}
SET ROWCOUNT 0

Select *
    FROM (
        SELECT * 
        FROM {table} 
        ORDER BY {unique_key}
    ) a 
where rownum <= {begin_base_0 + rows}

Select *
from {table}
order by {unique_key}
OFFSET {begin_base_0} ROWS
FETCH NEXT {rows} ROWS ONLY

Select *
from {table}
order by {unique_key}
LIMIT {rows} OFFSET {begin_base_0}

Select * 
from {table} 
order by {unique_key}
LIMIT {begin_base_0}, {rows}

Select *
from {table}
order by {unique_key}
ROWS LIMIT {rows} OFFSET {begin_base_0}

Select TOP {begin_base_0 + rows}
       *,  _offset=identity(10) 
into #temp
from {table}
ORDER BY {unique_key} 
select * from #temp where _offset > {begin_base_0}
DROP TABLE #temp

SET ROWCOUNT {begin_base_0 + rows}
select *,  _offset=identity(10) 
into #temp
from {table}
ORDER BY {unique_key} 
select * from #temp where _offset > {begin_base_0}
DROP TABLE #temp
SET ROWCOUNT 0

select TOP {rows} * 
from (
      select *, ROW_NUMBER() over (order by {unique_key}) as _offset
      from {table}
) xx 
where _offset > {begin_base_0}

SET ROWCOUNT {begin_base_0 + rows}
select *,  _offset=identity(int,1,1) 
into #temp
from {table}
ORDER BY {unique-key}
select * from #temp where _offset > {begin_base_0}
DROP TABLE #temp
SET ROWCOUNT 0

SELECT * FROM (
    SELECT rownum-1 as _offset, a.* 
    FROM(
        SELECT * 
        FROM {table} 
        ORDER BY {unique_key}
    ) a 
    WHERE rownum <= {begin_base_0 + cant_regs}
)
WHERE _offset >= {begin_base_0}

select *
from {table} 
order by {unique_key}
FETCH FIRST {rows} ROWS ONLY

select * 
from {table} 
where {unique_key} > {last_val}
order by {unique_key}
FETCH FIRST {rows} ROWS ONLY

select * 
 from (
   select * 
   from {table} 
   where {unique_key} < {first_val}
   order by {unique_key} DESC
   FETCH FIRST {rows} ROWS ONLY
 ) a
 order by {unique_key}

select *
from {table}
order by {unique_key}
LIMIT {rows}

select * 
from {table} 
where {unique_key} > {last_val}
order by {unique_key}
LIMIT {rows}

select * 
 from (
   select * 
   from {table} 
   where {unique_key} < {first_val}
   order by {unique_key} DESC
   LIMIT {rows}
 ) a
 order by {unique_key}

select TOP {rows} * 
from {table} 
order by {unique_key}

select TOP {rows} * 
from {table} 
where {unique_key} > {last_val}
order by {unique_key}

select * 
 from (
   select TOP {rows} * 
   from {table} 
   where {unique_key} < {first_val}
   order by {unique_key} DESC
 ) a
 order by {unique_key}

SET ROWCOUNT {rows}
select *
from {table} 
order by {unique_key}
SET ROWCOUNT 0

SET ROWCOUNT {rows}
select *
from {table} 
where {unique_key} > {last_val}
order by {unique_key}
SET ROWCOUNT 0

SET ROWCOUNT {rows}
 select *
 from (
   select * 
   from {table} 
   where {unique_key} < {first_val}
   order by {unique_key} DESC
 ) a
 order by {unique_key}
 SET ROWCOUNT 0

select *
from (
    select * 
    from {table} 
    order by {unique_key}
  ) a 
where rownum <= {rows}

select *
from (
  select * 
  from {table} 
  where {unique_key} > {last_val}
  order by {unique_key}
) a 
where rownum <= {rows}

select * 
 from (
   select *
   from (
     select * 
     from {table} 
     where {unique_key} < {first_val}
     order by {unique_key} DESC
   ) a1
   where rownum <= {rows}
 ) a2
 order by {unique_key}

Some databases provide specialised syntax for hierarchical data.

A window function in SQL:2003 is an aggregate function applied to a partition of the result set.

For example,

 sum(population) OVER( PARTITION BY city )

calculates the sum of the populations of all rows having the same city value as the current row.

Partitions are specified using the OVER clause which modifies the aggregate. Syntax:

<OVER_CLAUSE> :: =
    OVER ( [ PARTITION BY <expr>, ... ]
           [ ORDER BY <expression> ] )

The OVER clause can partition and order the result set. Ordering is used for order-relative functions such as row_number.

The processing of a SELECT statement according to ANSI SQL would be the following:[10]

The implementation of window function features by vendors of relational databases and SQL engines differs wildly. Most databases support at least some flavour of window functions. However, when we take a closer look it becomes clear that most vendors only implement a subset of the standard. Let's take the powerful RANGE clause as an example. Only Oracle, DB2, Spark/Hive, and Google Big Query fully implement this feature. More recently, vendors have added new extensions to the standard, e.g. array aggregation functions. These are particularly useful in the context of running SQL against a distributed file system (Hadoop, Spark, Google BigQuery) where we have weaker data co-locality guarantees than on a distributed relational database (MPP). Rather than evenly distributing the data across all nodes, SQL engines running queries against a distributed filesystem can achieve data co-locality guarantees by nesting data and thus avoiding potentially expensive joins involving heavy shuffling across the network. User-defined aggregate functions that can be used in window functions are another extremely powerful feature.

Method to generate data based on the union all

select 1 a, 1 b union all
select 1, 2 union all
select 1, 3 union all
select 2, 1 union all
select 5, 1

SQL Server 2008 supports the "row constructor" specified in the SQL3 ("SQL:1999") standard

select *
from (values (1, 1), (1, 2), (1, 3), (2, 1), (5, 1)) as x(a, b)

• Horizontal & Vertical Partitioning, Microsoft SQL Server 2000 Books Online.

• Windowed Tables and Window function in SQL, Stefan Deßloch
• Oracle SELECT syntax
• Firebird SELECT syntax
• MySQL SELECT syntax
• PostgreSQL SELECT syntax
• SQLite SELECT syntax