query SELECT The previous chapters explained how to create tables, how to fill them with data, and how to manipulate that data. Now we finally discuss how to retrieve the data from the database. The process of retrieving or the command to retrieve data from a database is called a query. In SQL the command is used to specify queries. The general syntax of the SELECT command is WITH with_queries SELECT select_list FROM table_expression sort_specification The following sections describe the details of the select list, the table expression, and the sort specification. WITH queries are treated last since they are an advanced feature. A simple kind of query has the form: SELECT * FROM table1; Assuming that there is a table called table1, this command would retrieve all rows and all columns from table1. (The method of retrieval depends on the client application. For example, the psql program will display an ASCII-art table on the screen, while client libraries will offer functions to extract individual values from the query result.) The select list specification * means all columns that the table expression happens to provide. A select list can also select a subset of the available columns or make calculations using the columns. For example, if table1 has columns named a, b, and c (and perhaps others) you can make the following query: SELECT a, b + c FROM table1; (assuming that b and c are of a numerical data type). See for more details. FROM table1 is a simple kind of table expression: it reads just one table. In general, table expressions can be complex constructs of base tables, joins, and subqueries. But you can also omit the table expression entirely and use the SELECT command as a calculator: SELECT 3 * 4; This is more useful if the expressions in the select list return varying results. For example, you could call a function this way: SELECT random(); table expression A table expression computes a table. The table expression contains a FROM clause that is optionally followed by WHERE, GROUP BY, and HAVING clauses. Trivial table expressions simply refer to a table on disk, a so-called base table, but more complex expressions can be used to modify or combine base tables in various ways. The optional WHERE, GROUP BY, and HAVING clauses in the table expression specify a pipeline of successive transformations performed on the table derived in the FROM clause. All these transformations produce a virtual table that provides the rows that are passed to the select list to compute the output rows of the query. The derives a table from one or more other tables given in a comma-separated table reference list. FROM table_reference , table_reference , ... A table reference can be a table name (possibly schema-qualified), or a derived table such as a subquery, a table join, or complex combinations of these. If more than one table reference is listed in the FROM clause they are cross-joined (see below) to form the intermediate virtual table that can then be subject to transformations by the WHERE, GROUP BY, and HAVING clauses and is finally the result of the overall table expression. ONLY When a table reference names a table that is the parent of a table inheritance hierarchy, the table reference produces rows of not only that table but all of its descendant tables, unless the key word ONLY precedes the table name. However, the reference produces only the columns that appear in the named table — any columns added in subtables are ignored. join A joined table is a table derived from two other (real or derived) tables according to the rules of the particular join type. Inner, outer, and cross-joins are available. Cross join join cross cross join T1 CROSS JOIN T2 For every possible combination of rows from T1 and T2 (i.e., a Cartesian product), the joined table will contain a row consisting of all columns in T1 followed by all columns in T2. If the tables have N and M rows respectively, the joined table will have N * M rows. FROM T1 CROSS JOIN T2 is equivalent to FROM T1, T2. It is also equivalent to FROM T1 INNER JOIN T2 ON TRUE (see below). Qualified joins join outer outer join T1 { INNER | { LEFT | RIGHT | FULL } OUTER } JOIN T2 ON boolean_expression T1 { INNER | { LEFT | RIGHT | FULL } OUTER } JOIN T2 USING ( join column list ) T1 NATURAL { INNER | { LEFT | RIGHT | FULL } OUTER } JOIN T2 The words INNER and OUTER are optional in all forms. INNER is the default; LEFT, RIGHT, and FULL imply an outer join. The join condition is specified in the ON or USING clause, or implicitly by the word NATURAL. The join condition determines which rows from the two source tables are considered to match, as explained in detail below. The ON clause is the most general kind of join condition: it takes a Boolean value expression of the same kind as is used in a WHERE clause. A pair of rows from T1 and T2 match if the ON expression evaluates to true for them. USING is a shorthand notation: it takes a comma-separated list of column names, which the joined tables must have in common, and forms a join condition specifying equality of each of these pairs of columns. Furthermore, the output of JOIN USING has one column for each of the equated pairs of input columns, followed by the remaining columns from each table. Thus, USING (a, b, c) is equivalent to ON (t1.a = t2.a AND t1.b = t2.b AND t1.c = t2.c) with the exception that if ON is used there will be two columns a, b, and c in the result, whereas with USING there will be only one of each (and they will appear first if SELECT * is used). join natural natural join Finally, NATURAL is a shorthand form of USING: it forms a USING list consisting of all column names that appear in both input tables. As with USING, these columns appear only once in the output table. The possible types of qualified join are: INNER JOIN For each row R1 of T1, the joined table has a row for each row in T2 that satisfies the join condition with R1. LEFT OUTER JOIN join left left join First, an inner join is performed. Then, for each row in T1 that does not satisfy the join condition with any row in T2, a joined row is added with null values in columns of T2. Thus, the joined table always has at least one row for each row in T1. RIGHT OUTER JOIN join right right join First, an inner join is performed. Then, for each row in T2 that does not satisfy the join condition with any row in T1, a joined row is added with null values in columns of T1. This is the converse of a left join: the result table will always have a row for each row in T2. FULL OUTER JOIN First, an inner join is performed. Then, for each row in T1 that does not satisfy the join condition with any row in T2, a joined row is added with null values in columns of T2. Also, for each row of T2 that does not satisfy the join condition with any row in T1, a joined row with null values in the columns of T1 is added. Joins of all types can be chained together or nested: either or both T1 and T2 can be joined tables. Parentheses can be used around JOIN clauses to control the join order. In the absence of parentheses, JOIN clauses nest left-to-right. To put this together, assume we have tables t1: num | name -----+------ 1 | a 2 | b 3 | c and t2: num | value -----+------- 1 | xxx 3 | yyy 5 | zzz then we get the following results for the various joins: => SELECT * FROM t1 CROSS JOIN t2; num | name | num | value -----+------+-----+------- 1 | a | 1 | xxx 1 | a | 3 | yyy 1 | a | 5 | zzz 2 | b | 1 | xxx 2 | b | 3 | yyy 2 | b | 5 | zzz 3 | c | 1 | xxx 3 | c | 3 | yyy 3 | c | 5 | zzz (9 rows) => SELECT * FROM t1 INNER JOIN t2 ON t1.num = t2.num; num | name | num | value -----+------+-----+------- 1 | a | 1 | xxx 3 | c | 3 | yyy (2 rows) => SELECT * FROM t1 INNER JOIN t2 USING (num); num | name | value -----+------+------- 1 | a | xxx 3 | c | yyy (2 rows) => SELECT * FROM t1 NATURAL INNER JOIN t2; num | name | value -----+------+------- 1 | a | xxx 3 | c | yyy (2 rows) => SELECT * FROM t1 LEFT JOIN t2 ON t1.num = t2.num; num | name | num | value -----+------+-----+------- 1 | a | 1 | xxx 2 | b | | 3 | c | 3 | yyy (3 rows) => SELECT * FROM t1 LEFT JOIN t2 USING (num); num | name | value -----+------+------- 1 | a | xxx 2 | b | 3 | c | yyy (3 rows) => SELECT * FROM t1 RIGHT JOIN t2 ON t1.num = t2.num; num | name | num | value -----+------+-----+------- 1 | a | 1 | xxx 3 | c | 3 | yyy | | 5 | zzz (3 rows) => SELECT * FROM t1 FULL JOIN t2 ON t1.num = t2.num; num | name | num | value -----+------+-----+------- 1 | a | 1 | xxx 2 | b | | 3 | c | 3 | yyy | | 5 | zzz (4 rows) The join condition specified with ON can also contain conditions that do not relate directly to the join. This can prove useful for some queries but needs to be thought out carefully. For example: => SELECT * FROM t1 LEFT JOIN t2 ON t1.num = t2.num AND t2.value = 'xxx'; num | name | num | value -----+------+-----+------- 1 | a | 1 | xxx 2 | b | | 3 | c | | (3 rows) Notice that placing the restriction in the WHERE clause produces a different result: => SELECT * FROM t1 LEFT JOIN t2 ON t1.num = t2.num WHERE t2.value = 'xxx'; num | name | num | value -----+------+-----+------- 1 | a | 1 | xxx (1 row) This is because a restriction placed in the ON clause is processed before the join, while a restriction placed in the WHERE clause is processed after the join. alias in the FROM clause label alias A temporary name can be given to tables and complex table references to be used for references to the derived table in the rest of the query. This is called a table alias. To create a table alias, write FROM table_reference AS alias or FROM table_reference alias The AS key word is optional noise. alias can be any identifier. A typical application of table aliases is to assign short identifiers to long table names to keep the join clauses readable. For example: SELECT * FROM some_very_long_table_name s JOIN another_fairly_long_name a ON s.id = a.num; The alias becomes the new name of the table reference so far as the current query is concerned — it is not allowed to refer to the table by the original name elsewhere in the query. Thus, this is not valid: SELECT * FROM my_table AS m WHERE my_table.a > 5; -- wrong Table aliases are mainly for notational convenience, but it is necessary to use them when joining a table to itself, e.g.: SELECT * FROM people AS mother JOIN people AS child ON mother.id = child.mother_id; Additionally, an alias is required if the table reference is a subquery (see ). Parentheses are used to resolve ambiguities. In the following example, the first statement assigns the alias b to the second instance of my_table, but the second statement assigns the alias to the result of the join: SELECT * FROM my_table AS a CROSS JOIN my_table AS b ... SELECT * FROM (my_table AS a CROSS JOIN my_table) AS b ... Another form of table aliasing gives temporary names to the columns of the table, as well as the table itself: FROM table_reference AS alias ( column1 , column2 , ... ) If fewer column aliases are specified than the actual table has columns, the remaining columns are not renamed. This syntax is especially useful for self-joins or subqueries. When an alias is applied to the output of a JOIN clause, the alias hides the original name(s) within the JOIN. For example: SELECT a.* FROM my_table AS a JOIN your_table AS b ON ... is valid SQL, but: SELECT a.* FROM (my_table AS a JOIN your_table AS b ON ...) AS c is not valid; the table alias a is not visible outside the alias c. subquery Subqueries specifying a derived table must be enclosed in parentheses and must be assigned a table alias name. (See .) For example: FROM (SELECT * FROM table1) AS alias_name This example is equivalent to FROM table1 AS alias_name. More interesting cases, which cannot be reduced to a plain join, arise when the subquery involves grouping or aggregation. A subquery can also be a VALUES list: FROM (VALUES ('anne', 'smith'), ('bob', 'jones'), ('joe', 'blow')) AS names(first, last) Again, a table alias is required. Assigning alias names to the columns of the VALUES list is optional, but is good practice. For more information see . table function function in the FROM clause Table functions are functions that produce a set of rows, made up of either base data types (scalar types) or composite data types (table rows). They are used like a table, view, or subquery in the FROM clause of a query. Columns returned by table functions can be included in SELECT, JOIN, or WHERE clauses in the same manner as a table, view, or subquery column. If a table function returns a base data type, the single result column name matches the function name. If the function returns a composite type, the result columns get the same names as the individual attributes of the type. A table function can be aliased in the FROM clause, but it also can be left unaliased. If a function is used in the FROM clause with no alias, the function name is used as the resulting table name. Some examples: CREATE TABLE foo (fooid int, foosubid int, fooname text); CREATE FUNCTION getfoo(int) RETURNS SETOF foo AS $$ SELECT * FROM foo WHERE fooid = $1; $$ LANGUAGE SQL; SELECT * FROM getfoo(1) AS t1; SELECT * FROM foo WHERE foosubid IN ( SELECT foosubid FROM getfoo(foo.fooid) z WHERE z.fooid = foo.fooid ); CREATE VIEW vw_getfoo AS SELECT * FROM getfoo(1); SELECT * FROM vw_getfoo; In some cases it is useful to define table functions that can return different column sets depending on how they are invoked. To support this, the table function can be declared as returning the pseudotype record. When such a function is used in a query, the expected row structure must be specified in the query itself, so that the system can know how to parse and plan the query. Consider this example: SELECT * FROM dblink('dbname=mydb', 'SELECT proname, prosrc FROM pg_proc') AS t1(proname name, prosrc text) WHERE proname LIKE 'bytea%'; The dblink function executes a remote query (see contrib/dblink). It is declared to return record since it might be used for any kind of query. The actual column set must be specified in the calling query so that the parser knows, for example, what * should expand to. WHERE The syntax of the is WHERE search_condition where search_condition is any value expression (see ) that returns a value of type boolean. After the processing of the FROM clause is done, each row of the derived virtual table is checked against the search condition. If the result of the condition is true, the row is kept in the output table, otherwise (i.e., if the result is false or null) it is discarded. The search condition typically references at least one column of the table generated in the FROM clause; this is not required, but otherwise the WHERE clause will be fairly useless. The join condition of an inner join can be written either in the WHERE clause or in the JOIN clause. For example, these table expressions are equivalent: FROM a, b WHERE a.id = b.id AND b.val > 5 and: FROM a INNER JOIN b ON (a.id = b.id) WHERE b.val > 5 or perhaps even: FROM a NATURAL JOIN b WHERE b.val > 5 Which one of these you use is mainly a matter of style. The JOIN syntax in the FROM clause is probably not as portable to other SQL database management systems, even though it is in the SQL standard. For outer joins there is no choice: they must be done in the FROM clause. The ON or USING clause of an outer join is not equivalent to a WHERE condition, because it results in the addition of rows (for unmatched input rows) as well as the removal of rows in the final result. Here are some examples of WHERE clauses: SELECT ... FROM fdt WHERE c1 > 5 SELECT ... FROM fdt WHERE c1 IN (1, 2, 3) SELECT ... FROM fdt WHERE c1 IN (SELECT c1 FROM t2) SELECT ... FROM fdt WHERE c1 IN (SELECT c3 FROM t2 WHERE c2 = fdt.c1 + 10) SELECT ... FROM fdt WHERE c1 BETWEEN (SELECT c3 FROM t2 WHERE c2 = fdt.c1 + 10) AND 100 SELECT ... FROM fdt WHERE EXISTS (SELECT c1 FROM t2 WHERE c2 > fdt.c1) fdt is the table derived in the FROM clause. Rows that do not meet the search condition of the WHERE clause are eliminated from fdt. Notice the use of scalar subqueries as value expressions. Just like any other query, the subqueries can employ complex table expressions. Notice also how fdt is referenced in the subqueries. Qualifying c1 as fdt.c1 is only necessary if c1 is also the name of a column in the derived input table of the subquery. But qualifying the column name adds clarity even when it is not needed. This example shows how the column naming scope of an outer query extends into its inner queries. GROUP BY grouping After passing the WHERE filter, the derived input table might be subject to grouping, using the GROUP BY clause, and elimination of group rows using the HAVING clause. SELECT select_list FROM ... WHERE ... GROUP BY grouping_column_reference , grouping_column_reference... The is used to group together those rows in a table that have the same values in all the columns listed. The order in which the columns are listed does not matter. The effect is to combine each set of rows having common values into one group row that represents all rows in the group. This is done to eliminate redundancy in the output and/or compute aggregates that apply to these groups. For instance: => SELECT * FROM test1; x | y ---+--- a | 3 c | 2 b | 5 a | 1 (4 rows) => SELECT x FROM test1 GROUP BY x; x --- a b c (3 rows) In the second query, we could not have written SELECT * FROM test1 GROUP BY x, because there is no single value for the column y that could be associated with each group. The grouped-by columns can be referenced in the select list since they have a single value in each group. In general, if a table is grouped, columns that are not listed in GROUP BY cannot be referenced except in aggregate expressions. An example with aggregate expressions is: => SELECT x, sum(y) FROM test1 GROUP BY x; x | sum ---+----- a | 4 b | 5 c | 2 (3 rows) Here sum is an aggregate function that computes a single value over the entire group. More information about the available aggregate functions can be found in . Grouping without aggregate expressions effectively calculates the set of distinct values in a column. This can also be achieved using the DISTINCT clause (see ). Here is another example: it calculates the total sales for each product (rather than the total sales of all products): SELECT product_id, p.name, (sum(s.units) * p.price) AS sales FROM products p LEFT JOIN sales s USING (product_id) GROUP BY product_id, p.name, p.price; In this example, the columns product_id, p.name, and p.price must be in the GROUP BY clause since they are referenced in the query select list. (Depending on how the products table is set up, name and price might be fully dependent on the product ID, so the additional groupings could theoretically be unnecessary, though this is not implemented.) The column s.units does not have to be in the GROUP BY list since it is only used in an aggregate expression (sum(...)), which represents the sales of a product. For each product, the query returns a summary row about all sales of the product. In strict SQL, GROUP BY can only group by columns of the source table but PostgreSQL extends this to also allow GROUP BY to group by columns in the select list. Grouping by value expressions instead of simple column names is also allowed. HAVING If a table has been grouped using GROUP BY, but only certain groups are of interest, the HAVING clause can be used, much like a WHERE clause, to eliminate groups from the result. The syntax is: SELECT select_list FROM ... WHERE ... GROUP BY ... HAVING boolean_expression Expressions in the HAVING clause can refer both to grouped expressions and to ungrouped expressions (which necessarily involve an aggregate function). Example: => SELECT x, sum(y) FROM test1 GROUP BY x HAVING sum(y) > 3; x | sum ---+----- a | 4 b | 5 (2 rows) => SELECT x, sum(y) FROM test1 GROUP BY x HAVING x < 'c'; x | sum ---+----- a | 4 b | 5 (2 rows) Again, a more realistic example: SELECT product_id, p.name, (sum(s.units) * (p.price - p.cost)) AS profit FROM products p LEFT JOIN sales s USING (product_id) WHERE s.date > CURRENT_DATE - INTERVAL '4 weeks' GROUP BY product_id, p.name, p.price, p.cost HAVING sum(p.price * s.units) > 5000; In the example above, the WHERE clause is selecting rows by a column that is not grouped (the expression is only true for sales during the last four weeks), while the HAVING clause restricts the output to groups with total gross sales over 5000. Note that the aggregate expressions do not necessarily need to be the same in all parts of the query. If a query contains aggregate function calls, but no GROUP BY clause, grouping still occurs: the result is a single group row (or perhaps no rows at all, if the single row is then eliminated by HAVING). The same is true if it contains a HAVING clause, even without any aggregate function calls or GROUP BY clause. SELECT select list As shown in the previous section, the table expression in the SELECT command constructs an intermediate virtual table by possibly combining tables, views, eliminating rows, grouping, etc. This table is finally passed on to processing by the select list. The select list determines which columns of the intermediate table are actually output. * The simplest kind of select list is * which emits all columns that the table expression produces. Otherwise, a select list is a comma-separated list of value expressions (as defined in ). For instance, it could be a list of column names: SELECT a, b, c FROM ... The columns names a, b, and c are either the actual names of the columns of tables referenced in the FROM clause, or the aliases given to them as explained in . The name space available in the select list is the same as in the WHERE clause, unless grouping is used, in which case it is the same as in the HAVING clause. If more than one table has a column of the same name, the table name must also be given, as in: SELECT tbl1.a, tbl2.a, tbl1.b FROM ... When working with multiple tables, it can also be useful to ask for all the columns of a particular table: SELECT tbl1.*, tbl2.a FROM ... (See also .) If an arbitrary value expression is used in the select list, it conceptually adds a new virtual column to the returned table. The value expression is evaluated once for each result row, with the row's values substituted for any column references. But the expressions in the select list do not have to reference any columns in the table expression of the FROM clause; they can be constant arithmetic expressions, for instance. alias in the select list The entries in the select list can be assigned names for subsequent processing, such as for use in an ORDER BY clause or for display by the client application. For example: SELECT a AS value, b + c AS sum FROM ... If no output column name is specified using AS, the system assigns a default column name. For simple column references, this is the name of the referenced column. For function calls, this is the name of the function. For complex expressions, the system will generate a generic name. The AS keyword is optional, but only if the new column name does not match any PostgreSQL keyword (see ). To avoid an accidental match to a keyword, you can double-quote the column name. For example, VALUE is a keyword, so this does not work: SELECT a value, b + c AS sum FROM ... but this does: SELECT a "value", b + c AS sum FROM ... For protection against possible future keyword additions, it is recommended that you always either write AS or double-quote the output column name. The naming of output columns here is different from that done in the FROM clause (see ). It is possible to rename the same column twice, but the name assigned in the select list is the one that will be passed on. DISTINCT duplicates After the select list has been processed, the result table can optionally be subject to the elimination of duplicate rows. The DISTINCT key word is written directly after SELECT to specify this: SELECT DISTINCT select_list ... (Instead of DISTINCT the key word ALL can be used to specify the default behavior of retaining all rows.) null valuein DISTINCT Obviously, two rows are considered distinct if they differ in at least one column value. Null values are considered equal in this comparison. Alternatively, an arbitrary expression can determine what rows are to be considered distinct: SELECT DISTINCT ON (expression , expression ...) select_list ... Here expression is an arbitrary value expression that is evaluated for all rows. A set of rows for which all the expressions are equal are considered duplicates, and only the first row of the set is kept in the output. Note that the first row of a set is unpredictable unless the query is sorted on enough columns to guarantee a unique ordering of the rows arriving at the DISTINCT filter. (DISTINCT ON processing occurs after ORDER BY sorting.) The DISTINCT ON clause is not part of the SQL standard and is sometimes considered bad style because of the potentially indeterminate nature of its results. With judicious use of GROUP BY and subqueries in FROM, this construct can be avoided, but it is often the most convenient alternative. UNION INTERSECT EXCEPT set union set intersection set difference set operation The results of two queries can be combined using the set operations union, intersection, and difference. The syntax is query1 UNION ALL query2 query1 INTERSECT ALL query2 query1 EXCEPT ALL query2 query1 and query2 are queries that can use any of the features discussed up to this point. Set operations can also be nested and chained, for example query1 UNION query2 UNION query3 which is executed as: (query1 UNION query2) UNION query3 UNION effectively appends the result of query2 to the result of query1 (although there is no guarantee that this is the order in which the rows are actually returned). Furthermore, it eliminates duplicate rows from its result, in the same way as DISTINCT, unless UNION ALL is used. INTERSECT returns all rows that are both in the result of query1 and in the result of query2. Duplicate rows are eliminated unless INTERSECT ALL is used. EXCEPT returns all rows that are in the result of query1 but not in the result of query2. (This is sometimes called the difference between two queries.) Again, duplicates are eliminated unless EXCEPT ALL is used. In order to calculate the union, intersection, or difference of two queries, the two queries must be union compatible, which means that they return the same number of columns and the corresponding columns have compatible data types, as described in . sorting ORDER BY After a query has produced an output table (after the select list has been processed) it can optionally be sorted. If sorting is not chosen, the rows will be returned in an unspecified order. The actual order in that case will depend on the scan and join plan types and the order on disk, but it must not be relied on. A particular output ordering can only be guaranteed if the sort step is explicitly chosen. The ORDER BY clause specifies the sort order: SELECT select_list FROM table_expression ORDER BY sort_expression1 ASC | DESC NULLS { FIRST | LAST } , sort_expression2 ASC | DESC NULLS { FIRST | LAST } ... The sort expression(s) can be any expression that would be valid in the query's select list. An example is: SELECT a, b FROM table1 ORDER BY a + b, c; When more than one expression is specified, the later values are used to sort rows that are equal according to the earlier values. Each expression can be followed by an optional ASC or DESC keyword to set the sort direction to ascending or descending. ASC order is the default. Ascending order puts smaller values first, where smaller is defined in terms of the < operator. Similarly, descending order is determined with the > operator. Actually, PostgreSQL uses the default B-tree operator class for the expression's data type to determine the sort ordering for ASC and DESC. Conventionally, data types will be set up so that the < and > operators correspond to this sort ordering, but a user-defined data type's designer could choose to do something different. The NULLS FIRST and NULLS LAST options can be used to determine whether nulls appear before or after non-null values in the sort ordering. By default, null values sort as if larger than any non-null value; that is, NULLS FIRST is the default for DESC order, and NULLS LAST otherwise. Note that the ordering options are considered independently for each sort column. For example ORDER BY x, y DESC means ORDER BY x ASC, y DESC, which is not the same as ORDER BY x DESC, y DESC. A sort_expression can also be the column label or number of an output column, as in: SELECT a + b AS sum, c FROM table1 ORDER BY sum; SELECT a, max(b) FROM table1 GROUP BY a ORDER BY 1; both of which sort by the first output column. Note that an output column name has to stand alone, that is, it cannot be used in an expression — for example, this is not correct: SELECT a + b AS sum, c FROM table1 ORDER BY sum + c; -- wrong This restriction is made to reduce ambiguity. There is still ambiguity if an ORDER BY item is a simple name that could match either an output column name or a column from the table expression. The output column is used in such cases. This would only cause confusion if you use AS to rename an output column to match some other table column's name. ORDER BY can be applied to the result of a UNION, INTERSECT, or EXCEPT combination, but in this case it is only permitted to sort by output column names or numbers, not by expressions. LIMIT OFFSET LIMIT and OFFSET allow you to retrieve just a portion of the rows that are generated by the rest of the query: SELECT select_list FROM table_expression ORDER BY ... LIMIT { number | ALL } OFFSET number If a limit count is given, no more than that many rows will be returned (but possibly less, if the query itself yields less rows). LIMIT ALL is the same as omitting the LIMIT clause. OFFSET says to skip that many rows before beginning to return rows. OFFSET 0 is the same as omitting the OFFSET clause, and LIMIT NULL is the same as omitting the LIMIT clause. If both OFFSET and LIMIT appear, then OFFSET rows are skipped before starting to count the LIMIT rows that are returned. When using LIMIT, it is important to use an ORDER BY clause that constrains the result rows into a unique order. Otherwise you will get an unpredictable subset of the query's rows. You might be asking for the tenth through twentieth rows, but tenth through twentieth in what ordering? The ordering is unknown, unless you specified ORDER BY. The query optimizer takes LIMIT into account when generating query plans, so you are very likely to get different plans (yielding different row orders) depending on what you give for LIMIT and OFFSET. Thus, using different LIMIT/OFFSET values to select different subsets of a query result will give inconsistent results unless you enforce a predictable result ordering with ORDER BY. This is not a bug; it is an inherent consequence of the fact that SQL does not promise to deliver the results of a query in any particular order unless ORDER BY is used to constrain the order. The rows skipped by an OFFSET clause still have to be computed inside the server; therefore a large OFFSET might be inefficient. VALUES VALUES provides a way to generate a constant table that can be used in a query without having to actually create and populate a table on-disk. The syntax is VALUES ( expression [, ...] ) [, ...] Each parenthesized list of expressions generates a row in the table. The lists must all have the same number of elements (i.e., the number of columns in the table), and corresponding entries in each list must have compatible data types. The actual data type assigned to each column of the result is determined using the same rules as for UNION (see ). As an example: VALUES (1, 'one'), (2, 'two'), (3, 'three'); will return a table of two columns and three rows. It's effectively equivalent to: SELECT 1 AS column1, 'one' AS column2 UNION ALL SELECT 2, 'two' UNION ALL SELECT 3, 'three'; By default, PostgreSQL assigns the names column1, column2, etc. to the columns of a VALUES table. The column names are not specified by the SQL standard and different database systems do it differently, so it's usually better to override the default names with a table alias list. Syntactically, VALUES followed by expression lists is treated as equivalent to: SELECT select_list FROM table_expression and can appear anywhere a SELECT can. For example, you can use it as part of a UNION, or attach a sort_specification (ORDER BY, LIMIT, and/or OFFSET) to it. VALUES is most commonly used as the data source in an INSERT command, and next most commonly as a subquery. For more information see . WITH in SELECT common table expression WITH WITH provides a way to write subqueries for use in a larger SELECT query. The subqueries can be thought of as defining temporary tables that exist just for this query. One use of this feature is to break down complicated queries into simpler parts. An example is: WITH regional_sales AS ( SELECT region, SUM(amount) AS total_sales FROM orders GROUP BY region ), top_regions AS ( SELECT region FROM regional_sales WHERE total_sales > (SELECT SUM(total_sales)/10 FROM regional_sales) ) SELECT region, product, SUM(quantity) AS product_units, SUM(amount) AS product_sales FROM orders WHERE region IN (SELECT region FROM top_regions) GROUP BY region, product; which displays per-product sales totals in only the top sales regions. This example could have been written without WITH, but we'd have needed two levels of nested sub-SELECTs. It's a bit easier to follow this way. The optional RECURSIVE modifier changes WITH from a mere syntactic convenience into a feature that accomplishes things not otherwise possible in standard SQL. Using RECURSIVE, a WITH query can refer to its own output. A very simple example is this query to sum the integers from 1 through 100: WITH RECURSIVE t(n) AS ( VALUES (1) UNION ALL SELECT n+1 FROM t WHERE n < 100 ) SELECT sum(n) FROM t; The general form of a recursive WITH query is always a non-recursive term, then UNION (or UNION ALL), then a recursive term, where only the recursive term can contain a reference to the query's own output. Such a query is executed as follows: Evaluate the non-recursive term. For UNION (but not UNION ALL), discard duplicate rows. Include all remaining rows in the result of the recursive query, and also place them in a temporary working table. So long as the working table is not empty, repeat these steps: Evaluate the recursive term, substituting the current contents of the working table for the recursive self-reference. For UNION (but not UNION ALL), discard duplicate rows and rows that duplicate any previous result row. Include all remaining rows in the result of the recursive query, and also place them in a temporary intermediate table. Replace the contents of the working table with the contents of the intermediate table, then empty the intermediate table. Strictly speaking, this process is iteration not recursion, but RECURSIVE is the terminology chosen by the SQL standards committee. In the example above, the working table has just a single row in each step, and it takes on the values from 1 through 100 in successive steps. In the 100th step, there is no output because of the WHERE clause, and so the query terminates. Recursive queries are typically used to deal with hierarchical or tree-structured data. A useful example is this query to find all the direct and indirect sub-parts of a product, given only a table that shows immediate inclusions: WITH RECURSIVE included_parts(sub_part, part, quantity) AS ( SELECT sub_part, part, quantity FROM parts WHERE part = 'our_product' UNION ALL SELECT p.sub_part, p.part, p.quantity FROM included_parts pr, parts p WHERE p.part = pr.sub_part ) SELECT sub_part, SUM(quantity) as total_quantity FROM included_parts GROUP BY sub_part When working with recursive queries it is important to be sure that the recursive part of the query will eventually return no tuples, or else the query will loop indefinitely. Sometimes, using UNION instead of UNION ALL can accomplish this by discarding rows that duplicate previous output rows. However, often a cycle does not involve output rows that are completely duplicate: it may be necessary to check just one or a few fields to see if the same point has been reached before. The standard method for handling such situations is to compute an array of the already-visited values. For example, consider the following query that searches a table graph using a link field: WITH RECURSIVE search_graph(id, link, data, depth) AS ( SELECT g.id, g.link, g.data, 1 FROM graph g UNION ALL SELECT g.id, g.link, g.data, sg.depth + 1 FROM graph g, search_graph sg WHERE g.id = sg.link ) SELECT * FROM search_graph; This query will loop if the link relationships contain cycles. Because we require a depth output, just changing UNION ALL to UNION would not eliminate the looping. Instead we need to recognize whether we have reached the same row again while following a particular path of links. We add two columns path and cycle to the loop-prone query: WITH RECURSIVE search_graph(id, link, data, depth, path, cycle) AS ( SELECT g.id, g.link, g.data, 1, ARRAY[g.id], false FROM graph g UNION ALL SELECT g.id, g.link, g.data, sg.depth + 1, path || g.id, g.id = ANY(path) FROM graph g, search_graph sg WHERE g.id = sg.link AND NOT cycle ) SELECT * FROM search_graph; Aside from preventing cycles, the array value is often useful in its own right as representing the path taken to reach any particular row. In the general case where more than one field needs to be checked to recognize a cycle, use an array of rows. For example, if we needed to compare fields f1 and f2: WITH RECURSIVE search_graph(id, link, data, depth, path, cycle) AS ( SELECT g.id, g.link, g.data, 1, ARRAY[ROW(g.f1, g.f2)], false FROM graph g UNION ALL SELECT g.id, g.link, g.data, sg.depth + 1, path || ROW(g.f1, g.f2), ROW(g.f1, g.f2) = ANY(path) FROM graph g, search_graph sg WHERE g.id = sg.link AND NOT cycle ) SELECT * FROM search_graph; Omit the ROW() syntax in the common case where only one field needs to be checked to recognize a cycle. This allows a simple array rather than a composite-type array to be used, gaining efficiency. The recursive query evaluation algorithm produces its output in breadth-first search order. You can display the results in depth-first search order by making the outer query ORDER BY a path column constructed in this way. A helpful trick for testing queries when you are not certain if they might loop is to place a LIMIT in the parent query. For example, this query would loop forever without the LIMIT: WITH RECURSIVE t(n) AS ( SELECT 1 UNION ALL SELECT n+1 FROM t ) SELECT n FROM t LIMIT 100; This works because PostgreSQL's implementation evaluates only as many rows of a WITH query as are actually fetched by the parent query. Using this trick in production is not recommended, because other systems might work differently. Also, it usually won't work if you make the outer query sort the recursive query's results or join them to some other table. A useful property of WITH queries is that they are evaluated only once per execution of the parent query, even if they are referred to more than once by the parent query or sibling WITH queries. Thus, expensive calculations that are needed in multiple places can be placed within a WITH query to avoid redundant work. Another possible application is to prevent unwanted multiple evaluations of functions with side-effects. However, the other side of this coin is that the optimizer is less able to push restrictions from the parent query down into a WITH query than an ordinary sub-query. The WITH query will generally be evaluated as stated, without suppression of rows that the parent query might discard afterwards. (But, as mentioned above, evaluation might stop early if the reference(s) to the query demand only a limited number of rows.)