SELECT

    where with_query: is:

    where grouping_element can be one of:

    1.   ()
    2.   <expression>
    3.   ROLLUP (<expression> [,...])
    4.   CUBE (<expression> [,...])
    5.   GROUPING SETS ((<grouping_element> [, ...]))

    where window_specification can be:

    1.   [<window_name>]
    2.   [PARTITION BY <expression >[, ...]]
    3.   [ORDER BY <expression> [ASC | DESC | USING <operator>] [NULLS {FIRST | LAST}] [, ...]
    4.      [{RANGE | ROWS} 
    5.           { UNBOUNDED PRECEDING
    6.           | <expression> PRECEDING
    7.           | CURRENT ROW
    8.           | BETWEEN <window_frame_bound> AND <window_frame_bound> }]]
    9.                     where <window_frame_bound> can be one of:
    10.                         UNBOUNDED PRECEDING
    11.                         <expression> PRECEDING
    12.                         CURRENT ROW
    13.                         <expression> FOLLOWING
    14.                         UNBOUNDED FOLLOWING

    where from_item can be one of:

    1. [ONLY] <table_name> [[AS] <alias> [( <column_alias> [, ...] )]]
    2. (select) [AS] <alias> [( <column_alias> [, ...] )]
    3. with\_query\_name [ [AS] <alias> [( <column_alias> [, ...] )]]
    4. <function_name> ( [<argument> [, ...]] ) [AS] <alias>
    5.              [( <column_alias> [, ...] 
    6.                 | <column_definition> [, ...] )]
    7. <function_name> ( [<argument> [, ...]] ) AS 
    8.               ( <column_definition> [, ...] )
    9. <from_item> [NATURAL] <join_type> <from_item>
    10.           [ON <join_condition> | USING ( <join_column> [, ...] )]

    Note: 1The RECURSIVE keyword is a Beta feature.

    Description

    SELECT retrieves rows from zero or more tables. The general processing of SELECT is as follows:

    1. All queries in the WITH clause are computed. These effectively serve as temporary tables that can be referenced in the FROM list.
    2. All elements in the FROM list are computed. (Each element in the FROM list is a real or virtual table.) If more than one element is specified in the FROM list, they are cross-joined together.
    3. If the WHERE clause is specified, all rows that do not satisfy the condition are eliminated from the output.
    4. If the GROUP BY clause is specified, the output is divided into groups of rows that match on one or more of the defined grouping elements. If the HAVING clause is present, it eliminates groups that do not satisfy the given condition.
    5. If a window expression is specified (and optional WINDOW clause), the output is organized according to the positional (row) or value-based (range) window frame.
    6. DISTINCT eliminates duplicate rows from the result. DISTINCT ON eliminates rows that match on all the specified expressions. ALL (the default) will return all candidate rows, including duplicates.
    7. The actual output rows are computed using the SELECT output expressions for each selected row.
    8. Using the operators UNION, INTERSECT, and EXCEPT, the output of more than one SELECT statement can be combined to form a single result set. The UNION operator returns all rows that are in one or both of the result sets. The INTERSECT operator returns all rows that are strictly in both result sets. The EXCEPT operator returns the rows that are in the first result set but not in the second. In all three cases, duplicate rows are eliminated unless ALL is specified.
    9. If the ORDER BY clause is specified, the returned rows are sorted in the specified order. If ORDER BY is not given, the rows are returned in whatever order the system finds fastest to produce.
    10. If the LIMIT or OFFSET clause is specified, the SELECT statement only returns a subset of the result rows.
    11. If FOR UPDATE or FOR SHARE is specified, the SELECT statement locks the entire table against concurrent updates.

    You must have SELECT privilege on a table to read its values. The use of FOR UPDATE or FOR SHARE requires UPDATE privilege as well.

    The WITH Clause

    The optional WITH clause allows you to specify one or more subqueries that can be referenced by name in the primary query. The subqueries effectively act as temporary tables or views for the duration of the primary query. Each subquery can be a SELECT, or VALUES command.

    A with_query_name without schema qualification must be specified for each query in the WITH clause. Optionally, a list of column names can be specified; if the list of column names is omitted, the names are inferred from the subquery. The primary query and the WITH queries are all (notionally) executed at the same time.

    The RECURSIVE keyword can be enabled by setting the server configuration parameter gp_recursive_cte_prototype to true. For information about the parameter, see .

    Note: The RECURSIVE keyword is a Beta feature.

    If RECURSIVE is specified, it allows a subquery to reference itself by name. Such a subquery, the select portion of the with_query , must have the form

    1. <non_recursive_term> UNION [ALL] <recursive_term>

    The recursive self-reference must appear on the right-hand side of the UNION [ALL]. Only one recursive self-reference is permitted per query.

    If the RECURSIVE keyword is specified, the WITH queries need not be ordered: a query can reference another query that is later in the list. However, circular references, or mutual recursion, are not supported.

    Without the RECURSIVE keyword, WITH queries can only reference sibling WITH queries that are earlier in the WITH list.

    WITH RECURSIVE limitations. These items are not supported,

    • A recursive WITH clause that contains the following in the recursive_term.
      • Subqueries with a self-reference
      • DISTINCT clause
      • GROUP BY clause
      • A window function
    • A recursive WITH clause where the with_query_name is a part of a set operation.

    An example the set operation limitation. This query returns an error because the set operation UNION contains a reference to the table foo.

    1. WITH RECURSIVE foo(i) AS (
    2.     SELECT 1
    3.   UNION ALL
    4. )
    5. SELECT * FROM foo LIMIT 5;

    This recursive CTE is allowed because the set operation UNION does not have a reference to the CTE foo.

    1. WITH RECURSIVE foo(i) AS (
    2.     SELECT 1
    3.   UNION ALL
    4.     SELECT i+1 FROM (SELECT * FROM bar UNION SELECT 0) bar, foo
    5.     WHERE foo.i = bar.a
    6. )
    7. SELECT * FROM foo LIMIT 5;

    See WITH Queries (Common Table Expressions)in the Greenplum Database Administrator Guide for additional information.

    The SELECT List

    The SELECT list (between the key words SELECT and FROM) specifies expressions that form the output rows of the SELECT statement. The expressions can (and usually do) refer to columns computed in the FROM clause.

    Using the clause [AS] output_name, another name can be specified for an output column. This name is primarily used to label the column for display. It can also be used to refer to the column’s value in ORDER BY and GROUP BY clauses, but not in the WHERE or HAVING clauses; there you must write out the expression instead. The AS keyword is optional in most cases (such as when declaring an alias for column names, constants, function calls, and simple unary operator expressions). In cases where the declared alias is a reserved SQL keyword, the output_name must be enclosed in double quotes to avoid ambiguity.

    An expression in the SELECT list can be a constant value, a column reference, an operator invocation, a function call, an aggregate expression, a window expression, a scalar subquery, and so on. A number of constructs can be classified as an expression but do not follow any general syntax rules. These generally have the semantics of a function or operator. For information about SQL value expressions and function calls, see “Querying Data” in the Greenplum Database Administrator Guide.

    Instead of an expression, * can be written in the output list as a shorthand for all the columns of the selected rows. Also, you can write table\_name.* as a shorthand for the columns coming from just that table.

    The FROM Clause

    The FROM clause specifies one or more source tables for the SELECT. If multiple sources are specified, the result is the Cartesian product (cross join) of all the sources. But usually qualification conditions are added to restrict the returned rows to a small subset of the Cartesian product. The FROM clause can contain the following elements:

    table_name

    The name (optionally schema-qualified) of an existing table or view. If ONLY is specified, only that table is scanned. If ONLY is not specified, the table and all its descendant tables (if any) are scanned.

    alias

    A substitute name for the FROM item containing the alias. An alias is used for brevity or to eliminate ambiguity for self-joins (where the same table is scanned multiple times). When an alias is provided, it completely hides the actual name of the table or function; for example given FROM foo AS f, the remainder of the SELECT must refer to this FROM item as f not foo. If an alias is written, a column alias list can also be written to provide substitute names for one or more columns of the table.

    select

    A sub- SELECT can appear in the FROM clause. This acts as though its output were created as a temporary table for the duration of this single SELECT command. Note that the sub- SELECT must be surrounded by parentheses, and an alias must be provided for it. A VALUES command can also be used here. See “Non-standard Clauses” in the section for limitations of using correlated sub-selects in Greenplum Database.

    with_query_name

    A with_query is referenced in the FROM clause by specifying its with_query_name, just as though the name were a table name. The with_query_name cannot contain a schema qualifier. An alias can be provided in the same way as for a table.

    The with_query hides a table of the same name for the purposes of the primary query. If necessary, you can refer to a table of the same name by qualifying the table name with the schema.

    function_name

    Function calls can appear in the FROM clause. (This is especially useful for functions that return result sets, but any function can be used.) This acts as though its output were created as a temporary table for the duration of this single SELECT command. An alias may also be used. If an alias is written, a column alias list can also be written to provide substitute names for one or more attributes of the function’s composite return type. If the function has been defined as returning the record data type, then an alias or the key word AS must be present, followed by a column definition list in the form ( column_name data_type [, ... ] ). The column definition list must match the actual number and types of columns returned by the function.

    join_type

    One of:

    • [INNER] JOIN
    • LEFT [OUTER] JOIN
    • RIGHT [OUTER] JOIN
    • FULL [OUTER] JOIN
    • CROSS JOIN

    For the INNER and OUTER join types, a join condition must be specified, namely exactly one of NATURAL, ON join\_condition, or USING ( join\_column [, ...]). See below for the meaning. For CROSS JOIN, none of these clauses may appear.

    A JOIN clause combines two FROM items. Use parentheses if necessary to determine the order of nesting. In the absence of parentheses, JOINs nest left-to-right. In any case JOIN binds more tightly than the commas separating FROM items.

    CROSS JOIN and INNER JOIN produce a simple Cartesian product, the same result as you get from listing the two items at the top level of FROM, but restricted by the join condition (if any). CROSS JOIN is equivalent to INNER JOIN ON``(TRUE), that is, no rows are removed by qualification. These join types are just a notational convenience, since they do nothing you could not do with plain FROM and WHERE.

    LEFT OUTER JOIN returns all rows in the qualified Cartesian product (i.e., all combined rows that pass its join condition), plus one copy of each row in the left-hand table for which there was no right-hand row that passed the join condition. This left-hand row is extended to the full width of the joined table by inserting null values for the right-hand columns. Note that only the JOIN clause’s own condition is considered while deciding which rows have matches. Outer conditions are applied afterwards.

    Conversely, RIGHT OUTER JOIN returns all the joined rows, plus one row for each unmatched right-hand row (extended with nulls on the left). This is just a notational convenience, since you could convert it to a LEFT OUTER JOIN by switching the left and right inputs.

    FULL OUTER JOIN returns all the joined rows, plus one row for each unmatched left-hand row (extended with nulls on the right), plus one row for each unmatched right-hand row (extended with nulls on the left).

    ON join_condition

    join_condition is an expression resulting in a value of type boolean (similar to a WHERE clause) that specifies which rows in a join are considered to match.

    USING (join_column [, …])

    A clause of the form USING ( a, b, ... ) is shorthand for ON left_table.a = right_table.a AND left_table.b = right_table.b .... Also, USING implies that only one of each pair of equivalent columns will be included in the join output, not both.

    NATURAL

    NATURAL is shorthand for a USING list that mentions all columns in the two tables that have the same names.

    The WHERE Clause

    The optional WHERE clause has the general form:

    1. WHERE <condition>

    where condition is any expression that evaluates to a result of type boolean. Any row that does not satisfy this condition will be eliminated from the output. A row satisfies the condition if it returns true when the actual row values are substituted for any variable references.

    The GROUP BY Clause

    The optional GROUP BY clause has the general form:

    1. GROUP BY <grouping_element >[, ...]
    1. ()
    2. <expression>
    3. ROLLUP (<expression> [,...])
    4. CUBE (<expression> [,...])
    5. GROUPING SETS ((<grouping_element> [, ...]))

    GROUP BY will condense into a single row all selected rows that share the same values for the grouped expressions. expression can be an input column name, or the name or ordinal number of an output column (SELECT list item), or an arbitrary expression formed from input-column values. In case of ambiguity, a GROUP BY name will be interpreted as an input-column name rather than an output column name.

    Aggregate functions, if any are used, are computed across all rows making up each group, producing a separate value for each group (whereas without GROUP BY, an aggregate produces a single value computed across all the selected rows). When GROUP BY is present, it is not valid for the list expressions to refer to ungrouped columns except within aggregate functions, since there would be more than one possible value to return for an ungrouped column.

    Greenplum Database has the following additional OLAP grouping extensions (often referred to as supergroups):

    ROLLUP

    A ROLLUP grouping is an extension to the GROUP BY clause that creates aggregate subtotals that roll up from the most detailed level to a grand total, following a list of grouping columns (or expressions). ROLLUP takes an ordered list of grouping columns, calculates the standard aggregate values specified in the GROUP BY clause, then creates progressively higher-level subtotals, moving from right to left through the list. Finally, it creates a grand total. A ROLLUP grouping can be thought of as a series of grouping sets. For example:

    1. GROUP BY ROLLUP (a,b,c)

    : is equivalent to:

    1. GROUP BY GROUPING SETS( (a,b,c), (a,b), (a), () )

    : Notice that the n elements of a ROLLUP translate to n+1 grouping sets. Also, the order in which the grouping expressions are specified is significant in a ROLLUP.

    CUBE

    A CUBE grouping is an extension to the GROUP BY clause that creates subtotals for all of the possible combinations of the given list of grouping columns (or expressions). In terms of multidimensional analysis, CUBE generates all the subtotals that could be calculated for a data cube with the specified dimensions. For example:

    1. GROUP BY CUBE (a,b,c)

    : is equivalent to:

    : Notice that n elements of a CUBE translate to 2n grouping sets. Consider using CUBE in any situation requiring cross-tabular reports. CUBE is typically most suitable in queries that use columns from multiple dimensions rather than columns representing different levels of a single dimension. For instance, a commonly requested cross-tabulation might need subtotals for all the combinations of month, state, and product.

    GROUPING SETS

    You can selectively specify the set of groups that you want to create using a GROUPING SETS expression within a GROUP BY clause. This allows precise specification across multiple dimensions without computing a whole ROLLUP or CUBE. For example:

    1. GROUP BY GROUPING SETS( (a,c), (a,b) )

    : If using the grouping extension clauses ROLLUP, CUBE, or GROUPING SETS, two challenges arise. First, how do you determine which result rows are subtotals, and then the exact level of aggregation for a given subtotal. Or, how do you differentiate between result rows that contain both stored NULL values and “NULL” values created by the ROLLUP or CUBE. Secondly, when duplicate grouping sets are specified in the GROUP BY clause, how do you determine which result rows are duplicates? There are two additional grouping functions you can use in the SELECT list to help with this:

    1. -   **grouping\(column \[, ...\]\)** — The `grouping` function can be applied to one or more grouping attributes to distinguish super-aggregated rows from regular grouped rows. This can be helpful in distinguishing a "NULL" representing the set of all values in a super-aggregated row from a `NULL` value in a regular row. Each argument in this function produces a bit — either `1` or `0`, where `1` means the result row is super-aggregated, and `0` means the result row is from a regular grouping. The `grouping` function returns an integer by treating these bits as a binary number and then converting it to a base-10 integer.
    2. -   **group\_id\(\)** — For grouping extension queries that contain duplicate grouping sets, the `group_id` function is used to identify duplicate rows in the output. All *unique* grouping set output rows will have a group\_id value of 0. For each duplicate grouping set detected, the `group_id` function assigns a group\_id number greater than 0. All output rows in a particular duplicate grouping set are identified by the same group\_id number.

    The WINDOW Clause

    The WINDOW clause is used to define a window that can be used in the OVER() expression of a window function such as rank or avg. For example:

    1. SELECT vendor, rank() OVER (mywindow) FROM sale
    2. GROUP BY vendor
    3. WINDOW mywindow AS (ORDER BY sum(prc*qty));

    A WINDOW clause has this general form:

    1. WINDOW <window_name> AS (<window_specification>)

    where window_specification can be:

    1. [<window_name>]
    2. [PARTITION BY <expression >[, ...]]
    3. [ORDER BY <expression> [ASC | DESC | USING <operator>] [NULLS {FIRST | LAST}] [, ...]
    4.     [{RANGE | ROWS} 
    5.       { UNBOUNDED PRECEDING
    6.       | <expression> PRECEDING
    7.       | CURRENT ROW
    8.       | BETWEEN <window_frame_bound> AND <window_frame_bound> }]]
    9.              where <window_frame_bound> can be one of:
    10.                UNBOUNDED PRECEDING
    11.                <expression> PRECEDING
    12.                CURRENT ROW
    13.                <expression> FOLLOWING
    14.                UNBOUNDED FOLLOWING

    window_name

    Gives a name to the window specification.

    PARTITION BY

    The PARTITION BY clause organizes the result set into logical groups based on the unique values of the specified expression. When used with window functions, the functions are applied to each partition independently. For example, if you follow PARTITION BY with a column name, the result set is partitioned by the distinct values of that column. If omitted, the entire result set is considered one partition.

    ORDER BY

    The ORDER BY clause defines how to sort the rows in each partition of the result set. If omitted, rows are returned in whatever order is most efficient and may vary. Note: Columns of data types that lack a coherent ordering, such as time, are not good candidates for use in the ORDER BY clause of a window specification. Time, with or without time zone, lacks a coherent ordering because addition and subtraction do not have the expected effects. For example, the following is not generally true: x::time < x::time + '2 hour'::interval

    ROWS | RANGE

    Use either a ROWS or RANGE clause to express the bounds of the window. The window bound can be one, many, or all rows of a partition. You can express the bound of the window either in terms of a range of data values offset from the value in the current row (RANGE), or in terms of the number of rows offset from the current row (ROWS). When using the RANGE clause, you must also use an ORDER BY clause. This is because the calculation performed to produce the window requires that the values be sorted. Additionally, the ORDER BY clause cannot contain more than one expression, and the expression must result in either a date or a numeric value. When using the ROWS or RANGE clauses, if you specify only a starting row, the current row is used as the last row in the window.

    PRECEDING — The PRECEDING clause defines the first row of the window using the current row as a reference point. The starting row is expressed in terms of the number of rows preceding the current row. For example, in the case of ROWS framing, 5 PRECEDING sets the window to start with the fifth row preceding the current row. In the case of RANGE framing, it sets the window to start with the first row whose ordering column value precedes that of the current row by 5 in the given order. If the specified order is ascending by date, this will be the first row within 5 days before the current row. UNBOUNDED PRECEDING sets the first row in the window to be the first row in the partition.

    BETWEEN — The BETWEEN clause defines the first and last row of the window, using the current row as a reference point. First and last rows are expressed in terms of the number of rows preceding and following the current row, respectively. For example, BETWEEN 3 PRECEDING AND 5 FOLLOWING sets the window to start with the third row preceding the current row, and end with the fifth row following the current row. Use BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING to set the first and last rows in the window to be the first and last row in the partition, respectively. This is equivalent to the default behavior if no ROW or RANGE clause is specified.

    FOLLOWING — The FOLLOWING clause defines the last row of the window using the current row as a reference point. The last row is expressed in terms of the number of rows following the current row. For example, in the case of ROWS framing, 5 FOLLOWING sets the window to end with the fifth row following the current row. In the case of RANGE framing, it sets the window to end with the last row whose ordering column value follows that of the current row by 5 in the given order. If the specified order is ascending by date, this will be the last row within 5 days after the current row. Use UNBOUNDED FOLLOWING to set the last row in the window to be the last row in the partition.

    If you do not specify a ROW or a RANGE clause, the window bound starts with the first row in the partition ( UNBOUNDED PRECEDING) and ends with the current row ( CURRENT ROW) if ORDER BY is used. If an ORDER BY is not specified, the window starts with the first row in the partition ( UNBOUNDED PRECEDING) and ends with last row in the partition ( UNBOUNDED FOLLOWING).

    The HAVING Clause

    The optional HAVING clause has the general form:

    1. HAVING <condition>

    where condition is the same as specified for the WHERE clause. HAVING eliminates group rows that do not satisfy the condition. HAVING is different from WHERE: WHERE filters individual rows before the application of GROUP BY, while HAVING filters group rows created by GROUP BY. Each column referenced in condition must unambiguously reference a grouping column, unless the reference appears within an aggregate function.

    The presence of HAVING turns a query into a grouped query even if there is no GROUP BY clause. This is the same as what happens when the query contains aggregate functions but no GROUP BY clause. All the selected rows are considered to form a single group, and the SELECT list and HAVING clause can only reference table columns from within aggregate functions. Such a query will emit a single row if the HAVING condition is true, zero rows if it is not true.

    The UNION Clause

    The UNION clause has this general form:

    1. <select_statement> UNION [ALL] <select_statement>

    where select_statement is any SELECT statement without an ORDER BY, LIMIT, FOR UPDATE, or FOR SHARE clause. (ORDER BY and LIMIT can be attached to a subquery expression if it is enclosed in parentheses. Without parentheses, these clauses will be taken to apply to the result of the UNION, not to its right-hand input expression.)

    The UNION operator computes the set union of the rows returned by the involved SELECT statements. A row is in the set union of two result sets if it appears in at least one of the result sets. The two SELECT statements that represent the direct operands of the UNION must produce the same number of columns, and corresponding columns must be of compatible data types.

    The result of UNION does not contain any duplicate rows unless the ALL option is specified. ALL prevents elimination of duplicates. (Therefore, UNION ALL is usually significantly quicker than UNION; use ALL when you can.)

    Multiple UNION operators in the same SELECT statement are evaluated left to right, unless otherwise indicated by parentheses.

    Currently, FOR UPDATE and FOR SHARE may not be specified either for a UNION result or for any input of a UNION.

    The INTERSECT Clause

    The INTERSECT clause has this general form:

    1. <select_statement> INTERSECT [ALL] <select_statement>

    where select_statement is any SELECT statement without an ORDER BY, LIMIT, FOR UPDATE, or FOR SHARE clause.

    The INTERSECT operator computes the set intersection of the rows returned by the involved SELECT statements. A row is in the intersection of two result sets if it appears in both result sets.

    The result of INTERSECT does not contain any duplicate rows unless the ALL option is specified. With ALL, a row that has m duplicates in the left table and n duplicates in the right table will appear min(m, n) times in the result set.

    Multiple INTERSECT operators in the same SELECT statement are evaluated left to right, unless parentheses dictate otherwise. INTERSECT binds more tightly than UNION. That is, A UNION B INTERSECT C will be read as A UNION (B INTERSECT C).

    Currently, FOR UPDATE and FOR SHARE may not be specified either for an INTERSECT result or for any input of an INTERSECT.

    The EXCEPT Clause

    The EXCEPT clause has this general form:

    1. <select_statement> EXCEPT [ALL] <select_statement>

    where select_statement is any SELECT statement without an ORDER BY, LIMIT, FOR UPDATE, or FOR SHARE clause.

    The EXCEPT operator computes the set of rows that are in the result of the left SELECT statement but not in the result of the right one.

    The result of EXCEPT does not contain any duplicate rows unless the ALL option is specified. With ALL, a row that has m duplicates in the left table and n duplicates in the right table will appear max(m-n,0) times in the result set.

    Multiple EXCEPT operators in the same SELECT statement are evaluated left to right, unless parentheses dictate otherwise. EXCEPT binds at the same level as UNION.

    Currently, FOR UPDATE and FOR SHARE may not be specified either for an EXCEPT result or for any input of an EXCEPT.

    The ORDER BY Clause

    The optional ORDER BY clause has this general form:

    1. ORDER BY <expression> [ASC | DESC | USING <operator>] [NULLS { FIRST | LAST}] [, ...]

    The ORDER BY clause causes the result rows to be sorted according to the specified expressions. If two rows are equal according to the left-most expression, they are compared according to the next expression and so on. If they are equal according to all specified expressions, they are returned in an implementation-dependent order.

    The ordinal number refers to the ordinal (left-to-right) position of the result column. This feature makes it possible to define an ordering on the basis of a column that does not have a unique name. This is never absolutely necessary because it is always possible to assign a name to a result column using the AS clause.

    It is also possible to use arbitrary expressions in the ORDER BY clause, including columns that do not appear in the SELECT result list. Thus the following statement is valid:

    1. SELECT name FROM distributors ORDER BY code;

    A limitation of this feature is that an ORDER BY clause applying to the result of a UNION, , or EXCEPT clause may only specify an output column name or number, not an expression.

    If an ORDER BY expression is a simple name that matches both a result column name and an input column name, ORDER BY will interpret it as the result column name. This is the opposite of the choice that GROUP BY will make in the same situation. This inconsistency is made to be compatible with the SQL standard.

    Optionally one may add the key word ASC (ascending) or DESC (descending) after any expression in the ORDER BY clause. If not specified, ASC is assumed by default. Alternatively, a specific ordering operator name may be specified in the USING clause. ASC is usually equivalent to USING < and DESC is usually equivalent to USING >. (But the creator of a user-defined data type can define exactly what the default sort ordering is, and it might correspond to operators with other names.)

    If NULLS LAST is specified, null values sort after all non-null values; if NULLS FIRST is specified, null values sort before all non-null values. If neither is specified, the default behavior is NULLS LAST when ASC is specified or implied, and NULLS FIRST when DESC is specified (thus, the default is to act as though nulls are larger than non-nulls). When USING is specified, the default nulls ordering depends upon whether the operator is a less-than or greater-than operator.

    Note that ordering options apply only to the expression they follow; for example ORDER BY x, y DESC does not mean the same thing as ORDER BY x DESC, y DESC.

    Character-string data is sorted according to the locale-specific collation order that was established when the Greenplum Database system was initialized.

    The DISTINCT Clause

    If DISTINCT is specified, all duplicate rows are removed from the result set (one row is kept from each group of duplicates). ALL specifies the opposite: all rows are kept. ALL is the default.

    DISTINCT ON ( expression [, ...] ) keeps only the first row of each set of rows where the given expressions evaluate to equal. The DISTINCT ON expressions are interpreted using the same rules as for ORDER BY. Note that the ‘first row’ of each set is unpredictable unless ORDER BY is used to ensure that the desired row appears first. For example:

    1. SELECT DISTINCT ON (location) location, time, report FROM 
    2. weather_reports ORDER BY location, time DESC;

    retrieves the most recent weather report for each location. But if we had not used ORDER BY to force descending order of time values for each location, we would have gotten a report from an unpredictable time for each location.

    The DISTINCT ON expression(s) must match the left-most ORDER BY expression(s). The ORDER BY clause will normally contain additional expression(s) that determine the desired precedence of rows within each DISTINCT ON group.

    When Greenplum Database processes queries that contain the DISTINCT clause, the queries are transformed into GROUP BY queries. In many cases, the transformation provides significant performance gains. However, when the number of distinct values is close to the total number of rows, the transformation might result in the generation of a multi-level grouping plan. In this case, there is an expected performance degradation because of the overhead introduced by the lower aggregation level.

    The LIMIT Clause

    The LIMIT clause consists of two independent sub-clauses:

    1. LIMIT {<count> | ALL}
    2. OFFSET <start>

    where count specifies the maximum number of rows to return, while start specifies the number of rows to skip before starting to return rows. When both are specified, start rows are skipped before starting to count the count rows to be returned.

    When using LIMIT, it is a good idea 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 may be asking for the tenth through twentieth rows, but tenth through twentieth in what ordering? You don’t know what ordering unless you specify ORDER BY.

    The query optimizer takes LIMIT into account when generating a query plan, so you are very likely to get different plans (yielding different row orders) depending on what you use 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 defect; 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 FOR UPDATE/FOR SHARE Clause

    The FOR UPDATE clause has this form:

    The closely related FOR SHARE clause has this form:

    1. FOR SHARE [OF <table_name> [, ...]] [NOWAIT]

    FOR UPDATE causes the tables accessed by the SELECT statement to be locked as though for update. This prevents the table from being modified or deleted by other transactions until the current transaction ends. That is, other transactions that attempt UPDATE, DELETE, or SELECT FOR UPDATE of this table will be blocked until the current transaction ends. Also, if an UPDATE, DELETE, or SELECT FOR UPDATE from another transaction has already locked a selected table, SELECT FOR UPDATE will wait for the other transaction to complete, and will then lock and return the updated table.

    To prevent the operation from waiting for other transactions to commit, use the NOWAIT option. With NOWAIT, the statement reports an error, rather than waiting, if a selected row cannot be locked immediately. Note that NOWAIT only affects whether the SELECT statement waits to obtain row-level locks. A required table-level lock is always taken in the ordinary way. For example, a SELECT FOR UPDATE NOWAIT statement will always wait for the required table-level lock; it behaves as if NOWAIT was omitted. You can use LOCK with the NOWAIT option first, if you need to acquire the table-level lock without waiting.

    FOR SHARE behaves similarly, except that it acquires a shared rather than exclusive lock on the table. A shared lock blocks other transactions from performing UPDATE, DELETE, or SELECT FOR UPDATE on the table, but it does not prevent them from performing SELECT FOR SHARE.

    If specific tables are named in FOR UPDATE or FOR SHARE, then only those tables are locked; any other tables used in the SELECT are simply read as usual. A FOR UPDATE or FOR SHARE clause without a table list affects all tables used in the command. If FOR UPDATE or FOR SHARE is applied to a view or subquery, it affects all tables used in the view or subquery.

    FOR UPDATE or FOR SHARE do not apply to a with_query referenced by the primary query. If you want row locking to occur within a with_query, specify FOR UPDATE or FOR SHARE within the with_query.

    Multiple FOR UPDATE and FOR SHARE clauses can be written if it is necessary to specify different locking behavior for different tables. If the same table is mentioned (or implicitly affected) by both FOR UPDATE and FOR SHARE clauses, then it is processed as FOR UPDATE. Similarly, a table is processed as NOWAIT if that is specified in any of the clauses affecting it.

    Examples

    To join the table films with the table distributors:

    1. SELECT f.title, f.did, d.name, f.date_prod, f.kind FROM

    To sum the column length of all films and group the results by kind:

    1. SELECT kind, sum(length) AS total FROM films GROUP BY kind;

    To sum the column length of all films, group the results by kind and show those group totals that are less than 5 hours:

    1. SELECT kind, sum(length) AS total FROM films GROUP BY kind 
    2. HAVING sum(length) < interval '5 hours';

    Calculate the subtotals and grand totals of all sales for movie kind and distributor.

    1. SELECT kind, distributor, sum(prc*qty) FROM sales
    2. GROUP BY ROLLUP(kind, distributor)
    3. ORDER BY 1,2,3;

    Calculate the rank of movie distributors based on total sales:

    1. SELECT distributor, sum(prc*qty), 
    2.        rank() OVER (ORDER BY sum(prc*qty) DESC) 
    3. FROM sale
    4. GROUP BY distributor ORDER BY 2 DESC;

    The following two examples are identical ways of sorting the individual results according to the contents of the second column (name):

    1. SELECT * FROM distributors ORDER BY name;
    2. SELECT * FROM distributors ORDER BY 2;

    The next example shows how to obtain the union of the tables distributors and actors, restricting the results to those that begin with the letter W in each table. Only distinct rows are wanted, so the key word ALL is omitted:

    1. SELECT distributors.name FROM distributors WHERE 
    2. distributors.name LIKE 'W%' UNION SELECT actors.name FROM 
    3. actors WHERE actors.name LIKE 'W%';

    This example shows how to use a function in the FROM clause, both with and without a column definition list:

    1. CREATE FUNCTION distributors(int) RETURNS SETOF distributors 
    2. AS $$ SELECT * FROM distributors WHERE did = $1; $$ LANGUAGE 
    3. SQL;
    4. SELECT * FROM distributors(111);
    6. CREATE FUNCTION distributors_2(int) RETURNS SETOF record AS 
    7. $$ SELECT * FROM distributors WHERE did = $1; $$ LANGUAGE 
    8. SQL;
    9. SELECT * FROM distributors_2(111) AS (dist_id int, dist_name 
    10. text);

    This example uses a simple WITH clause:

    1. WITH test AS (
    2.   SELECT random() as x FROM generate_series(1, 3)
    3.   )
    4. SELECT * FROM test
    5. UNION ALL
    6. SELECT * FROM test;

    This example uses the WITH clause to display per-product sales totals in only the top sales regions.

    1. WITH regional_sales AS 
    2.     SELECT region, SUM(amount) AS total_sales
    3.     FROM orders
    4.     GROUP BY region
    5.   ), top_regions AS (
    6.     SELECT region
    7.     FROM regional_sales
    8.     WHERE total_sales > (SELECT SUM(total_sales) FROM
    9.        regional_sales)
    10.   )
    11. SELECT region, product, SUM(quantity) AS product_units,
    12.    SUM(amount) AS product_sales
    13. FROM orders
    14. WHERE region IN (SELECT region FROM top_regions) 
    15. GROUP BY region, product;

    The example could have been written without the WITH clause but would have required two levels of nested sub-SELECT statements.

    This example uses the WITH RECURSIVE clause to find all subordinates (direct or indirect) of the employee Mary, and their level of indirectness, from a table that shows only direct subordinates:

    1. WITH RECURSIVE employee_recursive(distance, employee_name, manager_name) AS (
    2.     SELECT 1, employee_name, manager_name
    3.     FROM employee
    4.     WHERE manager_name = 'Mary'
    5.   UNION ALL
    6.     SELECT er.distance + 1, e.employee_name, e.manager_name
    7.     FROM employee_recursive er, employee e
    8.     WHERE er.employee_name = e.manager_name
    9.   )
    10. SELECT distance, employee_name FROM employee_recursive;

    The typical form of recursive queries: an initial condition, followed by UNION [ALL], followed by the recursive part of the query. Be sure that the recursive part of the query will eventually return no tuples, or else the query will loop indefinitely. See in the Greenplum Database Administrator Guide for more examples.

    The SELECT statement is compatible with the SQL standard, but there are some extensions and some missing features.

    Omitted FROM Clauses

    Greenplum Database allows one to omit the FROM clause. It has a straightforward use to compute the results of simple expressions. For example:

    1. SELECT 2+2;

    Some other SQL databases cannot do this except by introducing a dummy one-row table from which to do the SELECT.

    Note that if a FROM clause is not specified, the query cannot reference any database tables. For compatibility with applications that rely on this behavior the add_missing_from configuration variable can be enabled.

    The AS Key Word

    In the SQL standard, the optional key word AS is just noise and can be omitted without affecting the meaning. The Greenplum Database parser requires this key word when renaming output columns because the type extensibility features lead to parsing ambiguities without it. AS is optional in FROM items, however.

    Namespace Available to GROUP BY and ORDER BY

    In the SQL-92 standard, an ORDER BY clause may only use result column names or numbers, while a GROUP BY clause may only use expressions based on input column names. Greenplum Database extends each of these clauses to allow the other choice as well (but it uses the standard’s interpretation if there is ambiguity). Greenplum Database also allows both clauses to specify arbitrary expressions. Note that names appearing in an expression are always taken as input-column names, not as result-column names.

    SQL:1999 and later use a slightly different definition which is not entirely upward compatible with SQL-92. In most cases, however, Greenplum Database interprets an ORDER BY or GROUP BY expression the same way SQL:1999 does.

    Nonstandard Clauses

    The clauses DISTINCT ON, LIMIT, and OFFSET are not defined in the SQL standard.

    Limited Use of STABLE and VOLATILE Functions

    To prevent data from becoming out-of-sync across the segments in Greenplum Database, any function classified as or VOLATILE cannot be executed at the segment database level if it contains SQL or modifies the database in any way. See for more information.

    See Also