Summary of Built-in Functions

Greenplum Database supports built-in functions and operators including analytic functions and window functions that can be used in window expressions. For information about using built-in Greenplum Database functions see, "Using Functions and Operators" in the Greenplum Database Administrator Guide.

Parent topic: Greenplum Database Reference Guide

Greenplum Database Function Types

Greenplum Database evaluates functions and operators used in SQL expressions. Some functions and operators are only allowed to execute on the master since they could lead to inconsistencies in Greenplum Database segment instances. This table describes the Greenplum Database Function Types.

Function Type Greenplum Support Description Comments
IMMUTABLE Yes Relies only on information directly in its argument list. Given the same argument values, always returns the same result.
STABLE Yes, in most cases Within a single table scan, returns the same result for same argument values, but results change across SQL statements. Results depend on database lookups or parameter values. current_timestamp family of functions is STABLE; values do not change within an execution.
VOLATILE Restricted Function values can change within a single table scan. For example: random(), timeofday(). Any function with side effects is volatile, even if its result is predictable. For example: setval().

In Greenplum Database, data is divided up across segments — each segment is a distinct PostgreSQL database. To prevent inconsistent or unexpected results, do not execute functions classified as VOLATILE at the segment level if they contain SQL commands or modify the database in any way. For example, functions such as setval() are not allowed to execute on distributed data in Greenplum Database because they can cause inconsistent data between segment instances.

To ensure data consistency, you can safely use VOLATILE and STABLE functions in statements that are evaluated on and run from the master. For example, the following statements run on the master (statements without a FROM clause):

SELECT setval('myseq', 201);
SELECT foo();

If a statement has a FROM clause containing a distributed table and the function in the FROM clause returns a set of rows, the statement can run on the segments:

SELECT * from foo();

Greenplum Database does not support functions that return a table reference (rangeFuncs) or functions that use the refCursor datatype.

Built-in Functions and Operators

The following table lists the categories of built-in functions and operators supported by PostgreSQL. All functions and operators are supported in Greenplum Database as in PostgreSQL with the exception of STABLE and VOLATILE functions, which are subject to the restrictions noted in Greenplum Database Function Types. See the Functions and Operators section of the PostgreSQL documentation for more information about these built-in functions and operators.

Operator/Function Category VOLATILE Functions STABLE Functions Restrictions
Logical Operators
Comparison Operators
Mathematical Functions and Operators random
setseed
String Functions and Operators All built-in
conversion
functions		 convert
pg_client_encoding
Binary String Functions and Operators
Bit String Functions and Operators
Pattern Matching
Data Type Formatting Functions to_char
to_timestamp
Date/Time Functions and Operators timeofday age
current_date
current_time
current_timestamp
localtime
localtimestamp
now
Enum Support Functions
Geometric Functions and Operators
Network Address Functions and Operators
Sequence Manipulation Functions nextval()
setval()
Conditional Expressions
Array Functions and Operators All array functions
Aggregate Functions
Subquery Expressions
Row and Array Comparisons
Set Returning Functions generate_series
System Information Functions All session information functions
All access privilege inquiry functions
All schema visibility inquiry functions
All system catalog information functions
All comment information functions
All transaction ids and snapshots
System Administration Functions set_configpg_cancel_backend
pg_reload_conf
pg_rotate_logfile
pg_start_backup
pg_stop_backup
pg_size_pretty
pg_ls_dir
pg_read_file
pg_stat_file		 current_setting
All database object size functions		 Note: The function pg_column_size displays bytes required to store the value, possibly with TOAST compression.
XML Functions
and function-like expressions		 cursor_to_xml(cursor refcursor, count int, nulls boolean, tableforest boolean, targetns text)

cursor_to_xmlschema(cursor refcursor, nulls boolean, tableforest boolean, targetns text)

database_to_xml(nulls boolean, tableforest boolean, targetns text)

database_to_xmlschema(nulls boolean, tableforest boolean, targetns text)

database_to_xml_and_xmlschema(nulls boolean, tableforest boolean, targetns text)

query_to_xml(query text, nulls boolean, tableforest boolean, targetns text)

query_to_xmlschema(query text, nulls boolean, tableforest boolean, targetns text)

query_to_xml_and_xmlschema(query text, nulls boolean, tableforest boolean, targetns text)

schema_to_xml(schema name, nulls boolean, tableforest boolean, targetns text)

schema_to_xmlschema(schema name, nulls boolean, tableforest boolean, targetns text<br/>

schema_to_xml_and_xmlschema(schema name, nulls boolean, tableforest boolean, targetns text)

table_to_xml(tbl regclass, nulls boolean, tableforest boolean, targetns text)

table_to_xmlschema(tbl regclass, nulls boolean, tableforest boolean, targetns text

table_to_xml_and_xmlschema(tbl regclass, nulls boolean, tableforest boolean, targetns text)

xmlagg(xml)

xmlconcat(xml[, ...])

xmlelement(name name [, xmlattributes(value [AS attname] [, ... ])] [, content, ...])

xmlexists(text, xml)

xmlforest(content [AS name] [, ...]

xml_is_well_formed(text)

xml_is_well_formed_document(text)

xml_is_well_formed_content(text)

xmlparse ( { DOCUMENT | CONTENT } value)

xpath(text, xml)

xpath(text, xml, text[])

xpath_exists(text, xml)

xpath_exists(text, xml, text[])

xmlpi(name target [, content])

xmlroot(xml, version text | no value [, standalone yes|no|no value])

xmlserialize ( { DOCUMENT | CONTENT } value AS type )

xml(text)

text(xml)

xmlcomment(xml)

xmlconcat2(xml, xml)

JSON Functions and Operators

Built-in functions and operators that create and manipulate JSON data.

Note: For json values, all key/value pairs are kept even if a JSON object contains duplicate key/value pairs. The processing functions consider the last value as the operative one.

JSON Operators

This table describes the operators that are available for use with the json data type.

Operator Right Operand Type Description Example Example Result
-> int Get JSON array element (indexed from zero). '[{"a":"foo"},{"b":"bar"},{"c":"baz"}]'::json->2 {"c":"baz"}
-> text Get JSON object field by key. '{"a": {"b":"foo"}}'::json->'a' {"b":"foo"}
->> int Get JSON array element as text. '[1,2,3]'::json->>2 3
->> text Get JSON object field as text. '{"a":1,"b":2}'::json->>'b' 2
#> text[] Get JSON object at specified path. '{"a": {"b":{"c": "foo"}}}'::json#>'{a,b}' {"c": "foo"}
#>> text[] Get JSON object at specified path as text. '{"a":[1,2,3],"b":[4,5,6]}'::json#>>'{a,2}' 3

JSON Creation Functions

This table describes the functions that create json values.

Function Description Example Example Result
array_to_json(anyarray [, pretty_bool]) Returns the array as a JSON array. A Greenplum Database multidimensional array becomes a JSON array of arrays.
Line feeds are added between dimension 1 elements if pretty_bool is true.
 array_to_json('{{1,5},{99,100}}'::int[]) [[1,5],[99,100]]
row_to_json(record [, pretty_bool]) Returns the row as a JSON object. Line feeds are added between level 1 elements if pretty_bool is true. row_to_json(row(1,'foo')) {"f1":1,"f2":"foo"}

JSON Processing Functions

This table describes the functions that process json values.

0
Operator Right Operand Type Description Example Example Result
-> int Get JSON array element (indexed from zero). '[{"a":"foo"},{"b":"bar"},{"c":"baz"}]'::json->2 {"c":"baz"}
-> text Get JSON object field by key. '{"a": {"b":"foo"}}'::json->'a' {"b":"foo"}
->> int Get JSON array element as text. '[1,2,3]'::json->>2 3
->> text Get JSON object field as text. '{"a":1,"b":2}'::json->>'b' 2
#> text[] Get JSON object at specified path. '{"a": {"b":{"c": "foo"}}}'::json#>'{a,b}' {"c": "foo"}
#>> text[] Get JSON object at specified path as text. '{"a":[1,2,3],"b":[4,5,6]}'::json#>>'{a,2}' 3

Note: Many of these functions and operators convert Unicode escapes in JSON strings to regular characters. The functions throw an error for characters that cannot be represented in the database encoding.

For json_populate_record and json_populate_recordset, type coercion from JSON is best effort and might not result in desired values for some types. JSON keys are matched to identical column names in the target row type. JSON fields that do not appear in the target row type are omitted from the output, and target columns that do not match any JSON field return NULL.

Window Functions

The following built-in window functions are Greenplum extensions to the PostgreSQL database. All window functions are immutable. For more information about window functions, see "Window Expressions" in the Greenplum Database Administrator Guide.

Function Return Type Full Syntax Description
cume_dist() double precision CUME_DIST() OVER ( [PARTITION BY expr ] ORDER BY expr ) Calculates the cumulative distribution of a value in a group of values. Rows with equal values always evaluate to the same cumulative distribution value.
dense_rank() bigint DENSE_RANK () OVER ( [PARTITION BY expr ] ORDER BY expr ) Computes the rank of a row in an ordered group of rows without skipping rank values. Rows with equal values are given the same rank value.
first_value(expr) same as input expr type FIRST_VALUE( expr ) OVER ( [PARTITION BY expr ] ORDER BY expr [ROWS|RANGE frame_expr ] ) Returns the first value in an ordered set of values.
lag(expr [,offset] [,default]) same as input expr type LAG( expr [, offset ] [, default ]) OVER ( [PARTITION BY expr ] ORDER BY expr ) Provides access to more than one row of the same table without doing a self join. Given a series of rows returned from a query and a position of the cursor, LAG provides access to a row at a given physical offset prior to that position. The default offset is 1. default sets the value that is returned if the offset goes beyond the scope of the window. If default is not specified, the default value is null.
last_value(expr) same as input expr type LAST_VALUE(expr) OVER ( [PARTITION BY expr] ORDER BY expr [ROWS|RANGE frame_expr] ) Returns the last value in an ordered set of values.
lead(expr [,offset] [,default]) same as input expr type LEAD(expr[,offset] [,expr**default]) OVER ( [PARTITION BY expr] ORDER BY expr ) Provides access to more than one row of the same table without doing a self join. Given a series of rows returned from a query and a position of the cursor, lead provides access to a row at a given physical offset after that position. If offset is not specified, the default offset is 1. default sets the value that is returned if the offset goes beyond the scope of the window. If default is not specified, the default value is null.
ntile(expr) bigint NTILE(expr) OVER ( [PARTITION BY expr] ORDER BY expr ) Divides an ordered data set into a number of buckets (as defined by expr) and assigns a bucket number to each row.
percent_rank() double precision PERCENT_RANK () OVER ( [PARTITION BY expr] ORDER BY expr) Calculates the rank of a hypothetical row R minus 1, divided by 1 less than the number of rows being evaluated (within a window partition).
rank() bigint RANK () OVER ( [PARTITION BY expr] ORDER BY expr) Calculates the rank of a row in an ordered group of values. Rows with equal values for the ranking criteria receive the same rank. The number of tied rows are added to the rank number to calculate the next rank value. Ranks may not be consecutive numbers in this case.
row_number() bigint ROW_NUMBER () OVER ( [PARTITION BY expr] ORDER BY expr) Assigns a unique number to each row to which it is applied (either each row in a window partition or each row of the query).

Advanced Aggregate Functions

Function Return Type Full Syntax Description
MEDIAN (expr) timestamp, timestamptz, interval, float MEDIAN (expression)

Example: 

SELECT department_id, MEDIAN(salary) FROM employees 
GROUP BY department_id;
Can take a two-dimensional array as input. Treats such arrays as matrices.
PERCENTILE_CONT (expr) WITHIN GROUP (ORDER BY expr [DESC/ASC]) timestamp, timestamptz, interval, float PERCENTILE_CONT(percentage) WITHIN GROUP (ORDER BY expression)

Example: 

SELECT department_id,
PERCENTILE_CONT (0.5) WITHIN GROUP (ORDER BY salary DESC)
"Median_cont"; 
FROM employees GROUP BY department_id;
 Performs an inverse distribution function that assumes a continuous distribution model. It takes a percentile value and a sort specification and returns the same datatype as the numeric datatype of the argument. This returned value is a computed result after performing linear interpolation. Null are ignored in this calculation.
PERCENTILE_DISC (expr) WITHIN GROUP (ORDER BY expr [DESC/ASC]) timestamp, timestamptz, interval, float PERCENTILE_DISC(percentage) WITHIN GROUP (ORDER BY expression)

Example: 

SELECT department_id, 
PERCENTILE_DISC (0.5) WITHIN GROUP (ORDER BY salary DESC)
"Median_desc"; 
FROM employees GROUP BY department_id;
 Performs an inverse distribution function that assumes a discrete distribution model. It takes a percentile value and a sort specification. This returned value is an element from the set. Null are ignored in this calculation.
sum(array[]) smallint[]int[], bigint[], float[] sum(array[[1,2],[3,4]])

Example: 

CREATE TABLE mymatrix (myvalue int[]);
INSERT INTO mymatrix VALUES (array[[1,2],[3,4]]);
INSERT INTO mymatrix VALUES (array[[0,1],[1,0]]);
SELECT sum(myvalue) FROM mymatrix;
 sum 
---------------
 {{1,3},{4,4}}
 Performs matrix summation. Can take as input a two-dimensional array that is treated as a matrix.
pivot_sum (label[], label, expr) int[], bigint[], float[] pivot_sum( array['A1','A2'], attr, value) A pivot aggregation using sum to resolve duplicate entries.
unnest (array[]) set of anyelement unnest( array['one', 'row', 'per', 'item']) Transforms a one dimensional array into rows. Returns a set of anyelement, a polymorphic pseudotype in PostgreSQL.
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