Using Functions and Operators

Description of user-defined and built-in functions and operators in Greenplum Database.

Parent topic: Querying Data

Using Functions in Greenplum Database

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().

Refer to the PostgreSQL Function Volatility Categories documentation for additional information about the Greenplum Database function volatility classifications.

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.

Function Volatility and Plan Caching

There is relatively little difference between the STABLE and IMMUTABLE function volatility categories for simple interactive queries that are planned and immediately executed. It does not matter much whether a function is executed once during planning or once during query execution startup. But there is a big difference when you save the plan and reuse it later. If you mislabel a function IMMUTABLE, Greenplum Database may prematurely fold it to a constant during planning, possibly reusing a stale value during subsequent execution of the plan. You may run into this hazard when using PREPAREd statements, or when using languages such as PL/pgSQL that cache plans.

User-Defined Functions

Greenplum Database supports user-defined functions. See Extending SQL in the PostgreSQL documentation for more information.

Use the CREATE FUNCTION statement to register user-defined functions that are used as described in Using Functions in Greenplum Database. By default, user-defined functions are declared as VOLATILE, so if your user-defined function is IMMUTABLE or STABLE, you must specify the correct volatility level when you register your function.

When you create user-defined functions, avoid using fatal errors or destructive calls. Greenplum Database may respond to such errors with a sudden shutdown or restart.

In Greenplum Database, the shared library files for user-created functions must reside in the same library path location on every host in the Greenplum Database array (masters, segments, and mirrors).

You can also create and execute anonymous code blocks that are written in a Greenplum Database procedural language such as PL/pgSQL. The anonymous blocks run as transient anonymous functions. For information about creating and executing anonymous blocks, see the DO command.

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 Using Functions in Greenplum Database. See the Functions and Operators section of the PostgreSQL documentation for more information about these built-in functions and operators.

Greenplum Database includes JSON processing functions that manipulate values the json data type. For information about JSON data, see Working with JSON Data.

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_charto_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_config

pg_cancel_backend

pg_terminate_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)

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)

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.

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

The following built-in advanced aggregate functions are Greenplum extensions of the PostgreSQL database. These functions are immutable. Greenplum Database does not support the PostgreSQL ordered-set aggregate functions.

Note: The Greenplum MADlib Extension for Analytics provides additional advanced functions to perform statistical analysis and machine learning with Greenplum Database data. See Greenplum MADlib Extension for Analytics in the Greenplum Database Reference Guide.

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.
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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